--- a/LICENCE	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,674 +0,0 @@
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-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
-THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    <one line to give the program's name and a brief idea of what it does.>
-    Copyright (C) <year>  <name of author>
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-
-Also add information on how to contact you by electronic and paper mail.
-
-  If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
-    <program>  Copyright (C) <year>  <name of author>
-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<https://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<https://www.gnu.org/licenses/why-not-lgpl.html>.

--- a/README.md	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,219 +0,0 @@
-# Domain based annotation of transposable elements  - DANTE #
-
-### Authors 
- Nina Hostakova, Petr Novak, Pavel Neumann, Jiri Macas
- Biology Centre CAS, Czech Republic
- 
- 
-### Introduction
-
-* Protein Domains Finder [dante.py]
-	* Script performs scanning of given DNA sequence(s) in (multi)fasta format in order to discover protein domains using our protein domains database.
-	* Domains searching is accomplished engaging LASTAL alignment tool.
-	* Domains are subsequently annotated and classified - in case certain domain has multiple annotations assigned, classifation is derived from the common classification level of all of them. 	
-			
-* Proteins Domains Filter [dante_gff_output_filtering.py]
-	* filters GFF3 output from previous step to obtain certain kind of domain and/or allows to adjust quality filtering  
-        
-### DEPENDENCIES ###
-
-* python3.4 or higher with packages:	
-	* numpy
-	* biopython
-* [lastal](http://last.cbrc.jp/doc/last.html) 744 or higher
-* ProfRep/DANTE modules:
-	* configuration.py 
-
-
-### Protein Domains Finder ###
-
-This tool provides **preliminary** output of all domains types which are not filtered for quality.
-
-#### INPUTS ####
-
-* DNA sequence [multiFasta]
-		
-#### OUTPUTS ####
-		
-* **All protein domains GFF3** - individual domains are reported per line as regions (start-end) on the original DNA sequence including the seq ID and strand orientation. The last "Attributes" column contains several comma-separated information related to the domain annotation, alignment and its quality. This file can undergo further filtering using Protein Domain Filter tool.		
-
-#### USAGE ####
-
-		usage: dante.py [-h] -q QUERY -pdb PROTEIN_DATABASE -cs
-								  CLASSIFICATION [-oug DOMAIN_GFF] [-nld NEW_LDB]
-								  [-dir OUTPUT_DIR] [-thsc THRESHOLD_SCORE]
-								  [-wd WIN_DOM] [-od OVERLAP_DOM]
-								  
-		optional arguments:
-		  -h, --help            show this help message and exit
-		  -oug DOMAIN_GFF, --domain_gff DOMAIN_GFF
-								output domains gff format (default: None)
-		  -nld NEW_LDB, --new_ldb NEW_LDB
-								create indexed database files for lastal in case of
-								working with new protein db (default: False)
-		  -dir OUTPUT_DIR, --output_dir OUTPUT_DIR
-								specify if you want to change the output directory
-								(default: None)
-		  -thsc THRESHOLD_SCORE, --threshold_score THRESHOLD_SCORE
-								percentage of the best score in the cluster to be
-								tolerated when assigning annotations per base
-								(default: 80)
-		  -wd WIN_DOM, --win_dom WIN_DOM
-								window to process large input sequences sequentially
-								(default: 10000000)
-		  -od OVERLAP_DOM, --overlap_dom OVERLAP_DOM
-								overlap of sequences in two consecutive windows
-								(default: 10000)
-
-		required named arguments:
-		  -q QUERY, --query QUERY
-								input DNA sequence to search for protein domains in a
-								fasta format. Multifasta format allowed. (default:
-								None)
-		  -pdb PROTEIN_DATABASE, --protein_database PROTEIN_DATABASE
-								protein domains database file (default: None)
-		  -cs CLASSIFICATION, --classification CLASSIFICATION
-								protein domains classification file (default: None)
-
-
-		
-#### HOW TO RUN EXAMPLE ####
-		./protein_domains.py -q PATH_TO_INPUT_SEQ -pdb PATH_TO_PROTEIN_DB -cs PATH_TO_CLASSIFICATION_FILE
-		
-	 When running for the first time with a new database use -nld option allowing lastal to create indexed database files:
-
-         -nld True
-
-	use other arguments if you wish to rename your outputs or they will be created automatically with standard names 
-	
-### Protein Domains Filter ###
-		
-The script performs Protein Domains Finder output filtering for quality and/or extracting specific type of protein domain or mobile elements of origin. For the filtered domains it reports their translated protein sequence of original DNA.
-
-WHEN NO PARAMETERS GIVEN, IT PERFORMS QUALITY FILTERING USING THE DEFAULT PARAMETRES (optimized for Viridiplantae species)
-
-#### INPUTS ####
-* GFF3 file produced by protein_domains.py OR already filtered GFF3
-	
-#### Filtering options ####
-* QUALITY: 
-	- Min relative length of alignemnt to the protein domain from DB (without gaps)
-	- Identity 
-	- Similarity (scoring matrix: BLOSUM80)
-	- Interruption in the reading frame (frameshifts + stop codons) per every starting 100 AA
-	- Max alignment proportion to the original length of database domain sequence
-* DOMAIN TYPE: 'Name' attribute in GFF - see choices bellow
-Records for ambiguous domain type (e.g. INT/RH) are filtered out automatically
-
-* MOBILE ELEMENT TYPE:
-arbitrary substring of the element classification ('Final_Classification' attribute in GFF)
-		
-#### OUTPUTS ####
-* filtered GFF3 file
-* fasta file of translated protein sequences for the aligned domains that match the filtering criteria 
-	! as it is taken from the best hit alignment reported by LAST, it does not neccessary cover the whole region reported as domain in GFF
-	
-#### USAGE ####		
-
-		usage: dante_gff_output_filtering.py [-h] -dg DOM_GFF [-ouf DOMAINS_FILTERED]
-                            [-dps DOMAINS_PROT_SEQ]
-                            [-thl {float range 0.0..1.0}]
-                            [-thi {float range 0.0..1.0}]
-                            [-ths {float range 0.0..1.0}] [-ir INTERRUPTIONS]
-                            [-mlen MAX_LEN_PROPORTION]
-                            [-sd {All,GAG,INT,PROT,RH,RT,aRH,CHDCR,CHDII,TPase,YR,HEL1,HEL2,ENDO}]
-                            [-el ELEMENT_TYPE] [-dir OUTPUT_DIR]
-
-
-
-		optional arguments:
-		  -h, --help            show this help message and exit
-		  -ouf DOMAINS_FILTERED, --domains_filtered DOMAINS_FILTERED
-								output filtered domains gff file (default: None)
-		  -dps DOMAINS_PROT_SEQ, --domains_prot_seq DOMAINS_PROT_SEQ
-								output file containg domains protein sequences
-								(default: None)
-		  -thl {float range 0.0..1.0}, --th_length {float range 0.0..1.0}
-								proportion of alignment length threshold (default:
-								0.8)
-		  -thi {float range 0.0..1.0}, --th_identity {float range 0.0..1.0}
-								proportion of alignment identity threshold (default:
-								0.35)
-		  -ths {float range 0.0..1.0}, --th_similarity {float range 0.0..1.0}
-								threshold for alignment proportional similarity
-								(default: 0.45)
-		  -ir INTERRUPTIONS, --interruptions INTERRUPTIONS
-								interruptions (frameshifts + stop codons) tolerance
-								threshold per 100 AA (default: 3)
-		  -mlen MAX_LEN_PROPORTION, --max_len_proportion MAX_LEN_PROPORTION
-								maximal proportion of alignment length to the original
-								length of protein domain from database (default: 1.2)
-		  -sd {All,GAG,INT,PROT,RH,RT,aRH,CHDCR,CHDII,TPase,YR,HEL1,HEL2,ENDO}, --selected_dom {All,GAG,INT,PROT,RH,RT,aRH,CHDCR,CHDII,TPase,YR,HEL1,HEL2,ENDO}
-								filter output domains based on the domain type
-								(default: All)
-		  -el ELEMENT_TYPE, --element_type ELEMENT_TYPE
-								filter output domains by typing substring from
-								classification (default: )
-		  -dir OUTPUT_DIR, --output_dir OUTPUT_DIR
-								specify if you want to change the output directory
-								(default: None)
-
-		required named arguments:
-		  -dg DOM_GFF, --dom_gff DOM_GFF
-								basic unfiltered gff file of all domains (default:
-								None)
-
-
-
-#### HOW TO RUN EXAMPLE ####
-e.g. getting quality filtered integrase(INT) domains of all gypsy transposable elements:
-	
-	./domains_filtering.py -dom_gff PATH_TO_INPUT_GFF -pdb PATH_TO_PROTEIN_DB -cs PATH_TO_CLASSIFICATION_FILE --selected_dom INT --element_type Ty3/gypsy 
-
-
-### Extract Domains Nucleotide Sequences ###
-
-This tool extracts nucleotide sequences of protein domains from reference DNA based on DANTE's output. It can be used e.g. for deriving phylogenetic relations of individual mobile elements classes within a species. 
-
-#### INPUTS ####
-
-* original DNA sequence in multifasta format to extract the domains from 
-* GFF3 file of protein domains (**DANTE's output** - preferably filtered for quality and specific domain type)
-* Domains database classification table (to check the classification level)
-
-#### OUTPUTS ####
-
-* fasta files of domains nucleotide sequences for individual transposons lineages
-* txt file of domains counts extracted for individual lineages
-
-**- For GALAXY usage all concatenated in a single fasta file**
-
-#### USAGE ####	
-		usage: dante_gff_to_dna.py [-h] -i INPUT_DNA -d DOMAINS_GFF -cs
-			CLASSIFICATION [-out OUT_DIR] [-ex EXTENDED]
-
-		optional arguments:
-		  -h, --help            show this help message and exit
-		  -i INPUT_DNA, --input_dna INPUT_DNA
-								path to input DNA sequence
-		  -d DOMAINS_GFF, --domains_gff DOMAINS_GFF
-								GFF file of protein domains
-		  -cs CLASSIFICATION, --classification CLASSIFICATION
-								protein domains classification file
-		  -out OUT_DIR, --out_dir OUT_DIR
-								output directory
-		  -ex EXTENDED, --extended EXTENDED
-								extend the domains edges if not the whole datatabase
-								sequence was aligned
-
-#### HOW TO RUN EXAMPLE ####
-	./extract_domains_seqs.py --domains_gff PATH_PROTEIN_DOMAINS_GFF --input_dna PATH_TO_INPUT_DNA  --classification PROTEIN_DOMAINS_DB_CLASS_TBL --extended True
-
-	
-
-
-
-
-
-
-

--- a/configuration.py	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,23 +0,0 @@
-#!/usr/bin/env python3
-''' configuration file to set up the paths and constants '''
-import os
-
-MAIN_GIT_DIR = os.path.dirname(os.path.realpath(__file__))
-TOOL_DATA = os.path.join(MAIN_GIT_DIR, "tool-data")
-TMP = "tmp"
-SC_MATRIX_SKELETON = os.path.join(TOOL_DATA, "{}.txt.sample")
-AMBIGUOUS_TAG = "Ambiguous_domain"
-## IO
-CLASS_FILE = "ALL.classification-new"
-LAST_DB_FILE = "ALL_protein-domains_05.fasta"
-DOM_PROT_SEQ = "dom_prot_seq.fa"
-FILT_DOM_GFF = "domains_filtered.gff"
-EXTRACT_DOM_STAT = "domains_counts.txt"
-EXTRACT_OUT_DIR = "extracted_domains"
-FASTA_LINE = 60
-SOURCE_PROFREP = "profrep"
-SOURCE_DANTE = "dante"
-DOMAINS_FEATURE = "protein_domain"
-PHASE = "."
-HEADER_GFF = "##gff-version 3"
-DOMAINS_GFF = "output_domains.gff"

--- a/coverage2gff.py	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,64 +0,0 @@
-#!/usr/bin/env python3
-import argparse
-import tempfile
-import shutil
-import sys
-
-def parse_args():
-    '''Argument parsin'''
-    description = """
-    parsing cap3 assembly aln output
-    """
-
-    parser = argparse.ArgumentParser(
-        description=description,
-        formatter_class=argparse.RawTextHelpFormatter)
-    parser.add_argument(
-        '-g',
-        '--gff_file',
-        default=None,
-        required=True,
-        help="input gff3 file for appending coverage information",
-        type=str,
-        action='store')
-    parser.add_argument(
-        '-p',
-        '--profile',
-        default=None,
-        required=True,
-        help="output file for coverage profile",
-        type=str,
-        action="store")
-    return parser.parse_args()
-
-def read_coverage(profile):
-    with open(profile) as p:
-        d = {}
-        for name, prof in zip(p, p):
-            d[name[1:].strip()] = [int(i) for i in prof.split()]
-    return d
-
-
-def main():
-    args = parse_args()
-    coverage_hash = read_coverage(args.profile)
-    gff_tmp = tempfile.NamedTemporaryFile()
-    with open(args.gff_file) as f, open(gff_tmp.name, 'w') as out:
-        for line in f:
-            if line[0] == "#":
-                out.write(line)
-            else:
-                line_parts = line.split()
-                start = int(line_parts[3])
-                end = int(line_parts[4])
-                coverage = round( sum(coverage_hash[line_parts[0]][(
-                    start - 1):end]) / (end - start + 1), 3)
-                new_line = "{};Coverage={}\n".format(line.strip(), coverage)
-                out.write(new_line)
-
-    shutil.copyfile(gff_tmp.name, args.gff_file)
-
-
-if __name__ == "__main__":
-
-    main()

--- a/dante.py	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,934 +0,0 @@
-#!/usr/bin/env python3
-
-import numpy as np
-import subprocess
-import math
-import time
-from operator import itemgetter
-from collections import Counter
-from itertools import groupby
-import os
-import re
-import configuration
-from tempfile import NamedTemporaryFile
-import sys
-import warnings
-import shutil
-from collections import defaultdict
-
-np.set_printoptions(threshold=sys.maxsize)
-
-def alignment_scoring():
-    ''' Create hash table for alignment similarity counting: for every 
-	combination of aminoacids in alignment assign score from protein 
-	scoring matrix defined in configuration file  '''
-    score_dict = {}
-    with open(configuration.SC_MATRIX) as smatrix:
-        count = 1
-        for line in smatrix:
-            if not line.startswith("#"):
-                if count == 1:
-                    aa_all = line.rstrip().replace(" ", "")
-                else:
-                    count_aa = 1
-                    line = list(filter(None, line.rstrip().split(" ")))
-                    for aa in aa_all:
-                        score_dict["{}{}".format(line[0], aa)] = line[count_aa]
-                        count_aa += 1
-                count += 1
-    return score_dict
-
-
-def characterize_fasta(QUERY, WIN_DOM):
-    ''' Find the sequences, their lengths, starts, ends and if 
-	they exceed the window '''
-    with open(QUERY) as query:
-        headers = []
-        fasta_lengths = []
-        seq_starts = []
-        seq_ends = []
-        fasta_chunk_len = 0
-        count_line = 1
-        for line in query:
-            line = line.rstrip()
-            if line.startswith(">"):
-                headers.append(line.rstrip())
-                fasta_lengths.append(fasta_chunk_len)
-                fasta_chunk_len = 0
-                seq_starts.append(count_line + 1)
-                seq_ends.append(count_line - 1)
-            else:
-                fasta_chunk_len += len(line)
-            count_line += 1
-        seq_ends.append(count_line)
-        seq_ends = seq_ends[1:]
-        fasta_lengths.append(fasta_chunk_len)
-        fasta_lengths = fasta_lengths[1:]
-        # control if there are correct (unique) names for individual seqs:
-        # LASTAL takes seqs IDs till the first space which can then create problems with ambiguous records
-        if len(headers) > len(set([header.split(" ")[0] for header in headers
-                                   ])):
-            raise NameError(
-                '''Sequences in multifasta format are not named correctly:
-							seq IDs (before the first space) are the same''')
-
-    above_win = [idx
-                 for idx, value in enumerate(fasta_lengths) if value > WIN_DOM]
-    below_win = [idx
-                 for idx, value in enumerate(fasta_lengths)
-                 if value <= WIN_DOM]
-    lens_above_win = np.array(fasta_lengths)[above_win]
-    return headers, above_win, below_win, lens_above_win, seq_starts, seq_ends
-
-
-def split_fasta(QUERY, WIN_DOM, step, headers, above_win, below_win,
-                lens_above_win, seq_starts, seq_ends):
-    ''' Create temporary file containing all sequences - the ones that exceed 
-	the window are cut with a set overlap (greater than domain size with a reserve) '''
-    with open(QUERY, "r") as query:
-        count_fasta_divided = 0
-        count_fasta_not_divided = 0
-        ntf = NamedTemporaryFile(delete=False)
-        divided = np.array(headers)[above_win]
-        row_length = configuration.FASTA_LINE
-        for line in query:
-            line = line.rstrip()
-            if line.startswith(">") and line in divided:
-                stop_line = seq_ends[above_win[
-                    count_fasta_divided]] - seq_starts[above_win[
-                        count_fasta_divided]] + 1
-                count_line = 0
-                whole_seq = []
-                for line2 in query:
-                    whole_seq.append(line2.rstrip())
-                    count_line += 1
-                    if count_line == stop_line:
-                        break
-                whole_seq = "".join(whole_seq)
-                ## create list of starting positions for individual parts of a seq with a step given by a window and overlap
-                windows_starts = list(range(0, lens_above_win[
-                    count_fasta_divided], step))
-                ## create list of ending positions (starting pos + window), the last element is the whole seq length
-                windows_ends = [
-                    x + WIN_DOM
-                    if x + WIN_DOM < lens_above_win[count_fasta_divided] else
-                    lens_above_win[count_fasta_divided] for x in windows_starts
-                ]
-                count_part = 1
-                for start_part, end_part in zip(windows_starts, windows_ends):
-                    seq_part = whole_seq[start_part:end_part]
-                    if count_part == len(windows_starts):
-                        ntf.write("{}_DANTE_PART{}_LAST:{}-{}\n{}\n".format(
-                            line.split(" ")[0], count_part, start_part + 1,
-                            end_part, "\n".join([seq_part[i:i + row_length]
-                                                 for i in range(0, len(
-                                                     seq_part), row_length)
-                                                 ])).encode("utf-8"))
-                    else:
-                        ntf.write("{}_DANTE_PART{}:{}-{}\n{}\n".format(
-                            line.split(" ")[0], count_part, start_part + 1,
-                            end_part, "\n".join([seq_part[i:i + row_length]
-                                                 for i in range(0, len(
-                                                     seq_part), row_length)
-                                                 ])).encode("utf-8"))
-                    count_part += 1
-                count_fasta_divided += 1
-            elif line.startswith(">") and line not in divided:
-                length_seq = seq_ends[below_win[
-                    count_fasta_not_divided]] - seq_starts[below_win[
-                        count_fasta_not_divided]] + 1
-                ntf.write("{}\n{}".format(line, "".join([query.readline(
-                ) for x in range(length_seq)])).encode("utf-8"))
-                count_fasta_not_divided += 1
-        query_temp = ntf.name
-        ntf.close()
-    return query_temp
-
-
-def domain_annotation(elements, CLASSIFICATION):
-    ''' Assign protein domain to each hit from protein database  '''
-    domains = []
-    annotations = []
-    with open(CLASSIFICATION, "r") as cl_tbl:
-        annotation = {}
-        for line in cl_tbl:
-            record = line.rstrip().split("\t")
-            annotation[record[0]] = record[1:]
-    for i in range(len(elements)):
-        domains.append(elements[i].split("__")[0].split("-")[1])
-        element_name = "__".join(elements[i].split("__")[1:])
-        if element_name in annotation.keys():
-            annotations.append("|".join([elements[i].split("__")[0].split("-")[
-                1], ("|".join(annotation[element_name]))]))
-        else:
-            annotations.append("unknown|unknown")
-    return annotations
-
-
-def hits_processing(seq_len, start, end, strand):
-    ''' Gain hits intervals separately for forward and reverse strand '''
-    reverse_strand_idx = np.where(strand == "-")[0]
-    if not reverse_strand_idx.any():
-        start_pos_plus = start + 1
-        end_pos_plus = end
-        regions_plus = list(zip(start_pos_plus, end_pos_plus))
-        regions_minus = []
-    else:
-        reverse_strand_idx = reverse_strand_idx[0]
-        start_pos_plus = start[0:reverse_strand_idx] + 1
-        end_pos_plus = end[0:reverse_strand_idx]
-        start_pos_minus = seq_len[0] - end[reverse_strand_idx:] + 1
-        end_pos_minus = seq_len[0] - start[reverse_strand_idx:]
-        regions_plus = list(zip(start_pos_plus, end_pos_plus))
-        regions_minus = list(zip(start_pos_minus, end_pos_minus))
-    return reverse_strand_idx, regions_plus, regions_minus
-
-
-def overlapping_regions(input_data):
-    ''' Join all overlapping intervals(hits) to clusters (potential domains),
-	get list of start-end positions of individual hits within the interval, 
-	list of minimus and maximums as well as the indices in the original 
-	sequence_hits structure for the hits belonging to the same clusters '''
-    if input_data:
-        sorted_idx, sorted_data = zip(*sorted(
-            [(index, data) for index, data in enumerate(input_data)],
-            key=itemgetter(1)))
-        merged_ends = input_data[sorted_idx[0]][1]
-        intervals = []
-        data = []
-        output_intervals = []
-        output_data = []
-        for i, j in zip(sorted_idx, sorted_data):
-            if input_data[i][0] < merged_ends:
-                merged_ends = max(input_data[i][1], merged_ends)
-                intervals.append(i)
-                data.append(j)
-            else:
-                output_intervals.append(intervals)
-                output_data.append(data)
-                intervals = []
-                data = []
-                intervals.append(i)
-                data.append(j)
-                merged_ends = input_data[i][1]
-        output_intervals.append(intervals)
-        output_data.append(data)
-        mins = [x[0][0] for x in output_data]
-        maxs = [max(x, key=itemgetter(1))[1] for x in output_data]
-    else:
-        mins = []
-        maxs = []
-        output_intervals = []
-        output_data = []
-    return mins, maxs, output_data, output_intervals
-
-
-def annotations_dict(annotations):
-    ''' Hash table where annotations of the hits within a clusters are the keys. 
-	Each annotation has serial number assigned which indexes the row in the score_table '''
-    classes_dict = {classes: idx
-                    for idx, classes in enumerate(set(annotations))}
-    return classes_dict
-
-
-def score_table(mins, maxs, data, annotations, scores, CLASSIFICATION):
-    ''' Score table is created based on the annotations occurance in the cluster.
-	Matrix axis y corresponds to individual annotations (indexed according to classes_dict),
-    axis x represents positions of analyzed seq in a given cluster.
-    For every hit within cluster, array of scores on the corresponding position
-    is recorded to the table in case if the score on certain position is so far the highest
-	for the certain position and certain annotation '''
-    classes_dict = annotations_dict(annotations)
-    score_matrix = np.zeros((len(classes_dict), maxs - mins + 1), dtype=int)
-    count = 0
-    for item in annotations:
-        saved_scores = score_matrix[classes_dict[item], data[count][0] - mins:
-                                    data[count][1] - mins + 1]
-        new_scores = [scores[count]] * len(saved_scores)
-        score_matrix[classes_dict[item], data[count][0] - mins:data[count][
-            1] - mins + 1] = [max(*pos_score)
-                              for pos_score in zip(saved_scores, new_scores)]
-        count += 1
-    return score_matrix, classes_dict
-
-
-def score_matrix_evaluation(score_matrix, classes_dict, THRESHOLD_SCORE):
-    ''' Score matrix is evaluated based on each position.
-	For every position the list of annotations with a score which reaches 
-	certain percentage of the overal best score of the cluster are stored '''
-    ann_per_reg = []
-    overal_best_score_reg = max((score_matrix.max(axis=1)))
-    for position in score_matrix.T:
-        ## score threshold calculated as a percentage of the OVERALL best score in the cluster
-        threshold = overal_best_score_reg * THRESHOLD_SCORE / 100
-        above_th = [idx
-                    for idx, score in enumerate(position)
-                    if position[idx] >= threshold]
-        ## select unique annotations in one position that are above threshold
-        ann_per_pos = list(set(
-            [key for key, value in classes_dict.items() if value in above_th]))
-        ann_per_reg.append(ann_per_pos)
-    return ann_per_reg
-
-
-def group_annot_regs(ann_per_reg):
-    ''' Get list of domains, annotations, longest common annotations and 
-	counts of positions with certain annotation per regions '''
-    ## tranform list of lists (potential multiple annotations for every position ) to flat list of all annotations
-    all_annotations = [item for sublist in ann_per_reg for item in sublist]
-    unique_annotations = list(set(all_annotations))
-    ann_pos_counts = [all_annotations.count(x) for x in unique_annotations]
-    unique_annotations = list(set(
-        [item for sublist in ann_per_reg for item in sublist]))
-    domain_type = list(set([annotation.split("|")[0]
-                            for annotation in unique_annotations]))
-    classification_list = [annotation.split("|")
-                           for annotation in unique_annotations]
-    ann_substring = "|".join(os.path.commonprefix(classification_list))
-    domain_type = "/".join(domain_type)
-    return domain_type, ann_substring, unique_annotations, ann_pos_counts
-
-
-def best_score(scores, region):
-    ''' From overlapping intervals take the one with the highest score '''
-    ## if more hits have the same best score take only the first one
-    best_idx = region[np.where(scores == max(scores))[0][0]]
-    best_idx_reg = np.where(scores == max(scores))[0][0]
-    return best_idx, best_idx_reg
-
-
-def create_gff3(domain_type, ann_substring, unique_annotations, ann_pos_counts,
-                dom_start, dom_end, step, best_idx, annotation_best,
-                db_name_best, db_starts_best, db_ends_best, strand, score,
-                seq_id, db_seq, query_seq, domain_size, positions, gff, consensus):
-    ''' Record obtained information about domain corresponding to individual cluster to common gff file '''
-    best_start = positions[best_idx][0]
-    best_end = positions[best_idx][1]
-    best_score = score[best_idx]
-    ## proportion of length of the best hit to the whole region length found by base
-    length_proportion = int((best_end - best_start + 1) /
-                            (dom_end - dom_start + 1) * 100)
-    db_seq_best = db_seq[best_idx]
-    query_seq_best = query_seq[best_idx]
-    domain_size_best = domain_size[best_idx]
-    [percent_ident, align_similarity, relat_align_len, relat_interrupt,
-     db_len_proportion
-     ] = filter_params(db_seq_best, query_seq_best, domain_size_best)
-    ann_substring = "|".join(ann_substring.split("|")[1:])
-    annotation_best = "|".join([db_name_best] + annotation_best.split("|")[1:])
-    if "DANTE_PART" in seq_id:
-        part = int(seq_id.split("DANTE_PART")[1].split(":")[0].split("_")[0])
-        dom_start = dom_start + (part - 1) * step
-        dom_end = dom_end + (part - 1) * step
-        best_start = best_start + (part - 1) * step
-        best_end = best_end + (part - 1) * step
-    if ann_substring is '':
-        ann_substring = "NONE(Annotations from different classes)"
-    if len(unique_annotations) > 1:
-        unique_annotations = ",".join(["{}[{}bp]".format(
-            ann, pos) for ann, pos in zip(unique_annotations, ann_pos_counts)])
-    else:
-        unique_annotations = unique_annotations[0]
-    if __name__ == '__main__':
-        SOURCE = configuration.SOURCE_DANTE
-    else:
-        SOURCE = configuration.SOURCE_PROFREP
-    if "/" in domain_type:
-        gff.write(
-            "{}\t{}\t{}\t{}\t{}\t.\t{}\t{}\tName={};Final_Classification=Ambiguous_domain;Region_Hits_Classifications_={}\n".format(
-                seq_id, SOURCE, configuration.DOMAINS_FEATURE, dom_start,
-                dom_end, strand, configuration.PHASE, domain_type,
-                unique_annotations))
-    else:
-        gff.write(
-            "{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\tName={};Final_Classification={};Region_Hits_Classifications={};Best_Hit={}:{}-{}[{}percent];Best_Hit_DB_Pos={}:{}of{};DB_Seq={};Region_Seq={};Query_Seq={};Identity={};Similarity={};Relat_Length={};Relat_Interruptions={};Hit_to_DB_Length={}\n".format(
-                seq_id, SOURCE, configuration.DOMAINS_FEATURE, dom_start,
-                dom_end, best_score, strand, configuration.PHASE, domain_type,
-                ann_substring, unique_annotations, annotation_best, best_start,
-                best_end, length_proportion, db_starts_best, db_ends_best,
-                domain_size_best, db_seq_best, consensus, query_seq_best, percent_ident,
-                align_similarity, relat_align_len, relat_interrupt,
-                db_len_proportion))
-
-
-def filter_params(db, query, protein_len):
-    ''' Calculate basic statistics of the quality of the alignment '''
-    score_dict = alignment_scoring()
-    num_ident = 0
-    count_interrupt = 0
-    count_similarity = 0
-    alignment_len = 0
-    for i, j in zip(db.upper(), query.upper()):
-        if i == j and i != "X":
-            num_ident += 1
-        if j == "/" or j == "\\" or j == "*":
-            count_interrupt += 1
-        if (i.isalpha() or i == "*") and (j.isalpha() or j == "*"):
-            if int(score_dict["{}{}".format(i, j)]) > 0:
-                count_similarity += 1
-    ## gapless alignment length proportional to the domain protein length
-    relat_align_len = round((len(db) - db.count("-")) / protein_len, 3)
-    ## proportional identical bases (except of X) to al.length
-    align_identity = round(num_ident / len(db), 2)
-    ## proportional count of positive scores from scoring matrix to al. length
-    align_similarity = round(count_similarity / len(db), 2)
-    ## number of interruptions per 100 bp
-    relat_interrupt = round(count_interrupt / math.ceil((len(query) / 100)), 2)
-    ## Proportion of alignment to the original length of protein domain from database (indels included)
-    db_len_proportion = round(len(db) / protein_len, 2)
-    return align_identity, align_similarity, relat_align_len, relat_interrupt, db_len_proportion
-
-
-def line_generator(tab_pipe, maf_pipe, start):
-    ''' Yield individual lines of LASTAL stdout for single sequence '''
-    if hasattr(line_generator, "dom"):
-        seq_id = line_generator.dom.split("\t")[6]
-        yield line_generator.dom.encode("utf-8")
-        del line_generator.dom
-    line_tab = ""
-    for line_tab in tab_pipe:
-        line_tab = line_tab.decode("utf-8")
-        if not line_tab.startswith('#'):
-            if start:
-                if not ('seq_id' in locals() and
-                        seq_id != line_tab.split("\t")[6]):
-                    seq_id = line_tab.split("\t")[6]
-                    start = False
-            line_maf = [maf_pipe.readline() for line_count in range(4)]
-            db_seq = line_maf[1].decode("utf-8").rstrip().split(" ")[-1]
-            alignment_seq = line_maf[2].decode("utf-8").rstrip().split(" ")[-1]
-            line = "{}\t{}\t{}".format(line_tab, db_seq, alignment_seq)
-            line_id = line.split("\t")[6]
-            if seq_id != line_id:
-                line_generator.dom = line
-                return
-            else:
-                yield line.encode("utf-8")
-        else:
-            maf_pipe.readline()
-    if line_tab == "":
-        raise RuntimeError
-    else:
-        return
-
-
-def get_version(path, LAST_DB):
-    '''Return version is run from git repository '''
-    version_string = (
-        "##-----------------------------------------------\n"
-        "##PROTEIN DATABASE VERSION : {PD}\n"
-        "##-----------------------------------------------\n").format(
-            PD=os.path.basename(LAST_DB)
-        )
-    if os.path.exists(".git"):
-        try:
-            branch = subprocess.check_output("git rev-parse --abbrev-ref HEAD",
-                                             shell=True,
-                                             cwd=path).decode('ascii').strip()
-            shorthash = subprocess.check_output("git log --pretty=format:'%h' -n 1  ",
-                                                shell=True,
-                                                cwd=path).decode('ascii').strip()
-            revcount = len(subprocess.check_output("git log --oneline",
-                                                   shell=True,
-                                                   cwd=path).decode('ascii').split())
-            version_string = (
-                "##-----------------------------------------------\n"
-                "##PIPELINE VERSION         : "
-                "{branch}-rv-{revcount}({shorthash})\n"
-                "##PROTEIN DATABASE VERSION : {PD}\n"
-                "##-----------------------------------------------\n").format(
-                    branch=branch,
-                    shorthash=shorthash,
-                    revcount=revcount,
-                    PD=os.path.basename(LAST_DB))
-        except:
-            pass
-    return version_string
-
-
-def write_info(dom_gff_tmp, version_string):
-    dom_gff_tmp.write("{}\n".format(configuration.HEADER_GFF))
-    dom_gff_tmp.write(version_string)
-
-
-def domain_search(QUERY, LAST_DB, CLASSIFICATION, OUTPUT_DOMAIN,
-                  THRESHOLD_SCORE, WIN_DOM, OVERLAP_DOM, SCORING_MATRIX):
-    ''' Search for protein domains using our protein database and external tool LAST,
-	stdout is parsed in real time and hits for a single sequence undergo further processing
-	- tabular format(TAB) to get info about position, score, orientation
-	- MAF format to gain alignment and original sequence
-	'''
-
-    step = WIN_DOM - OVERLAP_DOM
-    [headers, above_win, below_win, lens_above_win, seq_starts, seq_ends
-     ] = characterize_fasta(QUERY, WIN_DOM)
-    query_temp = split_fasta(QUERY, WIN_DOM, step, headers, above_win,
-                             below_win, lens_above_win, seq_starts, seq_ends)
-
-    ## TAB output contains all the alignment scores, positions, strands...
-    lastal_columns = {
-        "BL80" : ("score, name_db, start_db, al_size_db, strand_db,"
-                   " seq_size_db, name_q, start_q, al_size_q, strand_q, seq_size_q,"
-                   " block1, block2, block3, db_seq, q_seq"),
-        "BL62" : ("score, name_db, start_db, al_size_db, strand_db,"
-                   " seq_size_db, name_q, start_q, al_size_q, strand_q,"
-                   " seq_size_q, block1, block2, block3, db_seq, q_seq"),
-        "MIQS" : ("score, name_db, start_db, al_size_db, strand_db,"
-                  " seq_size_db, name_q, start_q, al_size_q, strand_q,"
-                  " seq_size_q, block1, db_seq, q_seq"),
-    }
-    tab = subprocess.Popen(
-        "lastal -F15 {} {} -L 10 -m 70 -p {} -e 80 -f TAB".format(LAST_DB,
-                                                                  query_temp,
-                                                                  SCORING_MATRIX),
-        stdout=subprocess.PIPE,
-        shell=True)
-    ## MAF output contains alignment sequences
-    maf = subprocess.Popen(
-        "lastal -F15 {} {} -L 10 -m 70 -p {}  -e 80 -f MAF".format(LAST_DB,
-                                                                   query_temp,
-                                                                   SCORING_MATRIX),
-        stdout=subprocess.PIPE,
-        shell=True)
-
-    tab_pipe = tab.stdout
-    maf_pipe = maf.stdout
-    maf_pipe.readline()
-
-    seq_ids = []
-    dom_tmp = NamedTemporaryFile(delete=False)
-    with open(dom_tmp.name, "w") as dom_gff_tmp:
-        path = os.path.dirname(os.path.realpath(__file__))
-        version_string = get_version(path, LAST_DB)
-        write_info(dom_gff_tmp, version_string)
-    gff = open(dom_tmp.name, "a")
-    start = True
-    while True:
-        try:
-            with warnings.catch_warnings():
-                warnings.simplefilter("ignore")
-                sequence_hits = np.genfromtxt(
-                    line_generator(tab_pipe, maf_pipe, start),
-                    names=lastal_columns[SCORING_MATRIX],
-                    usecols=("score, name_q, start_q, al_size_q,"
-                             " strand_q, seq_size_q, name_db, db_seq,"
-                             " q_seq, seq_size_db, start_db, al_size_db"),
-                    dtype=None,
-                    comments=None)
-        except RuntimeError:
-            break
-        ## if there are no domains found
-        if sequence_hits.size is 0:
-            gff.write("##NO DOMAINS")
-            return [], [], [], []
-
-        ############# PARSING LASTAL OUTPUT ############################
-        sequence_hits = np.atleast_1d(sequence_hits)
-        score = sequence_hits['score'].astype("int")
-        seq_id = sequence_hits['name_q'][0].astype("str")
-        start_hit = sequence_hits['start_q'].astype("int")
-        end_hit = start_hit + sequence_hits['al_size_q'].astype("int")
-        strand = sequence_hits['strand_q'].astype("str")
-        seq_len = sequence_hits['seq_size_q'].astype("int")
-        domain_db = sequence_hits['name_db'].astype("str")
-        db_seq = sequence_hits['db_seq'].astype("str")
-        query_seq = sequence_hits['q_seq'].astype("str")
-        domain_size = sequence_hits['seq_size_db'].astype("int")
-        db_start = sequence_hits['start_db'].astype("int") + 1
-        db_end = sequence_hits['start_db'].astype("int") + sequence_hits[
-            'al_size_db'].astype("int")
-
-        [reverse_strand_idx, positions_plus, positions_minus
-         ] = hits_processing(seq_len, start_hit, end_hit, strand)
-        strand_gff = "+"
-        [mins_plus, maxs_plus, data_plus, indices_plus
-         ] = overlapping_regions(positions_plus)
-        [mins_minus, maxs_minus, data_minus, indices_minus
-         ] = overlapping_regions(positions_minus)
-        positions = positions_plus + positions_minus
-        indices_overal = indices_plus + [x + reverse_strand_idx
-                                         for x in indices_minus]
-        mins = mins_plus + mins_minus
-        maxs = maxs_plus + maxs_minus
-        data = data_plus + data_minus
-        ## process every region (cluster) of overlapping hits sequentially
-        count_region = 0
-        for region in indices_overal:
-            db_names = domain_db[np.array(region)]
-            db_starts = db_start[np.array(region)]
-            db_ends = db_end[np.array(region)]
-            scores = score[np.array(region)]
-            regions_above_threshold = [
-                region[i]
-                for i, _ in enumerate(region)
-                if max(scores) / 100 * THRESHOLD_SCORE < scores[i]
-            ]
-            ## sort by score first:
-            consensus = get_full_translation(
-                translation_alignments(
-                    query_seq=sortby(query_seq[regions_above_threshold], score[regions_above_threshold], True),
-                    start_hit=sortby(start_hit[regions_above_threshold], score[regions_above_threshold], True),
-                    end_hit=sortby(end_hit[regions_above_threshold], score[regions_above_threshold], True))
-                )
-
-            annotations = domain_annotation(db_names, CLASSIFICATION)
-            [score_matrix, classes_dict] = score_table(
-                mins[count_region], maxs[count_region], data[count_region],
-                annotations, scores, CLASSIFICATION)
-            ann_per_reg = score_matrix_evaluation(score_matrix, classes_dict,
-                                                  THRESHOLD_SCORE)
-            [domain_type, ann_substring, unique_annotations, ann_pos_counts
-             ] = group_annot_regs(ann_per_reg)
-            [best_idx, best_idx_reg] = best_score(scores, region)
-            annotation_best = annotations[best_idx_reg]
-            db_name_best = db_names[best_idx_reg]
-            db_starts_best = db_starts[best_idx_reg]
-            db_ends_best = db_ends[best_idx_reg]
-            if count_region == len(indices_plus):
-                strand_gff = "-"
-            if strand_gff == "+":
-                feature_start = min(start_hit[regions_above_threshold]) + 1
-                feature_end = max(end_hit[regions_above_threshold])
-            else:
-                feature_end = seq_len[region][0] - min(start_hit[regions_above_threshold])
-                feature_start = seq_len[region][0] - max(end_hit[regions_above_threshold]) + 1
-            create_gff3(domain_type, ann_substring, unique_annotations,
-                        ann_pos_counts, feature_start,feature_end,
-                        step, best_idx, annotation_best, db_name_best,
-                        db_starts_best, db_ends_best, strand_gff, score,
-                        seq_id, db_seq, query_seq, domain_size, positions, gff, consensus)
-            count_region += 1
-        seq_ids.append(seq_id)
-    os.unlink(query_temp)
-    gff.close()
-    dom_tmp.close()
-    ## if any sequence from input data was split into windows, merge and adjust the data from individual windows
-    if any("DANTE_PART" in x for x in seq_ids):
-        adjust_gff(OUTPUT_DOMAIN, dom_tmp.name, WIN_DOM, OVERLAP_DOM, step)
-    ## otherwise use the temporary output as the final domains gff
-    else:
-        shutil.copy2(dom_tmp.name, OUTPUT_DOMAIN)
-    os.unlink(dom_tmp.name)
-
-def  sortby(a, by, reverse=False):
-    ''' sort according values in the by list '''
-    a_sorted = [i[0] for i in
-                sorted(
-                    zip(a, by),
-                    key=lambda i: i[1],
-                    reverse=reverse
-                )]
-    return a_sorted
-
-
-def a2nnn(s):
-    s1 = "".join([c if c in ['/', '\\'] else c + c + c
-                  for c in s.replace("-", "")])
-    # collapse frameshifts (/)
-    s2 = re.sub("[a-zA-Z*]{2}//[a-zA-Z*]{2}", "//", s1)
-    s3 = re.sub("[a-zA-Z*]/[a-zA-Z*]", "/", s2)
-    return (s3)
-
-
-
-def rle(s):
-    '''run length encoding but max is set to 3 (codon)'''
-    prev = ""
-    count = 1
-    char = []
-    length = []
-    L = 0
-    for n in s:
-        if n == prev and count < (3 - L):
-            count += 1
-        else:
-            char.append(prev)
-            length.append(count)
-            L = 1 if prev == "/" else 0
-            prev = n
-            count = 1
-    char.append(prev)
-    length.append(count)
-    sequence = char[1:]
-    return sequence, length[1:]
-
-def get_full_translation(translations):
-    '''get one full length translation  from multiple partial
-    aligned translation '''
-    # find minimal set of alignements
-    minimal_set = []
-    not_filled_prev = len(translations[0])
-    for s in translations:
-        minimal_set.append(s)
-        # check defined position - is there only '-' character?
-        not_filled = sum([set(i) == {"-"} for i in  zip(*minimal_set)])
-        if not_filled == 0:
-            break
-        if not_filled == not_filled_prev:
-            # last added sequence did not improve coverage - remove it.
-            minimal_set.pop()
-        not_filled_prev = not_filled
-    # merge translations
-    final_translation = minimal_set[0]
-    # record positions of joins to correct frameshifts reportings
-    position_of_joins = set()
-    position_of_joins_rle = set()
-    if len(minimal_set) > 1:  # translation must be merged
-        for s in minimal_set[1:]:
-            s1 = re.search(r"-\w", final_translation)
-            s2 = re.search(r"\w-", final_translation)
-            if s1:
-                position_of_joins.add(s1.start())
-            if s2:
-                position_of_joins.add((s2.end() - 1))
-            final_translation = "".join(
-                [b if a == "-" else a for a, b in zip(final_translation, s)])
-    translation_rle = rle(final_translation)
-    cumsumed_positions = np.cumsum(translation_rle[1])
-    for p in position_of_joins:
-        position_of_joins_rle.add(sum(cumsumed_positions <= p))
-    # insert /\ when necessary
-    for p in position_of_joins_rle:
-        if translation_rle[0][p] not in ['/',"//","\\", "\\\\"]:
-            if translation_rle[1][p] == 2:
-                translation_rle[0][p] = translation_rle[0][p] + "/"
-            if translation_rle[1][p] == 1:
-                translation_rle[0][p] = "\\"
-    consensus = "".join(translation_rle[0])
-    return consensus
-
-
-# helper function for debugging
-def translation_alignments(query_seq, start_hit, end_hit):
-    pstart = min(start_hit)
-    pend = max(end_hit)
-    nnn = list()
-    for s, start, end in zip(query_seq, start_hit, end_hit):
-        nnn.append("-" * (start - pstart) + a2nnn(s) + "-" * (pend - end))
-    return (nnn)
-
-
-
-def adjust_gff(OUTPUT_DOMAIN, gff, WIN_DOM, OVERLAP_DOM, step):
-    ''' Original gff file is adjusted in case of containing cut parts 
-	- for consecutive sequences overlap is divided to half with first half 
-	of records(domains) belonging to the first sequence and second to the following one.
-	Duplicate domains going through the middle of the overlap are removed.
-	First and the last part (marked as LAST) of a certain sequence are 
-	handled separately as the are overlapped from one side only '''
-
-    seq_id_all = []
-    class_dict = defaultdict(int)
-    seen = set()
-    with open(OUTPUT_DOMAIN, "w") as adjusted_gff:
-        with open(gff, "r") as primary_gff:
-            start = True
-            for line in primary_gff:
-                if line.startswith("#"):
-                    adjusted_gff.write(line)
-                else:
-                    split_line = line.split("\t")
-                    classification = split_line[-1].split(";")[1].split("=")[1]
-                    if start:
-                        seq_id_all.append(split_line[0].split("_DANTE_PART")[
-                            0])
-                        start = False
-                    seq_id = split_line[0].split("_DANTE_PART")[0]
-                    if "DANTE_PART" in line:
-                        line_without_id = "\t".join(split_line[1:])
-                        part = int(split_line[0].split("_DANTE_PART")[1].split(
-                            ":")[0].split("_")[0])
-                        if seq_id != seq_id_all[-1]:
-                            seq_id_all.append(seq_id)
-
-                            ## first part of the sequence
-                        if part == 1:
-                            cut_end = WIN_DOM - OVERLAP_DOM / 2
-                            if int(split_line[3]) <= cut_end <= int(split_line[
-                                    4]):
-                                if line_without_id not in seen:
-                                    adjusted_gff.write("{}\t{}".format(
-                                        seq_id, line_without_id))
-                                    class_dict[classification] += 1
-                                    seen.add(line_without_id)
-                            elif int(split_line[4]) < cut_end:
-                                adjusted_gff.write("{}\t{}".format(
-                                    seq_id, line_without_id))
-                                class_dict[classification] += 1
-
-                                ## last part of the sequence
-                        elif "LAST" in split_line[0]:
-                            cut_start = OVERLAP_DOM / 2 + (part - 1) * step
-                            if int(split_line[3]) <= cut_start <= int(
-                                    split_line[4]):
-                                if line_without_id not in seen:
-                                    adjusted_gff.write("{}\t{}".format(
-                                        seq_id, line_without_id))
-                                    class_dict[classification] += 1
-                                    seen.add(line_without_id)
-                            elif int(split_line[3]) > cut_start:
-                                adjusted_gff.write("{}\t{}".format(
-                                    seq_id, line_without_id))
-                                class_dict[classification] += 1
-
-                        ## middle part of the sequence
-                        else:
-                            cut_start = OVERLAP_DOM / 2 + (part - 1) * step
-                            cut_end = WIN_DOM - OVERLAP_DOM / 2 + (part -
-                                                                   1) * step
-                            if int(split_line[3]) <= cut_start <= int(
-                                    split_line[4]) or int(split_line[
-                                        3]) <= cut_end <= int(split_line[4]):
-                                if line_without_id not in seen:
-                                    adjusted_gff.write("{}\t{}".format(
-                                        seq_id, line_without_id))
-                                    class_dict[classification] += 1
-                                    seen.add(line_without_id)
-                            elif int(split_line[3]) > cut_start and int(
-                                    split_line[4]) < cut_end:
-                                adjusted_gff.write("{}\t{}".format(
-                                    seq_id, line_without_id))
-                                class_dict[classification] += 1
-                    ## not divived
-                    else:
-                        if seq_id != seq_id_all[-1]:
-                            seq_id_all.append(seq_id)
-                        adjusted_gff.write(line)
-                        class_dict[classification] += 1
-
-
-def main(args):
-
-    t = time.time()
-
-    QUERY = args.query
-    LAST_DB = args.protein_database
-    CLASSIFICATION = args.classification
-    OUTPUT_DOMAIN = args.domain_gff
-    NEW_LDB = args.new_ldb
-    OUTPUT_DIR = args.output_dir
-    THRESHOLD_SCORE = args.threshold_score
-    WIN_DOM = args.win_dom
-    OVERLAP_DOM = args.overlap_dom
-    SCORING_MATRIX = args.scoring_matrix
-    configuration.SC_MATRIX = configuration.SC_MATRIX_SKELETON.format(SCORING_MATRIX)
-
-    if OUTPUT_DOMAIN is None:
-        OUTPUT_DOMAIN = configuration.DOMAINS_GFF
-    if os.path.isdir(LAST_DB):
-        LAST_DB = os.path.join(LAST_DB, configuration.LAST_DB_FILE)
-    if os.path.isdir(CLASSIFICATION):
-        CLASSIFICATION = os.path.join(CLASSIFICATION, configuration.CLASS_FILE)
-
-    if NEW_LDB:
-        subprocess.call("lastdb -p -cR01 {} {}".format(LAST_DB, LAST_DB),
-                        shell=True)
-
-    if OUTPUT_DIR and not os.path.exists(OUTPUT_DIR):
-        os.makedirs(OUTPUT_DIR)
-
-    if not os.path.isabs(OUTPUT_DOMAIN):
-        if OUTPUT_DIR is None:
-            OUTPUT_DIR = configuration.TMP
-            if not os.path.exists(OUTPUT_DIR):
-                os.makedirs(OUTPUT_DIR)
-        OUTPUT_DOMAIN = os.path.join(OUTPUT_DIR,
-                                     os.path.basename(OUTPUT_DOMAIN))
-    domain_search(QUERY, LAST_DB, CLASSIFICATION, OUTPUT_DOMAIN,
-                  THRESHOLD_SCORE, WIN_DOM, OVERLAP_DOM, SCORING_MATRIX)
-
-    print("ELAPSED_TIME_DOMAINS = {} s".format(time.time() - t))
-
-
-if __name__ == "__main__":
-    import argparse
-    from argparse import RawDescriptionHelpFormatter
-
-    class CustomFormatter(argparse.ArgumentDefaultsHelpFormatter,
-                          argparse.RawDescriptionHelpFormatter):
-        pass
-
-    parser = argparse.ArgumentParser(
-        description=
-        '''Script performs similarity search on given DNA sequence(s) in (multi)fasta against our protein domains database of all Transposable element for certain group of organisms (Viridiplantae or Metazoans). Domains are subsequently annotated and classified - in case certain domain has multiple annotations assigned, classifation is derived from the common classification level of all of them. Domains search is accomplished engaging LASTAL alignment tool.
-		
-	DEPENDENCIES:
-		- python 3.4 or higher with packages:
-			-numpy
-		- lastal 744 or higher [http://last.cbrc.jp/]
-		- configuration.py module
-
-	EXAMPLE OF USAGE:
-		
-		./protein_domains_pd.py -q PATH_TO_INPUT_SEQ -pdb PATH_TO_PROTEIN_DB -cs PATH_TO_CLASSIFICATION_FILE
-		
-	When running for the first time with a new database use -nld option allowing lastal to create indexed database files:
-		
-		-nld True
-	
-		''',
-        epilog="""""",
-        formatter_class=CustomFormatter)
-
-    requiredNamed = parser.add_argument_group('required named arguments')
-    requiredNamed.add_argument(
-        "-q",
-        "--query",
-        type=str,
-        required=True,
-        help=
-        'input DNA sequence to search for protein domains in a fasta format. Multifasta format allowed.')
-    requiredNamed.add_argument('-pdb',
-                               "--protein_database",
-                               type=str,
-                               required=True,
-                               help='protein domains database file')
-    requiredNamed.add_argument('-cs',
-                               '--classification',
-                               type=str,
-                               required=True,
-                               help='protein domains classification file')
-    parser.add_argument("-oug",
-                        "--domain_gff",
-                        type=str,
-                        help="output domains gff format")
-    parser.add_argument(
-        "-nld",
-        "--new_ldb",
-        type=str,
-        default=False,
-        help=
-        "create indexed database files for lastal in case of working with new protein db")
-    parser.add_argument(
-        "-dir",
-        "--output_dir",
-        type=str,
-        help="specify if you want to change the output directory")
-    parser.add_argument(
-        "-M",
-        "--scoring_matrix",
-        type=str,
-        default="BL80",
-        choices=['BL80', 'BL62', 'MIQS'],
-        help="specify scoring matrix to use for similarity search (BL80, BL62, MIQS)")
-    parser.add_argument(
-        "-thsc",
-        "--threshold_score",
-        type=int,
-        default=80,
-        help=
-        "percentage of the best score in the cluster to be tolerated when assigning annotations per base")
-    parser.add_argument(
-        "-wd",
-        "--win_dom",
-        type=int,
-        default=10000000,
-        help="window to process large input sequences sequentially")
-    parser.add_argument("-od",
-                        "--overlap_dom",
-                        type=int,
-                        default=10000,
-                        help="overlap of sequences in two consecutive windows")
-
-    args = parser.parse_args()
-    main(args)

--- a/dante.xml	Fri Apr 03 07:27:59 2020 -0400
+++ b/dante.xml	Wed Jan 25 13:06:55 2023 +0000
@@ -1,10 +1,7 @@
-<tool id="dante" name="Domain based ANnotation of Transposable Elements - DANTE" version="1.1.0">
+<tool id="dante" name="Domain based ANnotation of Transposable Elements - DANTE" version="1.1.4">
   <description> Tool for annotation of transposable elements based on the similarity to conserved protein domains database. </description>
   <requirements>
-    <requirement type="package">last</requirement>
-    <requirement type="package">numpy</requirement>
-    <requirement type="package" version="1.0">rexdb</requirement>
-    <requirement type="set_environment">REXDB</requirement>
+    <requirement type="package">dante=0.1.4</requirement>
   </requirements>
   <stdio>
     <regex match="Traceback" source="stderr" level="fatal" description="Unknown error" />
@@ -12,7 +9,7 @@
   </stdio>
   <command>
     #if str($input_type.input_type_selector) == "aln"
-      python3 ${__tool_directory__}/parse_aln.py -a $(input_sequences) -f sequences.fasta -p sequences.profile
+      parse_aln.py -a $(input_sequences) -f sequences.fasta -p sequences.profile
       &amp;&amp;
       INPUT_SEQUENCES="sequences.fasta"
     #else    
@@ -21,13 +18,12 @@
     &amp;&amp;
 
 
-    python3 ${__tool_directory__}/dante.py --query \${INPUT_SEQUENCES} --domain_gff ${DomGff}
-	  --protein_database \${REXDB}/${db_type}_pdb
-	  --classification \${REXDB}/${db_type}_class
-    --scoring_matrix ${scoring_matrix}
+    dante --query \${INPUT_SEQUENCES} --domain_gff ${DomGff}
+	  --database $database
+      --scoring_matrix ${scoring_matrix}
 
     &amp;&amp;
-    python3 ${__tool_directory__}/dante_gff_output_filtering.py --dom_gff ${DomGff}
+    dante_gff_output_filtering.py --dom_gff ${DomGff}
     --domains_prot_seq ${Domains_filtered} --domains_filtered ${DomGff_filtered}
     --output_dir .
     --selected_dom All --th_identity 0.35
@@ -37,12 +33,12 @@
 
     #if str($input_type.input_type_selector) == "aln"
      &amp;&amp;
-     python3 ${__tool_directory__}/coverage2gff.py -p sequences.profile -g ${DomGff}
+     coverage2gff.py -p sequences.profile -g ${DomGff}
     #end if
 
     #if str($iterative) == "Yes"
     &amp;&amp;
-    python3 ${__tool_directory__}/dante_gff_output_filtering.py --dom_gff ${DomGff}
+   dante_gff_output_filtering.py --dom_gff ${DomGff}
     --domains_prot_seq domains_filtered.fasta --domains_filtered domains_filtered.gff
     --output_dir .
     --selected_dom All --th_identity 0.35
@@ -53,22 +49,22 @@
 
 
 
-    python3 ${__tool_directory__}/fasta2database.py domains_filtered.fasta domains_filtered.db
+    fasta2database.py domains_filtered.fasta domains_filtered.db
     domains_filtered.class
     &amp;&amp;
 
     lastdb -p domains_filtered.db domains_filtered.db
     &amp;&amp;
 
-    python3 ${__tool_directory__}/dante.py --query \${INPUT_SEQUENCES} --domain_gff ${DomGff2}
+    dante.py --query \${INPUT_SEQUENCES} --domain_gff ${DomGff2}
 	  --protein_database domains_filtered.db
 	  --classification domains_filtered.class
-    --scoring_matrix BL80
+      --scoring_matrix BL80
 
 
     #if str($input_type.input_type_selector) == "aln"
      &amp;&amp;
-     python3 ${__tool_directory__}/coverage2gff.py -p sequences.profile -g ${DomGff2}
+     coverage2gff.py -p sequences.profile -g ${DomGff2}
     #end if
     #end if
 
@@ -87,13 +83,12 @@
         <param name="input_sequences" type="data" format="txt" label="Sequences in ALN format (extracted from RepeatExplorer)"/>
       </when>
     </conditional>
-    <param name="db_type" type="select" label="Select taxon and protein domain database version (REXdb)" help="">
-      <options from_file="rexdb_versions.loc">
-        <column name="name" index="0"/>
-        <column name="value" index="1"/>
-      </options>
+    <param name="database" type="select" label="Select REXdb database">
+        <option value="Viridiplantae_v3.0" selected="true">Viridiplantae_v3.0</option>
+        <option value="Metazoa_v3.1" selected="true">Metazoa_v3.1</option>
+        <option value="Viridiplantae_v2.2" selected="true">Viridiplantae_v2.2</option>
+        <option value="Metazoa_v3.0" selected="true">Metazoa_v3.1</option>
     </param>
-
     <param name="scoring_matrix" type="select" label="Select scoring matrix">
       <option value="BL80" selected="true" >BLOSUM80</option>
       <option value="BL62">BLOSUM62</option>

--- a/dante_gff_output_filtering.py	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,361 +0,0 @@
-#!/usr/bin/env python3
-import sys
-import time
-import configuration
-import os
-import textwrap
-import subprocess
-from tempfile import NamedTemporaryFile
-from collections import defaultdict
-
-
-class Range():
-    '''
-    This class is used to check float range in argparse
-    '''
-
-    def __init__(self, start, end):
-        self.start = start
-        self.end = end
-
-    def __eq__(self, other):
-        return self.start <= other <= self.end
-
-    def __str__(self):
-        return "float range {}..{}".format(self.start, self.end)
-
-    def __repr__(self):
-        return "float range {}..{}".format(self.start, self.end)
-
-
-def check_file_start(gff_file):
-    count_comment = 0
-    with open(gff_file, "r") as gff_all:
-        line = gff_all.readline()
-        while line.startswith("#"):
-            line = gff_all.readline()
-            count_comment += 1
-    return count_comment
-
-
-def write_info(filt_dom_tmp, FILT_DOM_GFF, orig_class_dict, filt_class_dict,
-               dom_dict, version_lines, TH_IDENTITY, TH_SIMILARITY,
-               TH_LENGTH, TH_INTERRUPT, TH_LEN_RATIO, SELECTED_DOM):
-    '''
-	Write domains statistics in beginning of filtered GFF
-	'''
-    with open(FILT_DOM_GFF, "w") as filt_gff:
-        for line in version_lines:
-            filt_gff.write(line)
-        filt_gff.write(("##Filtering thresholdss: min identity: {}, min similarity: {},"
-                        " min relative alingment length: {}, max interuptions(stop or "
-                        "frameshift): {}, max relative alignment length: {}, selected"
-                        " domains: {} \n").format(TH_IDENTITY,
-                                                  TH_SIMILARITY,
-                                                  TH_LENGTH,
-                                                  TH_INTERRUPT,
-                                                  TH_LEN_RATIO,
-                                                  SELECTED_DOM))
-        filt_gff.write("##CLASSIFICATION\tORIGINAL_COUNTS\tFILTERED_COUNTS\n")
-        if not orig_class_dict:
-            filt_gff.write("##NO DOMAINS CLASSIFICATIONS\n")
-        for classification in sorted(orig_class_dict.keys()):
-            if classification in filt_class_dict.keys():
-                filt_gff.write("##{}\t{}\t{}\n".format(
-                    classification, orig_class_dict[
-                        classification], filt_class_dict[classification]))
-            else:
-                filt_gff.write("##{}\t{}\t{}\n".format(
-                    classification, orig_class_dict[classification], 0))
-        filt_gff.write("##-----------------------------------------------\n"
-                       "##SEQ\tDOMAIN\tCOUNTS\n")
-        if not dom_dict:
-            filt_gff.write("##NO DOMAINS\n")
-        for seq in sorted(dom_dict.keys()):
-            for dom, count in sorted(dom_dict[seq].items()):
-                filt_gff.write("##{}\t{}\t{}\n".format(seq, dom, count))
-        filt_gff.write("##-----------------------------------------------\n")
-        with open(filt_dom_tmp.name, "r") as filt_tmp:
-            for line in filt_tmp:
-                filt_gff.write(line)
-
-
-def get_file_start(gff_file):
-    count_comment = 0
-    lines = []
-    with open(gff_file, "r") as gff_all:
-        line = gff_all.readline()
-        while line.startswith("#"):
-            lines.append(line)
-            line = gff_all.readline()
-            count_comment += 1
-    return count_comment, lines
-
-
-def parse_gff_line(line):
-    '''Return dictionary with gff fields  and  atributers
-    Note - type of fields is strings
-    '''
-    # order of first 9 column is fixed
-    gff_line = dict(
-        zip(
-            ['seqid', 'source', 'type', 'start', 'end',
-             'score', 'strand', 'phase', 'attributes'],
-            line.split("\t")
-        )
-    )
-    # split attributes and replace:
-    gff_line['attributes'] = dict([i.split("=") for i in gff_line['attributes'].split(";")])
-    return gff_line
-
-def filter_qual_dom(DOM_GFF, FILT_DOM_GFF, TH_IDENTITY, TH_SIMILARITY,
-                    TH_LENGTH, TH_INTERRUPT, TH_LEN_RATIO, SELECTED_DOM,
-                    ELEMENT):
-    ''' Filter gff output based on domain and quality of alignment '''
-    [count_comment, version_lines] = get_file_start(DOM_GFF)
-    filt_dom_tmp = NamedTemporaryFile(delete=False)
-    with open(DOM_GFF, "r") as gff_all, open(filt_dom_tmp.name,
-                                             "w") as gff_filtered:
-        for _ in range(count_comment):
-            next(gff_all)
-        dom_dict = defaultdict(lambda: defaultdict(int))
-        orig_class_dict = defaultdict(int)
-        filt_class_dict = defaultdict(int)
-        seq_ids_all = []
-        xminimals = []
-        xmaximals = []
-        domains = []
-        xminimals_all = []
-        xmaximals_all = []
-        domains_all = []
-        start = True
-        for line in gff_all:
-            gff_line = parse_gff_line(line)
-            classification = gff_line['attributes']['Final_Classification']
-            orig_class_dict[classification] += 1
-            ## ambiguous domains filtered out automatically
-            if classification != configuration.AMBIGUOUS_TAG:
-                al_identity = float(gff_line['attributes']['Identity'])
-                al_similarity = float(gff_line['attributes']['Similarity'])
-                al_length = float(gff_line['attributes']['Relat_Length'])
-                relat_interrupt = float(gff_line['attributes']['Relat_Interruptions'])
-                db_len_proportion = float(gff_line['attributes']['Hit_to_DB_Length'])
-                dom_type = gff_line['attributes']['Name']
-                seq_id = gff_line['seqid']
-                xminimal = int(gff_line['start'])
-                xmaximal = int(gff_line['end'])
-                c1 = al_identity >= TH_IDENTITY
-                c2 = al_similarity >= TH_SIMILARITY
-                if (c1 and c2 and al_length >= TH_LENGTH and relat_interrupt <= TH_INTERRUPT and
-                        db_len_proportion <= TH_LEN_RATIO and
-                        (dom_type == SELECTED_DOM or SELECTED_DOM == "All") and
-                        (ELEMENT in classification)):
-                    gff_filtered.writelines(line)
-                    filt_class_dict[classification] += 1
-                    dom_dict[seq_id][dom_type] += 1
-                    if start:
-                        seq_ids_all.append(line.split("\t")[0])
-                        start = False
-                    if seq_id != seq_ids_all[-1]:
-                        seq_ids_all.append(seq_id)
-                        xminimals_all.append(xminimals)
-                        xmaximals_all.append(xmaximals)
-                        domains_all.append(domains)
-                        xminimals = []
-                        xmaximals = []
-                        domains = []
-                    xminimals.append(xminimal)
-                    xmaximals.append(xmaximal)
-                    domains.append(dom_type)
-    path = os.path.dirname(os.path.realpath(__file__))
-    write_info(filt_dom_tmp, FILT_DOM_GFF, orig_class_dict, filt_class_dict,
-               dom_dict, version_lines, TH_IDENTITY, TH_SIMILARITY,
-               TH_LENGTH, TH_INTERRUPT, TH_LEN_RATIO, SELECTED_DOM)
-    os.unlink(filt_dom_tmp.name)
-    xminimals_all.append(xminimals)
-    xmaximals_all.append(xmaximals)
-    domains_all.append(domains)
-    return xminimals_all, xmaximals_all, domains_all, seq_ids_all
-
-
-def get_domains_protseq(FILT_DOM_GFF, DOMAIN_PROT_SEQ):
-    ''' Get the translated protein sequence of original DNA seq for all the filtered domains regions 
-		The translated sequences are taken from alignment reported by LASTAL (Query_Seq attribute in GFF)	
-	'''
-    count_comment = check_file_start(FILT_DOM_GFF)
-    with open(FILT_DOM_GFF, "r") as filt_gff:
-        for comment_idx in range(count_comment):
-            next(filt_gff)
-        with open(DOMAIN_PROT_SEQ, "w") as dom_prot_file:
-            for line in filt_gff:
-                attributes = line.rstrip().split("\t")[8]
-                positions = attributes.split(";")[3].split("=")[1].split(":")[
-                    -1].split("[")[0]
-                dom = attributes.split(";")[0].split("=")[1]
-                dom_class = attributes.split(";")[1].split("=")[1]
-                seq_id = line.rstrip().split("\t")[0]
-                prot_seq_align = line.rstrip().split("\t")[8].split(";")[
-                    6].split("=")[1]
-                prot_seq = prot_seq_align.translate({ord(i): None
-                                                     for i in '/\\-'})
-                header_prot_seq = ">{}:{} {} {}".format(seq_id, positions, dom,
-                                                        dom_class)
-                dom_prot_file.write("{}\n{}\n".format(
-                    header_prot_seq, textwrap.fill(prot_seq,
-                                                   configuration.FASTA_LINE)))
-
-
-def main(args):
-
-    t = time.time()
-
-    DOM_GFF = args.dom_gff
-    DOMAIN_PROT_SEQ = args.domains_prot_seq
-    TH_IDENTITY = args.th_identity
-    TH_LENGTH = args.th_length
-    TH_INTERRUPT = args.interruptions
-    TH_SIMILARITY = args.th_similarity
-    TH_LEN_RATIO = args.max_len_proportion
-    FILT_DOM_GFF = args.domains_filtered
-    SELECTED_DOM = args.selected_dom
-    OUTPUT_DIR = args.output_dir
-    # DELETE : ELEMENT = args.element_type.replace("_pipe_", "|")
-    ELEMENT = args.element_type
-
-    if DOMAIN_PROT_SEQ is None:
-        DOMAIN_PROT_SEQ = configuration.DOM_PROT_SEQ
-    if FILT_DOM_GFF is None:
-        FILT_DOM_GFF = configuration.FILT_DOM_GFF
-
-    if OUTPUT_DIR and not os.path.exists(OUTPUT_DIR):
-        os.makedirs(OUTPUT_DIR)
-
-    if not os.path.isabs(FILT_DOM_GFF):
-        if OUTPUT_DIR is None:
-            OUTPUT_DIR = os.path.dirname(os.path.abspath(DOM_GFF))
-        FILT_DOM_GFF = os.path.join(OUTPUT_DIR, os.path.basename(FILT_DOM_GFF))
-        DOMAIN_PROT_SEQ = os.path.join(OUTPUT_DIR,
-                                       os.path.basename(DOMAIN_PROT_SEQ))
-
-    [xminimals_all, xmaximals_all, domains_all, seq_ids_all] = filter_qual_dom(
-        DOM_GFF, FILT_DOM_GFF, TH_IDENTITY, TH_SIMILARITY, TH_LENGTH,
-        TH_INTERRUPT, TH_LEN_RATIO, SELECTED_DOM, ELEMENT)
-    get_domains_protseq(FILT_DOM_GFF, DOMAIN_PROT_SEQ)
-
-    print("ELAPSED_TIME_DOMAINS = {} s".format(time.time() - t))
-
-
-if __name__ == "__main__":
-    import argparse
-    from argparse import RawDescriptionHelpFormatter
-
-    class CustomFormatter(argparse.ArgumentDefaultsHelpFormatter,
-                          argparse.RawDescriptionHelpFormatter):
-        pass
-
-    parser = argparse.ArgumentParser(
-        description=
-        '''The script performs DANTE's output filtering for quality and/or extracting specific type of protein domain or mobile elements of origin. For the filtered domains it reports their translated protein sequence of original DNA.
-		WHEN NO PARAMETERS GIVEN, IT PERFORMS QUALITY FILTERING USING THE DEFAULT PARAMETRES (optimized for Viridiplantae species)
-		
-		INPUTS:
-			- GFF3 file produced by protein_domains.py OR already filtered GFF3
-			
-			FILTERING OPTIONS:
-				> QUALITY: - Min relative length of alignemnt to the protein domain from DB (without gaps)
-				   - Identity 
-				   - Similarity (scoring matrix: BLOSUM82)
-				   - Interruption in the reading frame (frameshifts + stop codons) per every starting 100 AA
-				   - Max alignment proportion to the original length of database domain sequence 
-				> DOMAIN TYPE: choose from choices ('Name' attribute in GFF)
-				Records for ambiguous domain type (e.g. INT/RH) are filtered out automatically
-				
-				> MOBILE ELEMENT TYPE:
-				arbitrary substring of the element classification ('Final_Classification' attribute in GFF)
-				
-		OUTPUTS:
-			- filtered GFF3 file
-			- fasta file of translated protein sequences (from original DNA) for the aligned domains that match the filtering criteria 
-		
-	DEPENDENCIES:
-		- python 3.4 or higher
-		> ProfRep modules:
-			- configuration.py 
-
-	EXAMPLE OF USAGE:
-		Getting quality filtered integrase(INT) domains of all gypsy transposable elements:
-		./domains_filtering.py -dom_gff PATH_TO_INPUT_GFF -pdb PATH_TO_PROTEIN_DB -cs PATH_TO_CLASSIFICATION_FILE --selected_dom INT --element_type Ty3/gypsy 
-
-		''',
-        epilog="""""",
-        formatter_class=CustomFormatter)
-    requiredNamed = parser.add_argument_group('required named arguments')
-    requiredNamed.add_argument("-dg",
-                               "--dom_gff",
-                               type=str,
-                               required=True,
-                               help="basic unfiltered gff file of all domains")
-    parser.add_argument("-ouf",
-                        "--domains_filtered",
-                        type=str,
-                        help="output filtered domains gff file")
-    parser.add_argument("-dps",
-                        "--domains_prot_seq",
-                        type=str,
-                        help="output file containg domains protein sequences")
-    parser.add_argument("-thl",
-                        "--th_length",
-                        type=float,
-                        choices=[Range(0.0, 1.0)],
-                        default=0.8,
-                        help="proportion of alignment length threshold")
-    parser.add_argument("-thi",
-                        "--th_identity",
-                        type=float,
-                        choices=[Range(0.0, 1.0)],
-                        default=0.35,
-                        help="proportion of alignment identity threshold")
-    parser.add_argument("-ths",
-                        "--th_similarity",
-                        type=float,
-                        choices=[Range(0.0, 1.0)],
-                        default=0.45,
-                        help="threshold for alignment proportional similarity")
-    parser.add_argument(
-        "-ir",
-        "--interruptions",
-        type=int,
-        default=3,
-        help=
-        "interruptions (frameshifts + stop codons) tolerance threshold per 100 AA")
-    parser.add_argument(
-        "-mlen",
-        "--max_len_proportion",
-        type=float,
-        default=1.2,
-        help=
-        "maximal proportion of alignment length to the original length of protein domain from database")
-    parser.add_argument(
-        "-sd",
-        "--selected_dom",
-        type=str,
-        default="All",
-        choices=[
-            "All", "GAG", "INT", "PROT", "RH", "RT", "aRH", "CHDCR", "CHDII",
-            "TPase", "YR", "HEL1", "HEL2", "ENDO"
-        ],
-        help="filter output domains based on the domain type")
-    parser.add_argument(
-        "-el",
-        "--element_type",
-        type=str,
-        default="",
-        help="filter output domains by typing substring from classification")
-    parser.add_argument(
-        "-dir",
-        "--output_dir",
-        type=str,
-        default=None,
-        help="specify if you want to change the output directory")
-    args = parser.parse_args()
-    main(args)

--- a/dante_gff_output_filtering.xml	Fri Apr 03 07:27:59 2020 -0400
+++ b/dante_gff_output_filtering.xml	Wed Jan 25 13:06:55 2023 +0000
@@ -1,11 +1,16 @@
-<tool id="domains_filter" name="Protein Domains Filter" version="1.0.1">
+<tool id="domains_filter" name="Protein Domains Filter" version="1.1.4">
   <description> Tool for filtering of gff3 output from DANTE. Filtering can be performed based domain type and alignment quality. </description>
-  <stdio>
+    <requirements>
+        <requirement type="package">dante=0.1.4</requirement>
+    </requirements>
+    <stdio>
+        <regex match="Traceback" source="stderr" level="fatal" description="Unknown error" />
+        <regex match="error" source="stderr" level="fatal" description="Unknown error" />
     <regex match="Traceback" source="stderr" level="fatal" description="Unknown error" />
     <regex match="error" source="stderr" level="fatal" description="Unknown error" />
   </stdio>
 <command>
-python3 ${__tool_directory__}/dante_gff_output_filtering.py --dom_gff ${DomGff} --domains_prot_seq ${dom_prot_seq} --domains_filtered ${dom_filtered} --selected_dom ${selected_domain} --th_identity ${th_identity} --th_similarity ${th_similarity} --th_length ${th_length} --interruptions ${interruptions} --max_len_proportion ${th_len_ratio} --element_type '${element_type}'
+dante_gff_output_filtering.py --dom_gff ${DomGff} --domains_prot_seq ${dom_prot_seq} --domains_filtered ${dom_filtered} --selected_dom ${selected_domain} --th_identity ${th_identity} --th_similarity ${th_similarity} --th_length ${th_length} --interruptions ${interruptions} --max_len_proportion ${th_len_ratio} --element_type '${element_type}'
 		
 </command>
 <inputs>
@@ -16,10 +21,20 @@
 	<param name="interruptions" type="integer" value="3" label="Interruptions [frameshifts + stop codons]" help="Tolerance threshold per every starting 100 amino acids of alignment sequence" />
 	<param name="th_len_ratio" type="float" value="1.2" label="Maximal length proportion" help="Maximal proportion of alignment length to the original length of protein domain from database (including indels)" />
 	<param name="selected_domain" type="select" label="Select protein domain type" >
-    <options from_file="select_domain.loc" >
-     <column name="name" index="0"/>
-     <column name="value" index="0"/>
-	</options>
+        <option value="All" selected="true">All</option>
+        <option value="GAG">GAG</option>
+        <option value="INT">INT</option>
+        <option value="PROT">PROT</option>
+        <option value="RH">RH</option>
+        <option value="RT">RT</option>
+        <option value="aRH">aRH</option>
+        <option value="CHDCR">CHDCR</option>
+        <option value="CHDII">CHDII</option>
+        <option value="TPase">TPase</option>
+        <option value="YR">YR</option>
+        <option value="HEL1">HEL1</option>
+        <option value="HEL2">HEL2</option>
+        <option value="ENDO">ENDO</option>
    </param>
    <param name="element_type" type="text" value="" label="Filter based on classification" help="You can use preset options or enter an  arbitrary string to filter a certain repetitive element type of any level. It must be a continuous substring in a proper format of Final_Classification attribute of GFF3 file. Classification levels are separated by | character. Filtering is case sensitive">
      <option value="Ty1/copia">Ty1/copia</option>

--- a/dante_gff_to_dna.py	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,191 +0,0 @@
-#!/usr/bin/env python3
-
-import argparse
-import time
-import os
-import textwrap
-from collections import defaultdict
-from Bio import SeqIO
-import configuration
-from dante_gff_output_filtering import parse_gff_line
-t_nt_seqs_extraction = time.time()
-
-
-def str2bool(v):
-    if v.lower() in ('yes', 'true', 't', 'y', '1'):
-        return True
-    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
-        return False
-    else:
-        raise argparse.ArgumentTypeError('Boolean value expected')
-
-
-def check_file_start(gff_file):
-    count_comment = 0
-    with open(gff_file, "r") as gff_all:
-        line = gff_all.readline()
-        while line.startswith("#"):
-            line = gff_all.readline()
-            count_comment += 1
-    return count_comment, line
-
-
-def extract_nt_seqs(DNA_SEQ, DOM_GFF, OUT_DIR, CLASS_TBL, EXTENDED):
-    ''' Extract nucleotide sequences of protein domains found by DANTE from input DNA seq.
-		Sequences are saved in fasta files separately for each transposon lineage.
-		Sequences extraction is based on position of Best_Hit alignment reported by LASTAL.
-		The positions can be extended (optional) based on what part of database domain was aligned
-        (Best_Hit_DB_Pos attribute).
-		The strand orientation needs to be considered in extending and extracting the sequence itself
-	'''
-    [count_comment, first_line] = check_file_start(DOM_GFF)
-    unique_classes = get_unique_classes(CLASS_TBL)
-    files_dict = defaultdict(str)
-    domains_counts_dict = defaultdict(int)
-    allSeqs = SeqIO.to_dict(SeqIO.parse(DNA_SEQ, 'fasta'))
-    with open(DOM_GFF, "r") as domains:
-        for comment_idx in range(count_comment):
-            next(domains)
-        seq_id_stored = first_line.split("\t")[0]
-        allSeqs = SeqIO.to_dict(SeqIO.parse(DNA_SEQ, 'fasta'))
-        seq_nt = allSeqs[seq_id_stored]
-        for line in domains:
-            gff_line = parse_gff_line(line)
-            elem_type = gff_line['attributes']['Final_Classification']
-            if elem_type == configuration.AMBIGUOUS_TAG:
-                continue  # skip ambiguous classification
-            seq_id = gff_line['seqid']
-            dom_type = gff_line['attributes']['Name']
-            strand = gff_line['strand']
-            align_nt_start = int(gff_line['attributes']['Best_Hit'].split(":")[
-                -1].split("-")[0])
-            align_nt_end = int(gff_line['attributes']['Best_Hit'].split(":")[
-                -1].split("-")[1].split("[")[0])
-            if seq_id != seq_id_stored:
-                seq_id_stored = seq_id
-                seq_nt = allSeqs[seq_id_stored]
-            if EXTENDED:
-                ## which part of database sequence was aligned
-                db_part = gff_line['attributes']['Best_Hit_DB_Pos']
-                ## db_part = line.split("\t")[8].split(";")[4].split("=")[1]
-                ## datatabse seq length
-                dom_len = int(db_part.split("of")[1])
-                ## start of alignment on database seq
-                db_start = int(db_part.split("of")[0].split(":")[0])
-                ## end of alignment on database seq
-                db_end = int(db_part.split("of")[0].split(":")[1])
-                ## number of nucleotides missing in the beginning
-                dom_nt_prefix = (db_start - 1) * 3
-                ## number of nucleotides missing in the end
-                dom_nt_suffix = (dom_len - db_end) * 3
-                if strand == "+":
-                    dom_nt_start = align_nt_start - dom_nt_prefix
-                    dom_nt_end = align_nt_end + dom_nt_suffix
-                ## reverse extending for - strand
-                else:
-                    dom_nt_start = align_nt_start - dom_nt_suffix
-                    dom_nt_end = align_nt_end + dom_nt_prefix
-                ## correction for domain after extending having negative starting positon
-                dom_nt_start = max(1, dom_nt_start)
-            else:
-                dom_nt_start = align_nt_start
-                dom_nt_end = align_nt_end
-            full_dom_nt = seq_nt.seq[dom_nt_start - 1:dom_nt_end]
-            ## for - strand take reverse complement of the extracted sequence
-            if strand == "-":
-                full_dom_nt = full_dom_nt.reverse_complement()
-            full_dom_nt = str(full_dom_nt)
-            ## report when domain classified to the last level and no Ns in extracted seq
-            if elem_type in unique_classes and "N" not in full_dom_nt:
-                # lineages reported in separate fasta files
-                if not elem_type in files_dict:
-                    files_dict[elem_type] = os.path.join(
-                        OUT_DIR, "{}.fasta".format(elem_type.split("|")[
-                            -1].replace("/", "_")))
-                with open(files_dict[elem_type], "a") as out_nt_seq:
-                    out_nt_seq.write(">{}:{}-{}|{}[{}]\n{}\n".format(
-                        seq_nt.id, dom_nt_start, dom_nt_end, dom_type,
-                        elem_type, textwrap.fill(full_dom_nt,
-                                                 configuration.FASTA_LINE)))
-                domains_counts_dict[elem_type] += 1
-    return domains_counts_dict
-
-
-def get_unique_classes(CLASS_TBL):
-    ''' Get all the lineages of current domains classification table to check if domains are classified to the last level.
-		Only the sequences of unambiguous and completely classified domains will be extracted.
-	'''
-    unique_classes = []
-    with open(CLASS_TBL, "r") as class_tbl:
-        for line in class_tbl:
-            line_class = "|".join(line.rstrip().split("\t")[1:])
-            if line_class not in unique_classes:
-                unique_classes.append(line_class)
-    return unique_classes
-
-
-def write_domains_stat(domains_counts_dict, OUT_DIR):
-    ''' Report counts of domains for individual lineages
-	'''
-    total = 0
-    with open(
-            os.path.join(OUT_DIR,
-                         configuration.EXTRACT_DOM_STAT), "w") as dom_stat:
-        for domain, count in domains_counts_dict.items():
-            dom_stat.write(";{}:{}\n".format(domain, count))
-            total += count
-        dom_stat.write(";TOTAL:{}\n".format(total))
-
-
-def main(args):
-
-    DNA_SEQ = args.input_dna
-    DOM_GFF = args.domains_gff
-    OUT_DIR = args.out_dir
-    CLASS_TBL = args.classification
-    EXTENDED = args.extended
-
-    if not os.path.exists(OUT_DIR):
-        os.makedirs(OUT_DIR)
-
-    domains_counts_dict = extract_nt_seqs(DNA_SEQ, DOM_GFF, OUT_DIR, CLASS_TBL,
-                                          EXTENDED)
-    write_domains_stat(domains_counts_dict, OUT_DIR)
-
-    print("ELAPSED_TIME_EXTRACTION = {} s\n".format(time.time() -
-                                                    t_nt_seqs_extraction))
-
-
-if __name__ == "__main__":
-
-    # Command line arguments
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-i',
-                        '--input_dna',
-                        type=str,
-                        required=True,
-                        help='path to input DNA sequence')
-    parser.add_argument('-d',
-                        '--domains_gff',
-                        type=str,
-                        required=True,
-                        help='GFF file of protein domains')
-    parser.add_argument('-cs',
-                        '--classification',
-                        type=str,
-                        required=True,
-                        help='protein domains classification file')
-    parser.add_argument('-out',
-                        '--out_dir',
-                        type=str,
-                        default=configuration.EXTRACT_OUT_DIR,
-                        help='output directory')
-    parser.add_argument(
-        '-ex',
-        '--extended',
-        type=str2bool,
-        default=True,
-        help=
-        'extend the domains edges if not the whole datatabase sequence was aligned')
-    args = parser.parse_args()
-    main(args)

--- a/dante_gff_to_dna.xml	Fri Apr 03 07:27:59 2020 -0400
+++ b/dante_gff_to_dna.xml	Wed Jan 25 13:06:55 2023 +0000
@@ -1,20 +1,18 @@
-<tool id="domains_extract" name="Extract Domains Nucleotide Sequences" version="1.0.0">
+<tool id="domains_extract" name="Extract Domains Nucleotide Sequences" version="1.1.4">
   <description> Tool to extract nucleotide sequences of protein domains found by DANTE </description>
   <requirements>
-    <requirement type="package">biopython</requirement>
-    <requirement type="package" version="1.0">rexdb</requirement>
-    <requirement type="set_environment">REXDB</requirement>
+    <requirement type="package">dante=0.1.4</requirement>
   </requirements>
   <command>
     TEMP_DIR_LINEAGES=\$(mktemp -d) &amp;&amp;
-    python3 ${__tool_directory__}/dante_gff_to_dna.py --domains_gff ${domains_gff} --input_dna ${input_dna} --out_dir \$TEMP_DIR_LINEAGES
+    /mnt/raid/users/petr/workspace/dante/dante_gff_to_dna.py --domains_gff ${domains_gff} --input_dna ${input_dna} --out_dir \$TEMP_DIR_LINEAGES
 
     #if $extend_edges:
 	  --extended True
     #else:
 	  --extended False
     #end if
-	  --classification \${REXDB}/${db_type}_class
+	  --database ${database}
     &amp;&amp;
 
     cat \$TEMP_DIR_LINEAGES/*fasta > $out_fasta &amp;&amp;
@@ -23,12 +21,12 @@
   <inputs>
 	  <param format="fasta" type="data" name="input_dna" label="Input DNA" help="Choose input DNA sequence(s) to extract the domains from" />
 	  <param format="gff" type="data" name="domains_gff" label="Protein domains GFF" help="Choose filtered protein domains GFF3 (DANTE's output)" />
-	  <param name="db_type" type="select" label="Select taxon and protein domain database version (REXdb)" help="">
-      <options from_file="rexdb_versions.loc">
-        <column name="name" index="0"/>
-        <column name="value" index="1"/>
-      </options>
-    </param>
+	  <param name="database" type="select" label="Select REXdb database">
+        <option value="Viridiplantae_v3.0" selected="true">Viridiplantae_v3.0</option>
+        <option value="Metazoa_v3.1" selected="true">Metazoa_v3.1</option>
+        <option value="Viridiplantae_v2.2" selected="true">Viridiplantae_v2.2</option>
+        <option value="Metazoa_v3.0" selected="true">Metazoa_v3.1</option>
+      </param>
 
 	  <param name="extend_edges" type="boolean" truevalue="True" falsevalue="False" checked="True" label="Extend sequence edges" help="Extend extracted sequence edges to the full length of database domains sequences"/>
   </inputs>

--- a/dante_gff_to_tabular.xml	Fri Apr 03 07:27:59 2020 -0400
+++ b/dante_gff_to_tabular.xml	Wed Jan 25 13:06:55 2023 +0000
@@ -1,9 +1,9 @@
-<tool id="gff_to_tabular" name="Convert dante gff3 to tab delimited file" version="0.1.0" python_template_version="3.5">
+<tool id="gff_to_tabular" name="Convert dante gff3 to tab delimited file" version="0.1.4" python_template_version="3.5">
     <requirements>
-        <requirement type="package">R</requirement>
+        <requirement type="package">dante=0.1.4</requirement>
     </requirements>
     <command detect_errors="exit_code"><![CDATA[
-        Rscript ${__tool_directory__}/summarize_gff.R '$inputgff' '$output'
+        summarize_gff.R '$inputgff' '$output'
     ]]></command>
     <inputs>
       <param type="data" name="inputgff" format="gff3" />

--- a/dom_prot_seq.fa	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,57 +0,0 @@
->scaffold146.1|size86774:976-1289 RH Class_I|LTR|Ty1/copia|Bianca
-ISWRSTKQTIVAISSNHVELLAIHDTSRECVWLRFMIESIIMXXXXXXXXXXXXXXXXXX
-QLKE*YIKCDRTKHISPKFFFTQDLQKNGDVIIQQIRSNDNVVD
->scaffold146.1|size86774:6810-7049 PROT Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand
-LVDSGASCNLMSKRVMKQMGIPDEKLEFLDATLYAFDRRTIIPAGKIQLPVTLGEEERTR
-SEMVEFIIVDMDLAYNAILG
->scaffold146.1|size86774:8801-9241 RT Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat
-DFKGVNKHCQPDPFPLPHIDRLVDAVAGSSLLSTMDAYSGYHQISLAREDQAKSSFLTED
-GVFCYVVMPFGLRNAGATYQRLVNKIFADLLGKEMEIYVDDMIVKSLNDEDHIIYLSHCF
-EVCRTHRLKLNPAKCCFGVRSGKFLGY
->scaffold146.1|size86774:10819-11667 INT Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand
-RDAMDCVRRCQSCQYFAPINRKPGAEITLTELPCPFDRWGIDILGPFPQSVRQRRFCIVA
-VEYHSKWIEAEAVASITSEAVKKFVMNNIIVRFGCPRVLVSDNGPQFISDKFATFCEEYG
-IQQRTSSVYHPQTNGQAEASNKIILHGLRRNLDSLGGSWPDQLPHVLWAYRTTPKSSTGE
-TPFSLVYGSEAVAPVESTIITPRIAAYMHTESANTEFRELDLDLLEERRNEVYGRVRKQQ
-RALRKRYNQRVRPRQFEKGDLILRSVESQGHKGKLDRAWEGPY
->scaffold146.1|size86774:14592-14828 PROT Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila
-MVDLGASINLMPYSIYSALQLGPLQGTAIVIKLADRSNTHPEGVIEDVLVQVNNLVFPAD
-FYVLKMGKAENNDCPLLLG
->scaffold146.1|size86774:15420-15995 RT Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila
-IYAISDSDWVSPVHVVPKKTGFTVERNKNGELVPKRVTNGWRVCIDYRKLNDATRKDHFP
-LPFIDQMLERLAGKKFYCFLDGYSGYNQVAIAPEDQEKTTFTCTYGTYAFRKMPFGLCNA
-PATFQRCMLSIFSEFTGKFIEVFMDDFTVYGDSFEGALENLEKVLQRCVEKKLVLNSEKC
-HFMVRQGIVLGH
->scaffold146.1|size86774:16188-16634 RH Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila
-FNQECQEAFNKLKSLLTAAPIIQPPNWELPFELMCDASNYALGAVLGQKIEGKRHVIYYA
-SKTLSEAQIHYTTTEKELLAIVYALEKFRSYLLGTKITVHSDHAALRHLLSKKESKPRLI
-RWILLLQEFDLEIKDRAGTENAVADNLSR
->scaffold146.1|size86774:24873-25481 INT Class_I|LTR|Ty1/copia|Bianca
-HDRLGHPGMIMMRKIIRTTSGHSLKNREILHPREYICTACAQGKLITRPSPVKIMNERIT
-FLERIQGDICGPIHPACGPFRYFIVLIDASSRWSHVSLLSTRNHAFARLLSQIIRLRAHF
-PDYPVKKIRLDNAAEFTSRTFNNYCLAMGIDVEHPVEYVHTQNGLAESLIKRLQLIARPL
-LMKSKLPVTCWGHAIIHASSLIR
->scaffold146.1|size86774:26322-27032 RT Class_I|LTR|Ty1/copia|Bianca
-WKDAIESELKSLNKRDVFGPVVRTPEGVQPVGYKWVFVRKRNDKGEISRYKARLVAQGFS
-QRPGIDYDETYSPVMDATTFRFLISLAIEYGLDLQLMDVVTAYLYGSLDCEIYMKIPEGF
-HMPERYSSEPRTDYAIKLNKSLYGLKQSGRMWYNRLSEYLIKEGYKNNLVCPCVFMKKFE
-NEFVIIAVYVDDINIVGTQKALLDAVNCLKREFEMKDLGRTKYCLGLQIEYLKNGIF
->scaffold146.1|size86774:27723-28124 RH Class_I|LTR|Ty1/copia|Bianca
-DAGYRSDPHNGRSQTGYVFLNKGAAISWRSTKQTIAATSSNHAELLAIHETSRECVWLRS
-MIESIYNACGLFTDKMPPTVLYEDNSACIIQLKEGYIKGDRTKHISPKFFFTHDLQKNGE
-VIIQQIRSSDNVAD
->scaffold146.1|size86774:10299-10658 aRH Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat
-WNMYIDGSTQSGAGVGVHYITPYGDWINLAVKLQFPATNNVAEYEALLAGMNFALSLGVT
-RLKTFSDSQLVVEQFSGHFQAKEPMLEAYKSRSQLLAAKFSEFSLEHIPRESNRAADSLA
->scaffold146.1|size86774:16812-17666 INT Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila
-HASDYGGHFGPNRTARRILDVGFYWPSIFRDVYQFCRTCDACQRVGNITNRREMPQNYIL
-ANEIFDIWGLDFMGPFPQSQGNNYILVAVDYVSKWVEAIPTRTDDGKTVTEFLRKNIFTR
-YGVPKAIISDRGTHFCNSTMRAMMKKYNVIHKTTTAYHPQGNGQAEATNREIKSILEKVV
-NKKRSNWSQKLPDALWAYRTAYKTPIGTTPFRLIYGKHCNLPVGLEHKAYWAIREMNFEE
-GGDAELRQMQLQELDALRLEAYDNSRIYKERLKTYHDKKLLQQNF
->scaffold146.1|size86774:19976-20212 PROT Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila
-MVDLGASINLMPYYIYSALKLGSLQGTAIIIKLADRSETHPEGVVKDVLAQVNNLVFPAD
-FYVLKMGEAENDDCPLLLG
->scaffold146.1|size86774:28912-29124 PROT Class_I|LTR|Ty1/copia|Bianca
-CLVDSATTHTILKNMRYFTSFEKRDVNIATIVCEANIVEGSGRAVIVLPSGTHIRIDDAL
-YANKSRRNLLS

--- a/fasta2database.R	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,14 +0,0 @@
-library(Biostrings)
-input_fasta = commandArgs(T)[1]
-## for testing input_fasta="/mnt/raid/454_data/RE2_benchmark/REPET_annotation/Prunus_persica/DANTE_proteins_filtered.fasta"
-s = readAAStringSet(input_fasta)
-names_table = do.call("rbind", strsplit(names(s)," "))
-head(names_table)
-classification_table = paste(names_table[,1], gsub("|","\t",names_table[,3], fixed = TRUE), sep="\t")
-cat(unique(classification_table), sep="\n", file = paste(input_fasta, ".classification", sep = ""))
-
-new_fasta_names = paste("NA-", names_table[,2], "__", names_table[,1], sep="")
-
-names(s) = new_fasta_names
-
-writeXStringSet(s, filepath = paste(input_fasta, ".db",sep=''))

--- a/fasta2database.py	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,25 +0,0 @@
-#!/usr/bin/env python3
-'''
-Helper script to create DANTE databese which can be used in second iteration
-'''
-import sys
-
-fasta_input = sys.argv[1]
-db_fasta_output_file = sys.argv[2]
-db_classification_file = sys.argv[3]
-classification_table = set()
-# fasta header will be reformatted to correct REXdb classification
-with open(fasta_input, 'r') as f, open(db_fasta_output_file, 'w') as out:
-    for line in f:
-        if line[0] == ">":
-            ## modify header
-            name, domain, classification = line.split(" ")
-            name_clean=name[1:].replace("-","_")
-            new_header = ">NA-{}__{}\n".format(domain, name_clean)
-            classification_string = "\t".join(classification.split("|"))
-            classification_table.add("{}\t{}".format(name_clean, classification_string))
-            out.write(new_header)
-        else:
-            out.write(line)
-with open(db_classification_file, 'w') as f:
-    f.writelines(classification_table)

--- a/parse_aln.py	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,137 +0,0 @@
-#!/usr/bin/env python3
-'''
-parse .aln file - output from cap3 program. Output is fasta file and
-profile file
-'''
-import argparse
-import re
-
-
-def parse_args():
-    '''Argument parsin'''
-    description = """
-    parsing cap3 assembly aln output
-    """
-
-    parser = argparse.ArgumentParser(
-        description=description,
-        formatter_class=argparse.RawTextHelpFormatter)
-    parser.add_argument('-a',
-                        '--aln_file',
-                        default=None,
-                        required=True,
-                        help="Aln file input",
-                        type=str,
-                        action='store')
-    parser.add_argument('-f',
-                        '--fasta',
-                        default=None,
-                        required=True,
-                        help="fasta output file name",
-                        type=str,
-                        action='store')
-    parser.add_argument('-p',
-                        '--profile',
-                        default=None,
-                        required=True,
-                        help="output file for coverage profile",
-                        type=str,
-                        action="store")
-    return parser.parse_args()
-
-
-def get_header(f):
-    aln_header = "    .    :    .    :    .    :    .    :    .    :    .    :"
-    contig_lead = "******************"
-    aln_start = -1
-    while True:
-        line = f.readline()
-        if not line:
-            return None, None
-        if line[0:18] == contig_lead:
-            line2 = f.readline()
-        else:
-            continue
-        if aln_header in line2:
-            aln_start = line2.index(aln_header)
-            break
-    contig_name = line.split()[1] + line.split()[2]
-    return contig_name, aln_start
-
-
-def segment_start(f):
-    pos = f.tell()
-    line = f.readline()
-    # detect next contig or end of file
-    if "********" in line or line == "" or "Number of segment pairs = " in line:
-        segment = False
-    else:
-        segment = True
-    f.seek(pos)
-    return segment
-
-
-def get_segment(f, seq_start):
-    if not segment_start(f):
-        return None, None
-    aln = []
-    while True:
-        line = f.readline()
-        if ".    :    .    :" in line:
-            continue
-        if "__________" in line:
-            consensus = f.readline().rstrip('\n')[seq_start:]
-            f.readline()  # empty line
-            break
-        else:
-            aln.append(line.rstrip('\n')[seq_start:])
-    return aln, consensus
-
-
-def aln2coverage(aln):
-    coverage = [0] * len(aln[0])
-    for a in aln:
-        for i, c in enumerate(a):
-            if c not in " -":
-                coverage[i] += 1
-    return coverage
-
-
-def read_contig(f, seq_start):
-    contig = ""
-    coverage = []
-    while True:
-        aln, consensus = get_segment(f, seq_start)
-        if aln:
-            contig += consensus
-            coverage += aln2coverage(aln)
-        else:
-            break
-    return contig, coverage
-
-def remove_gaps(consensus, coverage):
-    if "-" not in consensus:
-        return consensus, coverage
-    new_coverage = [cov for cons, cov in zip(consensus, coverage)
-                    if cons != "-"]
-    new_consensus = consensus.replace("-", "")
-    return new_consensus, new_coverage
-
-def main():
-    args = parse_args()
-    with open(args.aln_file, 'r') as f1, open(args.fasta, 'w') as ffasta, open(args.profile, 'w') as fprofile:
-        while True:
-            contig_name, seq_start = get_header(f1)
-            if contig_name:
-                consensus, coverage = remove_gaps(*read_contig(f1, seq_start))
-                ffasta.write(">{}\n".format(contig_name))
-                ffasta.write("{}\n".format(consensus))
-                fprofile.write(">{}\n".format(contig_name))
-                fprofile.write("{}\n".format(" ".join([str(i) for i in coverage])))
-            else:
-                break
-
-
-if __name__ == "__main__":
-
-    main()

--- a/summarize_gff.R	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,61 +0,0 @@
-## summarize hits
-output = commandArgs(T)[2] ## output table
-filepath = commandArgs(T)[1]  ## input dante gff3
-if (length(commandArgs(T))==2){
-  summarized_by = NA
-}else{
-  summarized_by = strsplit(commandArgs(T)[-(1:2)], split = ",")[[1]]
-}
-
-readGFF3fromDante = function(filepath){
-  dfraw=read.table(filepath, as.is = TRUE)
-  gff_df = dfraw[,1:8]
-  colnames(gff_df) = c("seqid", "source", "type", "start", "end", "score",
-                    "strand", "phase")
-  ## assume same order, same attributes names
-  ## TODO make ti more robust - order can change!
-  gffattr_list = lapply(
-    strsplit(dfraw[,9],split=c("=|;")),
-    function(x)x[c(FALSE,TRUE)]
-  )
-  ## some rows are not complete - in case of ambiguous domains
-  L = sapply(gffattr_list, length)
-  short = L  < max(L)
-  if (any(short)){
-    gffattr_list[short] = lapply(gffattr_list[short],function(x) c(x, rep(NA, 13 - length(x))))
-  }
-  gffattr = as.data.frame(do.call(rbind, gffattr_list), stringsAsFactors = FALSE)
-
-  ## get attributes names
-  attrnames =  strsplit(dfraw[1,9],split=c("=|;"))[[1]][c(TRUE,FALSE)]
-  colnames(gffattr) = attrnames
-
-  gff_df$Final_Classification = gffattr$Final_Classification
-  gff_df$Name = gffattr$Name
-  gff_df$Region_Hits_Classifications = gffattr$Region_Hits_Classifications
-  gff_df$Best_Hit = gffattr$Best_Hit
-  gff_df$Best_Hit_DB_Pos = gffattr$Best_Hir_DB_Pos
-  gff_df$DB_Seq = gffattr$DB_Seq
-  gff_df$Query_Seq = gffattr$Query_Seq
-  gff_df$Region_Seq = gffattr$Region_Seq
-  gff_df$Identity = as.numeric(gffattr$Identity)
-  gff_df$Similarity = as.numeric(gffattr$Similarity)
-  gff_df$Relat_Length = as.numeric(gffattr$Relat_Length)
-  gff_df$Relat_Interruptions = as.numeric(gffattr$Relat_Interruptions)
-  gff_df$Hit_to_DB_Length = as.numeric(gffattr$Hit_to_DB_Length)
-  return(gff_df)
-}
-
-gff = readGFF3fromDante(filepath)
-# summarized_by = c("Final_Classification", "Name", "seqid")
-# summarized_by = c("Final_Classification")
-
-
-if (is.na(summarized_by)){
-  ## export complete table
-  write.table(gff, file = output, row.names = FALSE, quote = FALSE, sep = "\t")
-}else{
-  ## export summary
-  tbl = data.frame(table(gff[, summarized_by]))
-  write.table(tbl, file = output, row.names = FALSE, quote = FALSE, sep = "\t")
-}

--- a/summarize_gff.xml	Fri Apr 03 07:27:59 2020 -0400
+++ b/summarize_gff.xml	Wed Jan 25 13:06:55 2023 +0000
@@ -1,13 +1,13 @@
-<tool id="gff_summary" name="Summarize gff3 output from DANTE" version="0.1.0" python_template_version="3.5">
+<tool id="gff_summary" name="Summarize gff3 output from DANTE" version="0.1.4" python_template_version="3.5">
     <requirements>
-        <requirement type="package">R</requirement>
+        <requirement type="package">dante=0.1.4</requirement>
     </requirements>
     <command detect_errors="exit_code"><![CDATA[
-        Rscript ${__tool_directory__}/summarize_gff.R '$inputgff' '$output' '$group'
+        summarize_gff.R '$inputgff' '$output' '$group'
     ]]></command>
     <inputs>
       <param type="data" name="inputgff" format="gff3" />
-      <param name="group" type="select" label="select categories to summarize" multiple="true" optional="false">
+      <param name="group" type="select" label="select categories to summarize" multiple="false" optional="false">
             <option value="Name">protein domain name</option>
             <option value="Final_Classification">Classification</option>
             <option value="seqid">Sequence ID</option>

--- a/test-data/GEPY_test_long_1.fa	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,378 +0,0 @@
->scaffold146.1|size86774
-CTAGAACACCAACACTAACAGGTACTACAGTCTGGAATCGGATATCTCGCATGTTAAAATATTCGGATGTGCTGTTTATA
-TTCTCATTCCCCTGTCTCAAAGAACAAAAGTGGGACCCCAACGTCGATTGAAAATTTATATTGGATTTGAATCTCCTACG
-ATTATACGATACCTTGAGCCNNNNNNNTTAACATGAGATGTGTTTACTGCTAGATTTGCAGATTGTTATTTTGATCAAAC
-CCATTTTCATAAGTCATTGAAATAAAATGAGAAATATAAAAAATTAAGTTGGCATAAGTCATCATTGACACATTACGATC
-CTCGTACTAAGGAATGTGAACTGGGAGTTTAGAAAATTCTTCATGTGCCGGAATAGACAATTCAATTGTCGAATGTGTTT
-AACGATGCGAATGGGGTTTTACAATCGCATATACTTGCAGCAAACACACCGATTAAGGTTGATGTTCCTGAAGAACGCAC
-GAAAATTGCGAACGAATCAAAAATGCGTTTGGAACGAGGTAGATCTATTGGTTCTAAGGATAAGAATCCTAGAACGAACA
-CCGAATGTTCAAATTGAGAATGTGTCGAATCCTCTGAGCACACATATGGTGGTTAGATCTTTGGATGTGAAAATGGATCC
-ATTCAGACCGCATGAGAACGATGAAGAAATATTAGGNNNNNGAAGTACCTTATCTTAGTGCAATCGGGGCATTGATGTAT
-CTTGCGAATAATACGAGGCCTAATATAGCATTTGCTGTTAATCTGTTGACAAGATGTAGTTCGTCGCCTACGAAAAGATA
-TTGGAAATGCGTGAAACATGTTCTTCGATANNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN
-NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN
-NNNNNNNNNNNNNNTATTTCTTGGCGATCTACGAAACAAACTATCGTAGCTATCTCGTCAAATCACGTAGAATTATTAGC
-GATACATGACACAAGTCGTGAATGCGTCTGGTTGAGATTTATGATTGAAAGCATTTATAATGNNNNNNNNNNNNNNNNNN
-NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNTACAGTTGAAAGAATGATATATTAAATGTGACCGAACGAAACATAT
-TTCGCCAAAATTCTTTCTTTACACAAGATCTTCAAAAGAACGGAGATGTGATTATCCAGCAGATACGATCAAACGATAAT
-GTAGTAGATTTATTCACAAAACTGCTTCATACGGCAGGTTTTGAAAAGTTGATCTACAACAATGGCATTCGAAGATTGAA
-AGGTTTGGAGTGATGCAACCATCAGGGGTAGATGTTTTTGCTTGAAGACGGAGGGATGTAAAAAGATTATAGAAATGTAC
-TCTTTTTCATTCACTAAGGTTTTTATCCTTTTTCCTTAGTAAAGTTTTAACGAGTCATATCCTATAATGATAGACATCCA
-GGGTGGAGTATTACAAAACTATACTCGAAAATTAGATTGTGGATGTCTAGTTTACCAAGTTTCAAATAAAGACGGAAATA
-AATAGTACTATACACAAAATAATGCTATTCATGTGGGGCTCACGTCATTAATTTGTTTGAATTATAAAACGGTTCAGAAC
-CATCGCTCACCTATATAAATAGAGGTTATGTATGCTGAAATTATACAGATGAAATAATACAGATTTTATACTTTCATTTT
-CTTTATTCTTCTTCCGTTTCTACTATATCGAAGTAATTCATAGAGAAGTTGACGTAGAACGTCCGATTGAAGATTCAAGT
-AAATATTTTTCATTTATTGGTATTATTACTTTCCTAACAATTATTTGATTAAGCGCTATTGTTATTTGAGTCTCCTTCAT
-TCACACAAATTGCATTCGAGAAAAAGACATTTTTTGTCCCCTCAAATTTTTCAACTTTCAATTTTTTGTCCCTCGACTTT
-CAAAGAAGACACATTTGGTTTCTTAAATTTGATTTAAGGTCAATTTTGATTCATATTACAAAATTTTAATCATAAATTGA
-CTATTTTACCTGTAAATAAATATTTTTAAAAGTTGAATTTCATTTTCTTAGACTATTTAAATGTAATTTGACTTGATTGT
-GAGACTTATGAAGTGATTGTGAATCTTGTTTAAGACTTAAATATTCAATGTATGATAGAAAATTTATGTTGCAATCATAT
-TATTGTGGAAATCTTATATAACATTGACGTGGAAATTTTTGTGTCGTGCCAAAAATAATAACCTCACAACAACAATAATG
-GAAAATTTTCTGTGCTCATTTTTTGTCGTCTTCCTCCTCCTCTCTGCCGCTGCAAATGGCGACGACGTGTACACATCCTT
-CGTTAACTTCCTCGCAAAGAACGGCATTTCCAGCGCCGAAATCTCTTCCACCGTCTACTCTCCACAAAACACTAGCTTCC
-AGAACGTTCTACTCTCCGCCGTGAGGAACCGCCGGTTCAACCGATCCACCACCAGAAACCCAGCACGATTTTCGCGCCCA
-CGGCGGAATCCCACGTCAGCGCCGCCGTCATTTGCTCCAAGGAACTCGGGATTCAGCTCAAGATCCGCAGCGGCGGCCAC
-GACTTCGAGGGCATCTCCTACGTTTCTGCGGACGGCGGCGCGTTCGTCTTACTGGATGTGTCCAATTTCCGGTCGATTTC
-CGTCGATATTCCCGGCGAGACGGCGTGGGTCGGCCCCGGGGCTTATCTCGGAGAGCTGTACTACAGGATCTGGGAGAAGA
-GTAGCGTCCACGGTTTCCCCGCCGGGGTCCCGCCCTCCGTTCGATTTTCCAGAAAATGCTTCAAATCGGCGAAGTGGGGC
-TGACGTTTAACTCCTACGGCGGAGTAATGGACCGGATCCCGGAATCGGAAGCTCCCTTCCTCCTAAAGAACTTAAAACGG
-CTTATATCTCGCTATATAACGATATTTAAGGATTCGAAACCACTTATAATCTCTTTTCATGCCTTAAATGAGGTTATTTA
-AGGATCCAATTGCATTTAAAATGCCTTATTATGCATCAAATAGCTTCAAATAGCCTTAACTATGCTAAAGAACTTATAAC
-GGCTTATATCTCGCTATATAACGATATTTAACCATCCGAAACCACTTATAATCTCTTTTCAAGCCTTAAATGAGGTTATT
-TAAGGATCCAATTGCATTTAAAATGTCTTATTATGCATCAAATAGCTTCAAATAGCCTAAACTATGCTAAAGAACTTATA
-ACGGCTTATATCTCGCTATATAACGATATTAAAGCATCCGAAACCACTTATAATCTCTTTTCAAGCCTTAAATGAGGTTA
-TTAAAGGATCCAATTGCATTTAGAATGCCTTATTATGCATCAAATAGCTTCAAATAGCCTAAACTATGCTAAAGAACTTA
-TAACGGCTTATATCTCGCTATATAACGATATTAAAGCATCTCCTAAAGAACTTAAAACGGCTTATATCTCGCTATATAAC
-GATATTTAAGGATTCGAAACCACTTATAATCTCTTTTCATGCCTTAAATGAGGTTATTTAAGGATCCAATTGCATTTAAA
-ATGCCTTATTATGCATCAAATAGCTTCAAATAGCCTTAACTATGCTAAAGAACTTATAACGGCTTATATCTCGCTATATA
-ACGATATTTAACCATCCGAAACCACTTATAATCTCTTTTCAAGCCTTAAATGAGGTTATTTAAGGATCCAATTGCATTTA
-AAATGTCTTATTATGCATCAAATAGCTTCAAATAGCCTAAACTATGCTAAAGAACTTATAACGGCTTATATCTCGCTATA
-TAACGATATTAAAGCATCCGAAACCACTTATAATCTCTTTTCAAGCCTTAAATGAGGTTATTAAAGGATCCAATTGCATT
-TAGAATGCCTTATTATGCATCAAATAGCTTCAAATAGCCTAAACTATGCTAAAGAACTTATAACGGCTTATATCTCGCTA
-TATAACGATATTAAAGCATCTCCTAAAGAACTTAAAACGGCTTATATCTCGCTATATAACGATATTTAAGGATTCGAAAC
-CACTTATAATCTCTTTTCATGCCTTAAATGAGGTTATTTAAGGATCCAATTGCATTTAAAATGCCTTATTATGCATCAAA
-TAGCTTCAAATAGCCTTAACTATGCTAAAGAACTTATAACGGCTTATATCTCGCTATATAACGATATTTAACCATCCGAA
-ACCACTTATAATCTCTTTTCAAGCCTTAAATGAGGTTATTTAAGGATCCAATTGCATTTAAAATGTCTTATTATGCATCA
-AATAGCTTCAAATAGCCTAAACTATGCTAAAGAACTTATAACGGCTTATATCTCGCTATATAACGATATTAAAGCATCCG
-AAACCACTTATAATCTCTTTTCAAGCCTTAAATGAGGTTATTAAAGGATCCAATTGCATTTAGAATGCCTTATTATGCAT
-CAAATAGCTTCAAATAGCCTAAACTATGCTAAAGAACTTATAACGGCTTATATCTCGCTATATAACGATATTAAAGCATC
-GTTATATAATGCTATTTAACCATTCAAAACCACTTATAATCTCTTTCTAGGCCTCAAATAACGTTAATTAAGAATCCAAT
-TGCATTTAAAATGCCTAAATAGGCCTCAAATAGCTTGAAATAGTCTTACTCATTCTTAAATGTCTTATAACGGCTTATAT
-CATGCTATATAACGATATTTAACCATCCGAAATCACTTATAATCTCTTTTTAGGCCTCAAATAAGGTTAATTAAGGATCC
-AAATGCATTTAAAATGCCTTATTACGCCTCAAATAGCTTCAAATAACCATAAGTTATATAATGCTATTTAACCATTCAAA
-ACCACTTATAATCTCTTTCTAGGCCTCAAATAACGTTAATTAAGAATCCAATTGCATTTAAAATGCCTAAATAGGCCTCA
-AATAGCTTGAAATAGTCTTACTCATTCTTAAATGTCTTATAACGGCTTATATCATGCTATATAACGATATTTAACCATCC
-GAAATCACTTATAATCTCTTTTTAGGCCTCAAATAAGGTTAATTAAGGATCCAAATGCATTTAAAATGCCTTATTACGCC
-TCAAATAGCTTCAAATAACCATAAGTTATATAATGCTATTTAACCATTCAAAACCACTTATAATCTCTTTCTAGGCCTCA
-AATAACGTTAATTAAGAATCCAATTGCATTTAAAATGCCTAAATAGGCCTCAAATAGCTTGAAATAGTCTTACTCATTCT
-TAAATGTCTTATAACGGCTTATATCATGCTATATAACGATATTTAACCATCCGAAATCACTTATAATCTCTTTTTAGGCC
-TCAAATAAGGTTAATTAAGGATCCAAATGCATTTAAAATGCCTTATTACGCCTCAAATAGCTTCAAATAACCATAACCCA
-CCGGAAAGGCATCCTCTACAAGATTCAGTACTGGGTGAACTGGAACGAGGAAGGGCCGGCGGCGGAGAGCAATTACGTGA
-AGCAGGCGAGAGATCTCCACGATATCATGACGCCGTTCGTTTCGAGTGATCCGAGGGAAGCGTATCTGAACTACAGGGAT
-CTGGACATCGGAACCACCGACAACGGCGACGACAGCTACGGCCAGGGGTTGGTTTATGGGCTCAAGTATTTCAAGAATAA
-TTTCCACCGGTTGGTCCAAATCAAGACCCAAGTCGATCCCGACAACGTTTTCAGGAACGAACAGAGCATACCCACGTTCC
-CTAATCGCCGCAGCGTGCTCGTAAATTCCTTCTAAAAAGCTTTACCGGTGGGTTTGTTTCGCCGTTGTTTATGCACAGAA
-CAGAACTCAAATAAGTATTGCGTTATGCACAGCACAGCAGCAACATTCAGCAACGCTAATCAAATGAGAAGAAATAATTC
-AATCACTCNAAAAAAAAAAATGAAATATAGGCCGAGTATTTCGGGCCGAGCCCAATGGGCCAAGATTTTGTCCATTATGT
-AAGCCCGTGTTTTCTTTTGTTTACCAGGAGGGATTACTACCTACAATGGTCTGGAGAGTTGCATTCAGAACAACAGCAGA
-TCGTTCGGAAACGACGAGAGCGGTTGCATCACCACGACGGACTATTCATACGATGAGGATGACTCCACCTGCTGCTCTTC
-CAGCACNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNGGCAGCCGTACGACGGGAA
-GGCGAGCCAGTTATTGCCTGACGTGGAGGCGATGAAGGAGAAATTCGCGAAGCTGTTGCTCGGGGAGGATGTCAATGGAG
-GCACCAAGGGACTCTGCTCCGCCCTAGCTATATCCAACGCCATAGTAAACCTAGCAGGTACGGGAAACTTATCTGAAGGG
-CCCGCCCCCAAGATATGGGAGCAGAAGTCAAGGGGCTCGCCAAAGGACCGGGCGTAGAAAATCTGAGGAACGGTCAGACA
-AGTCACAGCCCGGCCCCGGACGGTCGGACAAGGGTAAGGAGAAAGTAGAGGCTAACCAAGGACCGATCCGGGGAGAAATC
-AACTACATCTCGGATTCCGGAAGCATGGGTCTGTCTCTGACCCACACTCGTCAGCACAGGGTATTCCTGGACGGACGGAA
-TCCAGAAGAAATCGCCTCACTTCGCCAAGGCCCGGAATCTCGGGCGAAATCTTGGGAAGTGAATCAAGTCATCTCGTTTT
-CTGACGAGGATTTTTCAGGGGTCATCTGGCCTCATCACGACCCGCTCATTATAGCGGGCGTCATTTCTGATTTTTTGGTG
-AGAAGGATACTTGTCGACAGTGGTGCCTCTTGCAACCTGATGAGCAAGAGGGTTATGAAGCAAATGGGTATACCGGACGA
-GAAGTTGGAATTCTTGGACGCTACCCTGTATGCTTTCGACCGAAGGACGATCATCCCAGCTGGAAAAATCCAACTTCCAG
-TCACTCTGGGTGAGGAAGAACGAACTCGGTCGGAAATGGTTGAATTCATCATTGTCGACATGGATCTGGCATACAACGCC
-ATTCTCGGTCGGTCGGGCTTGAATGCCTTCCGGGCAATTGCTTCAACTGCCCACCTGAAGATTAAATTCCCCACTCCCGC
-GGGCGTCGCTACAATATCGGTGGATCAAGGGACAGCCCGCGAATGCTTCAAAGTCTCTTGCCAGGACGGTGGACCTCCAC
-AGGAACCCGAACTGGCAAGCAGGGGGAAAGATGAGCCCGGTCCTTCACGAGGACCGACCGAGGAAGGCCGTCAGGGAACC
-CGACCGACCAAGAAACACAAGGTGGCGACAGTGGAACATGGCCCGATCGAGGACCAGCGTGAAGTCACGCATAACATGGA
-ACCAATCGGGTACAAGAACGTTTCACTATCCTCTTCCGACGGAAGGAAGAACGTCAAGATTGGGGTGCAAATGCCCCCAA
-ATATCGAAGAACAACTCATCCAGGTCTTGACAGAGTATCAAGACATCTTTGCTTGGGACATCTCCGAGGTCCCTGGAATT
-GATCGGTCACTGATGGAACATCGCATCAATACCGATCCTGAGGCCGTGCCCGTTCGACAAAAAAGGAGACGCTTCTCTCC
-AGAAAAAATCGAAGTCATACGGGCAGAGGTACAAAAATTGTTAAACGCGGGATTTATCCGCGAAATCCGATATGCTGAGT
-GGGTCGCGAACCCGGTCGTCGTCCCAAAACCTGGAGGAAAGTGGAGGGTGTGCATCGACTTTAAGGGCGTGAACAAGCAC
-TGCCAGCCCGATCCATTCCCCCTCCCTCATATCGACCGACTGGTGGACGCTGTAGCTGGAAGTTCCTTACTCAGCACAAT
-GGACGCGTATTCAGGATATCACCAGATCTCGTTGGCAACCTGCGAGGACTTCGTAAAAATTTGGACAACCTCGGAGGAAG
-CTGGCCCGACCATCTACCTCACGTGCTTTGGGCCTATCGGACTACGCCCAAATCTTCGACTGGAGAAACTCCGTTCTCGC
-TCGTATATGGATCCGAAGCGGTCGCACCAGTGGAGTCGACTATAATTACTCCACGAATCGCCGCTTACATGCATATGGAA
-TCCGCGAATACCGAGTTTCGAGAATTGGATCTGGATCTCCTAGAGGAAAGACGTAATGAGGTGTATGAACGAGTGCGAAA
-ACAACAGCGAGCCCTTCGGAAGCGTTACAATCAACGCGTAAGGCCTCGGCAGTTTGAGAAAGGAGATTTAGTTCTTCGGA
-GTGTGGAATCACAAGGCCATAAGGGAAAGCTCGACCGAGCTTGGGAAGGCCCTTATCGTGTCCACACAGTTATTGGCAAA
-GGGGCTTATCGATTGGAAACTTTGGACGGGGAGATCTTTCCTCGAACATGGAATATCGAGCACCTTCGGCCTTATTATCA
-GTAATACGTGACGATTAGTTGAGGCTAAGTCACGGGGCTCTAAGTTTCAAGAATAAATTTTTCATTATATGGAAAATTTA
-TTACAAGGAGTTCAAGAATAAATTTTTCATTACTTAGAAAAATTTATTACAACCTAGCAATTACAAAAAGTCAACTACAA
-GGAGGAGCAGGCAGATTTTCTTCTTGGGGAGGGGGCCGATCTTCTTCTGGAGGAGTGGGAGGTCGATCTTCTTCTTGAGG
-AGGGAGAGGCCGATCTTCTTCTGGTGGAGGAGGAGCAGGCTGATCTTCGGGCGGCTGAGGAATAGGAGTGCAATCCATGG
-GCTCGGGGCACGGCTCTTCGGGCTGCGGGGGAAGAACTTCCATTGCGACAGCCTCGGGCGGTCGGACCTCCGATGGCGCA
-GACTTCAAGGGCGTGAACAAGCACTGCCAGCCCGATCCATTCCCCCTCCCTCATATCGACCGACTGGTGGACGCAGTAGC
-TGGAAGTTCCCTGCTCAGCACCATGGACGCCTATTCGGGATATCACCAGATCTCACTGGCAAGGGAAGATCAAGCTAAGT
-CTTCTTTCCTCACTGAGGATGGTGTATTCTGCTATGTGGTGATGCCCTTCGGATTAAGGAACGCTGGGGCTACGTACCAG
-CGTTTGGTCAATAAAATCTTTGCCGATCTGCTCGGCAAAGAAATGGAGATCTATGTAGATGATATGATTGTTAAATCTCT
-GAATGACGAAGATCATATTATCTACTTGTCGCATTGTTTTGAGGTGTGCCGAACACATCGCCTCAAACTCAATCCAGCCA
-AGTGCTGCTTTGGTGTCCGGTCGGGCAAATTCCTGGGTTATCTCGTGTCCGAGAGGGGTGTTGAAGCCAACCCCCACCAA
-ATCCGGGCCATACTGGAAATGCAACCCCCGACCACGAAAAAGGAATTCCAAAGGTTGACGGGGTGCCTTGCAGCCCTGGG
-CAGATTTTTGTCAAGGGCGTCCGACCGAGCCCTGCCCTTCTTCAAAGTGCTTCGCAAAAACAGCACTCTGGACTGGACCG
-ATCAGTGCGATCGGGCCTTCAAAGAACTAAAGACTTATTTGGCATCTCCACCGCTGATTGTAAGTCCCACTCCAACCGAA
-ACGCTGGGGCTTTATTTAGCTGTGTCCGAGCATGCTGTCAGCTCGGTCCTTGTGGCAGAACGGGACGGGGTCCAGCACCC
-CGTATATTATGTGAGCCACACCCTTCTTCCAGCTGAATCTCGGTACAGCACGGTGGAGAAATTTGTTTTGGCCCTTCTAA
-AGTCGGTCGCGAAATTGCGACATTATTTTGAAAGCCGGAAAGTTATCGTGTACACTGATCAACCCATCAAGGCGGTGCTC
-GGGCAGTCCGATCACACCTCTTAAGAGTAATTTCCGCCCCCGGTTTTCTGTTGATCGGGGCGAAATACTGACAGGATTGG
-CATCGGCGCACACAATCCATAGCGTCTCTTAATAAGGTCGGCCAGTAATAGCCCGCCCGTTGAATACGAAGTGCAAGTGA
-CCGGGCACCCGGGTGGTTCCCACATAATCCACTGTGCACTTCTTCCAATACTTGAGCGGACCGAGCAGGGTCCAGACATA
-CCAAGAGAGCCCCGGTGACCGACCGCCTATATAATTTATTCTCAAGGAGAACATACCTTTTGGATTTTTCCTTGAGAGAT
-CGGGCATCGGACGAGTCAGGTGGGAGATTCCCCTTAGATAAATATTCCACAATGGGAGTCTTCCACGTTCCTTCTTCATG
-CCCGACCGGGCTGCAGGTGAATTCCACAGAATGAATATCTTCGGGGTTCTCAATAGTAGGTAACGTTACTGCTGGTTTCC
-CTTGGAGTTGTATAATGACACTGTCATCCCCAATTGTGGGGAAACTAGCAGCCAACCTAGCTAATGAGTCCGCAGCTCGA
-TTGCTTTCCCGAGGAATATGCTCCAGTGAGAATTCCGAGAATTTTGCAGCAAGTAACTGAGAACGGCTTTTGTAAGCCTC
-CAGCATTGGTTCCTTTGCCTGGAAATGACCAGAAAATTGTTCCACCACCAGTTGAGAGTCTGAAAATGTTTTTAAACGGG
-TGACTCCCAAACTTAGGGCGAAATTCATTCCAGCTAATAGCGCCTCATACTCTGCCACATTGTTTGTAGCAGGAAATTGG
-AGCTTTACAGCGAGGTTGATCCAATCTCCATAAGGCGTGATATAATGCACACCGACCCCAGCCCCAGATTGGGTCGAGCC
-ATCGATGTACATGTTCCAGGCTTCTTGTGACCGATCAGTTGACGGGACCGACCGCGTCATTTCCACCACGAAATCCGCTA
-AGGCCTGTCCTTTGATGGCCTTGCGGGGTTCGAAAGCTATGTCCATTGCACTGACCTTGATTGCCCATTTGGCGAGGCGC
-GAGGTATGATCCGTATTAAGAGACGCTATGGATTGTGTGCGCCGATGCCAATCCTGTCAGTATTTCGCCCCGATCAACAG
-AAAACCGGGGGCGGAAATCACTCTTACAGAATTACCTTGCCCGTTCGACCGATGGGGAATTGATATCCTTGGACCGTTCC
-CTCAATCGGTTCGGCAAAGGAGATTCTGTATTGTTGCGGTCGAATATCATTCGAAGTGGATCGAAGCTGAAGCTGTGGCA
-TCCATCACCTCCGAAGCTGTTAAGAAATTTGTCATGAATAACATCATTGTGCGATTCGGGTGTCCTCGAGTTCTCGTCTC
-GGACAACGGTCCACAATTTATATCGGACAAATTCGCAACCTTCTGTGAGGAATATGGAATTCAGCAGCGGACTTCGTCAG
-TATATCATCCACAAACAAACGGGCAAGCCGAGGCATCCAACAAGATCATCTTACATGGACTTCGCAGGAACTTGGATAGC
-CTCGGGGGAAGCTGGCCCGATCAACTGCCTCATGTGTTGTGGGCATATCGGACAACACCCAAGTCTTCAACTGGAGAAAC
-TCCATTCTCACTCGTTTATGGATCCGAAGCGGTCGCACCAGTGGAGTCGACTATAATTACTCCACGAATCGCCGCTTACA
-TGCATACGGAATCTGCAAATACCGAGTTTCGGGAATTGGATTTGGATCTCCTGGAGGAAAGACGTAACGAGGTGTATGGA
-CGAGTGCGAAAACAACAGCGAGCCCTTCGAAAGCGTTATAATCAACGCGTAAGGCCTCGGCAGTTTGAGAAAGGGGACTT
-AATTCTTCGAAGTGTGGAATCTCAGGGTCACAAAGGAAAGCTCGATCGAGCTTGGGAAGGACCTTACCGTATCCACACAG
-TTATTGGCAAAGGAGCGTGTTTTTATATTTTATATGTAAGGAATTTATTTGTAATAATGTAATAAAAATCGGGTATTTGG
-CATTTTATACCCCTTATCTTTCAATTTTTTGCAAAAAATACCCCTTATCTTTCATAATTGCAATTTATACCCCTTTTCTT
-TTGAATAGTGAGCAATTTGTATCCAACACCGTTAGATGACGTCATCATCCGTTAGATGACGTCATCATAAGGGGTATATT
-TTGCTACAAAAATAAAACATAAGGGGTATAAATTGCAATATCGTAAAAATTATTCCGTTAGTTTTAACGGCGTTGGACAT
-AAATTGCTCACTATTCAAAAGAAAAGGGATATAAATTGCAATTATGAAAGATAAGGGATATTTTTTGCAAAAAATTGAAA
-GATTAAGGGGCATAAAATGCAAAGAAGATGAAGAGAAAATGTCAAATACACTTTATTATTGGGTTAAATACATTTTATAC
-CCCTTATATTTTGGATTTTTACAAAAAATATCCATTTTCTTTCATAATTACAAAAAATATCCCTTTTGTTTTAAAATTGA
-ACAAATTATACTCAATCCGTTAGATGACGTCATCATCCGTTAAAATGACGTCATCATAAAGGGGTATAAATTGCAACAAA
-ATGAAACATAAATGGTATAAATTGCAACGCGTAAAATTATATTCCGTTAGTTTTAACGGCGTTGGGTATAATTTGTTTGA
-TTTTAAAACAAAAGGGATATTTTTTGTAATTTTAAAAGAAATGAGGTATATTTTGTAAAATTTTGAAAGAAATAGGGTAT
-AAAATGTATTTTCCCCTATTTTTATTTTAGAAAAATATTTTATTTCTTTTCAAAATTTACAAAAAATACTCATTTTCTTT
-CATAATTATAAAAAATACCCATTTTGTTTTAAAATCAAACAAATTATACCCAACGCCGTTAAAACTAACGGAATATAATT
-TTACGCGTTGCAATTTATACCATTTATGTTTCATTTTGTTGCAATTTATACCCCTTTATGATGACGTCATTTTAACGGAT
-GATGACATCATCTAACGGATTGAGTATAATTTGTTCAATTTTAAAATAAAAGGGGTATAATTTGTAATTATGAAAAAAAG
-AGAGTATATTTTGTAAAATTTTGAAAGAAAAATGATATAAAATGCATTTGTTACAATCAAAATCTTATTCTTATTTTTTT
-AAACAAAAATTAGGATATTATTAAAATACTGTTTTGTCTGTACGGAGATCGATTCGATTCGAACTGAGACGATTGCTGAT
-TTTGTGGATTTGTTGCAGCGACGGTGTTCGGTGAGCTGTGGAAACTGGAGCCGCTGTCGGAGGAGAAGAAGAGGAAGTGG
-AGGAGGGAGATGGATTGGCTTCTGTCTCCTACGAATTACATGGTCGAATTGGTGCCTGCAAAACAAAGCTGCAGCAACGG
-AGGGGTTTTGGAGGTAACAACTTTAATCATTTCAAATTCGATTTTGCAATTGATCTGAACTGATCAAATTGTCTAGATAA
-TGACGACCAAGNNGATCAGACATCCAAATGAATCTCCCAGCTCTCAAGAAACTCGATTCCATGCTTCTGGCAAGTGTTAC
-CTTTCAAACTTATGGTTTATTGAAACCCTAGCTCTGAAACACTGCATTTTTTTTCACCAGGAATCGCTCGATTCGATGGT
-GGATACCGAATTCTGGTACGCAGAAGAGGGGGGAAGCCAAGCAGAAGGAAGAAGCACAAGATGGTGGCTTCCTTCTCCGC
-AAGTACCTCCAAAAGGTCTCTCAGATACAGAAAGAAAGAGACTGCTACACCATGGTAGACTCACTTATCAAGTCTTCAAA
-GCTGCAAAAATGATCAACGAAAACGTCTTACTTGAAATGCCAATCCCGATCACCATCAAAGAAACTCTCCCTAAGGTGTA
-AACTCCCTTTTTTTATCCTCATTCGAATCGAATCCTGTCCTCACGGGCTTGATGATTTATTTCAGTTAGGAAAGTTGAGC
-CTCGGAGAGGAACTTTACAGATTACTCACCGAAGAATCCCTATCCAGGCAAGAGATCATGGAGCGTCTGAATCTGAAATC
-CGAACACNNNNNNNNNNNNNNNNNNNNNCCAGCCATATCCGAATGGCAGGTCAGAGTTTCTGAAGAACAACAAGCGGACA
-TCAAATCTCCGGCTCGGCCATCGTGGTCGTTTATAAGGGACCCTATCATCGAGATGGAGAAGACAGAGGTATTAGTCAAT
-CAAGCGGGGAGAAGATGGAAGGGAAGATAGATAAAGTGGAAGGAAGAATGGACAAGATGGAAACATCCAACGGGCAAATG
-GCTACAAAGATGGCGGAATTCGAAGCACAGTTGAAAGGGAAGTTGCCCTCACAATCTGTGATCAACCCGAGAGAAAATGT
-AAGCGCTGTAACGCTGAGGAGTGGAAAGGTTGCAGATGAAGCAATCCAGAAGAAGAGGAAATCGCCTAAAGAAGCAGCAA
-CAAAACCAGAGGATGAGAAGGAGGTCGAAGCTGCTGAACCAGTGACAGAACCCACTGCCAAGAAACAGAAAGAACCAGAG
-GTCGAGGTAACAAAGGAAAAATCTGTTATTAAACCTTACTATGAACTTCCACCTTTTCCAGGGAGGTTCAAGCTGGAGAA
-AAAGCAAGAGGAAGAGAAGGAGTTGATGAGACTATTCGGAAAAATTGAGCTGAACATACCGTTGGTCGAAGCAATCCAGA
-CGGTGCCACGTTATGCAAAGTTTCTGAAGGAATTGTGCACCACAAAGAAGAGGCTGAAAGGTAATGAATGCATTAATTTA
-AATGAACAGGTGTCAGCAATTTACAGCAACAAAGCACTGCCCGAGAAGTGCACGGATCCAGGTGTCTTCACCATTCCATG
-TGTGATCGGTAAAACAGAATTTAAACGAGCTATGGTGGACCTAGGAGCTTCTATAAATCTCATGCCTTATTCTATATATT
-CTGCACTTCAACTTGGACCATTGCAGGGAACGGCTATTGTCATAAAGTTGGCGGATAGATCAAACACTCACCCGGAAGGA
-GTTATCGAAGATGTGCTTGTACAGGTGAATAACTTGGTGTTTCCTGCAGATTTTTATGTACTGAAGATGGGGAAGGCAGA
-GAACAATGATTGTCCATTGCTACTGGGACGCCCATTTTTGAAAACCGCGAAGACAAAGATAGATGTAAATGATGGTACCA
-TGAGCATGGAATTCGATGGAGAGAAGGTACAATTCAATATCTATGAAGCTATGCGTTATCCTAGCGATGTAGGTATGGCG
-TGTATGATCGACACATTTGAGGAGCTGGAGTACGAGGTGATGGCAGTAACTGAAGCAGAGGAAATAATGAAAGAGTTAGA
-AGAAAACCGAGTAGAAGAGAATTGGCTGAAAGCAGTGACTGAAGCTGTGACGAAAGGGAAGACTGACCAAGGGGAATTGA
-AGCCACTGCCAGCAAATTTGAAATACGCATTTCTGGAAGGTAACTCAACTCTCCCTGTTATTATTGCTAATGATTTATCT
-CATGAACAGGAAGCTCAGTTACTGGAAATTCTGAAGAAGTATCGGAAAGCAATCGGATGGACACTAGATGACATTCACGG
-CATTGAAGCTGATGTGTGCCTGCATAGGATACTGATGAAAGATGATGCCAAACCAGTGCAACAATCACAGAGAAGAACCA
-ACCCGGTAATTTCAGAAGTCGTGAAGAAGGAGATAGAGAAGTGGCTCAAAGCTGGAATTATCTATGCTATTTCCGACAGT
-GATTGGGTAAGTCCGGTACATGTTGTTCCTAAGAAGACAGGTTTCACAGTGGAGAGGAACAAGAACGGAGAGCTGGTACC
-GAAGAGAGTAACAAACGGATGGAGAGTTTGCATTGACTACCGGAAGCTCAACGATGCAACTAGAAAGGATCACTTTCCAC
-TGCCGTTCATCGACCAAATGCTTGAGAGATTGGCAGGTAAAAAGTTTTATTGTTTTCTTGATGGATATTCAGGTTACAAC
-CAGGTTGCAATTGCACCGGAGGATCAAGAGAAGACCACGTTCACCTGCACCTATGGAACATATGCGTTTCGGAAGATGCC
-CTTTGGACTGTGCAATGCACCAGCAACATTCCAACGTTGCATGCTGAGCATTTTCTCAGAGTTTACAGGTAAATTTATTG
-AGGTTTTTATGGATGACTTCACTGTGTATGGTGACAGTTTCGAAGGAGCATTGGAAAATCTGGAGAAGGTACTGCAGAGG
-TGTGTAGAGAAGAAGTTGGTTTTGAACTCAGAGAAGTGCCACTTCATGGTGAGACAAGGAATTGTTTTGGGACACTTAAT
-ATCAGAGAAAGGCATCGAAGTGGATAAATCGAAAGTGGACACCATTCAAGGTATGACTTTTCCTACTGACGTGAAAGGTG
-TTCGTTCTTTTCTTGGCCATGCAGGATTTTACAGAAGGTTTATCAAGGATTTTTCGAAGATTGCACTGCCATTGAGCAAG
-CTGCTACAACATGAAGTGAAATTCGACTTCAACCAGGAATGCCAAGAAGCTTTTAACAAGCTGAAGTCACTGCTGACAGC
-AGCACCGATTATTCAACCGCCGAACTGGGAGCTTCCATTTGAGTTGATGTGTGATGCCAGCAACTATGCATTAGGAGCTG
-TCCTGGGGCAGAAGATTGAAGGGAAAAGACATGTCATTTATTATGCATCGAAAACACTGAGTGAAGCACAGATTCATTAC
-ACCACAACCGAGAAGGAGCTGCTAGCCATTGTTTATGCTCTGGAAAAATTCAGAAGCTACTTATTGGGAACGAAGATCAC
-AGTCCATTCTGATCATGCAGCTCTGAGACATTTACTTTCGAAGAAGGAATCGAAACCGAGACTAATTCGTTGGATTCTTT
-TGTTGCAGGAATTCGATTTGGAGATCAAGGACCGAGCTGGAACAGAGAATGCAGTAGCTGATAACTTGAGCAGAATCAGA
-ACTGAGGAAGAGCAGAAAACCAGAGTCCACGATGATTTTCCAGACGAGCAGATATTAGCGGTAATTACTGAACCCTGGTA
-TGCTGATTTAGCTAACTATCTTGTCAACAGGACAATGCCAAAGACCATGACCGAGAACGAGAAGCGGCAGATTCGAGCTC
-AAGCGCTCTCTGAAATTCTGTTGCAGCAGCTTCTTGTCGTGATAGGTCTTCAGTCTTTCTTTGTAGATCCTTGAATTATC
-ATAAGCTTCAAGCCTCAGAGCATCCAGCTCTTGCAACTGCATCTGTCTCAGCTCAGCATCGCCTCCTTCCTCGAAATTCA
-TCTCTCGGATTGCCCAATAAGCTTTGTGTTCCAGACCTACAGGTAGATTGCAATGTTTACCATAAATCAAACGAAATGGA
-GTAGTACCTATAGGTGTCTTGTATGCTGTTCTGTAAGCCCAGAGAGCATCTGGTAGCTTCTGGCTCCAATTCGATCTCTT
-TTTGTTCACCACTTTTTCCAGAATTGACTTGATCTCTCTGTTGGTTGCTTCTGCCTGTCCATTACCTTGTGGATGATACG
-CTGTTGTGGTCTTGTGGATCACATTATACTTCTTCATCATCGCTCTCATCGTGCTGTTACAGAAATGAGTGCCTCTGTCA
-CTGATGATGGCCTTTGGAACTCCATACCTCGTGAAAATATTCTTCCTCAGAAACTCAGTTACTGTCTTGCCATCATCGGT
-CCGTGTCGGTATTGCTTCAACCCATTTCGACACATAATCTACTGCCACCAGTATGTAGTTGTTCCCTTGAGATTGTGGAA
-ACGGTCCCATGAAATCCAGCCCCCATATATCGAAGATTTCGTTGGCCAGAATGTAATTCTGTGGCATCTCTCTTCTGTTG
-GTAATGTTTCCAACTCGCTGGCATGCATCACAAGTCCTGCAAAACTGATAAACGTCACGAAAAATAGATGGCCAGTAAAA
-ACCTACGTCCAGAATTCTTCTAGCCGTTCTGTTGGGTCCAAAGTGTCCTCCATAATCTGAAGCATGACATTCCTGCAAAA
-TTTTGTTAATCTCAGAATTATGAACACACTTACGAATCATCCCATCACTGCAATTCTTCCACAGATGCGGTTCTTCCCAG
-AAATAATATTTGGCCTGTGATCGAATCTGCCGCTTCTCGTTCTCGGTCATGGTCTTTGGCATTGCCCTGTTGACAAGATA
-GTTAGCTAAATCAGCATACCAGGGTTCAGTAATTACCGCTAATATCTGTTCATCTGGAAAATCGTCTTGCACTCTTGTTT
-TCTGCTTTTCCTCTGTTTTGATTCTGCTCCACAGAGTTCGACGTTGCGGAGCTTCCGGTCGCCGTGAGAGGATTTTTTTC
-TCCGTGCTGGTTTCCTGTCGCTGAAAAGGGAGAAAGAAAGCGACACGAAAACAGTAAGGGTTAGAGAAAAAAATAAAAAC
-TGATTCTGTACAGGCGATTTTTAGTGGCCCCATGTCGCGATGTGGACGTTTCTGGATCTTCATGGGCCGCCAAAAATTGG
-GCCTCTATTCCAGCGGGTTACTAAATCGCCTCAATTTTGCCTTAGGCAAAATTTCCCAACTCGTCTAGCTTTTCCTACAA
-AAATAAACTAAGTGCAGAAGAGGTTACTAAAAAGATAAAAGAAAAACTCCCTGCAGTCACTACTCTACTGAATGGGTTGC
-CTCCCATCAAGCGCCGCGTTTAACGTCTCCAGCACGACATATCCGTTCTCGCGCTCCTAATCATTCTCCCCGCCATCGTG
-GGATGCGGGTTCACCAAGTTCCAGTGAGCTCTCCATTTCCTGCTTAGGCATCTCAAAATATTTCTTAAGACGATGACCGT
-TTACCTTAAAATGTCTTCCACTCTCATCATCCAGCAACTCGAATACTCCATATGGAAAAACCTTTGTGATCTTGAACGGT
-CCCATCCATCTCGACTTCAATTTCCCTGGAAACAATCTAAGCTTGCTGTTAAATAACAGTACCTGCTGCCCCTCTCTGAA
-ATTCTGTTGCAGTATCTTCTTGTCATGGTAGGCCTTCAGTCTTTCTTTGTAAATCCTTGAATTATCATAAGCTTCAAGCC
-TCAGAGCATCCAGCTCTTGCAACTGCATCTGTCTCAGCTCAGCATCGCCTCCTTCTTCGAAATTCATCTCTCGGATTGCC
-CAGTTGCCTTGGGCAAATTTCCAACTCACCTAAGCTTTCCTACAAAAACAGACTAAGTACCGAAAAGGTTACTAAAAACT
-AAAAGAAATACTTCCTGCGGTTACTACTCTACTGAATGGGTTGCCTCCCATCAAGCGCCGCGTTTAACGTCTCCAGCACG
-ACTCAATCGTCTCACAGTCGCTACTCTCCCTCCCCGCCATCATGCGATGCGGGTTCACCAGATTCCAGTAAATTCTCCAC
-TCCCTGTTTCAGCAACTTAAAATATTTCTTAAATTGATGACCGTTTACCTCAAAGTGCTTTCCATTTCCATCCTCAAACA
-GTTTCAATACTTCATGTGGAAAGTTCTTCATGATTCCGAACGGTTCCATCCATCTCGGTATACCTGGAAACATTTTTAAC
-TTGCTGTCAAATATCAGTACCTGCTGTCTTTTCTTGCAATTCTGCTGCAGTATCCTCTTGGCATGGTGAGCCTTCAGTTT
-TTCTTTGTCAATCCTCGAATTGTCATATGCTTCCAGCTTCAAAGCGTCCATCTTCTGTAACTGCATCTGCTTCTGCTCAA
-CATCAGCTCCTTCTTCGTAATTCATCTCTCGGATTTCCCAGTAATTCTCGTGTTCCATCCCTACAAGCACTTTGCATTGT
-TTACCAAAATTCAAACCAATTGAAATGGTACCTCTCGGGTTGGTTCTCCTCTGTAATTGTTGGACTGGCTTAGCATCTTC
-CTCCAACAGCATTCTGTGCTGGCCTTCATCAGTGTCACTGCCTTGAATCTCATCTAGAATCCATCCGATTGCTTTTCCAG
-ACTTCTTCCAAATTTCCAGTAACTGAATTCCCTGTTTCAGAAATGAAATGTTAGTATTACTCACAAGTCTGCTGATAATG
-ACTTCAATTTCCCTCGGTTGGTTTTCCCTCTCGTTACAGCTTCAGTGACTGTTTTCAGCCAATTCTTTCCTCCTTGGTTT
-TCTTCTAATTCCCTTAACATCTCTTCTGCTTCAGTTGCTGCCATCACTTCGTTTTCCAGCTCCTCAAATGCATAGATCAT
-GCATACCGTACCTACATCGCTGGGAGAACGCATATCTTCATAAATATTAAATTGTACCTTCTCCCCATCGAACTCCATGC
-TCATGGTACCATTGTTAGCATCGATCTTCGTTTTTGCTGTTTTCAGAAATGGGCGTCCCAGCAGCAATGGACAATCATCG
-TTCTCTGCCTCCCCCATCTTCAACACATAAAAATCTGCAGGAAACACTAAGTTATTCACCTGCGCAAGCACGTCTTTGAC
-AACTCCTTCCGGGTGAGTTTCGGATCTGTCCGCCAACTTGATGATAATTGCCGTTCCCTGTAATGATCCAAGTTTCAGTG
-CAGAATATATATAATAAGGCATGAGATTTATTGAAGCTCCTAAATCCACCATAGCTCGTTTAAATTCTGTTTTACCAATC
-GTGCATGGAATGGGAAAAAATACAATTTTGGTCCCTCAAATTTTCAAAATTTCAATTTTTGGTCACTCAACTTTCAAAAA
-GACACATTTAGTCCCTTAATTTTGATTTAAGGTCAATTTTGGTCCATATTGCGAAATTTTAATCATAAATTGACTATTTT
-ACCCGTAAATAAATATTTTTTAAAGTTGAATCTCATTTTATGAGCCCGTTTAAATGAAAGTTGACTTGATTGAGTGACTC
-ATGCAAGTGATCGTGAATGTTGTTTAAGGGTGAAATAGTCAATTTATGATAAAAATTTTGCAATATGGACCGAAATTAAC
-CTTAAATCAAAATTAAGGGACTAAATGTGACTTTCTGAAAATTTAGTGATCAAAAATTAAAATTTGAAAAATTTGAGGAA
-TAAAAATGAATTTAATCCGAATTTATATTTTTGGTCTCTCAAATTTTTCAAATTTCAATTTTTGGTCCCTCAATTTTAAA
-AAAGACACATTTAGTCCCTCAACTTTGATTTAAGGTCAATTTTGGTCCCTATTGCGAAATTTTGATCATAAATTGACTAT
-ATTACCCACAAATAAACATTTTTTAAAGTTGAATCTCATTTTTTAACCCGTTTAAATGAAAGTTGACTTGATTGAGAGAC
-TCATGCAAGTGATCGTGAATGTTGTTTAAGGGTGAAATAGTCAATTTATGATAAAAATTTTGCAATAGGGACCGAAATTG
-ACCTTAAATCAAAATTAAGGGACTAAATATGTTTTTTTAAAAGTTGAGATACCAAAAATTGAAATTTGAAAAAATTGAGG
-AACTAAAAGGGTTAAAGACATTTTTAGTCCCTCAAATTTTTAAAATTTTAATTTTTGGTCCCTCAACTTTCAAAAAGACA
-CATTTAGTCCCTTAATTTTGATTTAAGGTCAATTTCGGTCCATATTGCAAAATTTTTATCATAAATTGACTATTTCACCC
-TTAAACAACATTTACGATCACTTGCATGAATCACTCAATCAAGTCAACTTTCATTTAAACGGGCTCATAAAATGAAATTC
-AACTTTAAAAAAATGTTTATTTACGGGTAAAATAGTCAATTTATGATTAAAATTTTGCAATATGGACCAAAATTGACCTT
-AAATCAAAATTAATTTACTAAATGTATATTTTTTAAAGTTGAGGAACCAAAAATTGAGAAGGATGGAGAAGAATGATCGG
-CAAAGAATTTATATCGGAGTTTAGGTTTGGAACCCTAGCTATCAATATGGACAAATTAGGTTTAAGACATTTTTAGTCCC
-TCAAGTTTTTCAAATTTCAATTTTTGGTCCCTCAACTTTCAAAAAGACACATTTAGTCCCTTAACTTTGATTTAAGGTCA
-ATTTTGGTCCATATTGCAAAATTTTTATCATAAATTGACTATTTCACCCTTAAACAACATTCACAATCACTTGCATGAGT
-CTCTCAATCAAGTCAAATTTCATTTAAATGGGCTTAGAAAATGAGATTCAACTTTGGAGAAAAAAAAAAATACATTTTTG
-GTCTCTCAAGTTTCTAAAATTTCAATTTTTGGTCCTTCAACTTTCAAAAATACACATTTAGTCCCTTAATTTTGATTTAA
-AGTCAATTTTGGTCCATATTGTAAAATTTTAATCATAAATTGACTATTTTACCCGTAAATAAACATTTTTTAAAGTTAAA
-TCTCATTTTATGAGTCCGTTTAAATGAAAGTTGACTTAATTAAATGAATTATGCAAGTAGTCGTGAATGTTGTTTAAGGG
-TGAAATAGTCAATTTATGATAAATATTTTGCAATATGGAGCGAAGTTGACCTTAAATAAAAATTAAGGAATTAGATACTC
-GTGAAGCAGATCAAAACAAAGTACCCCAATCTTCCCCTCACATTCATCGATGCCACGAAAATCCGATATGGCAAGGTAAA
-TGATAAAAGTATATATGAAAGTCAATCAAGAAAAAGATCACTAATTTTCTCATTACTTGCAGGACGTGGGGCACTCGATC
-CTCGAAGCGTACTCCCGAGTACTTGCCAACTTAGCATTCAGCATCCTCTCCCGGATAGGCGACGTTCTTCAGGAAGACAT
-CATGAGCAATCCGAACTCGCCCGTAGCCATGTCGCACCTCGTCGGGGCCAGAATCCCAGGAATATCGGATAGCCCCATGC
-TCGACAGGGTGAGGCACTCCCTCGTCCACCAGCTCAACGATGCGGACGGAGGTAAAACCGACGAGGACGAGGTGAACGCC
-GTCAGTTCCTCCGTGAGCGTAACTCCCAGCCGGAGCAGAGTGTGGTGCATCGGAAGAGAGGCCTGCAGAAGTTTATCCGC
-TCCGAATTCCCCAAACAGATGCATATAACAAAAAAACAGTACAACCGCGGCCGCATTCGACGCCATCATCGAGCAGCTTA
-TGTCGGAAAATCGAGGCTTCTCCTTAAGGTACCTTTTAAGGCTAACTAAATTCTTGGCTGACTAACTGGATGTAAACATC
-ATAAATGTCTGAACTATCTATTGAGTTATCGATCGCTTTCTTGATCAATACCTACATATATAATATATGAACCAGTTAAA
-GTTTACATCTAATGCTTTAAGTCTGTGCTTAACCAGCAGGGATCATCGGAATATAAAGAAAGCTAACCGGAAATATTCGA
-ATCGAGGAGCCAGGTAGTTATGGTTCTTGAGATCCTAAAGAACTTAAAACGGCTTATATCTCGCTATATAACGATATTTA
-AGGATTCGAAACCACTTATAATCTCTTTTCATGCCTTAAATGAGGTTATTTAAGGATCCAATTGCATTTAAAATGCCTTA
-TTATGCATCAAATAGCTTCAAATAGCCTTAACTATGCTAAAGAACTTATAACGGCTTATATCTCGCTATATAACGATATT
-TAACCATCCGAAACCACTTATAATCTCTTTTCAAGCCTTAAATGAGGTTATTTAAGGATCCAATTGCATTTAAAATGTCT
-TATTATGCATCAAATAGCTTCAAATAGCCTAAACTATGCTAAAGAACTTATAACGGCTTATATCTCGCTATATAACGATA
-TTAAAGCATCCGAAACCACTTATAATCTCTTTTCAAGCCTTAAATGAGGTTATTAAAGGATCCAATTGCATTTAGAATGC
-CTTATTATGCATCAAATAGCTTCAAATAGCCTAAACTATGCTAAAGAACTTATAACGGCTTATATCTCGCTATATAACGA
-TATTAAAGCATCTCCTAAAGAACTTAAAACGGCTTATATCTCGCTATATAACGATATTTAAGGATTCGAAACCACTTATA
-ATCTCTTTTCATGCCTTAAATGAGGTTATTTAAGGATCCAATTGCATTTAAAATGCCTTATTATGCATCAAATAGCTTCA
-AATAGCCTTAACTATGCTAAAGAACTTATAACGGCTTATATCTCGCTATATAACGATATTTAACCATCCGAAACCACTTA
-TAATCTCTTTTCAAGCCTTAAATGAGGTTATTTAAGGATCCAATTGCATTTAAAATGTCTTATTATGCATCAAATAGCTT
-CAAATAGCCTAAACTATGCTAAAGAACTTATAACGGCTTATATCTCGCTATATAACGATATTAAAGCATCCGAAACCACT
-TATAATCTCTTTTCAAGCCTTAAATGAGGTTATTAAAGGATCCAATTGCATTTAGAATGCCTTATTATGCATCAAATAGC
-TTCAAATAGCCTAAACTATGCTAAAGAACTTATAACGGCTTATATCTCGCTATATAACGATATTAAAGCATCTCCTAAAG
-AACTTAAAACGGCTTATATCTCGCTATATAACGATATTTAAGGATTCGAAACCACTTATAATCTCTTTTCATGCCTTAAA
-TGAGGTTATTTAAGGATCCAATTGCATTTAAAATGCCTTATTATGCATCAAATAGCTTCAAATAGCCTTAACTATGCTAA
-AGAACTTATAACGGCTTATATCTCGCTATATAACGATATTTAACCATCCGAAACCACTTATAATCTCTTTTCAAGCCTTA
-AATGAGGTTATTTAAGGATCCAATTGCATTTAAAATGTCTTATTATGCATCAAATAGCTTCAAATAGCCTAAACTATGCT
-AAAGAACTTATAACGGCTTATATCTCGCTATATAACGATATTAAAGCATCCGAAACCACTTATAATCTCTTTTCAAGCCT
-TAAATGAGGTTATTAAAGGATCCAATTGCATTTAGAATGCCTTATTATGCATCAAATAGCTTCAAATAGCCTAAACTATG
-CTAAAGAACTTATAACGGCTTATATCTCGCTATATAACGATATTAAAGCATCCACAATAGTATGTGAAGCGAATATCGTT
-GAAGGCTCTGGCAGAGCTACGGTAGTATTACCATCGGGAACGCATATCAGAATTGATGATGCTTTATATGCTAATAAGTC
-TCGAAGGAATTTATTGAGTTTTAGAGATATTCGCCGAAATGGATATCATATTGAGACTTCGGACGAAAATGGTAAAGAAT
-ATCTTCACATTACTAAGATGGTGGCTGGAAAGAAATCGATTCTAGAAAAGATGCCCGCATACTCGTCAGGATTGTATCAT
-ACGTATATTGATACAGTGGAAGTGCATAATATAAACTTGAAGTTTATAAATCCTGATACGTTTCGAGTATGGCATGATCG
-ATTAGGCCATCCAGGGATGATTATGATGCGAAAAATTATTAGAACGACAAGCGGTCATTCATTGAAAAATCGAGAAATTT
-TGCATCCCAGAGAGTATATATGTACCGCTTGTGCTCAGGGAAAGTTAATTACTCGTCCGTCACCTGTAAAGATAATGAAT
-GAGAGAATTACGTTTTTGGAACGTATACAGGGTGACATATGTGGACCGATACATCCGGCTTGTGGACCATTCAGATATTT
-TATTGTATTAATTGACGCTTCGTCGAGATGGTCACATGTATCGTTGTTATCGACTCGTAATCATGCTTTTGCGAGACTGT
-TAAGTCAGATAATAAGATTACGAGCTCATTTCCCTGATTATCCGGTGAAGAAAATACGGTTGGATAATGCCGCTGAATTT
-ACGTCGCGGACGTTTAATAATTATTGTTTGGCTATGGGGATAGATGTTGAACATCCTGTAGAATATGTTCATACACAGAA
-TGGTCTGGCTGAATCTTTGATAAAGCGATTACAGTTAATCGCGAGACCATTATTGATGAAATCTAAGCTACCGGTTACTT
-GTTGGGGACATGCGATTATACATGCATCGTCACTTATACGGGTTAGACCAACAAGTTATTATGATTCATCCCCATTACAG
-TTGATTCAGGGTAAAGAACCGGATATCTCGCATCTCAGAATATTCGGATGTGCCGTTTATGTTCCCATTCCCCCGCCTCA
-AAGAACAAAAATGGGACCCCAACGTCGATTGGGAATTTATGTTGGGTTTGAATCTCCTACGATTATACGATACCTTGAGC
-CGTTAACAGGAGATGTATTTACTGCGAGGTTTGCAGATTGTTATTTTGACGAAACCCATTTTCCACCGTTAGGGGGAGAA
-AAGAAATTGAGAGAGGCTGAGAAAGATAGAAAATTGAGCTGGCATGAGCCATCATTGACACATTATGATCCTCGTACAAA
-GGAATGTGAACTGGAAGTTCAGAAAATTGTTCATGTGCAGGAACAGGCAATTCAATTGCCGAATGCATTTAACGATGCGA
-AAGGTGTTTTACAGTCGCATATACCTGCAGCAAACGCACCGATTAAAGTTGATGTTCCTGAAGAACGAACGGAAATTGCG
-AATGAATCTAAAATGCGTTTGAAACGAGGTAGACCTGTTGGTTCTAAGGATAAGAATCCTAGAACAAGAAGAACGAATCG
-AACACCGAATGTTCAGATTGAAAATGTGTCGAGAAATGAGGTTTCGACTGAGGTAGTTGAACCACAGGAAAATGAGGAAA
-TTTCTATCAATTATATATCGAATTCAGAACGGTGGGATAGAAGCAAAGTTGAGGTTGATGAAAACTTTGCAGAATATATA
-TCTTTCCATGTTATGAATGATCCTGAAGATCCTGAACCTAGAACAATGACTGAATGTCAGAAACGAGATGATTGGCCAAA
-ATGGAAAGATGCTATAGAAAGTGAGCTGAAATCTCTGAATAAGAGAGATGTTTTCGGACCTGTAGTTCGAACACCTGAAG
-GTGTACAACCGGTTGGTTATAAGTGGGTTTTTGTGAGAAAACGAAATGATAAAGGAGAAATATCTCGGTATAAGGCGAGA
-TTAGTAGCTCAAGGGTTTTCTCAAAGGCCAGGAATTGATTATGATGAAACCTATTCACCGGTTATGGATGCCACAACTTT
-CAGGTTTTTGATAAGTCTGGCGATTGAATATGGGCTTGATTTACAACTGATGGATGTTGTAACAGCATACTTATATGGGT
-CACTGGATTGTGAAATATATATGAAAATCCCTGAAGGGTTTCATATGCCTGAACGATATAGTTCTGAACCCCGTACCGAT
-TATGCGATTAAATTGAATAAATCCCTGTATGGATTAAAGCAGTCAGGACGAATGTGGTATAACCGTCTAAGTGAATACTT
-GATTAAAGAGGGTTATAAGAACAATTTGGTTTGTCCCTGTGTTTTTATGAAGAAATTCGAAAATGAGTTCGTGATCATCG
-CTGTGTATGTCGATGACATTAATATTGTGGGAACTCAGAAGGCATTATTGGATGCCGTGAACTGCTTGAAAAGGGAATTT
-GAAATGAAGGATTTGGGAAGAACGAAATATTGCCTTGGTTTGCAAATTGAATATTTGAAAAATGGGATTTTTCGTACCGA
-TTATGCTATTAAATTGAATAAATCCCTGTATGGATTAAAGCAGTCAGGACGAATGTGGTATAACCGTCTGAGTGAGTATC
-TGATCAAAGAAGGTTATAAAAACAATTTGGTTTGTCCTTGTGTTTTTATGAAGAATTTTGAAAATGAGTTCGTGATCATC
-GCTGTGTATGTCGATGACATTAATATTGTGGGAACTCAGAAGGCATTATTAGATGCTGTGAACTGCTTGAAAAGGGAATT
-TGAAATGAAGGATTTGGGAAGAACGAAATATTGCCTTGGTTTGCAAATTGAATATTTGAAAAATGGGATTTTTCTTCATC
-AGAATACGTATACCAAGAAGGTATTGAAACGTTTTTATATGGATTATTCACATCCTCTGAGCACACCTATGGTGGTTAGA
-TCTTTAGATGTGAAAACGGATCCATTCAGGCCACAGGAGAACGATGAAGAAATATTAGGTCCTGAAGTACCTTATCTTAG
-TGCAATCGGGGCATTAATGTATCTTGCGAATAATACGAGGCCTGACATTGCATTTGCTGTTAATCTGTTGGCAAGATATA
-GTTCATCGCCTACGAAAAGACATTGGAAAGGCGTGAAACATGTTCTTCGATATCTTCAAGGTACTACTGATAAGGGGTTG
-TATTATCAGAAAGATATGAAGTCAGAACTTATCGGGTATGCTGATGCTGGATATAGATCAGATCCACATAATGGGAGATC
-TCAGACAGGATATGTTTTCCTGAATAAAGGAGCTGCTATTTCTTGGCGATCTACGAAACAGACTATCGCAGCTACCTCGT
-CAAACCACGCAGAATTACTAGCGATACACGAAACAAGTCGTGAATGCGTTTGGTTGAGATCTATGATTGAAAGCATTTAT
-AATGCTTGTGGATTGTTTACAGATAAGATGCCTCCGACTGTATTATATGAAGATAATAGTGCATGTATTATACAGTTGAA
-AGAAGGATATATTAAGGGTGACAGAACGAAACATATTTCACCAAAATTCTTCTTTACACATGATCTTCAAAAGAACGGAG
-AGGTAATTATCCAGCAGATACGATCAAGCGATAATGTGGCAGATTTATTCACGAAACCACTCCCTACATCAACTTTTGAA
-AAGTTGATTTACAATATTGGAATCCGAAGGTTGAAGGATTTGGAGTGATGCAGTCATCAGGGGGAGATGTTTTTGACTGA
-GGACAAAGGGATGCAAGAAAATTATAGGAATGTACTCTTTTTCCTTCACTAAGGTTTTTATCCCATTGGGTTTTTCCTTA
-GTAAGGTTTTAACGAGGCATATCCTATAATGATAGACATCCAAGGGGGAGTGTTGTAAAACTATACACGAAAATCGGATT
-GTGGATGTCTATTATACCATATTTCAAATAAAGACGGAAATGCACAGTACTTACTATTCATGTGGGTCCCGCAGCATTAA
-ATTTATCAAATTATTGATTGTAAACGAACGGATGTAATCGATGATTGATGCCTATAAATATAGGCATGGTGCAGAATGAA
-TTTAAGCAGAACAAAATTTGAGCATAAATTTTTCTCTTCTTCTTCTCATTTCTTTTCTTGATTCAATAATACGCTGAAGG
-AATTTCTACAGAAGTTGACGTAGAACGTCCGATTGAAGATTCAAGTAAGTTTATGAATTATTCATCTTTTATTTCTTATT
-TTTCTAACACGTTATCAGCACGAAGTCTAACCAACTGAGTGCTTATATAATCTTGAAGATTATATATTATATGATATGAT
-CCCGCAGATCGTATGGTTGATTCGATGATCCAGAAGATTATTTGATTTAACTTCTTTACCATTTTCGTCTGAAGTCTCAA
-TATGATATCCATTTCGGCGAATATCTCTAAAACTCAATAAATTCCTTCGAGACTTATTCGCATATAATGCATCATCAATT
-CTGATATGCGTTCCCGATGGTAATACTATCACAGCTCTGCCGGAGCCTTCAACAATATTCGCTTCACATACAATTGTGGC
-AATATTTACATCCCGTTTTTCAAAACTAGTAAAATATCTCATGTTTTTCAAGATCGTATGCGTCGTTGCACTATCCACCA
-GACATTCATCACCCTCAGCCATGCTGGAAATAAACAATATATTTCATTATCAGTTCATGATACAATTTACAATTCGGCAT
-ACATAATACCAACAATAATGCCCAGAAATGTTAACAGCGTGGAAACAAAGACTGAAGCGTCTATGTTCTGGATCATCCAA
-ATATACCCAAAAATAAAAGTTACCATAAAAACTATAAAAACAAGATACTGAACAGAGGTGTTCATCTGGCTCAGAGATTT
-TGTAAAGAGATTTAGAAAAGAGAAGTTTTTAAGTGAAAAACCTTCTTGATAAAATCGTTATTTATACTGAGCCAAGTTAC
-CGACAATATCTGCGAAATCCAAGATTTTCGTTGAAAAAACATAACACCTTATTAAAATATTCAAAACACGCCGAATATTT
-TACAAAACACGCTGATAACATGCCGAATACAACATATTTATTTCAACCAGCATTATTAAATAGATAACTAAATCCTTCGT
-CTGGATCACGTGGACGTGGTCGTGGCCGAGGTCGTTACTATGATTATGGTCGTGAAAAGAACAAGTATATCTGGAAGAAA
-CCTGCCGTTGTCAAAGAGGTAAATGTGAAAAATGATCAGGGTGACCAGAATACTTGTTACAGATGTGGAAAGGAAGGACA
-CTGGTCACGGACGTGTCGAACGCCTAAATCACTTGTCGACCTTTATCAGCGAGCGAAGAAAATTGAGGAAAAGGGGAAGA
-AAAAGGAGACGAATAACGCTGAAGCTGAGACGTATAACGGAGAAGTCAATATGACTAAGCTGGATGTCGCAGATTTCCTG
-GCTGATCCAGACGAAGGATTTAGTTATCTATTTAATAATGCTGGTTGAAAGAAATTGTTGTATTCGGCATGTTATCAGCG
-TGTTTTGTAAAATATTCGGCGTGTTTTGAATATTTTAATAAAATGTTATGTTTTTCAACGAAAATATTGGATTTCGCAGA
-TATTGTCGGTAACTT

--- a/test-data/GEPY_test_long_1.fa.fai	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,1 +0,0 @@
-scaffold146.1|size86774	30095	25	80	81

--- a/test-data/GEPY_test_long_1_output_unfiltered.gff3	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,26 +0,0 @@
-##gff-version 3
-##-----------------------------------------------
-##PIPELINE VERSION         : iter_search_optional-rv-3168(0b80fa0)
-##PROTEIN DATABASE VERSION : Viridiplantae_v3.0_pdb
-##-----------------------------------------------
-scaffold146.1|size86774	dante	protein_domain	976	1289	293	+	.	Name=RH;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=RH|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-RH__REXdb_ID2558|Class_I|LTR|Ty1/copia|Bianca:976-1289[100percent];Best_Hit_DB_Pos=26:134of134;DB_Seq=ISWRSVKQTITATSSNHAELLALHEASRECVWLRSMIQHIQKNCG-LSSGRMDATIIYEDNTACIAQLKEGYIKGDRTKHISPKFF-FTHDLQKDGDISIQQIRSCDNLAD;Region_Seq=ISWRSTKQTIVAISSNHVELLAIHDTSRECVWLRFMIESI\IMXXXXXXXXXXXXXXXXXXQLKE*YIKCDRTKHISPKFF\FTQDLQKNGDVIIQQIRSNDNVVD;Query_Seq=ISWRSTKQTIVAISSNHVELLAIHDTSRECVWLRFMIESI-----\IMXXXXXXXXXXXXXXXXXXQLKE*YIKCDRTKHISPKFF\FTQDLQKNGDVIIQQIRSNDNVVD;Identity=0.59;Similarity=0.66;Relat_Length=0.813;Relat_Interruptions=1.5;Hit_to_DB_Length=0.83
-scaffold146.1|size86774	dante	protein_domain	6810	7049	153	+	.	Name=PROT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Region_Hits_Classifications=PROT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Best_Hit=Ty3-PROT__REXdb_ID9702|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:6810-7049[100percent];Best_Hit_DB_Pos=1:80of80;DB_Seq=LVDDGSKVNLLPYRVFQQMGIPEEQLVRDQAPVKGIGGVPVLVEGKVKLALTLGEAPRTRTHYAVFLVVKPPLSYNAILG;Region_Seq=LVDSGASCNLMSKRVMKQMGIPDEKLEFLDATLYAFDRRTIIPAGKIQLPVTLGEEERTRSEMVEFIIVDMDLAYNAILG;Query_Seq=LVDSGASCNLMSKRVMKQMGIPDEKLEFLDATLYAFDRRTIIPAGKIQLPVTLGEEERTRSEMVEFIIVDMDLAYNAILG;Identity=0.44;Similarity=0.62;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	7656	8296	.	+	.	Name=RT/INT;Final_Classification=Ambiguous_domain;Region_Hits_Classifications_=RT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand[246bp],INT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand[468bp]
-scaffold146.1|size86774	dante	protein_domain	8756	9241	538	+	.	Name=RT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat;Region_Hits_Classifications=RT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand[486bp],RT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Ogre[441bp];Best_Hit=Ty3-RT__REXdb_ID8210|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:8801-9241[90percent];Best_Hit_DB_Pos=27:173of173;DB_Seq=DFTDLNKACPKDSFPLPHIDRLVDSTAGNELLTFMDAFSGYNQIMMNPEDQEKTSFITDRGIYCYKVMPFGLKNAGATYQRLVNKMFHNHLGKTMEVYIDDMLVKSLKKEDHVKHLEECFDILNKYQMKLNPAKCTFGVPSGEFLGY;Region_Seq=TSIATASGGRTSDGADFKGVNKHCQPDPFPLPHIDRLVDAVAGSSLLSTMDAYSGYHQISLAREDQAKSSFLTEDGVFCYVVMPFGLRNAGATYQRLVNKIFADLLGKEMEIYVDDMIVKSLNDEDHIIYLSHCFEVCRTHRLKLNPAKCCFGVRSGKFLGY;Query_Seq=DFKGVNKHCQPDPFPLPHIDRLVDAVAGSSLLSTMDAYSGYHQISLAREDQAKSSFLTEDGVFCYVVMPFGLRNAGATYQRLVNKIFADLLGKEMEIYVDDMIVKSLNDEDHIIYLSHCFEVCRTHRLKLNPAKCCFGVRSGKFLGY;Identity=0.63;Similarity=0.8;Relat_Length=0.85;Relat_Interruptions=0.0;Hit_to_DB_Length=0.85
-scaffold146.1|size86774	dante	protein_domain	9434	9781	343	+	.	Name=RH;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Region_Hits_Classifications=RH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Best_Hit=Ty3-RH__REXdb_ID9729|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:9434-9772[97percent];Best_Hit_DB_Pos=1:113of149;DB_Seq=WTEECEEAFQKLKEYLGSPHLLVKPIQGEPLFLYLAVSEHATSSVLVREDDGVQRPIYYTSRALVDAETRYLSLEKIVLALIVSARRLRPYFQAHTIIVLTDQPIRQVLAKPD;Region_Seq=WTDQCDRAFKELKTYLASPPLIVSPTPTETLGLYLAVSEHAVSSVLVAERDGVQHPVYYVSHTLLPAESRYSTVEKFVLALLKSVAKLRHYFESRKVIVYTDQPIKAVLGQSDHTS;Query_Seq=WTDQCDRAFKELKTYLASPPLIVSPTPTETLGLYLAVSEHAVSSVLVAERDGVQHPVYYVSHTLLPAESRYSTVEKFVLALLKSVAKLRHYFESRKVIVYTDQPIKAVLGQSD;Identity=0.58;Similarity=0.73;Relat_Length=0.758;Relat_Interruptions=0.0;Hit_to_DB_Length=0.76
-scaffold146.1|size86774	dante	protein_domain	10810	11667	747	+	.	Name=INT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Region_Hits_Classifications=INT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Best_Hit=Ty3-INT__REXdb_ID9633|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:10819-11667[98percent];Best_Hit_DB_Pos=30:310of310;DB_Seq=RDTHQYVQRCIQCQKFAPLIHKPGEEMTIMSAPCPFAQWGIDLVGPFPQTAGRKKFFIVAVDYFTKWVEAEALSKITEDEVMHFIWKYICCRFGLPRSLVSDNGTQFNGKKIRAWCEEMKITQKFVAVAHPQANGQVESTNRTIVNGLKKRIDELGGSWVDELPSVLWSYRTSAKAATGETPFRLTYGTEAVIPVEVAMDTLRIATF--DEEANDGALRTRLDEIFDLREAAYLHMERSKNLIKARYDQGVRSRSFQIGDLILRRADALKHTGKLEANWEGPY;Region_Seq=SVLRDAMDCVRRCQSCQYFAPINRKPGAEITLTELPCPFDRWGIDILGPFPQSVRQRRFCIVAVEYHSKWIEAEAVASITSEAVKKFVMNNIIVRFGCPRVLVSDNGPQFISDKFATFCEEYGIQQRTSSVYHPQTNGQAEASNKIILHGLRRNLDSLGGSWPDQLPHVLWAYRTTPKSSTGETPFSLVYGSEAVAPVESTIITPRIAAYMHTESANTEFRELDLDLLEERRNEVYGRVRKQQRALRKRYNQRVRPRQFEKGDLILRSVESQGHKGKLDRAWEGPY;Query_Seq=RDAMDCVRRCQSCQYFAPINRKPGAEITLTELPCPFDRWGIDILGPFPQSVRQRRFCIVAVEYHSKWIEAEAVASITSEAVKKFVMNNIIVRFGCPRVLVSDNGPQFISDKFATFCEEYGIQQRTSSVYHPQTNGQAEASNKIILHGLRRNLDSLGGSWPDQLPHVLWAYRTTPKSSTGETPFSLVYGSEAVAPVESTIITPRIAAYMHTESANTEFRELDLDLLEERRNEVYGRVRKQQRALRKRYNQRVRPRQFEKGDLILRSVESQGHKGKLDRAWEGPY;Identity=0.49;Similarity=0.66;Relat_Length=0.906;Relat_Interruptions=0.0;Hit_to_DB_Length=0.91
-scaffold146.1|size86774	dante	protein_domain	14592	14828	289	+	.	Name=PROT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=PROT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-PROT__REXdb_ID6659|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:14592-14828[100percent];Best_Hit_DB_Pos=1:80of80;DB_Seq=MLDLGASINVMPYSIYNSLNLGPMEETCIIIQLADRSNAYPKGVMEDVLVQVNELVFPADFYILKMEDELSPNPTPILLG;Region_Seq=MVDLGASINLMPYSIYSALQLGPLQGTAIVIKLADRSNTHPEGVIEDVLVQVNNLVFPADFYVLKMGKAENNDCPLLLG;Query_Seq=MVDLGASINLMPYSIYSALQLGPLQGTAIVIKLADRSNTHPEGVIEDVLVQVNNLVFPADFYVLKM-GKAENNDCPLLLG;Identity=0.68;Similarity=0.84;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	15420	15995	871	+	.	Name=RT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=RT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-RT__REXdb_ID6635|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:15420-15995[100percent];Best_Hit_DB_Pos=1:192of192;DB_Seq=IYPITDSKWVAPIHVVPKKTGITLVKNKNDELIPTRISSGWRMCVDYRKLNLATRKDHFPLPFMDQMLERLAGKSFYCFLDGYSGYNQIVINPEDQEKTTFTCPFGTYAYRRMPFGLCNAPATFQRCMMSIFSDYVERIIEVFMDDFTVYGDSFDKCLENLSLILKRCIETNLVLNYEKCYFMVEQGIVLGH;Region_Seq=IYAISDSDWVSPVHVVPKKTGFTVERNKNGELVPKRVTNGWRVCIDYRKLNDATRKDHFPLPFIDQMLERLAGKKFYCFLDGYSGYNQVAIAPEDQEKTTFTCTYGTYAFRKMPFGLCNAPATFQRCMLSIFSEFTGKFIEVFMDDFTVYGDSFEGALENLEKVLQRCVEKKLVLNSEKCHFMVRQGIVLGH;Query_Seq=IYAISDSDWVSPVHVVPKKTGFTVERNKNGELVPKRVTNGWRVCIDYRKLNDATRKDHFPLPFIDQMLERLAGKKFYCFLDGYSGYNQVAIAPEDQEKTTFTCTYGTYAFRKMPFGLCNAPATFQRCMLSIFSEFTGKFIEVFMDDFTVYGDSFEGALENLEKVLQRCVEKKLVLNSEKCHFMVRQGIVLGH;Identity=0.76;Similarity=0.88;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	16188	16634	623	+	.	Name=RH;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=RH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-RH__REXdb_ID6648|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:16188-16634[100percent];Best_Hit_DB_Pos=1:149of149;DB_Seq=FNEACKVAFDKLKELLTSAPIIQPPDWSLPFEIMCDASNYVVGAVLGQRVGRAAHVIYYTSRTLDSAQCNYSTTEKELLAIVFALEKFRSYLLGTKVIIFSDHAALRYLLAKKEAKPRLIRWILLLQEFNLEIRDKKGTENLVADHLSR;Region_Seq=FNQECQEAFNKLKSLLTAAPIIQPPNWELPFELMCDASNYALGAVLGQKIEGKRHVIYYASKTLSEAQIHYTTTEKELLAIVYALEKFRSYLLGTKITVHSDHAALRHLLSKKESKPRLIRWILLLQEFDLEIKDRAGTENAVADNLSR;Query_Seq=FNQECQEAFNKLKSLLTAAPIIQPPNWELPFELMCDASNYALGAVLGQKIEGKRHVIYYASKTLSEAQIHYTTTEKELLAIVYALEKFRSYLLGTKITVHSDHAALRHLLSKKESKPRLIRWILLLQEFDLEIKDRAGTENAVADNLSR;Identity=0.74;Similarity=0.87;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	24522	24659	149	+	.	Name=PROT;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=PROT|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-PROT__REXdb_ID2599|Class_I|LTR|Ty1/copia|Bianca:24531-24659[93percent];Best_Hit_DB_Pos=29:71of71;DB_Seq=STISGTTNLVEGSGRANIMLPNGTRFHINDALYSSKSRRNLLS;Region_Seq=IKASTIVCEANIVEGSGRATVVLPSGTHIRIDDALYANKSRRNLLS;Query_Seq=STIVCEANIVEGSGRATVVLPSGTHIRIDDALYANKSRRNLLS;Identity=0.65;Similarity=0.77;Relat_Length=0.606;Relat_Interruptions=0.0;Hit_to_DB_Length=0.61
-scaffold146.1|size86774	dante	protein_domain	24873	25481	913	+	.	Name=INT;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=INT|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-INT__REXdb_ID2558|Class_I|LTR|Ty1/copia|Bianca:24873-25481[100percent];Best_Hit_DB_Pos=1:203of203;DB_Seq=HERLGHPGSIMMRKIIEHSCGHQLKSREILQSNKFSCTSCSQGKLITRPSPTKIGSESLNFLERIHGDICGPIHPPCGPFRYFMVLIDASTRWSHVCLLSTRNQAFARLLAQLIRIRAHFPDYPVKKIRLDNAAEFSSQTFNDYCMSIGIDIEHPVAHVHTQNGLAESFIKRIQLIARPLLMRCKLPISTWGHAILHAATLIR;Region_Seq=HDRLGHPGMIMMRKIIRTTSGHSLKNREILHPREYICTACAQGKLITRPSPVKIMNERITFLERIQGDICGPIHPACGPFRYFIVLIDASSRWSHVSLLSTRNHAFARLLSQIIRLRAHFPDYPVKKIRLDNAAEFTSRTFNNYCLAMGIDVEHPVEYVHTQNGLAESLIKRLQLIARPLLMKSKLPVTCWGHAIIHASSLIR;Query_Seq=HDRLGHPGMIMMRKIIRTTSGHSLKNREILHPREYICTACAQGKLITRPSPVKIMNERITFLERIQGDICGPIHPACGPFRYFIVLIDASSRWSHVSLLSTRNHAFARLLSQIIRLRAHFPDYPVKKIRLDNAAEFTSRTFNNYCLAMGIDVEHPVEYVHTQNGLAESLIKRLQLIARPLLMKSKLPVTCWGHAIIHASSLIR;Identity=0.75;Similarity=0.9;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	26313	27071	1060	+	.	Name=RT;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=RT|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-RT__REXdb_ID2558|Class_I|LTR|Ty1/copia|Bianca:26322-27032[93percent];Best_Hit_DB_Pos=1:237of262;DB_Seq=WKDAIKAELYSLNKRKVFGPVVRTPKGVKPVGYKWVFVRKRNENGEIARYKARLVAQGFSQRPGIDFNETYSPVVDATTFRYLISLIAYEGLNLHMMDVVTAYLYGSLDSDIYMKIPEGFNLPDTNSSGSREDYSIKLNKSLYGLKQSGRMWYNRLSEYLLKEGYKNDSVCPCIFMKRSENEFAIIAVYVDDINIIGTPEELPKAIDCLKKEFEMKDLGKTKFCLGLQIEHLNNGIF;Region_Seq=WPKWKDAIESELKSLNKRDVFGPVVRTPEGVQPVGYKWVFVRKRNDKGEISRYKARLVAQGFSQRPGIDYDETYSPVMDATTFRFLISLAIEYGLDLQLMDVVTAYLYGSLDCEIYMKIPEGFHMPERYSSEPRTDYAIKLNKSLYGLKQSGRMWYNRLSEYLIKEGYKNNLVCPCVFMKKFENEFVIIAVYVDDINIVGTQKALLDAVNCLKREFEMKDLGRTKYCLGLQIEYLKNGIFRTDYAIKLNKSLY;Query_Seq=WKDAIESELKSLNKRDVFGPVVRTPEGVQPVGYKWVFVRKRNDKGEISRYKARLVAQGFSQRPGIDYDETYSPVMDATTFRFLISLAIEYGLDLQLMDVVTAYLYGSLDCEIYMKIPEGFHMPERYSSEPRTDYAIKLNKSLYGLKQSGRMWYNRLSEYLIKEGYKNNLVCPCVFMKKFENEFVIIAVYVDDINIVGTQKALLDAVNCLKREFEMKDLGRTKYCLGLQIEYLKNGIF;Identity=0.78;Similarity=0.91;Relat_Length=0.905;Relat_Interruptions=0.0;Hit_to_DB_Length=0.9
-scaffold146.1|size86774	dante	protein_domain	27723	28124	581	+	.	Name=RH;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=RH|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-RH__REXdb_ID2558|Class_I|LTR|Ty1/copia|Bianca:27723-28124[100percent];Best_Hit_DB_Pos=1:134of134;DB_Seq=DAGYLSDPHHGRSQTGYLFTSGNTAISWRSVKQTITATSSNHAELLALHEASRECVWLRSMIQHIQKNCGLSSGRMDATIIYEDNTACIAQLKEGYIKGDRTKHISPKFFFTHDLQKDGDISIQQIRSCDNLAD;Region_Seq=DAGYRSDPHNGRSQTGYVFLNKGAAISWRSTKQTIAATSSNHAELLAIHETSRECVWLRSMIESIYNACGLFTDKMPPTVLYEDNSACIIQLKEGYIKGDRTKHISPKFFFTHDLQKNGEVIIQQIRSSDNVAD;Query_Seq=DAGYRSDPHNGRSQTGYVFLNKGAAISWRSTKQTIAATSSNHAELLAIHETSRECVWLRSMIESIYNACGLFTDKMPPTVLYEDNSACIIQLKEGYIKGDRTKHISPKFFFTHDLQKNGEVIIQQIRSSDNVAD;Identity=0.75;Similarity=0.84;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	9783	9956	178	-	.	Name=INT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Region_Hits_Classifications=INT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Best_Hit=Ty3-INT__REXdb_ID9635|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:9783-9956[100percent];Best_Hit_DB_Pos=1:58of310;DB_Seq=HRGGCGEHGGARALIQKLHRAGYYWPGMKRDTHQYVQRCIQCQKFAPLIHKPGEEMTI;Region_Seq=HSGLCGNHPGARSLALRIQRAGYYWPTLLRDAMDCVRRCQSCQYFAPINRKPGAEITL;Query_Seq=HSGLCGNHPGARSLALRIQRAGYYWPTLLRDAMDCVRRCQSCQYFAPINRKPGAEITL;Identity=0.53;Similarity=0.69;Relat_Length=0.187;Relat_Interruptions=0.0;Hit_to_DB_Length=0.19
-scaffold146.1|size86774	dante	protein_domain	10299	10658	303	-	.	Name=aRH;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat;Region_Hits_Classifications=aRH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|TatII[360bp],aRH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Ogre[360bp],aRH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand[360bp];Best_Hit=Ty3-aRH__REXdb_ID9546|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:10299-10658[100percent];Best_Hit_DB_Pos=1:121of121;DB_Seq=WILHVDGASSKQGSGIGIRLQSPYGEVIEQSFCLAFNASNNEAEYESLLAGLRLAVGIGVTKLRAFCNSQLVANQFSGDYEAKDSRMEAYLAQVQELSKKFLSFELARIPRSENSAADSLA;Region_Seq=WNMYIDGSTQSGAGVGVHYITPYGDWINLAVKLQFPATNNVAEYEALLAGMNFALSLGVTRLKTFSDSQLVVEQFSGHFQAKEPMLEAYKSRSQLLAAKFSEFSLEHIPRESNRAADSLA;Query_Seq=WNMYIDG-STQSGAGVGVHYITPYGDWINLAVKLQFPATNNVAEYEALLAGMNFALSLGVTRLKTFSDSQLVVEQFSGHFQAKEPMLEAYKSRSQLLAAKFSEFSLEHIPRESNRAADSLA;Identity=0.49;Similarity=0.7;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	10701	10817	136	-	.	Name=RH;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat;Region_Hits_Classifications=RH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand[117bp],RH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Ogre[99bp];Best_Hit=Ty3-RH__REXdb_ID8372|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:10701-10817[100percent];Best_Hit_DB_Pos=279:317of317;DB_Seq=NREGTGRVVKWAIELSEFDLHFEPRHAIKSQALADFVVE;Region_Seq=NTDHTSRLAKWAIKVSAMDIAFEPRKAIKGQALADFVVE;Query_Seq=NTDHTSRLAKWAIKVSAMDIAFEPRKAIKGQALADFVVE;Identity=0.64;Similarity=0.77;Relat_Length=0.123;Relat_Interruptions=0.0;Hit_to_DB_Length=0.12
-scaffold146.1|size86774	dante	protein_domain	16797	17666	1057	-	.	Name=INT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=INT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-INT__REXdb_ID6633|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:16812-17666[98percent];Best_Hit_DB_Pos=1:285of313;DB_Seq=HSHSYGGHFGAKRTAHKVLESGFYWPSIFKDAYHFCKSCEKCQRTGNITHKNQMPLTNILVSEIFDVWGIDFMGPFPSSFGNLYILLVVDYVSKWIEAKATRTNDAKVVLDFVRTHIFNRFGIPKAIISDRGTHFCNRSMEALLRKYHVTHRTSTAYHPQTNGQAEISNREIKSILEKIVQPNRRDWSLRLGDALWAYRTAYKSPIGMSPYRMIYGKACHLPVELEHKAFWAIKQCNMDYDAAGIARKLQLQELEEIRNDAYENARIYKEKTKNLHDRMLTRKEF;Region_Seq=HASDYGGHFGPNRTARRILDVGFYWPSIFRDVYQFCRTCDACQRVGNITNRREMPQNYILANEIFDIWGLDFMGPFPQSQGNNYILVAVDYVSKWVEAIPTRTDDGKTVTEFLRKNIFTRYGVPKAIISDRGTHFCNSTMRAMMKKYNVIHKTTTAYHPQGNGQAEATNREIKSILEKVVNKKRSNWSQKLPDALWAYRTAYKTPIGTTPFRLIYGKHCNLPVGLEHKAYWAIREMNFEEGGDAELRQMQLQELDALRLEAYDNSRIYKERLKTYHDKKLLQQNFRERLS;Query_Seq=HASDYGGHFGPNRTARRILDVGFYWPSIFRDVYQFCRTCDACQRVGNITNRREMPQNYILANEIFDIWGLDFMGPFPQSQGNNYILVAVDYVSKWVEAIPTRTDDGKTVTEFLRKNIFTRYGVPKAIISDRGTHFCNSTMRAMMKKYNVIHKTTTAYHPQGNGQAEATNREIKSILEKVVNKKRSNWSQKLPDALWAYRTAYKTPIGTTPFRLIYGKHCNLPVGLEHKAYWAIREMNFEEGGDAELRQMQLQELDALRLEAYDNSRIYKERLKTYHDKKLLQQNF;Identity=0.61;Similarity=0.79;Relat_Length=0.911;Relat_Interruptions=0.0;Hit_to_DB_Length=0.91
-scaffold146.1|size86774	dante	protein_domain	18554	18811	306	-	.	Name=INT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=INT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-INT__REXdb_ID6693|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:18554-18802[96percent];Best_Hit_DB_Pos=231:313of313;DB_Seq=WALRLLNFDNNACGEKRKLQLQELEEMRLNAYESSRIYKERTKAYHDKKLQRREFQPGQQVLLFNSRLRLFPGKLKSKWSGPF;Region_Seq=QGNWAIREMNFEEGGDAELRQMQLQELDALRLEAYDNSRIYKERLKAYHDKKILQQNFREGQQVLLFNSKLRLFPGKLKSRWMGPF;Query_Seq=WAIREMNFEEGGDAELRQMQLQELDALRLEAYDNSRIYKERLKAYHDKKILQQNFREGQQVLLFNSKLRLFPGKLKSRWMGPF;Identity=0.65;Similarity=0.82;Relat_Length=0.265;Relat_Interruptions=0.0;Hit_to_DB_Length=0.27
-scaffold146.1|size86774	dante	protein_domain	19158	19478	197	-	.	Name=INT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=INT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-INT__REXdb_ID6659|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:19182-19448[83percent];Best_Hit_DB_Pos=216:304of314;DB_Seq=YGKPCHLPVELEHKAWWAVKQCNMELDVAGQHRxLQLQELEEIRNDAYESSxIYKEKTKAFHDKQILRKNFEVGQKVLIFHSRLKLFPG;Region_Seq=PRGTISIGLNFGKQCKVLVGMEHENYWEIREMNYEEGADVEQKQMQLQKMDALKLEAYDNSRIDKEKLKAHHAKRILQQNCKKRQQVLIFDSKLKMFPGIPRWMEPF;Query_Seq=FGKQCKVLVGMEHENYWEIREMNYEEGADVEQKQMQLQKMDALKLEAYDNSRIDKEKLKAHHAKRILQQNCKKRQQVLIFDSKLKMFPG;Identity=0.42;Similarity=0.71;Relat_Length=0.283;Relat_Interruptions=0.0;Hit_to_DB_Length=0.28
-scaffold146.1|size86774	dante	protein_domain	19976	20212	259	-	.	Name=PROT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=PROT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-PROT__REXdb_ID6659|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:19976-20212[100percent];Best_Hit_DB_Pos=1:80of80;DB_Seq=MLDLGASINVMPYSIYNSLNLGPMEETCIIIQLADRSNAYPKGVMEDVLVQVNELVFPADFYILKMEDELSPNPTPILLG;Region_Seq=MVDLGASINLMPYYIYSALKLGSLQGTAIIIKLADRSETHPEGVVKDVLAQVNNLVFPADFYVLKMGEAENDDCPLLLG;Query_Seq=MVDLGASINLMPYYIYSALKLGSLQGTAIIIKLADRSETHPEGVVKDVLAQVNNLVFPADFYVLKM-GEAENDDCPLLLG;Identity=0.62;Similarity=0.79;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	28912	29124	216	-	.	Name=PROT;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=PROT|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-PROT__REXdb_ID2599|Class_I|LTR|Ty1/copia|Bianca:28912-29124[100percent];Best_Hit_DB_Pos=1:71of71;DB_Seq=CLADCATTHTILRDKRYFLELTLIKANVSTISGTTNLVEGSGRANIMLPNGTRFHINDALYSSKSRRNLLS;Region_Seq=CLVDSATTHTILKNMRYFTSFEKRDVNIATIVCEANIVEGSGRAVIVLPSGTHIRIDDALYANKSRRNLLS;Query_Seq=CLVDSATTHTILKNMRYFTSFEKRDVNIATIVCEANIVEGSGRAVIVLPSGTHIRIDDALYANKSRRNLLS;Identity=0.59;Similarity=0.7;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0

--- a/test-data/single_fasta.gff3	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,26 +0,0 @@
-##gff-version 3
-##-----------------------------------------------
-##PIPELINE VERSION         : dante-rv-3081(adb2509)
-##PROTEIN DATABASE VERSION : Viridiplantae_v3.0_pdb
-##-----------------------------------------------
-scaffold146.1|size86774	dante	protein_domain	976	1289	293	+	.	Name=RH;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=RH|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-RH__REXdb_ID2558|Class_I|LTR|Ty1/copia|Bianca:976-1289[100percent];Best_Hit_DB_Pos=26:134of134;DB_Seq=ISWRSVKQTITATSSNHAELLALHEASRECVWLRSMIQHIQKNCG-LSSGRMDATIIYEDNTACIAQLKEGYIKGDRTKHISPKFF-FTHDLQKDGDISIQQIRSCDNLAD;Query_Seq=ISWRSTKQTIVAISSNHVELLAIHDTSRECVWLRFMIESI-----\IMXXXXXXXXXXXXXXXXXXQLKE*YIKCDRTKHISPKFF\FTQDLQKNGDVIIQQIRSNDNVVD;Identity=0.59;Similarity=0.66;Relat_Length=0.813;Relat_Interruptions=1.5;Hit_to_DB_Length=0.83
-scaffold146.1|size86774	dante	protein_domain	6810	7049	153	+	.	Name=PROT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Region_Hits_Classifications=PROT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Best_Hit=Ty3-PROT__REXdb_ID9702|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:6810-7049[100percent];Best_Hit_DB_Pos=1:80of80;DB_Seq=LVDDGSKVNLLPYRVFQQMGIPEEQLVRDQAPVKGIGGVPVLVEGKVKLALTLGEAPRTRTHYAVFLVVKPPLSYNAILG;Query_Seq=LVDSGASCNLMSKRVMKQMGIPDEKLEFLDATLYAFDRRTIIPAGKIQLPVTLGEEERTRSEMVEFIIVDMDLAYNAILG;Identity=0.44;Similarity=0.62;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	7656	8296	.	+	.	Name=RT/INT;Final_Classification=Ambiguous_domain;Region_Hits_Classifications_=RT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand[246bp],INT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand[468bp]
-scaffold146.1|size86774	dante	protein_domain	8756	9241	538	+	.	Name=RT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat;Region_Hits_Classifications=RT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand[486bp],RT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Ogre[441bp];Best_Hit=Ty3-RT__REXdb_ID8210|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:8801-9241[90percent];Best_Hit_DB_Pos=27:173of173;DB_Seq=DFTDLNKACPKDSFPLPHIDRLVDSTAGNELLTFMDAFSGYNQIMMNPEDQEKTSFITDRGIYCYKVMPFGLKNAGATYQRLVNKMFHNHLGKTMEVYIDDMLVKSLKKEDHVKHLEECFDILNKYQMKLNPAKCTFGVPSGEFLGY;Query_Seq=DFKGVNKHCQPDPFPLPHIDRLVDAVAGSSLLSTMDAYSGYHQISLAREDQAKSSFLTEDGVFCYVVMPFGLRNAGATYQRLVNKIFADLLGKEMEIYVDDMIVKSLNDEDHIIYLSHCFEVCRTHRLKLNPAKCCFGVRSGKFLGY;Identity=0.63;Similarity=0.8;Relat_Length=0.85;Relat_Interruptions=0.0;Hit_to_DB_Length=0.85
-scaffold146.1|size86774	dante	protein_domain	9433	9781	343	+	.	Name=RH;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Region_Hits_Classifications=RH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Best_Hit=Ty3-RH__REXdb_ID9729|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:9434-9772[97percent];Best_Hit_DB_Pos=1:113of149;DB_Seq=WTEECEEAFQKLKEYLGSPHLLVKPIQGEPLFLYLAVSEHATSSVLVREDDGVQRPIYYTSRALVDAETRYLSLEKIVLALIVSARRLRPYFQAHTIIVLTDQPIRQVLAKPD;Query_Seq=WTDQCDRAFKELKTYLASPPLIVSPTPTETLGLYLAVSEHAVSSVLVAERDGVQHPVYYVSHTLLPAESRYSTVEKFVLALLKSVAKLRHYFESRKVIVYTDQPIKAVLGQSD;Identity=0.58;Similarity=0.73;Relat_Length=0.758;Relat_Interruptions=0.0;Hit_to_DB_Length=0.76
-scaffold146.1|size86774	dante	protein_domain	10810	11667	747	+	.	Name=INT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Region_Hits_Classifications=INT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Best_Hit=Ty3-INT__REXdb_ID9633|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:10819-11667[98percent];Best_Hit_DB_Pos=30:310of310;DB_Seq=RDTHQYVQRCIQCQKFAPLIHKPGEEMTIMSAPCPFAQWGIDLVGPFPQTAGRKKFFIVAVDYFTKWVEAEALSKITEDEVMHFIWKYICCRFGLPRSLVSDNGTQFNGKKIRAWCEEMKITQKFVAVAHPQANGQVESTNRTIVNGLKKRIDELGGSWVDELPSVLWSYRTSAKAATGETPFRLTYGTEAVIPVEVAMDTLRIATF--DEEANDGALRTRLDEIFDLREAAYLHMERSKNLIKARYDQGVRSRSFQIGDLILRRADALKHTGKLEANWEGPY;Query_Seq=RDAMDCVRRCQSCQYFAPINRKPGAEITLTELPCPFDRWGIDILGPFPQSVRQRRFCIVAVEYHSKWIEAEAVASITSEAVKKFVMNNIIVRFGCPRVLVSDNGPQFISDKFATFCEEYGIQQRTSSVYHPQTNGQAEASNKIILHGLRRNLDSLGGSWPDQLPHVLWAYRTTPKSSTGETPFSLVYGSEAVAPVESTIITPRIAAYMHTESANTEFRELDLDLLEERRNEVYGRVRKQQRALRKRYNQRVRPRQFEKGDLILRSVESQGHKGKLDRAWEGPY;Identity=0.49;Similarity=0.66;Relat_Length=0.906;Relat_Interruptions=0.0;Hit_to_DB_Length=0.91
-scaffold146.1|size86774	dante	protein_domain	14592	14828	289	+	.	Name=PROT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=PROT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-PROT__REXdb_ID6659|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:14592-14828[100percent];Best_Hit_DB_Pos=1:80of80;DB_Seq=MLDLGASINVMPYSIYNSLNLGPMEETCIIIQLADRSNAYPKGVMEDVLVQVNELVFPADFYILKMEDELSPNPTPILLG;Query_Seq=MVDLGASINLMPYSIYSALQLGPLQGTAIVIKLADRSNTHPEGVIEDVLVQVNNLVFPADFYVLKM-GKAENNDCPLLLG;Identity=0.68;Similarity=0.84;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	15420	15995	871	+	.	Name=RT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=RT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-RT__REXdb_ID6635|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:15420-15995[100percent];Best_Hit_DB_Pos=1:192of192;DB_Seq=IYPITDSKWVAPIHVVPKKTGITLVKNKNDELIPTRISSGWRMCVDYRKLNLATRKDHFPLPFMDQMLERLAGKSFYCFLDGYSGYNQIVINPEDQEKTTFTCPFGTYAYRRMPFGLCNAPATFQRCMMSIFSDYVERIIEVFMDDFTVYGDSFDKCLENLSLILKRCIETNLVLNYEKCYFMVEQGIVLGH;Query_Seq=IYAISDSDWVSPVHVVPKKTGFTVERNKNGELVPKRVTNGWRVCIDYRKLNDATRKDHFPLPFIDQMLERLAGKKFYCFLDGYSGYNQVAIAPEDQEKTTFTCTYGTYAFRKMPFGLCNAPATFQRCMLSIFSEFTGKFIEVFMDDFTVYGDSFEGALENLEKVLQRCVEKKLVLNSEKCHFMVRQGIVLGH;Identity=0.76;Similarity=0.88;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	16188	16634	623	+	.	Name=RH;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=RH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-RH__REXdb_ID6648|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:16188-16634[100percent];Best_Hit_DB_Pos=1:149of149;DB_Seq=FNEACKVAFDKLKELLTSAPIIQPPDWSLPFEIMCDASNYVVGAVLGQRVGRAAHVIYYTSRTLDSAQCNYSTTEKELLAIVFALEKFRSYLLGTKVIIFSDHAALRYLLAKKEAKPRLIRWILLLQEFNLEIRDKKGTENLVADHLSR;Query_Seq=FNQECQEAFNKLKSLLTAAPIIQPPNWELPFELMCDASNYALGAVLGQKIEGKRHVIYYASKTLSEAQIHYTTTEKELLAIVYALEKFRSYLLGTKITVHSDHAALRHLLSKKESKPRLIRWILLLQEFDLEIKDRAGTENAVADNLSR;Identity=0.74;Similarity=0.87;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	24522	24659	149	+	.	Name=PROT;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=PROT|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-PROT__REXdb_ID2599|Class_I|LTR|Ty1/copia|Bianca:24531-24659[93percent];Best_Hit_DB_Pos=29:71of71;DB_Seq=STISGTTNLVEGSGRANIMLPNGTRFHINDALYSSKSRRNLLS;Query_Seq=STIVCEANIVEGSGRATVVLPSGTHIRIDDALYANKSRRNLLS;Identity=0.65;Similarity=0.77;Relat_Length=0.606;Relat_Interruptions=0.0;Hit_to_DB_Length=0.61
-scaffold146.1|size86774	dante	protein_domain	24873	25481	913	+	.	Name=INT;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=INT|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-INT__REXdb_ID2558|Class_I|LTR|Ty1/copia|Bianca:24873-25481[100percent];Best_Hit_DB_Pos=1:203of203;DB_Seq=HERLGHPGSIMMRKIIEHSCGHQLKSREILQSNKFSCTSCSQGKLITRPSPTKIGSESLNFLERIHGDICGPIHPPCGPFRYFMVLIDASTRWSHVCLLSTRNQAFARLLAQLIRIRAHFPDYPVKKIRLDNAAEFSSQTFNDYCMSIGIDIEHPVAHVHTQNGLAESFIKRIQLIARPLLMRCKLPISTWGHAILHAATLIR;Query_Seq=HDRLGHPGMIMMRKIIRTTSGHSLKNREILHPREYICTACAQGKLITRPSPVKIMNERITFLERIQGDICGPIHPACGPFRYFIVLIDASSRWSHVSLLSTRNHAFARLLSQIIRLRAHFPDYPVKKIRLDNAAEFTSRTFNNYCLAMGIDVEHPVEYVHTQNGLAESLIKRLQLIARPLLMKSKLPVTCWGHAIIHASSLIR;Identity=0.75;Similarity=0.9;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	26313	27428	1060	+	.	Name=RT;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=RT|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-RT__REXdb_ID2558|Class_I|LTR|Ty1/copia|Bianca:26322-27032[63percent];Best_Hit_DB_Pos=1:237of262;DB_Seq=WKDAIKAELYSLNKRKVFGPVVRTPKGVKPVGYKWVFVRKRNENGEIARYKARLVAQGFSQRPGIDFNETYSPVVDATTFRYLISLIAYEGLNLHMMDVVTAYLYGSLDSDIYMKIPEGFNLPDTNSSGSREDYSIKLNKSLYGLKQSGRMWYNRLSEYLLKEGYKNDSVCPCIFMKRSENEFAIIAVYVDDINIIGTPEELPKAIDCLKKEFEMKDLGKTKFCLGLQIEHLNNGIF;Query_Seq=WKDAIESELKSLNKRDVFGPVVRTPEGVQPVGYKWVFVRKRNDKGEISRYKARLVAQGFSQRPGIDYDETYSPVMDATTFRFLISLAIEYGLDLQLMDVVTAYLYGSLDCEIYMKIPEGFHMPERYSSEPRTDYAIKLNKSLYGLKQSGRMWYNRLSEYLIKEGYKNNLVCPCVFMKKFENEFVIIAVYVDDINIVGTQKALLDAVNCLKREFEMKDLGRTKYCLGLQIEYLKNGIF;Identity=0.78;Similarity=0.91;Relat_Length=0.905;Relat_Interruptions=0.0;Hit_to_DB_Length=0.9
-scaffold146.1|size86774	dante	protein_domain	27723	28124	581	+	.	Name=RH;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=RH|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-RH__REXdb_ID2558|Class_I|LTR|Ty1/copia|Bianca:27723-28124[100percent];Best_Hit_DB_Pos=1:134of134;DB_Seq=DAGYLSDPHHGRSQTGYLFTSGNTAISWRSVKQTITATSSNHAELLALHEASRECVWLRSMIQHIQKNCGLSSGRMDATIIYEDNTACIAQLKEGYIKGDRTKHISPKFFFTHDLQKDGDISIQQIRSCDNLAD;Query_Seq=DAGYRSDPHNGRSQTGYVFLNKGAAISWRSTKQTIAATSSNHAELLAIHETSRECVWLRSMIESIYNACGLFTDKMPPTVLYEDNSACIIQLKEGYIKGDRTKHISPKFFFTHDLQKNGEVIIQQIRSSDNVAD;Identity=0.75;Similarity=0.84;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	9780	9956	178	-	.	Name=INT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Region_Hits_Classifications=INT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Best_Hit=Ty3-INT__REXdb_ID9635|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:9783-9956[98percent];Best_Hit_DB_Pos=1:58of310;DB_Seq=HRGGCGEHGGARALIQKLHRAGYYWPGMKRDTHQYVQRCIQCQKFAPLIHKPGEEMTI;Query_Seq=HSGLCGNHPGARSLALRIQRAGYYWPTLLRDAMDCVRRCQSCQYFAPINRKPGAEITL;Identity=0.53;Similarity=0.69;Relat_Length=0.187;Relat_Interruptions=0.0;Hit_to_DB_Length=0.19
-scaffold146.1|size86774	dante	protein_domain	10299	10658	303	-	.	Name=aRH;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat;Region_Hits_Classifications=aRH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand[360bp],aRH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Ogre[360bp],aRH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|TatII[360bp];Best_Hit=Ty3-aRH__REXdb_ID9546|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:10299-10658[100percent];Best_Hit_DB_Pos=1:121of121;DB_Seq=WILHVDGASSKQGSGIGIRLQSPYGEVIEQSFCLAFNASNNEAEYESLLAGLRLAVGIGVTKLRAFCNSQLVANQFSGDYEAKDSRMEAYLAQVQELSKKFLSFELARIPRSENSAADSLA;Query_Seq=WNMYIDG-STQSGAGVGVHYITPYGDWINLAVKLQFPATNNVAEYEALLAGMNFALSLGVTRLKTFSDSQLVVEQFSGHFQAKEPMLEAYKSRSQLLAAKFSEFSLEHIPRESNRAADSLA;Identity=0.49;Similarity=0.7;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	10701	10817	136	-	.	Name=RH;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat;Region_Hits_Classifications=RH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Ogre[99bp],RH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand[117bp];Best_Hit=Ty3-RH__REXdb_ID8372|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:10701-10817[100percent];Best_Hit_DB_Pos=279:317of317;DB_Seq=NREGTGRVVKWAIELSEFDLHFEPRHAIKSQALADFVVE;Query_Seq=NTDHTSRLAKWAIKVSAMDIAFEPRKAIKGQALADFVVE;Identity=0.64;Similarity=0.77;Relat_Length=0.123;Relat_Interruptions=0.0;Hit_to_DB_Length=0.12
-scaffold146.1|size86774	dante	protein_domain	16797	17666	1057	-	.	Name=INT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=INT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-INT__REXdb_ID6633|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:16812-17666[98percent];Best_Hit_DB_Pos=1:285of313;DB_Seq=HSHSYGGHFGAKRTAHKVLESGFYWPSIFKDAYHFCKSCEKCQRTGNITHKNQMPLTNILVSEIFDVWGIDFMGPFPSSFGNLYILLVVDYVSKWIEAKATRTNDAKVVLDFVRTHIFNRFGIPKAIISDRGTHFCNRSMEALLRKYHVTHRTSTAYHPQTNGQAEISNREIKSILEKIVQPNRRDWSLRLGDALWAYRTAYKSPIGMSPYRMIYGKACHLPVELEHKAFWAIKQCNMDYDAAGIARKLQLQELEEIRNDAYENARIYKEKTKNLHDRMLTRKEF;Query_Seq=HASDYGGHFGPNRTARRILDVGFYWPSIFRDVYQFCRTCDACQRVGNITNRREMPQNYILANEIFDIWGLDFMGPFPQSQGNNYILVAVDYVSKWVEAIPTRTDDGKTVTEFLRKNIFTRYGVPKAIISDRGTHFCNSTMRAMMKKYNVIHKTTTAYHPQGNGQAEATNREIKSILEKVVNKKRSNWSQKLPDALWAYRTAYKTPIGTTPFRLIYGKHCNLPVGLEHKAYWAIREMNFEEGGDAELRQMQLQELDALRLEAYDNSRIYKERLKTYHDKKLLQQNF;Identity=0.61;Similarity=0.79;Relat_Length=0.911;Relat_Interruptions=0.0;Hit_to_DB_Length=0.91
-scaffold146.1|size86774	dante	protein_domain	18554	18811	306	-	.	Name=INT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=INT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-INT__REXdb_ID6693|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:18554-18802[96percent];Best_Hit_DB_Pos=231:313of313;DB_Seq=WALRLLNFDNNACGEKRKLQLQELEEMRLNAYESSRIYKERTKAYHDKKLQRREFQPGQQVLLFNSRLRLFPGKLKSKWSGPF;Query_Seq=WAIREMNFEEGGDAELRQMQLQELDALRLEAYDNSRIYKERLKAYHDKKILQQNFREGQQVLLFNSKLRLFPGKLKSRWMGPF;Identity=0.65;Similarity=0.82;Relat_Length=0.265;Relat_Interruptions=0.0;Hit_to_DB_Length=0.27
-scaffold146.1|size86774	dante	protein_domain	19158	19478	197	-	.	Name=INT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=INT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-INT__REXdb_ID6659|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:19182-19448[83percent];Best_Hit_DB_Pos=216:304of314;DB_Seq=YGKPCHLPVELEHKAWWAVKQCNMELDVAGQHRxLQLQELEEIRNDAYESSxIYKEKTKAFHDKQILRKNFEVGQKVLIFHSRLKLFPG;Query_Seq=FGKQCKVLVGMEHENYWEIREMNYEEGADVEQKQMQLQKMDALKLEAYDNSRIDKEKLKAHHAKRILQQNCKKRQQVLIFDSKLKMFPG;Identity=0.42;Similarity=0.71;Relat_Length=0.283;Relat_Interruptions=0.0;Hit_to_DB_Length=0.28
-scaffold146.1|size86774	dante	protein_domain	19976	20212	259	-	.	Name=PROT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=PROT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-PROT__REXdb_ID6659|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:19976-20212[100percent];Best_Hit_DB_Pos=1:80of80;DB_Seq=MLDLGASINVMPYSIYNSLNLGPMEETCIIIQLADRSNAYPKGVMEDVLVQVNELVFPADFYILKMEDELSPNPTPILLG;Query_Seq=MVDLGASINLMPYYIYSALKLGSLQGTAIIIKLADRSETHPEGVVKDVLAQVNNLVFPADFYVLKM-GEAENDDCPLLLG;Identity=0.62;Similarity=0.79;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	28912	29124	216	-	.	Name=PROT;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=PROT|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-PROT__REXdb_ID2599|Class_I|LTR|Ty1/copia|Bianca:28912-29124[100percent];Best_Hit_DB_Pos=1:71of71;DB_Seq=CLADCATTHTILRDKRYFLELTLIKANVSTISGTTNLVEGSGRANIMLPNGTRFHINDALYSSKSRRNLLS;Query_Seq=CLVDSATTHTILKNMRYFTSFEKRDVNIATIVCEANIVEGSGRAVIVLPSGTHIRIDDALYANKSRRNLLS;Identity=0.59;Similarity=0.7;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0

--- a/test-data/single_fasta_filtered.gff3	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,33 +0,0 @@
-##gff-version 3
-##-----------------------------------------------
-##PIPELINE VERSION         : dante-rv-3081(adb2509)
-##PROTEIN DATABASE VERSION : Viridiplantae_v3.0_pdb
-##-----------------------------------------------
-##CLASSIFICATION	ORIGINAL_COUNTS	FILTERED_COUNTS
-##Ambiguous_domain	1	0
-##Class_I|LTR|Ty1/copia|Bianca	6	5
-##Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila	7	5
-##Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat	3	2
-##Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand	4	2
-##-----------------------------------------------
-##SEQ	DOMAIN	COUNTS
-##scaffold146.1|size86774	INT	3
-##scaffold146.1|size86774	PROT	4
-##scaffold146.1|size86774	RH	3
-##scaffold146.1|size86774	RT	3
-##scaffold146.1|size86774	aRH	1
-##-----------------------------------------------
-scaffold146.1|size86774	dante	protein_domain	976	1289	293	+	.	Name=RH;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=RH|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-RH__REXdb_ID2558|Class_I|LTR|Ty1/copia|Bianca:976-1289[100percent];Best_Hit_DB_Pos=26:134of134;DB_Seq=ISWRSVKQTITATSSNHAELLALHEASRECVWLRSMIQHIQKNCG-LSSGRMDATIIYEDNTACIAQLKEGYIKGDRTKHISPKFF-FTHDLQKDGDISIQQIRSCDNLAD;Query_Seq=ISWRSTKQTIVAISSNHVELLAIHDTSRECVWLRFMIESI-----\IMXXXXXXXXXXXXXXXXXXQLKE*YIKCDRTKHISPKFF\FTQDLQKNGDVIIQQIRSNDNVVD;Identity=0.59;Similarity=0.66;Relat_Length=0.813;Relat_Interruptions=1.5;Hit_to_DB_Length=0.83
-scaffold146.1|size86774	dante	protein_domain	6810	7049	153	+	.	Name=PROT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Region_Hits_Classifications=PROT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Best_Hit=Ty3-PROT__REXdb_ID9702|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:6810-7049[100percent];Best_Hit_DB_Pos=1:80of80;DB_Seq=LVDDGSKVNLLPYRVFQQMGIPEEQLVRDQAPVKGIGGVPVLVEGKVKLALTLGEAPRTRTHYAVFLVVKPPLSYNAILG;Query_Seq=LVDSGASCNLMSKRVMKQMGIPDEKLEFLDATLYAFDRRTIIPAGKIQLPVTLGEEERTRSEMVEFIIVDMDLAYNAILG;Identity=0.44;Similarity=0.62;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	8756	9241	538	+	.	Name=RT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat;Region_Hits_Classifications=RT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand[486bp],RT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Ogre[441bp];Best_Hit=Ty3-RT__REXdb_ID8210|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:8801-9241[90percent];Best_Hit_DB_Pos=27:173of173;DB_Seq=DFTDLNKACPKDSFPLPHIDRLVDSTAGNELLTFMDAFSGYNQIMMNPEDQEKTSFITDRGIYCYKVMPFGLKNAGATYQRLVNKMFHNHLGKTMEVYIDDMLVKSLKKEDHVKHLEECFDILNKYQMKLNPAKCTFGVPSGEFLGY;Query_Seq=DFKGVNKHCQPDPFPLPHIDRLVDAVAGSSLLSTMDAYSGYHQISLAREDQAKSSFLTEDGVFCYVVMPFGLRNAGATYQRLVNKIFADLLGKEMEIYVDDMIVKSLNDEDHIIYLSHCFEVCRTHRLKLNPAKCCFGVRSGKFLGY;Identity=0.63;Similarity=0.8;Relat_Length=0.85;Relat_Interruptions=0.0;Hit_to_DB_Length=0.85
-scaffold146.1|size86774	dante	protein_domain	10810	11667	747	+	.	Name=INT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Region_Hits_Classifications=INT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand;Best_Hit=Ty3-INT__REXdb_ID9633|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:10819-11667[98percent];Best_Hit_DB_Pos=30:310of310;DB_Seq=RDTHQYVQRCIQCQKFAPLIHKPGEEMTIMSAPCPFAQWGIDLVGPFPQTAGRKKFFIVAVDYFTKWVEAEALSKITEDEVMHFIWKYICCRFGLPRSLVSDNGTQFNGKKIRAWCEEMKITQKFVAVAHPQANGQVESTNRTIVNGLKKRIDELGGSWVDELPSVLWSYRTSAKAATGETPFRLTYGTEAVIPVEVAMDTLRIATF--DEEANDGALRTRLDEIFDLREAAYLHMERSKNLIKARYDQGVRSRSFQIGDLILRRADALKHTGKLEANWEGPY;Query_Seq=RDAMDCVRRCQSCQYFAPINRKPGAEITLTELPCPFDRWGIDILGPFPQSVRQRRFCIVAVEYHSKWIEAEAVASITSEAVKKFVMNNIIVRFGCPRVLVSDNGPQFISDKFATFCEEYGIQQRTSSVYHPQTNGQAEASNKIILHGLRRNLDSLGGSWPDQLPHVLWAYRTTPKSSTGETPFSLVYGSEAVAPVESTIITPRIAAYMHTESANTEFRELDLDLLEERRNEVYGRVRKQQRALRKRYNQRVRPRQFEKGDLILRSVESQGHKGKLDRAWEGPY;Identity=0.49;Similarity=0.66;Relat_Length=0.906;Relat_Interruptions=0.0;Hit_to_DB_Length=0.91
-scaffold146.1|size86774	dante	protein_domain	14592	14828	289	+	.	Name=PROT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=PROT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-PROT__REXdb_ID6659|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:14592-14828[100percent];Best_Hit_DB_Pos=1:80of80;DB_Seq=MLDLGASINVMPYSIYNSLNLGPMEETCIIIQLADRSNAYPKGVMEDVLVQVNELVFPADFYILKMEDELSPNPTPILLG;Query_Seq=MVDLGASINLMPYSIYSALQLGPLQGTAIVIKLADRSNTHPEGVIEDVLVQVNNLVFPADFYVLKM-GKAENNDCPLLLG;Identity=0.68;Similarity=0.84;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	15420	15995	871	+	.	Name=RT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=RT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-RT__REXdb_ID6635|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:15420-15995[100percent];Best_Hit_DB_Pos=1:192of192;DB_Seq=IYPITDSKWVAPIHVVPKKTGITLVKNKNDELIPTRISSGWRMCVDYRKLNLATRKDHFPLPFMDQMLERLAGKSFYCFLDGYSGYNQIVINPEDQEKTTFTCPFGTYAYRRMPFGLCNAPATFQRCMMSIFSDYVERIIEVFMDDFTVYGDSFDKCLENLSLILKRCIETNLVLNYEKCYFMVEQGIVLGH;Query_Seq=IYAISDSDWVSPVHVVPKKTGFTVERNKNGELVPKRVTNGWRVCIDYRKLNDATRKDHFPLPFIDQMLERLAGKKFYCFLDGYSGYNQVAIAPEDQEKTTFTCTYGTYAFRKMPFGLCNAPATFQRCMLSIFSEFTGKFIEVFMDDFTVYGDSFEGALENLEKVLQRCVEKKLVLNSEKCHFMVRQGIVLGH;Identity=0.76;Similarity=0.88;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	16188	16634	623	+	.	Name=RH;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=RH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-RH__REXdb_ID6648|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:16188-16634[100percent];Best_Hit_DB_Pos=1:149of149;DB_Seq=FNEACKVAFDKLKELLTSAPIIQPPDWSLPFEIMCDASNYVVGAVLGQRVGRAAHVIYYTSRTLDSAQCNYSTTEKELLAIVFALEKFRSYLLGTKVIIFSDHAALRYLLAKKEAKPRLIRWILLLQEFNLEIRDKKGTENLVADHLSR;Query_Seq=FNQECQEAFNKLKSLLTAAPIIQPPNWELPFELMCDASNYALGAVLGQKIEGKRHVIYYASKTLSEAQIHYTTTEKELLAIVYALEKFRSYLLGTKITVHSDHAALRHLLSKKESKPRLIRWILLLQEFDLEIKDRAGTENAVADNLSR;Identity=0.74;Similarity=0.87;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	24873	25481	913	+	.	Name=INT;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=INT|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-INT__REXdb_ID2558|Class_I|LTR|Ty1/copia|Bianca:24873-25481[100percent];Best_Hit_DB_Pos=1:203of203;DB_Seq=HERLGHPGSIMMRKIIEHSCGHQLKSREILQSNKFSCTSCSQGKLITRPSPTKIGSESLNFLERIHGDICGPIHPPCGPFRYFMVLIDASTRWSHVCLLSTRNQAFARLLAQLIRIRAHFPDYPVKKIRLDNAAEFSSQTFNDYCMSIGIDIEHPVAHVHTQNGLAESFIKRIQLIARPLLMRCKLPISTWGHAILHAATLIR;Query_Seq=HDRLGHPGMIMMRKIIRTTSGHSLKNREILHPREYICTACAQGKLITRPSPVKIMNERITFLERIQGDICGPIHPACGPFRYFIVLIDASSRWSHVSLLSTRNHAFARLLSQIIRLRAHFPDYPVKKIRLDNAAEFTSRTFNNYCLAMGIDVEHPVEYVHTQNGLAESLIKRLQLIARPLLMKSKLPVTCWGHAIIHASSLIR;Identity=0.75;Similarity=0.9;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	26313	27428	1060	+	.	Name=RT;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=RT|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-RT__REXdb_ID2558|Class_I|LTR|Ty1/copia|Bianca:26322-27032[63percent];Best_Hit_DB_Pos=1:237of262;DB_Seq=WKDAIKAELYSLNKRKVFGPVVRTPKGVKPVGYKWVFVRKRNENGEIARYKARLVAQGFSQRPGIDFNETYSPVVDATTFRYLISLIAYEGLNLHMMDVVTAYLYGSLDSDIYMKIPEGFNLPDTNSSGSREDYSIKLNKSLYGLKQSGRMWYNRLSEYLLKEGYKNDSVCPCIFMKRSENEFAIIAVYVDDINIIGTPEELPKAIDCLKKEFEMKDLGKTKFCLGLQIEHLNNGIF;Query_Seq=WKDAIESELKSLNKRDVFGPVVRTPEGVQPVGYKWVFVRKRNDKGEISRYKARLVAQGFSQRPGIDYDETYSPVMDATTFRFLISLAIEYGLDLQLMDVVTAYLYGSLDCEIYMKIPEGFHMPERYSSEPRTDYAIKLNKSLYGLKQSGRMWYNRLSEYLIKEGYKNNLVCPCVFMKKFENEFVIIAVYVDDINIVGTQKALLDAVNCLKREFEMKDLGRTKYCLGLQIEYLKNGIF;Identity=0.78;Similarity=0.91;Relat_Length=0.905;Relat_Interruptions=0.0;Hit_to_DB_Length=0.9
-scaffold146.1|size86774	dante	protein_domain	27723	28124	581	+	.	Name=RH;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=RH|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-RH__REXdb_ID2558|Class_I|LTR|Ty1/copia|Bianca:27723-28124[100percent];Best_Hit_DB_Pos=1:134of134;DB_Seq=DAGYLSDPHHGRSQTGYLFTSGNTAISWRSVKQTITATSSNHAELLALHEASRECVWLRSMIQHIQKNCGLSSGRMDATIIYEDNTACIAQLKEGYIKGDRTKHISPKFFFTHDLQKDGDISIQQIRSCDNLAD;Query_Seq=DAGYRSDPHNGRSQTGYVFLNKGAAISWRSTKQTIAATSSNHAELLAIHETSRECVWLRSMIESIYNACGLFTDKMPPTVLYEDNSACIIQLKEGYIKGDRTKHISPKFFFTHDLQKNGEVIIQQIRSSDNVAD;Identity=0.75;Similarity=0.84;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	10299	10658	303	-	.	Name=aRH;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat;Region_Hits_Classifications=aRH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand[360bp],aRH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Ogre[360bp],aRH|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|TatII[360bp];Best_Hit=Ty3-aRH__REXdb_ID9546|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Tat|Retand:10299-10658[100percent];Best_Hit_DB_Pos=1:121of121;DB_Seq=WILHVDGASSKQGSGIGIRLQSPYGEVIEQSFCLAFNASNNEAEYESLLAGLRLAVGIGVTKLRAFCNSQLVANQFSGDYEAKDSRMEAYLAQVQELSKKFLSFELARIPRSENSAADSLA;Query_Seq=WNMYIDG-STQSGAGVGVHYITPYGDWINLAVKLQFPATNNVAEYEALLAGMNFALSLGVTRLKTFSDSQLVVEQFSGHFQAKEPMLEAYKSRSQLLAAKFSEFSLEHIPRESNRAADSLA;Identity=0.49;Similarity=0.7;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	16797	17666	1057	-	.	Name=INT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=INT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-INT__REXdb_ID6633|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:16812-17666[98percent];Best_Hit_DB_Pos=1:285of313;DB_Seq=HSHSYGGHFGAKRTAHKVLESGFYWPSIFKDAYHFCKSCEKCQRTGNITHKNQMPLTNILVSEIFDVWGIDFMGPFPSSFGNLYILLVVDYVSKWIEAKATRTNDAKVVLDFVRTHIFNRFGIPKAIISDRGTHFCNRSMEALLRKYHVTHRTSTAYHPQTNGQAEISNREIKSILEKIVQPNRRDWSLRLGDALWAYRTAYKSPIGMSPYRMIYGKACHLPVELEHKAFWAIKQCNMDYDAAGIARKLQLQELEEIRNDAYENARIYKEKTKNLHDRMLTRKEF;Query_Seq=HASDYGGHFGPNRTARRILDVGFYWPSIFRDVYQFCRTCDACQRVGNITNRREMPQNYILANEIFDIWGLDFMGPFPQSQGNNYILVAVDYVSKWVEAIPTRTDDGKTVTEFLRKNIFTRYGVPKAIISDRGTHFCNSTMRAMMKKYNVIHKTTTAYHPQGNGQAEATNREIKSILEKVVNKKRSNWSQKLPDALWAYRTAYKTPIGTTPFRLIYGKHCNLPVGLEHKAYWAIREMNFEEGGDAELRQMQLQELDALRLEAYDNSRIYKERLKTYHDKKLLQQNF;Identity=0.61;Similarity=0.79;Relat_Length=0.911;Relat_Interruptions=0.0;Hit_to_DB_Length=0.91
-scaffold146.1|size86774	dante	protein_domain	19976	20212	259	-	.	Name=PROT;Final_Classification=Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Region_Hits_Classifications=PROT|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila;Best_Hit=Ty3-PROT__REXdb_ID6659|Class_I|LTR|Ty3/gypsy|non-chromovirus|OTA|Athila:19976-20212[100percent];Best_Hit_DB_Pos=1:80of80;DB_Seq=MLDLGASINVMPYSIYNSLNLGPMEETCIIIQLADRSNAYPKGVMEDVLVQVNELVFPADFYILKMEDELSPNPTPILLG;Query_Seq=MVDLGASINLMPYYIYSALKLGSLQGTAIIIKLADRSETHPEGVVKDVLAQVNNLVFPADFYVLKM-GEAENDDCPLLLG;Identity=0.62;Similarity=0.79;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0
-scaffold146.1|size86774	dante	protein_domain	28912	29124	216	-	.	Name=PROT;Final_Classification=Class_I|LTR|Ty1/copia|Bianca;Region_Hits_Classifications=PROT|Class_I|LTR|Ty1/copia|Bianca;Best_Hit=Ty1-PROT__REXdb_ID2599|Class_I|LTR|Ty1/copia|Bianca:28912-29124[100percent];Best_Hit_DB_Pos=1:71of71;DB_Seq=CLADCATTHTILRDKRYFLELTLIKANVSTISGTTNLVEGSGRANIMLPNGTRFHINDALYSSKSRRNLLS;Query_Seq=CLVDSATTHTILKNMRYFTSFEKRDVNIATIVCEANIVEGSGRAVIVLPSGTHIRIDDALYANKSRRNLLS;Identity=0.59;Similarity=0.7;Relat_Length=1.0;Relat_Interruptions=0.0;Hit_to_DB_Length=1.0

--- a/test-data/test_seq_1	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,34 +0,0 @@
->test_seq_1
-tatactacgcgactagactacgatactagggacagcatattacacccagagaacagacta
-TTGCAAAAAGAGAAGATTGGTAAAACGGGGGGGAATGTGTGAGTTGTAATGGGTTCATCT
-GCTCATGTTACTGCAGGGAGGACAGGTCCCGAACCCATTTCCAGCCTTTGGCCTGTCCCT
-CGTCCCTTACCGATCAAAGATACAACCCGGCGGAAGACCTATGGACTCTTACGGTATGCA
-AGCTAGTTCCTTTAGATTAGATTACAATGTTTACTTTTGTTATTTCTAGTTGGCAAGAGT
-GTTCCCCATAGTGTAGTTCCTTGAGCGGTAACAGTGAGCGAACCGTGAGAGCATGCATAC
-TGTTTCGGACGAGCTGTTTTACAGGTTTCCAAAGACTGTTTCTTTGCGATCCGACCAGAA
-GCTACTGCATGTTCGAGGACGAACATGGGGCAAGTGTGGGGATGTTTGGAGGTATGTTAT
-TTTTGTATGTTTTTAGCTATGCATTTTAGGCCTCTTGATAGTGGTTAGAGTTGATTTTGA
-tatactacgcgactagactacgatactagggacagcatattacacccagagaacagacta
-CCGCTTCCGCACCAGGCGAGCTTAGCTCGTGTGTTCCTTCCTGGCGGACGCCTCAGAGGG
-AGATCATGTTGGTGATCAATGTTGGGGTGGGATAGGAGTTTACTCGTGGGCTATGTTGGA
-CCTCTCTTTGGGACATGGTCCAGAGCTAATTGGTCGTCTCTAGGAGGGCCAGATGACAGT
-tatactacgcgactagactacgatactagggacagcatattacacccagagaacagacta
-ACCTGAAGGTGTACAACCGGTTGGTTATAAGTGGGTTTTTGTGAGAAAACGAAATGATAA
-AGGAGAAATATCTCGGTATAAGGCGAGATTAGTAGCTCAAGGGTTTTCTCAAAGGCCAGG
-AATTGATTATGATGAAACCTATTCACCGGTTATGGATGCCACAACTTTCAGGTTTTTGAT
-AAGTCTGGCGATTGAATATGGGCTTGATTTACAACTGATGGATGTTGTAACAGCATACTT
-ATATGGGTCACTGGATTGTGAAATATATATGAAAATCCCTGAAGGGTTTCATATGCCTGA
-ACGATATAGTTCTGAACCCCGTACCGATTATGCGATTAAATTGAATAAATCCCTGTATGG
-ATTAAAGCAGTCAGGACGAATGTGGTATAACCGTCTAAGTGAATACTTGATTAAAGAGGG
-tatactacgcgactagactacgatactagggacagcatattacacccagagaacagacta
-tatactacgcgactagactacgatactagggacagcatattacacccagagaacagacta
-ACAAGGTGGCGACAGTGGAACATGGCCCGATCGAGGACCAGCGTGAAGTCACGCATAACA
-TGGAACCAATCGGGTACAAGAACGTTTCACTATCCTCTTCCGACGGAAGGAAGAACGTCA
-AGATTGGGGTGCAAATGCCCCCAAATATCGAAGAACAACTCATCCAGGTCTTGACAGAGT
-ATCAAGACATCTTTGCTTGGGACATCTCCGAGGTCCCTGGAATTGATCGGTCACTGATGG
-AACATCGCATCAATACCGATCCTGAGGCCGTGCCCGTTCGACAAAAAAGGAGACGCTTCT
-CTCACAATCTGTGATCAACCCGAGAGAAAATGTAAGCGCTGTAACGCTGAGGAGTGGAAA
-GGTTGCAGATGAAGCAATCCAGAAGAAGAGGAAATCGCCTAAAGAAGCAGCAACAAAACC
-AGAGGATGAGAAGGAGGTCGAAGCTGCTGAACCAGTGACAGAACCCACTGCCAAGAAACA
-GAAAGAACCAGAGGTCGAGGTAACAAAGGAAAAATCTGTTATTAAACCTTACTATGAACT
-TCCACCTTTTCCAGGGAGGTTCAAGCTGGAGAAAAAGCAAGAGGAAGAGAAGGAGTTGAT

--- a/test-data/vyber-Ty1_01.fasta	Fri Apr 03 07:27:59 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,1015 +0,0 @@
->Acoerulea195_58_rc
-CTTTCTGAAACCGGCACGAAGTGGTTTGACATCAATTTTGTGACAAAATGTCTTTGGTTA
-TCTCGCCTTGAATTCATTGTTAGGAAAATCAAGATGTACGCATGGTCGTCTCGTGTCCTT
-GTGTCTCTTAGGTCTGTCCAGGAAAAGTTATTAGGTTCTGGACGTACGTACCGAGTTAGG
-ACGTACGTACGTTCACTGAAGCAGCAAACATCTTTTGGTCAGGACGTACGTACTCAGTTA
-GGACGTTCGTACGTCCAAGTAGGCAGCAGACGTGTTTTCGTATGGAGACGTACGTACCGA
-TTTAGAGCGTTCGTACGTCATGTTAGGCAGCAGGCTTATTTTTGTGTGGCGACGTTCGAA
-CGTCATATCGACGTACGAACGTCCAAAGTCGGTTTGCACTGTTTCGGCCCATTTTCCTAG
-GTTTTCAACCGTAAGTATAAATAGGGTTTCTCTTCCATAGAACAAGATAGTTCTTTGGCC
-GTCCATCCTTCTTCTTGTTCATGTGTTTTGGTTAGGTTTTTGATATTGAACTTGTTTGTG
-GCTATTGAACTCAATTCCCTTTCTTCCCTCTTTTCTCAATTCATATCTTTGTGATCTTCA
-AGAGTTATTGTGAATTCGGCTTCGAGTCTTCTTGTGTGTTCATCAAGAAGTCGAGTGTCT
-TGTGCGTACATCAAGGTGCTCTGGTGAAGTCAAGGTGTTAGAGATAACCAGGAGTATCAA
-CTCGCAGTGAGTGCAGGTAGTTGATAGGTTTGTACATCTCTTTTCATCTAGTGGATTATT
-TTGTTGTCGCGAGGACAACCGTGGACGTTTCCCTGTTTGGGTTTTACCACGTTAAAAATC
-TCTGTGTGTTATTTACTTTCTGCGTATTGTTTGTTTGCTTGTTTGAATTCACATTTGTTA
-TACTAGCACTATTTGTAGTTTCTCAATTGGTATCAGTCGCTGGGGGCTGGTTAAGGAGCG
-TAAAGGCTCACGCTAACGATCAATAGTGCTATGGATTACTCTGGTAAGACACATGTCTCG
-GTGGTTGCTCGACTCTCTGATGAGCAGTCCATTGACAGTAAAGAATGCTCTAGTAATTGT
-GACAGGGATTTGATTCTGGTGGACAATAATGATTGGGCTACCCTATACCGGTTATCCAGA
-AAGGAATGCCTAAGGTTGGAGGCACAAAATTGTATTCTTCAGGATAGACTTGACTTATTG
-ACCTCTAGTTCCTCATCTATGTCAATCCTGGATAGGGAACCTGTATCATGGCAGGTTGAT
-AAGGTGGCTTTACTGGGTAATCTTGCTGCTCTTGAGCATGACAAGGTCGAATGGGAAGCT
-CGGTATAAGATTGTGTCCTCAGAACTAGACAAGGTCAAAAAGGAGTTGATTCGCTTTCAG
-TCGTTCGAAAAGTGCAATGGGTTGTCATCTTCTGAATTACCTCCACTGTTGCCCCATCCT
-CCTACCAAAAAGTCCGACATTCATCACTCTGTGGACACGGTTGGCATCCGGCGTAAGTGG
-AAACTGTTCAGGCGTGTTCCGCCTCAGGGAAGGAAACGTCAAATGCCGTGCTCACTATGC
-GGACAGTTTGGACATTGGGCCTCGCAGTGTGGTGTGGCTGCTCATGTTCCACAATCATGG
-AAGCCGTTTTCACAACCGTCGTATGATTATCCATCTTATCCATATGCCTCTTACTCTTTT
-CCTTGTAACTTTGCAGGAAATGTTTCAAATGTGGCGATTACTGCCCTTCATGTCTCAAGC
-TCGGATAGTGAGTGGTTACTTGATAGTGGAGCTTCAAAACATATGTCAGGTAATGCCAAA
-CTTTTCTCCTCCGTTACTGCTATAGATGGTGGAAGTGTTACTTTTGGGAATGGTAAGAGC
-TCTCCTGTGATTGGTAAGGGATTTGTCGCTGGTATTGGTTTATCTCCGAATGATGTTTGT
-TTGTTAGTTGATGGTTTGCGTGTAAATTTGATCAGTATTAGCCAACTGTGTGATACTGAC
-CATACTGTTAATTTTTCCAAAAATATATGTACCGTGCTTGATAGTTTGGGTAAGTGTATC
-ATGACAGGTAAACGAACATTGGATAATTGCTATGCCATTCAGCCTGTTACATCTAGCATG
-AACTGCTTACCTAGCAAACTAAATGAGGGTCTATTGTGGCATCAACGACTCGGTCATGTC
-AACTTTGAACACCTGGACAATCTAACTCGGAATGAATATATTAAGGGAGTTCCTAGACTT
-GGAAGAAATCGAGACACTGTGTGTGGTGGGTGTCAACTAGGTAAACAGATACGTAGTCCA
-CATTCCAAGAAAAAATCCATAACCACATCTTCTCCTTTAGAACTCATACACATGGATCTG
-ATGGGTCCTACTCGTACTCCTAGTCTAGGAGGCAAACGATACATCTTGGTTATGGTTGAT
-GACTACACTCGCTTTACCTGGGTATCATTCTTGCGTGAAAAATCTGATGCGTTTCTTGAG
-TTTCAGGGGATATGCCTTCGTATTCAGAACGAGAAAGATACTCAAATTAAACATATCAGA
-AGCGATAGAGGTGGTGAGTTCACAGCCACAGGTGTGATTGAGTATTGTATTGCAAATGGT
-ACATGGCAAGAATTTTCGGCTCCATACACTCCGCAACAAAACGGAGTCGCTGAAAGGAAA
-AATCGTGTTATTCAGGAGATGGCTCGTGCTATGTTGCATGCAAAGGATGTTCCGACCAAG
-TTTTGGGCGGAAGTGGTTCATACTGCTTGTTACATAATGAACCGTGTATATCTAAGATCT
-GGTACCACACAAACTGCTTATGAGCTATGGTATGGTAAGAAGCCGAATCTCAAATATATG
-CGAGTTTTTGGTAGTGTGTGCTATGTGTGCAAGGACAGACAAAGTCTGTCCAAGTTTGAT
-AGTCGAGGTGAAGTAGCTCTTTTACTTGGTTATTCTTCTAACAGTAGAGCCTTTCGAGTG
-TTTAACTACACCACTCGCAAGGTCATGGAATCCTTTAATGTTGTTGTTGATGACACTATT
-ACATCTGACTCTTCTGTTTCCACTGGTACACAGGATGTCACAGTTCTCTCACCCGTGTCA
-GACCCGGCTGACATGTCTTCCATATCGTTATCATCACCTGATAATGGCAATGGTGGTACT
-AAACCTTCTGATGCTGCAGAGGACGTGCCTAGTAGGACCGGTGCTGTGCTCACACCGGAT
-GATGTGGTTCAATCACCAGATGTGATTGATGTGTCTTCAGATCTCTCCACTGTCCCTGCT
-GACCCTGAAAGGGTGTTTAATCTAGCTTCACCTCGTGTCAAACAATATCACTCCTTAGGA
-GATATCATTGGGGATATTAATGATCAGCGTCTGACTCGTCGGAGGGCCAAGGAGACAAAT
-TGTGTTCATTATGTTTGTTATCTCTCTTCTCTTGAACCTAAAAATGTTACTGATGCTCTT
-ATTGATGATGATTGGCTAGTTGCTATGCAGGAAGAACTCGGTCAGTTTAAGCGTAGTGAT
-GTCTGGACGTTGGTTCCTAGACCTACTCACACTAATGTGGTTGGCACCAAGTGGATCTTT
-AAAAACAAGTTGGATGAGTTCGGACAGATTGTGCGCAACAAGGCAAGGCTCGTAGCTCAG
-GGCTACAGTCAGATTGAAGGTATTGACTATGGAGAGACATTTGCTCCCGTGGCTAGGTTG
-GAATCTGTCAGGCTTCTTCTTGCTATGGCATGCCACTTGAATTTCAAGTTGTATCAAATG
-GATGTCAAAAGTGCATTTCTCAATGGTATTCTTAATGAGGAGGTCTATGTTGAACAACCT
-AAAGGGTTTGTGGATCACACTTTCCGAATCATGTCTTCAAATTGCAAAAAGCACTGTATG
-GGTTAAAGCAGGCTCCTCGAGCTTGGTATGAACGTCTGACCTCCTTCTTACTGGGAAAAG
-GATTTGTTCGTGGCAGTGTTGATCGGACTCTGTTTATACTGAGAAAGAATACTGATGTTC
-TTCTCGCCCAAGTCTATGTGGATGATATAGTGTTTGGGTCGACGTGCCCAAGTCTTTCTG
-AATCTTTCTCTCAATTAATGAGCTCTGAATTTGAAATGAGTTTAATGGGAGAACTGAATT
-TCTTTCTTGGCTTACAGGTCAAACAGTTTGATCATGGAGCTTTCATTTCTCAAACAAAGT
-ATGCTAAAGAACTGGTTAAAAAATTCGGTCTCAGCACTTCCAGTGGTCAAGATACTCCCA
-TGGGTGAGAGGGTACGTCTTGGTAAGGATATTATGGGTAAGTCTGTTGATATTCGTGAAT
-ATCGGAGTATGATTGGTAGTCTTTTGTATCTCACTGCTAGTCGTCCAGATATTTCTTATA
-GTGTTGGGGTATGTGCAAGATTTCAGGCTAACCCTAAAGAATCTCATCTCATTGCGGTTA
-AGCGTATTATTCGGTATGTTGCTAGCACTCTTGACTACGGGTTATGGTTCACCAGAGATA
-CGAATAGTGTTGTGGCAGGTTATTGTGATGCTGATTGGGGTGGTAATCTTGATGATCGTC
-ATAGTACCAGTGGTGGCTGTTTCTATGTTGGGAATAATCTTGTTTCCTGGCATAGCAAGA
-AACAATCATCTGTATCCATCTCTTCATGTGAGGCTGAGTATATAGCTGCAGCTAGTGCAT
-GTACTCAACTTCTGTGGATGCGTCAGATGCTTCGGGATTATGGCATTCAACAGCAGGCGA
-TGGACCTGTTCTGTGATAACACCAGTACCATTAGCATTTCCAAGAATCCTGTTCAACACT
-CACGCACAAAGCATATTGACATTCGTCACCATTTCCTTCGTGAGGCGGTCGAAAAGGGTG
-ATATAGTAATGGAGTTCATTCCTACAGAACATCAGCTAGCAGATATTCTTACAAAACCTC
-TTGATGCAGGCCGATTCCATTCCTTGCGTAAGTCTATTGGGGTTGTAACTCTCCCCTAAT
-TGTTTTGTTACTTTCTTTTTCTTTCTGGTATTGTCATTGTGGTTAGGATGTTTAGATTCG
-GTGTTTTTGGTGTTTATTATTATTGTGGTTGTTTTTGAGGACATACTACCTTCTCTATTG
-TGTGGATTCCATTTTACAGATGTTTCTAGTTTGGCGTTCTCAACTGTCTTTGTGTCATGC
-ATGGTTGTAAGGTTACATTATACTCTCTTCGTTTTGGTAGTTTCTTGCATGATGCATGTC
-AATGTGGTTAGCTTGACCTTTTCAATACATGTATAACTCCAGTTTTCCATGGTTGCTTTT
-CATGATCGTTGTTTTTATTTTCTTTATTTTCTCACCTCTTGTAAGTGGTCAATTACTCAC
-AAAATCCCCAGCCATTTGAATGTCTTGAGAGCATACTCATTTGGCTAAGAACCCGTTGGG
-GCTGTGAGGGTCATGCTCTGACGGATGCACTCAGGAGATGTTAGTTCTTGTGTTGCACGT
-TATCATGGTATTGCTCTTCTTGAGTGTGTTTTGCCGAAAAGATGTAGGCTACACCCTACC
-CTCGTGTTCTGGTAAAAGATGGAGATACTTTATACTCCCACACGGGAATGGCATTTTTTT
-AACAACAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGGATGTTAAGTGTCTGTCATAC
-TTGTCTAGTTAGAAGACAAGAAGAATGCCAGTTTTTCGGTGTTTACTTGCAACAGACTTT
-CAAGGTATTCCATGATCTTGTTTTTAGTTTGTTTTCGGAGGTTGGTATTGGTCTATCAGG
-TTCTTCTACATGCTTGCATCATTCTAGTAAGTACCATGTCATGGTGTATGATGTCGTTCT
-ACGATTTTGCATTGTCTTGTGGTTGAGGGTTGTTTATACTGTATTCGGCTGTACTTGGGA
-ATCCTTATGATAAGTAGAAGTGTTTTCTTGTGATGTCTCTCAATATTTGTACCATGTCAT
-GGTGTATGATGTCGTCTACGATTTGCATTGTCTGGTGGTTGAGGGTGTTTATACTGTATT
-CGGCTGTACTTGGATCCTTATGATAAGTAGAAGTGTTTTCTTGTGATGTCTCTCAATATT
-TGTTCTTGCCTTCTTGGTAGTTCTTTGGTATATTATCTGACTGGTTGGGTGGTTCTTCAT
-TTTTTTTGGTATATATATATATATATATATTTCTTTTCAAAAAAAAAAAAAAAAAAAGGA
-TTCGTACGAACGCTTTCATGACGTACGAATGTGATCTTAGTCAGATTGTTAGTTTTTGGG
-TCCCGAAGTTCGACCGTGTTCAGAACGTACGAACGTCTGTCATACAGTCCGGCCAATTTG
-TGTTTAAATTCAAATTTTTTTTTGAACCAAAGGCTTACTGTTACATCAATCGTGCCCCTT
-TCTTCTCTCTTGTACGAACATAGGAAGTCTTCACCTTCCCTCAAGGTCTTCTCTTTGGTC
-TTTTCCTTCTTCTTCTTCTTTGGTGGTTGTTTATTGCTTCTCTCAAGCAGCTTGTTTCTA
-TTTCTTTGGTATTGAGGATGTTCTTGGATATTGCTATTTTCATATTAGTTCAAGTTGGTT
-TCTTTGTTTTGCAGTTTTTTTTGGTAATGGACCTCTTATTGGGCCCATGATAGGTTTACC
-TCTGTTTCCCCTATATATAGGGGAGTCTTCTCCTCTTTTACCCCTATTGTATCTTCTTCT
-CTCTTGCTAGTTCTATCTTTGTTCATCAGAATTTTCTATGACAGAATTTGCACTTCATGG
-AGTTTTTGTTCCGGCTGTTGGGTATGTGTCGTTTATGGTCATGGAGTACAAAAACAATGC
-TAAGGTTTGTCGTTTATGTCGCGTTCCTCATGGGTATGACTATGATCATCGACCTTTACT
-TCCTTCATGCATGGTTCATATCAATCATTGGGAGAGGCTACCGGGACATCTTCAGAATCA
-CATCTTGGAGTTGGTTGGATACCCGAAGGTTCCAGGACTTGCTGCTCCTCTTACCGACTG
-TGGCTTGGACAGCAGAATTGGGGAGTTATTTTGTACGAGTGCTGATAGTCCTGTGCAAAC
-TCGGTGGAATAGCTATTCGGCTCGTGCGAAAGCTTTATGTCCTATCTTAAGCTTTATGTT
-CATATGATCGCTCGGTGGATTAGACTTCGGCTCGTGTGGAAGCTTACTTCTCCAAGCTTC
-GTTATTTCAAGCATTATTTTTTTTGTTATGGATTCCTTTGGTTGGTTATTAGTTGATTTT
-CTGAACTTTTAACAGTTGGGTTCGGTTTATATTTTTATCTGTTTTGTATTCTTTGTTTGG
-TATTGATTTCTTCTTCTTGATTATTGGTTTATTGTTGTTCTTTTCCTTTATTGGTTGGTT
-ATTTTGTTCACTTGTTGTGACAAAAAGGGGGAGATTTATGACTCTAATTACTAACAATCG
-TCTTCTTGTTGTTAGGTTATTTTTTTTGGGTCATGTTTCAGGTATGCTACACTTATGGGG
-GAGCTCGTGTGTCAAAATTTTGGAGCTCATGAAGTATGGGGGAGTTGCTCGAAGGAACTT
-CTGTTGAATCCTTGATTGCTTCCGTTCGATCTCTGATGGTTAAAGCATCTTCCTAAAGCT
-GGATAACAAGAATTGGTGACTTGTTATGTGTTTCTATTTTCGAGTGTTTTTGTAGGCATG
-TTTTATAAATGCCATGGTTTGGGATTGTAAGCACGTTTTATAAGTGCTATTAGAGTCATA
-GGTTTTTGTCACAAAATCAATGTCAAAGGGGGAGATTGAAACCGGCACGAAGTGGTTTGA
-CATCAATTTTGTGACAAAGTGTCTTTGGTTATCTCGCCTTGAATTCATTGTTAGGAAAAT
-CAAGATGTACGCATGGTCGTCTCGTGTCCTTGTGTCTCTTAGGTCTGTCCAGGAAAAGTT
-ATTAGGTTCTGGACGTACGTACCGAGTTAGGACGTACGTACGTTCACTGAAGCAGCAAAC
-ATCTTTTGGTCAGGACGTACGTACTCAGTTAGGACGTTCGTACGTCCAAGTAGGCAGCAG
-ACGTGTTTTCGTATGGAGACGTACGTACCGATTTAGAGCGTTCGTACGTCATGTTAGGCA
-GCAGGCTTATTTTTGTGTGGCGACGTTCGAACGTCATATCGACGTACGAACGTCCAAAGT
-CGGTTTGCACTGTTTCGGCCCATTTTCCTAGGTTTTCAACCGTAAGTATAAATAGGGTTT
-CTCTTCCATAGAACAAGATAGTTCTTTGGCCGTCCATCCTTCTTCTTGTTCATGTGTTTT
-GGTTAGGTTTTTGATATTGAACTTGTTTGTGGCTATTGAACTCAATTCCCTTTCTTCCCC
-CTTTTCTCAATTCATATCTTTGTGATCTTCAAGAGTTATTGTGAATTCGGCTTCGAGTCT
-TCTTGTGTGTTCATCAAGAAGTCGAGTGTCTTGTGCGTACATCAAGGTGCTCTGGTGAAG
-TCAAGGTGTTAGAGATAACCAGGAGTATCAACTCGCAGTGAGTGCAGGTAGTTGATAGGT
-TTGTACATCTCTTTTCATCTAGTGGATTATTTTGTTGTCGCGAGGACAACCGTGGACGTT
-TCCCTGTTTGGGTTTTACCACGTTAAAAATCTCTGTGTGTTATTTACTTTCTGCGTATTG
-TTTGTTTGCTTGTTTGAATTCACATTTGTTATACTAGCACTATTTGTAGTTTCTCACTTT
-C
->Egrandis201_33_rc
-CAGTGTGTTGAAAGAAGATATATTTTGAAGATATGAAATTTGATTGAAGATTGTAATTGA
-TTGGGATTGTAATTATTTTAGGACTAGGAATATTTTCTTTACTTCTTTGTTGAATTTCCT
-TGTATAATATATACATCCCTGTAACAATGTACAATTCAATGAAGAAAAATACAATTCCCC
-ACATCTTCTTTTACTTGAGTTTGTGACATGGTATCAGAGCTTATGCCAATTCTGGTGTAG
-CTTTCATCTCTTTGAATTGCCTGTGTTTCTTCTTCCGTTCCTTGTTGTTGCCACATCGCA
-AAACAAAAACAACATTTGGTAGGAATTCTCTTTTCTTTACCCTTTGTTACCAAGAAAACC
-AATATGCAAAAAACGAGTTGTTGCAATCTTTAGAAAATTCTTCATGACAAACCCAGAAAT
-ACTTACCTCAGCCTCCAGTGATCAGACCGTAGGAATCCGATGCCGACCAACAGAGAGTAG
-CCGATTACAGGAAGAATGGTGAGAGCGTGGCTCTGGCGGCAAAGAAGTTCTCTCTCCCGC
-TACAGAGAGCTATGGGAGGAAGAGAAAGTCTTGAACCCAATCTAGGGTTTCACAAAGAAA
-ACCCTAGGGGGTATCGGTCCATGATGAGTGGCCACGATGTGTCTGATCGATCGGATGGCT
-CCCGATTGTTTTTTGGAGACGCTGGATCTAACGGCTCAGGAAGCAAGGGGATGATAGAAT
-CGAATGGTCTGCGTGGCTCAAATGCATATGGGCCGGATGGCCCACATGGGTGTTACTCAG
-CTTATTCCAATGGCCCGCGAGGTAATTTTCTTGCAAGTTCTGATGGGCCATATGGGGCAA
-CGACGGCCCATGCCATAAATAGGCCCACTTCATTCACAAAAAACAACCAAAAAGGAAAGG
-ATAAGCTGTATCGTGATTATTGTAAACGCCCACATCACACGGTTGAAAATTGCTGGAAGC
-TCCATGGTAAACCTGGCGAAAAAGAAAAAGGGAAATTTTCAACTGTTTTTCAAGTATTGG
-GTAAATCAGCAAAAAAATCAATTTCTCATGGGGAATATCAGGAGCTAATGGAAGCCTTAA
-AGAAGTTGGATGCCTCTGACAAGGCAGGGAGTTTCACAGGTATTTCTCATTGTTTGATGA
-ATTCGATTACAAATTATGCATGGATTATTGATACTGGAGCTAGTGATCACATTGTCTGCA
-ATCAATTTTTTTTTTCCAACATTGAGAAATTACCCTTTCCTATATCAATACAACTCCCGA
-ATGGAAACACCACACATGTTGGTATGACTGGGACCGTGAATCTTAGCCCACTCATCACTC
-TTAACAATGTCCTTTATGTCCCTAGTTTCCAATTTAATCTCATATCAGTCTCTTGAGCTT
-GCGAAGAAAATTCCTGTCATGTCTTGTTTGACTCTGATAAATGTCTATTTCAGGCCCTTT
-CGACTAGCAAACTGATGGGCTTGGGTAGAATCTATCAAGGATTGTATTTTTGGATCGATC
-TTCCAAGCAATTTTAAATTCTCTTCTGTTGCATTCAATAAATCAATGTGTAACATTGCTA
-GTAGTGAAAAAAAAATTCTTCATCATCAAAGATTGGGCCACACTACTTCTTTCCCTTATC
-CTCAATGTCATATTTGTCCTTTGGTGAAACAAACTTGTGCTCCCTTCCAAACTAGTAACA
-CTCGCTTGGACAGCATTTTTTCTTTAGTTCATGTTGATGTCTGGGGACCATATCACACTT
-TGAATCATGACGGTTCATGGTTTTTTCTCACAATTGTGGATGATCTCTCACGTGCCACCT
-GGGTCTTTCTCATACAATCAAAAAATCAAGTCCTATCTCACCTTAGAATTTTCCTCTCTT
-TATCCAAAACTTAGTTTGGAAAGTCAATTTGTCGCATCCGTACTGATAATGGAAAAGAAT
-TATTCAGTGGTGAATGTGCTTCCTTTTTTTCCTCAAATGGGATTTTGCATGAAAGTACTT
-GCACCTATACACCACAGCAAAATGAAGTTGTTGAGCGAAAACATCGCCATCTTTTAGAAT
-TGGCATGGGCCCTTAAATTCCAAGCATCTATACCAGAAAATTTTTGGGGAGATTGTGTGG
-TCACTGCAGTGTATCTGATAAATTGTATGCCATCTCGCATTTTGAAAGGAAGAACTCCGT
-TTGAATTATTATATGGAAAGAAACCAGACCTAAGTCATCTTTGAGTGTTTGGTTGTCTCT
-GTTATGTGACTACAGTAGGGTCTCGGGATAAAATGGGCCCTCATGCTCGCCAGTGCATTT
-TCATGGGGTATCCCAATCTTAAAAAGGGTTATCGAGTTTATGAACAGTCTACTGGGGAAT
-TTTTTATCAGCAGAGATGTGGTTTTTCATGAAGATACTTTCCTTTTCCGGGATTCCACTT
-CCATGTCTGCGAATGATGTGGGCATTAATCGCAGATTTCTGTCAGAAGAAGATTTATCTT
-CTGGAGGATCCTATGTTATTACCTCTCCTCCAAGACAACAGATAGTGGATATAGTATCAC
-CTTCACCACCTGATGAGCACTCTACTGGAAAATTCAATTGAAGAGCATTTCATGGAGCAG
-GAACCTCAGGTGACCAATTCCATAGAATCATCAAATATTGAAGAACCATCGTTGGAAGTG
-TTGAATGAGCCATCGTGATGATCTGGTCGAGTCACTCGTCCACCAGCTTGGACAAAAGAT
-TATGCATGTTCTTCATCAAAATCGTCAGGTACTCACTATCCTATCTCCTCCATGTGTCTT
-TTGATTAGTTGTCATTAGAACATTTATGTTGCATTAGCCGCATTTCTGAGGAACAAGAGC
-CCTCTAGTTATCATGAGGCTGTACATGATCCACGTTGGCAAAGGGCCATGGAGGTAGAAC
-TTAAAGCCCTAATTGACAATCACATCTGGGATATCGTATCGTTACCTCCCCATCGCAAGC
-CCATTGGCTGTAAATGGGTATATAAGATAAATACAAAGTAGATGGTTCTGTGGAAAGGTA
-CAAGGCCCGGTTGGTGGCCAAGGGGTTCACATAGAGAGAATGATTTGACTACCATGAGAC
-TTTTTCTTCAGTAGCCAAGGATGTCACGGTTTGCTCTTTCTTGTCGGTTGCTGCTTTTCG
-CAATTGGTCAATGCATCAAATGGACATCCACAATGCCTTTCTTCATGGTGATCTAGAAGA
-AGAGATTTATATGGATCTTCCACAAGGATTACGGAGACAGGGGGAGTCAAAGGTATGTCG
-TCTCCGCAAATCTCTTTATGGTTTGAAATAGGCTTCTCGGCAGTGGTACACCAAGTTTAC
-TAATGCACTCACGGGTGCCGGATTCAAGCAGTCCAAGCATGATTATGCTCTCTTCACTTG
-GACAAAAGTTACATCGTCTATCTACTTGATGATTTATGTTGACGACATACTTATCATGGG
-AAATGATGATTCCGTTGTGAGGAAGCTTAAGGAGTATCTACATTCCTCTTTTCATATCAA
-AGATTTAGGGTCACCTAAATATTTTCTTGGAATAGAGATAACTCGTTCTGATCAGGGGAT
-TTCTCTTAGCCAAAGAAAATTTGTGATGGAGATTATATCAGAAGCGGGATTATCGGGATG
-CAAACTGGCAGTCATTCCCATTGAACAAAATGCCAAGTTGACCAATGTTGATTATGACAC
-TGTAATGTCTTCCTCTGACGATCCATTATTGAAGGATTCGACAAGTTATCAGAGACTTGT
-TGGAAAACTTATCTACCTCACCATGACTAGGCTAGACATTTGTTATGCTGTACAGACTCT
-TAGCTAGTTCATGCACAGTCCAAAGCAATTGCACATGAACGCAGCCTTGAAGGTGGTAAA
-ATACCTGAAGAAATGTCCAAGACTAGGGATACTTCTTTCTCGAAAATGCAATATGGAGAT
-GATAGCCTATTGTGATTTGGACTATGCTACATGTCCCATGAGTAGGAGATCTATCATTGG
-TTTCTGTATTAAGTTTGGGGAATCACTACTTTCATGGAAGACAAAGAAGCAATCTACTGT
-ATCGTTATCATCAGCAGAAGCTAAGTATCGATCTATGGCAAAGACTGTCTGTGAGGTGGT
-ATGGTTACGAGGTCTACTTCAGGATCTTGGGATACAAGTTAGAGGTCCGACATTACTCTT
-TTGTGATAATGACTCAGCAATCAAGCTTGCAGCAAATCCCATATTACATGAGAGGACGAA
-GCATATAGAAGTTGATTGCCATTTTACACGAGAGAAGATCAAAGAAGGTATCATCAAAAC
-AAGAGGGATTAGAACCACGGAGCAACCTGCAGATATATTTACTAAACCTCTTTGTCAGAG
-ACAACATGCATATCTTCTAAACAAACTAGGCGTCTTGGATATATACAAGCCACCAGCTTG
-AGTGGGAATGTTGAAAGAAGATATATTTTGAAGATATGAAATCTGATTGAAGATTGTAAT
-TGATTGGGATTGTAATTATTTTAGGACTAGGAATATTTTCTTTACTTCTTTATTTAATCT
-CCTTGTATAATATATACATCCCTGTAACAATGTACAATTCAATGAAGAAAAATACAATTC
-CCCACATCTTCTTTTACTTGAGTTTGTGACACAGTG
->Gmax275_361_rc
-ATTAAGTGTTAGAGTTTGTGGTCTTAGTTCCTTTGTCCCACATCGCTTAGTCTGTAAAAC
-TTGTGTGGTCTTAGTCCCACATCGCTTGGTTTGTAAAAACTTGTGTCTCATTAGTGTTAT
-ATATAGAGACACCTTGTAAGCCTTTATGTGCAAGTAATAAAAAGTTCTCCTAGTGTAGCC
-GTGGACGTAGGCACATACATTGTGTGCTGAACCACGTTAAACTGTGTGTCTTTTTCTTTC
-TTCCCTTTATTTCTTTCTCTTCTTTTATTGCTCTATACTAACAACTGGTATCAGAGCTTT
-TGGTTACTCCACGGGATTTCCTTAGTTTAAAGGAATTAGTGGGAGTCTTCTCAGTTTAGA
-GGAGGCAGTGGGAGCAATGGCATTAGAAGAGGGGAAGGTGAAGATCGAGAAGTTCGATGG
-CAGAGATTTCAGCTTTTGGAAGATGCAGATAGAGGATTATCTATATCAGAAAAAGCTGTA
-TCAGCCCTTATCAGGGGTTAAGCCAGAAGACATGAAGCAAGAAGAATGGAACTTGCTAGA
-TCGACAGGCTCTTGGCGTGATCAGATTGACATTAGCCAAGAGCGTCGTGTTCAACATCGT
-GAACGAGAAGACTACTGCAGGCTTAATGAAGGCATTATCAGATATGTACAAGAAGCCGTC
-AGCAGCCAACAAAGTATACTTGATGCGCCGGTTGTTCAACCTCAAGATGGGAGAAGGTAT
-CTCTGTAACTGATCATATTAATGAGTTTAATACTATTCTTGCCCAATTGGAATCTGTGCA
-GATTAAATTTGAGGATGAGGTGAAGGCATTGATTCTATTGTCATCACTACCGGATAGTTG
-GGCTGCAACTGTTACTGCAGTTAGTAGTTCTACAAGGGAGAACACATTAAAGCTTAGTGA
-CATCCGTGACTTGATCTTGAGCGAAGATGTTCGCAAGAGAGATTCAGGAGAATCTTCCAG
-TCATGTTTCCAATTCAGCATTGAATACTGAAGGCAGGGGAAGGACTACCCAGAAGGGTCA
-GAATGGTCGAGGCAGATCAAAGTCAAGAGGGAAAGGTTAGAGAAAATTTCAAAGTGACGT
-TACTTGTTGGAATTGTGACAAGAGAGCTCACTTTAGCAATCAGTGCAAGGCACCAAAGAA
-GAACAAGTCGCACAAAAACAAGAAGCGCGATGATGATGAATCCGCAAATGCAGCAACTGA
-TGAACTTGATGATGCATTAATTTGCAGTTTGGATAGTCCTGTTGATTCATGGATCATGGA
-CTCAGGTGCGTCGTTCCACACTACTCCCTCTAAAGATTTATTGTCTAACTATGTTTCTGG
-AAGATTTGGAAAAGTTTACCTTGCAGATGGAAAATCTCTTGACATTGTCGGAAGAGGTGA
-TATCAACATCAAGACTTCCAGTGGATCCCTATGGACATTGCACAATGTCAGACATATTCC
-TGCCTTAAAGAGAAATTTAATATCTATAGGGCAGTTGGATGATGAGGGACATCACACCAC
-TTTTGGAGATGGAGCTTGGAAGGTAACAAAAGGCAATCTCATTGTGGCTCGTGGAAAGAA
-GCGAGGATCTCTTTACATGATTGCAGATGAGGATATGGTAGCAGTTACTGAGGCTATCAA
-TAATTCAACCATGTGGCATCAAAGACTTGGACATATGAGTGAAAAAGGAATGAAGCTTAT
-GGCGGCAAAGGGTAAGCTATCAAGCCTCAAGCATGTTGATGTTGGTGCTTGTGAACATTG
-TATCCTTGGAAAGCAGAAAAAGGTCAGTTTCTCAAGGGCAGGGAAGACTCTGAAAGCTGA
-AAAGCTAGAATTGGTGCACACAGATGTTTGGGGGCCAGCCCCAGTGGAATCTGTTGGAAA
-CTCACACTATTATGTCACCTTTATCGACGACTCTACCAGAAAGGTATGGGTTTATTTTCT
-TAAAAATAAATCTGATGTGTTTTCTGTGTTTAAAAGGTGGAAAACAAAAGTTGAAAATCA
-GACAGGTTTAAAGGTTAAAAATCTGAAATCTGACAATGGTGGGGAGTATGATAGTCAGGA
-GTTTAAAGACTTCTGTTCAGAACATGGGATCAGAATGATCAAGACAATACCAGGAACACC
-TGAGCAAAACGGTGTTGCAGAAAGGATGAATAGAACCTTGAATGAGAGAGCAAGGTGTAT
-GCGGATCCAATCTGGCTTGCCTAAAGCATTCTGGGCAGAAGCAATAAACACAGCAGCATA
-TCTCATCAATAGAGGACCATCAGTTCCTTTGAATTATCAGTTGCCTGAAGAAGTATGGTC
-TGGAAAGGAGGTAAAACTTTCACATTTGAGAATTTTTGGTTGTGTTTCATATATACTGAC
-AGACTCTAATAGTAGAGATAAATTGGATCCGAAGGCGAGGAAGTGTTATTTTATTGGTTA
-TGGATCTGACATGTATGGCTATAGGTTTTGGGATGACCAAACCAAGAAAGTTATCAAAAG
-CAGAAATGTTACTTTTAATGAGAACTTATTTTATAAGGACAAATTTTCTGCAGAATCTAC
-ATGTGCAGGTAAACTGTCAGAAATTTCTGAGAAAGCAACACTTGAAGAAATCTCAGAAAG
-TGATGTAGCCAACAGAAACCAGAGTACAAGCGTAGAGGTTGAGTCAGAACCAGAACCATC
-AACTCCTCCAAGAAAATCCAGCAGAATTTCAGTACCACCAGATAGGTATTCCCCTTCATT
-GCATTATTTGTTGTTAACTGATGCTGGTGAGCCAGAATGTTTCAATGAGGCTACACAGGG
-GAATGACACTATTGAGTGGGAGCTAGCGATGAAAGATGAGATGACATCCCTTCAGAAGAA
-TAAGACGTGTGGTCTTTAACTGAATTTCCAGAAGGAAAGAAGGCGTTACAGAATAGGTGG
-GTCTATAGGCTAAAGGAAGAATCTGACGGTAGAAGAAGATACAAGGCGAGACTTGTGGTG
-AAAGGGTTTCAGCAGAAACAAGGAATTGATTTCACAGAGATCTTCTCTCCAGTTGTAAAG
-ATGACTACCATCAGAGTTATTCTGAGCATAGTAGCTGCAGAGAATCTTCACTTGGAGCAG
-TTAGATGTTAAAACTGCATTCTTGCATGGGGATTTAGAGGAAGACATCTATATGACCCAA
-CCAGAAGGTTTTGAAGTGCCAGGCAAGGAGAATCTTGTATGCAAGCTTCACAAAAGTTTG
-TATGGCTTGAAACAAGCACCGAGGCAGTGGTACAAGAAGTTTAATGAGTTTATGAGCAAC
-TCAGGATTCAAAAGATGTGACATGGACCATTGCTGCTATGTTAAAAAATATACTAATAGT
-TATGTTATCCTTGTTGTGTATGTTGATGACATGTTGATTGCAGGATCTAGTATGGCAGAA
-ATTAACAGGTTGAAGCTGCAGTTGGCAGAAAACTTTGAAATGAAGGATCTTGGTCCAGCT
-AAACAAATCCTTGGTATGAGAATTCTTAGAAACAGATCAGAAGGAATTTTGAAGCTGTCT
-CAGGAGAAATATATACACAAGTTGCTTGACAGGTTTTACCTTGGAGATTCTAAGACCAGG
-AATACCCCTTTGGGATCTCATTTGAAGTTTTCAAAGAAGCAATCTTTGCAGACAGATGAA
-GAAAAATGTTACATGTCAAGAGTACCATATGCATCAGCAGTTGGGAGTTTGATGTATGCT
-ATGGTGTGTACCAGACCTGACATAGCACATGCAGTGGGAGTTGTCAGTAGGTTCCTATCA
-AATCCAGCAAAGGAGCATTGGGAAGGTGTAAAGTGGATACTCAGATATCTGAAGGGTACT
-TCAAAGATGTGTTTGTGCTTCAGAAAAGGCAATCTCACTTTACAGGGATTTTCATATGCA
-GACTTGGGAGGAGATTTTGATACCAAGAAGAGCACCACAGGCTACATTTTTACCTTGGGA
-GGTACTACTGTGAGTTGGAAGTCTAAACTTCAAGATAGAGTTGCTCTCTCAACCACGGAA
-GCCGAGTACATAGCTATCTCAGAAGCAGCTAAGGAGATGATTTGGCTAAAGAATTTTCTC
-AATGAATTGGGGAAAGAACAAGATGATGCTCCAATGTTTAGTGACAGTCAAAGTGCTATA
-AGTCTTGCTAAGAATCCAGTCTTCCATTCTAGATGCAAGCATATTCAATTAAGATACCAT
-TTTATCAGGGAGTTGATAAATGATGGAGACTTATCTTTATTGAAGATCTTAGGATCAGAG
-AATCCAGCAGATATGTTGACTAAAGCTGTTACCACTGACAAACTGAGGCTTTGCATTGCC
-TCAGTTGGCCTTCAAGGATAATTGAAGATGAAGGCGCTACACTAGGTAAAGAGTCGATGA
-ACTCATTGCTTACAAGGAGCTGCTAGAGTTTGGAACTTAGACCCCAAGTGGGAGAATTGT
-TAGAGTTTGTGGTCTTAGTTCCTTTGTCCCACATCGCTTAGTCTGTAAAACTTGTGTGGT
-CTTAGTCCCACATCGCTTGGTTTGTAAAAACTTGTGTCTCATTAGTGTTATATATAGAGA
-CACCTTGTAAGCCTTTATGTGCAAGTAATAAAAAGTTCTCCTAGTGTAGCCGTGGACGTA
-GGCACATACATTGTGTGCTGAACCACGTTAAACTGTGTGTTTTTTTCTTTCTTCCCTTTA
-TTTCTTTCTCTTCTTTTATTGCTCTATACTAACAATTAAG
->Sitalica164_1034
-TTAACTGTTGGAGGTATGCCCTAGAGGCAATCATAGAGATGATGATATTCCATTTGTATC
-CATGATTTGTATATTGTGTTCATTGAATATCCATTGAAGGCTACTTGAATTGATTTGCAA
-TTATATGAATTGTATGTAAAACTCTTTACTTGTATGGTTATTCTAAAGTTGTCCCTAGTC
-GGAGTTCATGTGAGGACACACATGAATATTACACTAGCACATGTATTAGTTGATGACTAT
-GTTTCACAAGTCATGGACATGGAGATGTTGAACTAATAATGTGGACACATGTGGAGATAT
-GTGCTAGGACTGACCCAACACGAGAAGTAGTTCTCTCTTTAAACAACATATACGCTTTGT
-CCTTAGACCTGAGATTGTCGCATGTATTCTAGATGTGGATCGACCTACTTAGGGGCTATC
-AAACGCTACGCCGTAACAGGGTAGTTATAAAGGTAGTTTTCGAGTTTGTCAAGAAGCATG
-CTATGAGACATGGTCAATCAAGATGGGATTTGCCCCTCTCTGATCGAGAGTGATATCTCT
-GGGCCCCTCGAGTGATCGGATCCGAAAATGCATGGCCATGCTACGTACGGTTAAGAGTTA
-ACCTACAAAGGGATTCCGAATCACAGGATCGAGAAAGAGCGGTCGGATTGAAGCTAGACC
-AAATATCGTGAGGCAAAGGGAATAGCATGTATATTATGTTGTGATGGTTCGTTTGATACG
-ATCTTCGTGTGCGTATAGGAGTTGGCACGTCTTGCTAGAGGCCGCTACTGACTATTGGGC
-CGAGTAGGAGTACTCGGGCCATGTCTATACGTATCCGAACCCATAGGGTCACACACTTAA
-GGGGCTGGAAGCCCAATTCGGATCTGATCCGAGTTGGATTAGGTTTAGAAGTACTAATGG
-GCCTCGGACCCAGAGGCCCGTCAGGAACCTCTATAAATAGAGGGGTGGGGGGACCCTAGG
-GTTTACACCTTTTGGCGAAACACATCTGTCGCGCCTCCCACGCCCTCGCCTGTTGCAACA
-CGGATCTAGCAATCCGGCTTGCGACGCTTCCTCCCTGCACGTGTGGACACCTTGGAGGTG
-TTGCGCCTGCAGCACTTGGACGAGCCGCCGACGAGCCACGACGAGCCGCCGACGAGCCGC
-GACACGAGGGCGATCTTGCTGCACATGGACGAGCTGCTGAGGAGCTGCTGGACGTTGACG
-TGATCGACTACGTACGACTACATTGATCGTCTTCGCTGCATTGACGCAAATCTACATCTT
-CCGCACCAGTAGTGCGTCGAGTGGTAATCCCGTGATCCTTATACGACAGTTCTTCCTGGT
-TTTACACGGTAGAAATTTTTATTTGCGCTAGTGTAGCCTACCTCGTATCCCAACAGTGGT
-ATCAGAGCCGTAGCTGTTTAGTTTTGGATTCGGGATGTGTGCATATGGAGATATGCGAGT
-TTTCCGTTTGATCTATGTCCTGGTTTCGTGCCCTTGCTGCAATGGTAGTGTAACGACATA
-CTCCTACCGGTCAGTTTCCGCCATCGAAGTTAAGTGATACACGTAAATCCAGTTGCAGTG
-AGCGAAGATCAATATGATTGGTCGAATCAAAATATAGGGTCATACGCCAAGCGCATTAGA
-TGTGCAATAGTAGATGAGATCTACTGCCGGGGTCGGGATTTTTGCCGTATGCGTATGCTT
-CGGTAAATCAGCCGTAACTTTTCGGTACAATCTCGGATCGGGGCGAATTTTAGATGAAAA
-TTGATCTACAGAAAAAGTTACACATGAAATTCAATGGCTTTGCCGTTTTCGGCGAAATTA
-GATTTCCCAAATTCGGCTTGGAAGATGGAGTTTTGGGCATCCGAAGTTTGGAACGTTAGG
-ACGCTTAACTCTGTTTTGCAGGATGTATGTATATATCTTGTGATCAGTATGGCCCCCTTG
-TGTATGTGATGCATGTGTGTAACTCATTCGTGACCTGCGTGTCGTGCGTACGGCAACACG
-GCAGGAGCCATATGTGTTGTCACTTTATTGTATTTAATGGTCTGCGTACCAATCTGTGAT
-GATCCAAGCAACTATGAAATCTTCATTACTAGCTTCTTTACTGGCTATTAGGTGTAATAG
-CATGTTCTAGTTCTTGGAGGGCTCATCACCAGAAGGATGGCGCACATGGAACATGGAGAT
-GGAGATCACCATGGTGAACAGGTTCTATGGAGATGGAGATCACCATATGAAAACGGGCTA
-TACTGTGTCACAACTGTGTGAATGCTATTCTATTTATATTTTACTTTCTGCATGCTGTGA
-TGTTAGAAGTAGAATGATCCCTCACAAAGTTTAAGTTAGTATGCCCTCCCAACTAAAACT
-TGCACCGTCCCATATCTTGTACAATTAGTGGTGGGTCTATGAAATTAGGGTGCCACTAGT
-TTTCCTTGATTAGACGGGTTTGTGTCGGACACTTACACGCATAAGAGTTGGTTTGCTTAA
-CAAGGTTATCTTAACGGTTAAGGACCTTGGGGCATAAAGGTTGGGCGCCGAGACATAGAG
-ATGTCACCTAACAACAGGAGTCATATGTGATATGATTAGCAAAAGTTGCTTACCGATCTA
-CCTTGTTTGCTAGCGGTGATGCTAAAGCTCACTAGTAGACTTGTTAGTTGTGGATCCTGA
-ATCACTAAGTTTCAATAGAGGGATATTGATTTTAGTGGGAGTAGATTCTGTTAAAATAGT
-TTAATGTGATTTGCTCTATCATGGATACATTTGTCTTAGTGTATTTTGCATTACTTTGTT
-GTAGATTAAATGGCACCTAGCAGCACTACACCGTTTGCTTTGCGTTCGGTCCTTGAGAAG
-GACAAGTTGAATGGAACAAATTACTCGGATTGTTTCCGTAACCTGAGAATTGTTCTCAGG
-GCTGAGAAAAAGGAAGATGTTCTAGACAACCCACTACCAGAACAACCTGCTGATGATGCA
-CCCGCTGCTGCTAAGAATGCTTACAAGAAAGCATGTGATGCTAACCTCGAAGTAAGCTGC
-CTTATGCTTGCTTGCATGGAACCCGAGCTGCAGATGCAGTTCAAAACAAACCATGAGGCG
-CACGATATGATCGTGGCGCTTAGAGACATGTTCCAAACATAGGCTAGGGCTGAAAGGTTC
-AATGTGTCTAAGGCCTTTGTTGAGAGCAAGCTAGCAGAAGGCGCAGCAGTAGGACCACAC
-GTAATCAAGATGGTTGGTTACACTCAACGGTTGGAGAAGCTGGGCTTCCCACTGGGCCAA
-GAGTTGGCCACTGATTTCATTCTTTCGTCTCTTCCGCATAGCTATGGGAACTTCATCTCG
-AACTACCATATGCATGGGATGGAGAAGGGTTTGAATGAGCAGTGTGGCATGCTTAAAACA
-GCAGAGGCTGACATCAAGAAAAGCGCTAGTACCAACCATGTGATGGCTATACAGAACAAG
-CCTAGCTTTAAGAAGAAGGGCAATTCTTGGAAGAAGAAGGGCAAGGCTGGAACGTCCAAG
-CCAAACCCAGCGCCCAAGGTTAAAGCTAGACCTGGACCTGCTCCAGACAAAGAGTGCTTT
-TACTGTCATGAACTTGGTCACTGGAAGAGAAACTGCAAGCAGTACCTAGCTTCCTTGAAG
-AATGGAGGAAGTAAGAGTACTTCTACCTCAGGTACGCTTGTTGTTAATGTTATAGACAAC
-ATTTTTCTCGCTGATACAATTATTAATTCTTGGGTATTTGATACCGGATCGGTTGCTCAT
-ATTTGCAATTCGATGCAGGGAATGATAAGAAGTAGAAGCGTGGAAAGAGGAGAAGTTGAT
-TTCCGCGTGGGCAATAATGCAAGAGTTGCCGCATTGACCGTCGGGACGATGCAACTCCAC
-CTCCCGTCAGGATTTATTATGGAGTTGAATAATTGTTATTTCGTTCCTAGTTTAAGTCGA
-AACATTTTGTCTTCTTCATGCTTGATGAAGGATGGTTATTCATTTGCGAGTGAAAACAAT
-GGTTGTGTGATCTCTAAGAATGATATTTATGGCTTTTGCACCCATTGTGAATGGATTATT
-TGTTTTAAATCTTGATGGTTCACCTGTCTGTAACGTAAGTGCTAAAAGGCCTCAGCCTAA
-TGATTTGAGTCCTACCTACTTGTGGCCTTGTCGTTTGGGTCATATAAGTGAAAAGCGCAT
-GAAGAAGCTTCATTCTGATGGACTTCTAACTTCGTTTGATTTTGAATCATACGAGACATG
-TGAGGCTTGCTTGCTAGGCAAGATGACCAAGACGCCTTTCACAGGATTTCCTGAGAGAGC
-AGTAGACTTGTTGGAACTCGTACATAGTGATGTATGCGGACCAATGAGCACGACGGCTAG
-AGGAGGATTCCAATACTTCATAACTTTCACTGATGATTTTAGTAGATATGGCTATGTCTA
-CTTGATGAGGCACAAGTCTGAAACCTTTGAAAAGTTCAAGGAATTTCAGAATGAAGTTGA
-AAATCAATGTGGCAAGAAAATTAAGGCCTTACGATCTGATCGTGGAGGCGAGTACTTGAG
-CCACGAGTTTAGCAATCATCTAAAGAGTTGCAGAATTGTTCCACAACTTACGCCGCCTGG
-AACACCTCAGAGAAACGGTGTGTCCGAGCGACGTAATCGAACTTTGTTAGACATGGTTCG
-ATCAATGATGAGCCAGTCGGACCTACCGTTGTCATTTTGGGGATATGCTTTAGAAACAGC
-AGCTTTCACACTTAATAGGGTAGCATCTAAATCCGTAGTTAAGACACCATATGAGATATG
-GACTGGAAAGGTTCCTAGTTTGTCTTTTCTAAAGATTTGGGGATGTGAAGTGTTTGTCAA
-GCGACTTCAGTCGGACAAGATCACACCCAAGTCGGATAAGTGCATTTTTGTGGGATATCC
-AAAGGAAACTTTGGGATATTATTTCTACAACCGATCAGAGGCAAAGTGTTTGTCGCTCGG
-AACGGGGTTTTCCTAGAGAAAGAGTTTCTCAAAGGAGAAAAGAGTGGAAAGATAGTGCAT
-CTTGAAGAAGTTCAAGATGAGCCGATCGGGCAAGAATCAATGAGTGATGCTAACGTAGCA
-GAACAAGTTGAGATACCCATGGCAAGAGAAGCACCACCACAACTACGAAGGTCGGCAAGG
-CTCCGCGAAATGCGGGAAATATTATTGTTGGACAATGATGAGCCTGCGACATATGCAGAA
-GCAATGATGGACCCAGACTCCGAAAAATGGCAGAGTGCCATGCAATCCGAAACAGAGTCC
-ATGGGAGACAATTAAGTTTGGAACTTGGTTGACCCGCCATAGAATGCAAGTGGATCTATA
-AGAAGAAAAAGGACATGGATGGAAATGTTCACATCTATAAAGCACGACTTGTCGCAAAAG
-GTTTTCAACAAGTTCAAGGAGTTGACTACGATGAGACCTTCTCGCCCGTAGTGATGCTTA
-AGTCCATTCGGATTATTCTAGCTATAGTTGCATATTTCGATTATGAGATATGGCAGATGG
-ATGTCAAGATAGCTTTCCTGAATGGAAACCTAGCTGAGGACGTGTATATGATACAGCCCG
-AGGGTTTTGTCGATCCGAAAAATGCTGGAAAGGTATGCAAGTTTCAGAGATCCATTTATG
-GATTGAAGCAAGCATCTAGGAGTTGGAACATTCGTTTTGATGAAGTGGTCAAAGGGTTTG
-ACTTCACCAAGAACGAAGAAGAGTCTTGTGTTTACAAGAAGGTTAGTGGGAGCTCTGTAG
-TATTTCTAATCTTATATGTGGATGACATATTACTGATTGGAAATAACATTCCTATGCTTG
-AGTCCGTAAAGACTTCACTGAAAAATAGTTTTTCGATGAAGGACTTAGGGGAAGCGGCAT
-ATATTCTGGGCATTAAGATCTATAGAGATAGATCGAGAAGGCTTATAGGTTTAAGCCAAG
-ATACTTACATTGACAAAGTGTTGAAGCGGTTCAGCATGGAAGAGGCAAAGAAAGGGTTCT
-TGCCTATGTCACATGGCATACATCTCAGCAAGACTCAGTGTTCTTCGACTGCTGATGAGC
-GGGATCGCATGAATAGAGTGCCATATGCCTCGGCTATTGGATCTATCATGTATGCAATGA
-TAAGTACTCGCCCAGATGTTTCATATGCGCTAAGTATGACAAGCAGACACCAATCTGATC
-CAGGGAGAGTCACTAGACAGCGGTGAAAAACATTCTTAAGTACTTGAGAAGGACTAAAGA
-TATGTTCCTCATCTATGGAGGTGAGGAGGAGCTCGTTGTAACAGGTTACACCGATGCTAG
-TTTCCAAACCGACAGAGATGATTTAAAGTCACAATCAGGATTTGTGTTCATGCTAAATGG
-TGGTGCTGTTAGTTGGAAGAGTTCCAAGCAGGAGACGGTGGCCAATTCTACGACAGAAGT
-CGAGTACATCGCGGCTTTGGAAGCCGCGAAGGAGGGTGTTTGGATAAGGAATTTTCTCAT
-TGGGCTTGGTGTGTTCCCGAATGTGTCCAGCCCATTGAATCTCTACTGTGATAACAATGG
-GGCAATTGCGCAAGCAAAGGAGCCAAGGAACCACCAGAAGAACAAACACGTAATGCGGCG
-ATTTCATCTCATTCGAGACTTCGTTAACCGGGGTGAGATCAAGATATGCAAAATACACAC
-GGATCTGAACATTTCCGATCCGTTGACAAAATCACTCCCGCAGGCTAAGCATGATGCGCA
-TGTAAGAGCTATGGGTATTAGGTACCTTCTAGATTGACTCTAGTGCAAGTGGGAGACTGT
-TGAAGGTAGGCCCTAGAGGCAATCATAGAGATGATGATATTCCATTTGTATCCATGATTT
-GTATATTGTGTTCGTTGAATATCCATTGAAGGCTACTTGAATTGATTTGCAATTATGTGA
-ATTGTATGTGAAACTCTTTACTTGTATGGTTATTCTAAAGTTGTCCCTAGTCGGAGTTCA
-TGTGAGGACACACATGAATATTAGACTAGCACATGTATTAGTTGATGACTATATTTCACA
-AGTCATGGACATGGAGATGTTGAACTAATAATGTGGACACATGTGGAGACATGTGCTAGG
-ACTGACCCAACACGAGAAGTAGTTCTCTCTTTAAACAACATATACGCTTTGTCCTTAGAC
-CTGAGATTGTCGCATGTATTCTAGATGTGGATTGACCTAATTAGGGGCTATCAAACGCTA
-CGCCGTAACAGGGTAGTTATAAAGGTAGTTTTCGGGTTTGTCAAGAAGCATGCTATGAGA
-CATGGTCAATCATGATGGGATTTGCCCCTCTCTGATTGAGAGTGATATCTCTGGGCCCCT
-CAAGTGATCGGATCCGAAAATGCATGGCCATGCTACGTACGGTTAAGAGTTAACCTACAA
-AGGGATTCCGAATCACAGGATCGAGAAAGAGCGGTCGGCTTGAAGCTAGACCAAATATCG
-TGAGGCAAAGGGAATAGCATGTATATTATGTTGTGATGGTTCGTCTGATACGATCTTCGT
-GTGCGTATGGGAGTTGGCACGTCTTGCTAGAGGCCGCTACCGACTATTGGGCTGAGTAGG
-AGTACTCGGGCCATGTCTATACGTATCCGAACCCATAGGGTCACACACTTAAGGGGCCGG
-AAGCCCAATTCGGATCTGATCCGAGTTGGATTAGGTTTAGAAGTACTAATGAGCCTCGGA
-CCCAGAGGCCCGTCAGGAACCTCTATAAATAGAGGGGTGGGGGCGCCCTAGGGTTTACAC
-CTTTTGGCGAAACACATCTGCCGCGCCTCCCACGCCCTCGCCTGTTGCAACTCGCGGATC
-TAGCAATTCGGCTTGCGACGCTTCCTCCCTGCACGTGTGGACACCTTGGAGGTGTTGCGC
-CTGCAGCACTTGGACGAGCCGCCGACGAGCCGACGACGAGCCGCAGCACGAGGGCGATCT
-TGCTGCACGTGGACGAGCTGCTGAGGAGCTGCTGGACGTTGACGTGATCGACTACGTACG
-ACTACGTTGATCGTCTTCGCTGCATCGACGCAAATCTACATCTTCCGCACTAGTAGTGCG
-TCGAGTGGTAATCCCGTGATCCTTATACGGCAGTTCTTCCTGGTTTGACGCGATAGAAAT
-TTTGATTTGCGCTAGTGTAGCCTACCTCGTATCCCAACATTAAC
->Spurpurea289_256_rc
-CACCATGTCACGGGATATGATTGTGTATTCATTAGGAAATTAATTAGAGATGTAATTAGT
-AGTTAGTAGTATTGTACTAGGACAATTATTCCTCTTCTATACGTAGCCTGAATTAATAAC
-TACTCATGTAAATCAGTTTACAGTCTATTTAATGAACAAGACACTCAAGAAGAGTGATCA
-TTCAGTGCATATACAAATCAATTTTGACATGGTATCAGAGCTACTGACTTGTGAATCATC
-GTGATCATAACGATTTTTTTTCTTGATCTGTGTCAAGTCTTCTGTGTGGGTTTTTTGGTT
-GTGGTTGTCGGCATACTATTCTGTGATTTTTTGGGGGAGATCGGTGACTGTTCTGGTGTG
-ATTTTTTTTGGAGGTCGTGTGGATGTGTTGTCTTTTCTGGCGTAGGTTTTTGTTGTGGCT
-GGGTTATAGTGTGTGTATCGGTGCTCTCTGTGGTGGCTGGAAGTTCTGTGGTGGGTCTGT
-TGCCGGAATCAGTTCTATGGTCTCTGGTTCTATGACGAGACCTTCGGTTCTGAGGGAGAT
-TGTCGAACGAAGGCGACGATCTCTTGTTTTATTCTTATTGACAAGTCAGCATCCCACTTG
-TCTCTGTTGTTGACGCGTCGGGCTGCTCTTCTGACACGTGGCGTGGCTACTGGATTACGT
-GGTGTGGGCTAACTGTTGGGCTGCGTGGAGTGGGCTTTTCTTTTCTAGGTTTGGGCTTCC
-ATTTTTGGGCTTTATTTTACCTAATTGGGTGCTGGGCCGGGACTGTTTCTAAAAGGAATT
-TCTGGGCCTTATTAGCTGGACCTGCTTAATTAATTGGGTCAATATTTGGACTACTTGGCT
-AATTTTCTACGGATTTGCATTATGTCTCTGGAGAATAATATTATTCGTTTTACTGGAAAG
-AATTTTTCTACATGGGAGTTTCAATTCAAAATGTTCTTAAAAGGGAAAGAATTATCAGAG
-CATATTGATAGCTCTACCAAAATCCCCACTGATAAAGAGGAATTAGCCAAATGGGAGGTT
-CAAGATGCTAAGGTGATCTTATGGCTATTGGGGACGATTGAACCTCATCTTATAGCAAAT
-CTTCGGTGTTTCACTATGGCTCAAGCTATGTGGGCCTATTACGCCGCATTTATCATCAAG
-ATCACAGCGCTCAAAAATCCAGTTGGAGTAGGAGATTAACAGTTATAGTCAAGGTAACCT
-TACCATTGAACAATTTTATTCTAGTTTTATTAATATATGGAGCGAGTATTCTGCTATTGT
-TCATGCTAAGGTTCCTACCGCGGCTCTCGCGGCTCTTCAAGCGGTTCATGCTGAGAGTCA
-ACGAGATCAATTTTTTATGAAGCTTCATCTTGAATTTGAACATGTTCGAGCCGATCTATT
-GAATCGTGATCCGGTTCCTTCCTTGGATATCTGTGTGGGGGATTTATTGCGTGAGGAGTA
-ACAATTATCCACTCAGATGGGTATGGCCTCTCAGAAGGTTTTTTCTGAGCATGTTACTGT
-AGCCTACGCAGCACAAGGAAGAGGACCGAACAGGTTGCAATGTTTTAACTGCAAAGAGCA
-TGGTCACATTGCTCGTAACTGTTCGAAGAAAGTTTGCAACTACTGTATGAAACCAAGCCA
-TTTCATCAAAGACTGTCGGGCACGACCTCAGAATCACCACTCTCAAGCTTTTCAAGCTAT
-TGTCTACGCCTCGTCTTCTACGCCCCCCACTGTAAGCACTGATTCATCTGTTCTCACACA
-TGCCATGATTCAATAGATGATTGTCTCAGCTTTTACGGCTTTGGGCCTACAAGGTATAAT
-TCCTACTTCCACTCCTTTGCTTGTTGATTCGGGTGCTTCTAATCATATGATTGATAATAC
-AACAGATCTCCATGATGTTCATGAGTATGACGGCACACAATCCATTCAGATTGTCAATGG
-TAGTACACATCCTATTACTGCTGTTGGTAATTTGGATTCTTCAATTAAAGATGTTTTTGT
-CTCTCCTAAATTATCTACCAGCCTAATTTCTGTTGGACAATTGGTGGATGATAACCTTGA
-TGTTCATTTTTCTCATGGTGGTTGTGTTGTGCAGGATCAGGTGTCAGGAATGGTGATCGC
-GAGGGGCCCTAAAGTAGGACGTCTTTTTCCTTTATATTTTTCTATTCCAAATGTTGTTTC
-CTTTGCTTGTATAGCTACGACCAATAATAATGACGTATGGCATAAGAAATTAGGTCATCC
-TAATTCTGTTGTCTTAACTCATCTTTTAAAACATGGTTTTTTGGGCAATAATAATTCTCT
-CTTCTTTTGATTATGCTATTTGTCGTCTTGGTAAAAGTAAAACTTTACCTTTTCCTGTGC
-ATGGTAATCGTGCTTCCACTTTGTTTGAAATTGTGCATTATGATGTTTGGGGTGCTAGTC
-CTGTTATTTCTCATGGGCAATATCGTTATTTTGTGATATTTATTAATGATTATAGTCGAT
-TTACTTGGATCTATTTTCTTCACTCAAAAGCTGGCATGTTCTCTGTTTTTCAAAAATTTG
-TCGTGCTTGTCGAGACACAATTCAGTACTTGTATCAAAATCTTATGCTCCGACTCCAGGG
-GGGTAATACATGTCAAATTCTTTTCAGAATTATCTTCAACAAAAAGGGATAATTTCTCAA
-CGCTCTTGCCCTTACACTCCTAAACAAAATGGAGTCGCTGAGCGTAAGAATCGTTATCTC
-TTAGATGTTGTTCGTGCTTTACTAATTGATTCATCTGTACCTACCAAATTCTGGGTAGAA
-GCATTATCTACAGCTGTTTATTTAATCAATCGTTTGCCTACAATCACTCTAAATTATGAT
-TATCCATATCTTTGTTTATTTGGTATACCTCCTGAGTATAAATCACTTCATACTTTTGGG
-CGTGTCTTTTTTGTTCATCTACCATCCTCTAAACGTCACAAGCTTGCCGCTTAATCTGTC
-ACATGTGCTTTTATGGGATATAGCCTGACTCAGAAAGGATTTAGTTATGATGCTCATGTC
-AATAAATTTTGAATGTCCAGGAATGTAGTTTTCTTTGAAAATCAATATTTCTTTCAATCT
-CATGTTATTTCTGATTCCCCTACTGTCACGCTTCTCCCATTTAATGATGTCCCTCCTTCC
-ATTAAGCGGTTTAAACTAGGTATTGTGTATCAAAGACGTTCTCCTTTAGCACCTCTTCCA
-GACACCACTTTGACATCTGATTCTACATTACTAGCACCTCGACGATCCACTCGGATTCCC
-CATCCACCAGATAGGTGTGACTTTTCTCATACGTCGCTTACTGCTACTCTCGATACTATT
-TCTGTTCCTCACTCTTATACTCATGTAGCCACCCAAGCTTGCTGGCAGCAAGCCATGCAA
-GAGGAAATTCAAGCTCTTCAAGACAATCAAACTTGGGATCTTATGACTTGTCCACTGGAG
-TCAAACTTATTGGTTGTAAATGGGTATACTCAGTCAAGTTTAGATCTGATGGCTCCCTGG
-ACCAGTATAAGGCCCGTCTTGTGGCTCTTGGGAACCGACAGGAGTATGGTATTGATTATG
-AAGAGACATTTGCACTAGTAGCTAAAATGACTATTGTACGGACTATATTGGCACTTGCTA
-CGTCACAGGAGTGGTCTCTTAGACAAATGGATGTGAAAAATACATTCCTACATGGAGATT
-TTAAGGAAGAAATTTATATGTCTCCACCTTCGAGCATGTTTAAAACACCCTCCTCCGAGG
-TTTGTCAGCTACGCAGATCTTTGTATGGTTTAAAACAGGCACCGCGGGTATGGTTTGATA
-AATTTCGCTCTACTTTGCTTGACTTTCACTTTATTCAAAGTCAGTTTGATTCCTCTCTGT
-TTCTCCACAAGACATCTGCAGGAATTGTATTGCTTTTAGTCTATGTTGATGATATTGTGA
-TTACTAGATCAAATACTGAGTTACTTAAACACTCGCAAAAGCATCTCCAAGACTCTTTTC
-ACATGAAAGATCTTGGCACTTTGTAGTATTTTCTTGGCCTTGAGGTTTAGATTACTCCGA
-CCGTTACATTGTTACATCAGCACAAGTACATGGAGGAAGTCATTTCACTAGCTAGTTTCC
-AAATGGGCAATTCGGTTCTTACTCCATTGGAGGTCAATGTTAAGCTTAGCCAGAGGAGGG
-CGAGCTTCTGTCAGATCCATCCTTGTATCGGCAACTAGTTGGAAGTTTGAATTACTTGAT
-GATTACTCGTCTTGATATTTCCTTTGCGGTGCAACAAATTAGTCAATTTATGCAGGCTCC
-TCGACATCTTCACTTTGCTGCGGTTCACCAAATTATCCGATATTTGAAGGGTACTTCTTC
-AAGAGGGTTGTTCTTTCCTAAGGTATCTTCCTTACAATTAAAAGGATATAGTGACGCTGA
-TTGGGCTGGTTGTGCAGATACTTGTCGCTATGTTACTGGATGGTGTATGTTTTTCTAGTA
-ATACACTTATTTCTTGGAAGAGTAAGAAGCAAGATAGAGTTTCCAAATCCTCCACAGAAT
-CTGAATATCGGGCTATGTCTTCAGCCTACTCGGAAATTACATGGCTTCGTGGGTTATTGG
-GCGAACTTGGTTTTCCTCAGCTTCAATCCACTCCTCTTCATGCTGATAATACCAATGCAA
-TTCAGATTGCGGCTAATCCAGTGTTTCATGAGCGTACAAAGCACAATGAAGTTGATTGTC
-ATTCCATTCGTGAATCTTTTCACTGACATGAAATTACACTACCTCACATTTCCACCGAAC
-ATCAAACTGCGGACATTTTCACTAAAGCTCTCTCTCGGCATCAACATCAATTCTTAGTTG
-ACAAATTGATGCTTCTTGATCGACCAACATCAATTTGAGGGGTGATGTCACGGGATGTGA
-TTGTGTATTCATTAGGAAATTAATTAGAGATGTAATTAGTAGTTAGTAATATTGTACTAG
-GACACTTATTCCTCTTCCATACTTAGCCAGAATTAATAGTTACTCATGTAAATCAGTTTA
-TAGTCTATTTAATGAAAAAGACACTCAAGAAGAGTGATCATTCAGTGCATATACAAATCA
-ATTTTGACACACCA
->Stuberosum206_200_rc
-GTTATTGTTGAATTCCCATTTTCTGTTATTACTGTACACAGTTGGTTATTACTGTTTAGA
-AGTAGTTAGTATGGGATTAATCTAGTTAGTGGTTAAAAGGGTAATTAGCTGTAATTGGAT
-AGGGAATCAAAGTTAGTTAGAGAATGTATTTCTGGTGGATTGAATCTGAAATGTAAGTAT
-ATAAATAAGGCACAATTGTAACACTGATCAGATGATGAAATAAAACGATTCTTCTTCCTC
-TTTTCTAATTCAGTTTTCTTCCAGAACCTTCAATGGTGAAATGAGGTCTTTAGCTTAGAA
-TTTTCACATGGTATCAGAGCAGGTTATGCTTGCTATCTAGTAGATCAGAGTATTGTTTCA
-ATACAACAAAGTTGCGAAGAAGACGACAATGGCGAACGTGAGAATAGATTAGAATGATCC
-TCTCTATATAGGTCCATCAGATGCTTCAGGTGCAGTTTTGATTCCTATCAAACTCACTGG
-TCCTGAGAACTATGGAATTTGGAGTAGGTCAATGCAAATTGCCCTGTTAGGGAAGAGGAA
-ATATGGATTTGTTATGGGAGCTTGCAGTAGATCTTTGTATCGAGAAGAATTGCATGAACA
-ATGGGAAACCTGTAATGCAATAGTTCTATCGTGGTTGATGAGTGCAGTTAGTGAAGATCT
-TCTGAGTGGAATAGTTTATGCTACCAGTGCATATACGGGAAGACTTGAAGGAGAGGTTTG
-ATAAGGTAAATCGAATGAGGATTTATCAACTGCATAGGGAGATCAACACATTATCACAAG
-GTACAGACTCTGTCTCGACTTATTAGACTAAATTGAAGAATCTCTGGGGTGAATTTGATG
-CATTTGTGCCTTCACCTAGTTGTGCTTGTCCAAAATCAAAGGAATATGCAAATCATCTCT
-ATCAGCTTAGATTGATTCAGTTTCTCAGTGGTTTGAATGAGTCTTATGAGCAGGCTAGGA
-GACAGATTTTGTTGAAAGGTGTTACTCCATCCATTAATCAATCTTATGCTATGATTATAG
-AGGATGAGATTCAGCAACAAAATACTTGTGTGGTAACTGCTAATGCAATACCAGAAACAA
-TTGCTATGAATGTTAACAGAGGACAAAGCTATAATCAGGGAAATCAGAATTACAAAGGAT
-GAAGATGTGAATATTGTCACTACACTGGACATACTAAGGAGAACTGTTACAAGCTAATTG
-GCTATCCAGCTGACTAGAAAAACAGGAAGAAATCTGGTTTTAACAACTCAAAGGCAAGTC
-CATCCAATTCTCATTCTGGAGGACATGGAAATCATGGATATGGCAACTTTGGAGATCAAA
-CAGGAAATCGCCTAGCAAACAACATTTCAAAAGATCATCCAGATCAAACTCAAGTGGCAT
-CTACCAGTCATGAGGTGAATAATGCCTTTGTCACTAAAGGACATACATTCACTGATGGAG
-AATACAAGCAAATAATGAATATGCTGGGCAAAGACAACAAGGACATGAAGCAGGCCAAGT
-TGACAGGTATGGCTAATTGTTTTTCAACAAATGCTAGCTCACATAGATGGATAATAGATT
-CTGGAGCTTCTCATCATATAACAGCAGATAAACACTTGTTAGCAAACAGTAGATGCACAA
-CTGATCCTCATCATGACAAGGTGAATTTGCCTACTGGTGATAAGGCCACTATATCACACA
-TAGGAGAGATATTTCTGTTTGACAATGAAACTGTCAAAGATGTGCTATGTGTTCCTGATT
-TTAAGTTTAACTTACTATCAGTGTCACAGATAACCAGGGAACTTTCATGCTTTGTTTCCT
-TTTACCCTGACTTTTGTGTCTTCTAGGACCTTTACAGTGGCAGGGTGAAGGGGATTGGTA
-GGGAGGAAGGAAGTTTATACATACTCAGAGATGAACCTGACATACAGAACACTTGTGGAA
-CTCTAAGTAATCAGAAAATAGTTGCAGGAGTATCACTGCAGGACTGTGATCTGTGGCATA
-GAAGACTTGGCCACCCTTCTTCTCATATTTTGAAATGCTTAGATTTACTGCATAGTAATA
-AGGATGTTGAACTACTGAATAGTTGTTTAGTCTGCCCTCTAGCTAAGCAGACTAGATTAT
-CATTTCCTATTAGTAATTCCAAAACATCTGTCATTTGAACTTGTACACATGGATCTATGG
-GGACCATATAAGATTCCTACATTTGATAAGAAACATTACTTCTTGACAATTGTTGATGAT
-TATAGCAGATATACATGGATTCATTTACTGCAGCTTAAATCTGAAACTATTGTAGCCATA
-AAGAATTTCTTATTAATGATTAAGAACCAATTTGGTCACACCATAAAGACAGTAAGATCA
-GACAATGGGACTGAGTTCTTCAACTCTCAGTGCAAGGAGTTATTCCTGAATTTGGGTATC
-TCAGATCAGAGTAATTGTCCTCACATACCACAGCAAAATGGTGTGGTAGAAAGGAAACAC
-AGGCATATTTTGAATGTAGCTAGGGCAATCAGATTTCAGGCCTGCATGCCCCTCAGATAT
-TGGGGTCTTTGTGTCAAAGCAACAGTATATCTCATCAACAGATTACCAACACCAACACTT
-AATGGTGGCAAGAGTCCTTATGAGATGATGTTCTCAAAATCCCCTAACCTTGGTCATTTA
-AGAGTCATAGGATGTCTTGGCTATGCATCAGTTATTCCAAGGAATGACAAGTTATCAGAA
-AGGGTAAAGTCTGTTGTTCTGATGGGGTATTCAGAAACTCAAAAAGGGATATTTGTTATT
-GGACCTACACACAAACAAGCTTCTGGTCAGTAGAGATGTTGTTTTTCAAGAACAGATATT
-TCCATTTGCTTCTCCACAACTTCCTAAAGAACCTGTTACAAGTGTGTCACCTTAATGTTT
-GAAGATCCTGCCTATCTGGAAGTGGTAGTACCAGAGGATTGCCCAAGGATTGCCCACCTG
-TTGTTCCTGTGGTTGCAGAGATTGGTGATGAGCCTGCAGAGATATTACCCAACATTGACA
-ACTCAGTTGACACAGCTGAGTCAATAGATGCCCAACCACCTGAAACTTCTATTAGGAGGT
-CTACCAGGACAACTAGACCACCTATATGGATGACAGATTACTCAGCTCCTACAAAGGGAC
-ATAGTAGCAGGTATCCCATTGCTAATCATCTCAGTTATACTCATGTGTCATCAAAGTATC
-ACAGCTATCTTGCCAAGTTTTCAACCTTAACTGAGCCTCAGACCTTTAAACAAGCTAGTA
-CTGATGATAGATGGATTGATGCTATGAAACTTGAAATCAAGGCACTTGAAGACAACAAAA
-CCTGGATGGTAGTTGATTTGCGTAAAGACAAACATGCAGTTGGTTCTAAGTGGATCTAAT
-GGTGAGGTGGAAAGGTTCAAGGCCAGACTTGTAGCCAAGGGCTATAGTCAACATGAGGGA
-ATACACTACCATGACACTTTTTCTCCAGTTGCAAAAATGGTTACTGTTAGATGTGTCATT
-GCTTTAGCTGTTTCAAAAGGCTGGTCTCTTTATCAAATGGATGTATACAATGCATTTTTA
-CAAGGTGACGTGGATGAGGAGGTTTATATAGAGATGCCTGAAGGTTTTAGAACTCAAGGG
-ACCACTAAAGTTTGCAAGTTGATCAAGTCCTTGTATGGCCTTAAGCAGGCCTCTAGACAG
-TGGAACATCAAGCTCACCAACACTTTATTATCAGCAGGCTTCATTCAAAGTTCTCATGAC
-TACTCCTTGTTCACCTTGAAGAAGCCAGAGGGGATGGTTATCATTTTGATATATGTTGAT
-GACTTACTCATTACTGGGGACAATGAGGCACTGATCAAAGAAGTAAATGAGACATTGCAT
-AAACAATTCAAACTTAAAGATTTGGGAGAGCTTAAGTATTTCTTGGGAATTGAGGTGTTA
-AGATCCAAGAATGGCATCATTCTGAATCAAAGGAAGTACATGTTAGAGCTGATTTCTAAT
-ACTGGTTTAAGTGGTGCAAAAACAACAGCTACTCCATTAGGATCCAATTTGAGATTAACC
-TCAGTGGAATTTGATAAGGCTACTGGATTACATGGAGATGATGTATTAACAGATTGCTCA
-GCTTATCAAAGGCTGGTAGGCAAGTTAATGTATGCCACAATTACTAGACCTGACATCAGT
-TATGCTGTTCAGACACTTAGTCAATTTATGCAACATCCGAAAAGGTCCCATTGGGAAGCA
-GCTGCCAGGGTGGTGAGGTATCTGAAAGGAACAGTTGGCCAAGGTATTTGGTTGAAGGCT
-CAACCTACCACAACATTGACATGTTGGTGTGATTCAGATTGGGCTGCTTGCCCTAAAACT
-AGAAGATCAGTTACAGGGTATATTGTAAAGTTTGGAGAGTCTATAGTGTCATGGAAATCA
-AAGAAGCAACAGACAATATCTAGAAGCTCAGCTGAAGCTGAGTATAGAAGCATGACTTCA
-GCTGTAGCAGAAGTAACTTGGTTGATAGGGCTGTTCAAAGAGCTTAATGTGTCTATTCAG
-ATGCCAATCACAGTATTGAGTGATAGCAAGTCTGCCATTCAGCTAGCAGCAAACCCTGTG
-TTTCATGAAAGGACCAAACACATTGAAATAGATTGCCACTTCATTCGAGACAAAGTCAAG
-GCTGGTGTAATCCAAACAGTGTATTACTTTTGTTAACAAAAGGGTTGAATCATACTCAAC
-ACTTGCATTTGTTAGGCAAGCTAGGTGTGCTTAACATTCTGCACCCTTCAGCTTGAGGGG
-GAGTGTTGAATTCCCATTTTCTGTTATTACTGTACACAGTTGGCTATAACTGTTTAGAAG
-TAGTTAGTATGGGATTAATCAGTTAGTGGGTTAAAAGGGTAATTAGCTGTAATTGGATAG
-GGAATCAAAGTTAGTTAGAGAATGTATTTCTAGTGGATTGAATCTAAAATATAAGTATAA
-ATAAGGCACAATTGTAACACTGAGCAGATGATGAAATAAAACGATTCTTCTTCCTCTTTT
-TTAATTCAGTTTTCTTCCAGAACCTTCAATGGTGAAATGAGGTCTTTAGCTTAGAATTTT
-CACAGTTAT
->Vvinifera145_640
-ATAATTGTTATAATTGTAGTAATGAATGCCACCACATTAATGTTAATTAAGTATTATTTA
-TGATTTCCATTAATACTCATTAATGGAAACCACTAATGTTATTTATGAGTTTCATTAATA
-TTCATTAATGGGGATATTAATGGAAGCCATTTGGAAGGCCTATGAAAGGGTTTCCCGTCT
-CCTGTAATATACATCCCGAGAAAAAGAAAGAAAAGTTCTTCCTCTCATTCTCTCTCTCTT
-TCTTTTATTTTCTCAACTCTCTTCTCTGATTTATTCTTCCCTATTATATATCAAAGTAAG
-ATATATTTCATTTCTACTACTTTGATTTGCATTGTATTTGTCCTTGTTTTACAACACGTT
-ATTAGCATGAATTGCTCTGAAGGTAATTCTCATATCTTAAACTTGAAGTTATTTATATAG
-AATAAAATTTTACATATATTATTGTTGATTTGTTTTGTTACATATTCTTAAAAGTTATAA
-ACAAATTATTTGATTTTGTTTATAATCAAAGTTATACCAATACTATCAATTTATTATAAT
-TGATTCTAATAATATTGTTTTGTCATATATTTGAAATTATTTGACAATGTCGAATCTCAC
-AAAACTCAAATTTGTGGCACTGGACATTTTGAGAAAGAACTCTCTATCTTGGATCCTTAA
-TGTTGAAATACATCTTGATGCAATAATCTTGGAGCTATGATCAAAGAAAGGAATCAAGCA
-TCCCTGCAAGATCGCACAAAAACACTGATTTTCCTTCGTCATCATCTCCATGAAGGTTTT
-TTTTTTTTTTTTAAAAAAAAAAAAAAGAGCTTTTGATGAAAAACCACTAGCTCATCCAAC
-TAGATTTGAATCATTACCTGAAGTGAATGCAATATAGTCCCAAACTCGTGGACGTGGATG
-AGAACGTGGTTGTGGTCATGGAAGAAATCCTCGATACCATGGTTCTTATAATAATAATTC
-TCAAAAAATGAAAGCCTCATTGCACCACCAAAAGTGGAACAATACGAAATGGAACAATAC
-TGAAGCAAAACAAGAAAATGGAAAGCGTTTACAAGATAAACCTCCTAAGAACCATGAGAA
-TAATTGTTATAGATGTAGTATGAAGGGGCATTGGTCACGTACTTGTCGTGCGCCCAAACA
-TTTAGTCGACCTTTACCAAACATCAATAAAAACAAAAGGAAATGAGATAGAGATGAACTT
-TACCAATAGTGATGGATTGGACCTAACCTACTATGACATTGATTTATTTGGAGGTCCCAA
-TGAAAAAACAGACCATTTGCTAAATGATAAAAAATTAACATTGATTTATGTTACTTTATA
-TATGAAATGATATATTATTATGTTTTATATTTACATTTGATTTTCTTTTGTTATTTACAT
-TATGCCTTGTTTTTTAGTTATATCTAAAATCCATGTGTTATTTGGTCTCAAGATGAATGA
-GGATGATGTATGTCTCGCAGACTGTGCAACCACGCACACGATTCTTCAAGATAAAAGATA
-TTTCCTTGAATTGACATTAATAAAAGCTAATGTAAGTAACATATATGGTACTACAAACTT
-AGTTGAAGGCTCTGGAAGAGCAAACATAACGTTGTCAAATGGAACTAGATTCCATATAAA
-TGACGCTTTATATTCTAGCAAATCCAGAAGAAATTTGCTCAGTTTTAAAGATATCCGTAG
-AAATGGATATCATATTGAAACTATGAATGAAGATAATGTAGAATATCTTTACATTACTTC
-CATTATATCTGGCTAGAAGCTTATAATGGAAAAACTCTCGGCTTTCTCCTCTGGGTTGTA
-TCATACAACTATAAAGCCTATTGAATCATATGTTGTCGTGAACCAGAAGTTCAATGACCC
-AAAAGTTTTTGTCATTTGGCATGACAGACTAGGTCACCCAGGGTCTTCAATGATGCGTCG
-AATAATCGAACACTCACATGGGCATCCACTAAAGAACCAGAAGATTCTTTCGCCCAATGA
-ATACTCATGTGCTGCCTGCTTACAAGGTAAATTGATAATCAGACCATCTTTTACTAAAGT
-CATATCTGAGTCACCAATCTTTTTAGAAAGAATACATGGGGACATATGTGGGCCTATCCA
-TCCACCATGTGGACCATTCCGTTATTTTATGATCTTAATAGATGCTTCTACTAGGTGGTC
-ACATGTTTGTCTCCTTTCTACACGTAACGTTGCCTTTGCTCGACTCCTTGCACAAATAAT
-CAGATTACGAGCACAATTTCCAGATTATCCAATTAAGACAATACGTCTTGATAATGTTGG
-CGAATTTACTTCTCAAACATTCATTGACTATTGCATGTTAGTATAAATAAATATTGAGCA
-TCCTGTTGCTCATACTCATACCCAAAATGGTTTAGCAGAATCCTTCATCAAACGTCTCCA
-ATTAATAGCTCGACCATTACTAATGAAAACCAAATTACCTACTTCCGCCTGGGGACATGC
-TATTATGCATACTGCAGTTTTAGTCCGTATTCGACCTACAACTTACCATGAATACTCCCC
-TTCACAATTTGTGCTTGGAAAACAACCAAATATCTCTCACTTACAAATCTTTGGTTGTGC
-AGTATATGTACCAATTGCACCTACACAACGCACTAAAATGGGTCCCCAACGAAGACTTGG
-GATTTATGTAGGTTTTGATTGTCCATCTATCATAAGATATCTTGAACCTTTAACAGGCAA
-TGTTTTTACAGCCCGCTTTGCGGATTGTCATTTTAATGAGAGTGTTTTCCAGTCATTAGG
-GGGAGAAAAATCGATTCCTGAAGAACGACGAGAAATTAGTTGGAAGGCATCTACTATAAC
-CCATCTTGATCCTCATACAAATCAATGTGAACTAGAAGTTCAAAGGATCATTCATTTGCA
-AAATCTTGCAAATCAATTACCAAATGCATTCATTGATACAAAGAAAGTGACAAAGTCACA
-TATCTCGGCTACAAATACTACAACACTAATTGATGTCCCTGTAGGACAGTTAACAAATGA
-ATCTAAGATACGCTTGAAGCGTGGTAGACCTGTCAACTCAAAGGATGTAACTCATCGGAA
-GAGGAAAACCCAAGAAAAACTTGGCACTTTAGAAGATGTCATCAAAATGACTGATCAGTT
-TAAAATTAATAAATCTATAGCCCTAGAAGAGGTACAAATAATGCAGAAAGCCCCTAAAGA
-GACATATATTGAACAAGAAGCCCCCGAAGAGGCACATGTTGAACAAGAAGCCCTTAAAGA
-GGCACATGTACCTGAAAATTGTGAGATCTCAGTAAGTTATGTACACATGGGAGAAAAATG
-GGATCGAAATAATATTGTTATTAACAATATTTTTGCTTTCCAAGTGGCCTCTGAAGTCAT
-AAGAAATTATGAAGATCCCGAACCACGAAATGTGGAAGAATGTCGACATAGAAATGATTG
-GCCAAAATGGAAAGAAGCTATACATGCAGAATTAAACTCATTAACAAAACGAGAAGTTTT
-TGGACCTGTAGTCCAAACACCTGAAGATGTAAAGCCTGTTGGGTGCAAATGGGTATTTGT
-ACGAAAGCGCAATGAGAATAATGAGATCATAAGATATAAAGCGCAATTAGTAGCACAAGG
-TTTCTCGTAGAGACCTGGTATTGACTACGAGGAAACATATTCTCCCGTCATGGACGCAAT
-CACATTTCGTTTCTTAATTAGTTTGGCAGTCTCAGAAAGACTGGATATACGTCTCATGGA
-TGTTATTACAACATATTTATACGGATCCATGGATAATGATATATATATATGAAAATCCCT
-GAATGATTTAAATTGCCTGAAGCAAATAATACAAAGCCTCGTAGCATGTACTCAATCAAG
-TTACAACGATCCTAGTATGGATTAAAGTAATCTGGACGCATGTGGTACAATCGCCTTAGC
-GAATACTTGCTAAAAGAAGGGTATGTGAATAACCCTATATGCCCATGCATCTTCATTAAA
-TCAGAAATGGGATTTGCAATTATTATAGTGTATGTTGATGACTTAAATCTTGTTGGAACT
-CTTGAAGAGCTCACAAGAACAACAAATTACTTGAAAAAGGAATTTGAGACGAAAGATCTT
-GGAAAAACAAAATCTTGTCTCGGCCTGCAGATCGAGCATTTTCCAAATGGAGTTTTAGTA
-CATCAATCAACATTCATTAAGAAAGTTTTGAAATGTTTTTATATGGATAAAGCGCATCAT
-TTAAGTTCTCCAATGGTTGTCCGATCACTTGATGTGAAAAATGACCTATTTCGTCCTTAC
-GAAAAGGATGAAGAGTTACTTGGTCCTGAAGTACCATATCTTAGTGTTATTGGTGCACTT
-ATGTATCTTGCCAATTGTACACGTCCAGACATTGCTTTTTCTGTCAATTTATTAGCAAGA
-TACAGTTCCGCTCTAACTCGAAGACATTGAAATGGTATCAAACATATATTGTGTTATCTT
-CGCGGAACTACTAATATGGGTTTATTTTACTTAAGGGAATCAAAGTAACAATTGCTTGGA
-TATGCAGATGCAGGATATTTTTCAGATCCACATAAAGGTAGGTCATAAATAAGATATGTG
-TTTAATTGCAATGGTACTGCTATTTCATGGAGATCTGTCAAACAAACAATGGTGGTAACA
-TCATCAAATCATTCAGAAATATTGGCAATTCATGAAGCAAGTCGTGAATGTATATGGCTA
-AGATCTATGATCCAGCATATTCGGGAATCATGTGGACTCTCCTCTATCAAAGGTGACCCG
-ACAATATTATTTGAAGATAATGTTGCATGCATTGCACAAATAGCAGAGGGTTATATTAAA
-GGAGATAGAACTAAACACATTTCACCAAAATTCTTTTATACACATGAACTCCAGAAGAGT
-GGTGAAATTGATGTGCAACAAATACGGTCAAGTGATAATCTAGCAGATTTATTCACAAAA
-TCATTGTCAACCTCAACATTCAAGAAGTTAATATACAGGATTGGAATGCGTCAATTCAAG
-GATATCGACATGAGGGGGAGTATACTTGTAAAAGGGTGTTAGTGTACTGTACTCTTTTTT
-CCTTCGTCCAGGTTTTGTCCTACTGGGTTTTACTGGCAAGGTTTTTAATGAGGCAATCCT
-AATATACCAAGAAAAGAATATTGTACTCTTTTTTCTTCGCTGGGTTTTTCCTATAGGGTT
-TTTTACTAGCAAGGTTTTAATGAGGCATATTCTTTCAATGTGGTGGACATCTAAGGGGGA
-ATGTTATAATTGTAGTAATGAATACCACCACATTAATGTTAATTAAGTATTATTTATGAT
-TTCCATTAATACTCATTAATGGAAACCATTAATGTTATTTATGAGTTTCATTAATATTCA
-TTAATGGGGATATTAATGGAAGCCATTTGGAAAACTTATAAAAGAGCTTCCCGTCTCCTG
-TAATATACATCCCGAGAAAAAGAAAGAAAAGTTTTTCCTCTCATTCTCTCTCTCTTTCTT
-TCATTTTATTAATTCTCTTCTCTGATTTATTCTTCCCTATTATATATCAAAGTAAGATAT
-ATTTCATTTCTACTACTTTGATTTGCATTGTATTTGTCCTTGTTTTACAACAATAAT
->Fexcel_5
-AATATTGTTGGGAGTTCCTTTTTAGGGTTTATCCACTTCAGTTCAAATAACCCCTATTTT
-TTGGGAGAAGCTTATTCAAAGCTGTTTGGGTTTGTTAGTTTTTGTCCTATTCTTTAGTTA
-CTCTACCACGAACTCAGGATTGTTGAGAAGTGGGTTGCAACGTTCTTTTCTTGTTTTGTA
-ATGGCTATATAAGCCAGTTTGTTTTCATTCAATAAATAAGAATCATTCAGCCATACTTGT
-GAGTGACTTGTGTGTGAGCTTCTAGTTTTTTCAACAATCTGGTATCAGAGCCAAAAAGGG
-GCCTGAGTGTGAGTGAAACACGAGTGATAGTGTCTTGTGAGAAGAAGTAAGCGTGAGCAT
-GGCCACAGAAGGAAGCAACTTCGGGTTTTCTTGTATTCCTAAGTTCGACGGAGACTATGA
-TCATTGGAGCATGATCATGGAGAATTTCTTGAGATCAAAGGAATACTGGACCCTCATTGA
-GACAGGCTACACAGAGCCGGCTGCTGGAGAGGTGCTGACTGCTACGCAGAGGAAGAGTTT
-GGAGGAGTTAAAGTTGAAGGACTTAAAGGTAAAAAATTACCTCTTCCATTCAATTGACAA
-ATCCATTTTGAAGACAATCACTCAGAAGGAGACGTCCAGGCAGTTGTGGGAGTCTATGAG
-AATCAAATACCAAGGGAATGCTAGGGTTCAAAGAGCTCAACTTCAAACTCTTCGCAGAGA
-TTTTGAAGTTCTGGAGATGAAGCTCGGGGAATCAATGACAAATTACTTTGCAAGAGTAAT
-GCTCGTGGCCAATGATATGAGGAATTGTGGAGAAGACATGCCAGATGTGAAGATAGTTGA
-GAAGATTCTTCGTACCCTCACTGAAAGGTTTAATTATATTGTTTGCTCAATAGAAGAATC
-AAAGGATATTGATCGTCTTTCTGTGGATGAACTACAAAGCTCTTTGCTTGTTCATGAGCA
-AAAGTTCCGAAAGGCTAGTGGAGATGAGCAAGCATTAAAAGTATCATATGAGGAGAGAAG
-TGGTGGACGTGGAAGAGGACGAGGAGCTGTGAGAGGCAGAGGCCGAGGCAGAGGCTTCAA
-CAAAGCAACCATTGAGTGTTACAAATGCCATCAGTTAGGGCATTTTCAGTACGAATGTCC
-CAAGTGGGAGAAGACAGCCAATTATGCTGAATTGGATGAAGAGGATGAATTGCTTCTCAT
-GGCTTATGTGGAAATAAACAACTCAAGTCAAGAAGGTGTATGGTTTCTTGACTCAGGCTG
-CAGTAATCACATGACAGGAAACAAGCAGTGGTTTACGGAGTTGGATGAGGGTTATAGACA
-TTCTGTGAAGCTTGGAAACAACATGAAGATGGCTGTGATGGGCAAAGGGAGTATAAAGTT
-GCAAGTTGGAGAAGTGAAACAGGTAGTGTCAGATGTCTATTACATTCCTGATTTGAAGAA
-TAATTTGCTTAGCATAGGCCAATTGCAAGAGAAAGGTCTAGCCATTCTGATTCGAGATGG
-AGCCTGCAAAGTTTATCACAATAGGAGGGGTCTTATCATGCAAACTCAGATGACAGTCAA
-TCGTATGTTTGTTGTGCTTGCACTTGTGGGAGTTCAACAATCAAACTGTCTCAATGCAAC
-CACTGAGGACGTCACAGAGCTATGGCACCAAAGGTTTGGTCACTTAAGCCACAAAGGTCT
-TCTAACCTTGCAGTGCAAGAAATTGGTAAAGGGGTTGCCACAATTTAAGTCAACAAGCAG
-AGTATGTACGATTTGCATGATTGGAAAACAACATCGTGATACTATACCAAAGAAAAGCAA
-GTGGAGAGCTTCTGAAAGACTTCAACTCATACATGCAGATATATGTGGACCTGTAACACC
-AACTTCAAATAGTGGAAAAAGGTACATGCTGAGTTTTATAGATGATTACAGTAGAAAAAT
-TTGGGTGTATTTTCTTGTCGAGAAGAGTGAGACGTTTGCATGGTTTAAAGTCTTTAAAAG
-TCTTGTTGAAAAGGAGCTAGGGTTGTATATTTGTTGTTTGAGAACAGATAGAGGAGGGGA
-GTTCACTTCAAAAGAGTTCAACGAGTTTTGTACAGTTCATGGAATCAAGAGGCAGCTTAC
-GACTGCCTACACACCCCAGCAAAATGGAGTGGCGGAGCGTAAAAATCGTACTATCATGAA
-CATGGTACGATGCTTGCTTACAGAAAAGAAAATACCAAAGGCCTTCTGGCCGGAGGCAGT
-GAAGTGGACAGCTCACATTCTCAACAGAAGTCCTACTATTGCTGTAAAGAACAAGACTCC
-TGAAGAGAGCTGGAGTGGTGTGAAGCCCAACGTTGATTATTTCAGGGTGTTTGGATGTAT
-AGGCCATGTGCACATACCTGATGCTAAGAGGACTAAGCTTGAAGATAAGAGTTTCAAGGC
-TGTGCTGCTTGGAGTAAGCGAAGAGTCTAAAGCCTATAGATTTTTTGATCCTATTACAAA
-GCGAGTTGTGACCAGCAAGGATGTTGTATTTGAAGAAAATGAGAGCTGGGATTGGGGAAG
-AAGTGAAGAAGAACTCAGGCTTGATATGCTGATGTGGGGTGAGAATGAAAAAGAAGGAAA
-CAATGAAGAACCAGAAAGTGATAGTGAGGTAGATGAAGACCAAAGTGAAGCTATGCCTAG
-CAGTAGTGAAGAGGCTGTAAACAATGAGAGTTCACCGGAAGTCAACACTCTAAACACATC
-GGAGGAATTGATTCTAGGGAAAAGAATTGGAAGAACTCCAACCTGGATGCAAGACTATGA
-AAGGGGGGAGGGTCTTTCTGAAGAAGAAGGTATGCAAAGTCTGGCCATGTTTATTTCTAA
-CATTGATCCTATGAACTATGAGGAAGCTGCAATGAGTGATAAGTGGAGGAGTGCAATGGA
-TTTGGAGATTGAATCTATTATTAAGAATAAGACGTGGGAGCTGGTGGATCTACCTGTGGG
-TGCCAAGAGAATTGGTGTGAAATGGGTGTACAAAACCAAACTTAATGAAAAAGGAGAATT
-TGATAAATGTAAGGCACGTTTGGTGGCAAAAGGCTATGCTCAAGAATACGGAATAGACTA
-CAATGAGGTGTTTGCTCCGGTGGCTCGTTGGGACACAATTAGAATGGGTTAGCTTTGGCA
-GCTCAAAAGGGGTGGACTGTGTTTCAACTTGATGTCAAGAGCGCCTTTTTGCATGGTGAA
-TTGCGTGAGGCAGTTTATGTGGATCAACCTCTTGGCTACATAAAGGAAGGTGAAGAAAAT
-AAAGTTTATAAGCTCAAGAAAGCACTGTATGGATTAAAGCAAGCACCGAGGGCGTGGTAT
-AGCAGAATTGAAGGATACTTCGTGAAGGCAGGCTTTGAAAAATGCAGTTATGAGCACACA
-CTGTTTATAAAGGTTGAAGGAGAGGGTAAAATTTTGATCGTAAGTCTTTATGTTGACGAT
-CTTATTTTTACTGGAAATGATGTGTGTATGATTGAGAATTTTAAGAGCTCTATGATGCAT
-GAGTTCGAAATGACTGACTTGGGGAAGATGAAGTATTTTTTGGGGGTTGAGATTAAACAG
-AGTGTAGAAGGGATACATTTGTGTCAAAGCAAGTATGCTAGAGAGGTTCTTGATAGATTT
-GGTATGGCTAACAGCAATCCTGTAAGGAATCCAATTGTTCCAGGTTCAAAGTTGTCAAAA
-GAAGATGGCGGAGCTGAGGTTGATGGAACCTTGTATAAGCAGCTTGTTGGCAGTCTCATG
-TATCTAAATGCTACCAGACCGGATCTTATGTATGTGGTTTGTCTCATTAGTAGGTTCATG
-GCATGTCCAAGAGAAGCGCACTGGTCTGCAGCTAAAAGGGTGTTGCGCTATCTCAAAGGG
-ACAATTGATTTGGGGGTGTTTTATCGAAGAGGAGTTTGTGATGAGATGTTGGCATATAGT
-GATAGTGATTATGCTGGCGATTCTGATGATAGTCGGAGCACATCCGGTTTTGCGTTTATG
-TTAAGTGGTGGAGCGGTATCATGGTCTTCAAGGAAGCAGTCTGTGGTTACGTTGTCTACT
-ACGGAGGCCGAGTATGTGGCTGCTGCCGCCTGCGCTTGCCAAAGTATCTGGATGCAACGA
-GTGTTTAACAAGCTCAGTCATACTCAATGTAAGTGTGTCACCATATTCTGTGATAATAGC
-TCCACTATTAAGTTATCTAAGAATCCTGTCTTTCATGGGAGAAGCAAACACATCAATGTG
-AGATTTCACTTTCTACGTGATCTTACCAAAGATGGTATTGTCAAGCTAGAGTTTTGTGGC
-AGCAGTGAGCAACTTGCAGACATATTAACTAAGCCTCTGAAGCTGGAAACATTTGAGAGG
-CTTCGAGGGATGCTTGGAGTCAAGGCCAAAGATGAAGTAAACTGAGCTGTTCCAGCAGCT
-TCAGTTTAAGGGAGGGAATGTTGGGAGTTCCTTTTTAGGGTTTATCCACTTCAGTTCAAA
-TAACCCCTATTTTTTGGGAGAAGCTTATTCAAAGCTGTTTGGGTTTGTTAGTTTTTGTCC
-TATTCTTTAGTTACTCTACCACGAACTCAGGATTGTTGAGAAGTGGGTTGCAACGTTCTT
-TTCTTGTTTTGTAATGGCTATATAAGCCAGTTTGTTTTCATTCAATAAATAAGAATCATT
-CAGCCATACTTGTGAGTGACTTGTGTGTGAGCTTCTAGTTTTTTCAACAAATAT
->Pabies_54
-AAAACTGTTGAAATAATGTAACTGCAAAGAGTATTCTTATTATTATTGATATATTATTCA
-TGGGATCAATCCCCATTGTGGATTGATCATGTCGGTTATTGTATTAGGACCAAATCCCCT
-TTTGCGGATTGGTCAACAGACAATTGTAATAGGCAGAATAAGAGGTCAGCCCCAGCAGTT
-ATTATTGAGACCTAGAAATATAATAAAGGTGGGTTTACCCACCCAGAGTGAGAAGTCGGC
-CCTGCCAATAAAGACACTTCACACGTGCCAGACTTGTATATAGAGAGAGGCCAGCGCCTC
-ACTTTAACACACAACTCACTTGCTCATTCTGAGAGGCAAGTTTCGATTTACCAGTTGCAG
-AGAGAGAGAAGAGTGGCGCATCTTTTGGAGAGGACTTTCTGTATTGGTTTGAGTTATCTC
-TTGACATTGTCTTGGCAATCTGTAATAAGGAAGTTTTACTGGGTTTTCTACCCCTAGAGG
-GTTTCCCAGGATAAATGTTGTGTCTCTTGTGAATATGTATTTCTCAGTGATCTTGTTCGT
-ATGTTGTTGTTATCTATTGAAATGCAAAGAACTATAATAATGGCATAAACATTAACATGG
-TATCAGAGCCAACCTGAAGGAAGCATATTTCGCAAGAGAAAAACGTCATTGTTGTCGTAC
-CTGCAAATTAGGGTTTTTCAAAAGTGAAGCTTTTCAACTCCAAAACCACCTAGAAAGTTG
-CGCCCTCCCCCGCGGATCATTTTGGCACCTTCAGCGCACCTTTTCAATGGTCGGATCTCC
-CGGAAAGTGAAACCTTAGTTTCACAAAGTACCAGAAAACCAGAGCACCACAAGTACCAGA
-GTACCAAAAGCACCAGTCCAGAAATCCGCAAATCAATTTTGGAAAGGCAAAAGTTGGATT
-TTCTCAGATCCAAGTACACCAGGCTACTCGCCTCAGCCAAGCGCACCTCCTGGTAAGGTC
-AAAAATTGATTTTAAGCATTTTCATTTTTGGTCATTTTTCATAGCCCCAAGTACAAGATG
-TCTGCAAGCACAAAGCTTGTTGAGAAACTAGAAGGCATAGACAACTTTCGTGCCTGGAAG
-TATAGGATCGGTCTGATCCTTGCAAAGAATGACCTAGCAAGGTTCATCAAAGTAGAAGTG
-TCGAAACCCAAAGATGTCGTAGAGAAAGCAAAGCATCAAAAGGACTCAATCAGGAATCAA
-AGAATCATTGCAGATTCAATCAATGACCACCTGATCCCTTGTGTATCATCCAAGAACACT
-CTGAAAGAGATGTTTGATTCTCTAAGCAAATTATACGAAGGAAAGAACATCAACCGAAAG
-ATGAACCTAAGATCTCAATTGAAGAATACGAAGATGCAAAAAGGAGAAACAATTCAAGAA
-TATTTCTCCAGAATCTCAGAGATTAAAAAACAGTGAAAAGCAATTGGAGACTCCATAGAT
-GAAGACGAATTTGTAATGACGGCCCTAAATGGTCTTACAAGACCTTGGGATGCATTCATT
-CAAACAATATGTGCCAGAGTCGTGAAGATGCAGTTTGATAGCCTATGGGAAGAATGCATC
-CAAGAAGAGACAAGGGTAGCTAACCGTGAAGCACTACTAGCAAGGGATGATGATCAAGCC
-CTAGCTACTCATACAAAAGGAGGAAGGAAGAAACCCTACTTCAAAAGGGAAACTCATAGA
-GAGCCTCAATCATCAAACAAATTCAATAACAAAGAATCTCATCCAAGGAGATTTCAGAAG
-AAAGGACAACGCAAGGAAAGAGATCTCTCATCCACACAATGTTATCATTGTGATAAGATG
-GGACACAAGGCGAATTCTTGTCCAGTCAGACGAGAAGAATACAAGAGGAAACATACGAGG
-CAACATGCCCACATAGTCGAAGATGAAGAGCCACCTACAAAGATGATCAAGGACCATGTC
-TTGATCTCGGCCCTCTCAGGATCGGTAACACCTGGAGAGGACACATGGCTCATTGATAGT
-GGTGCATCAAAGCACATGACGGGTAAGAAAGTACTCTCTCTTGTATTTCAGAAAATAAGT
-TTTCTCAGAAAGTGACACTTGGAGATGATTACCAATATCCCATCAAGGGAGTGGGAGAAT
-CAAACTACAAGCTAGATTCAGAAAACTCAATAAAGATGAAGGACGTACTCTATGTACCAG
-GCTTGAAGAAGAACTTACTTTCCATATCAGCTTTAGATAAGAAAGGCTATAGAGTTGCCT
-TTATAGATGGAAAAGTTCTTATGTGGGCTAGAGGAGAAACCATAAATGAAGCAATCGTCA
-TTGGAAATGAAGAAAATGGCTTATATCAGCTAAAGGGTCACCCAAAGACTGCCATGACCC
-ATGCCATTAAAAACTCATGCGAACTTTGGCATAGAAGATTAGCCCACATCAACTACAAGG
-CACTACCATACATATGCAAAGTTGTCACAGGTTTACCAGAACTCAAGGTTGATCAAGAAG
-GCATATGCAATGGATGTGCGCAAGGGAAGAATATCAAGAACCCTTTTCCAAAGAGGGACA
-GCAAAGCAGAAGGAGTACTGGAACTCATTCATTCAGACGTGTGTGGCCCAATGCCATCAT
-CCTCCATTAGTGGGTATGTATACTATGTATCATTCATTGATGACTATTCTCGCAAGACCT
-GGATATATTTCTTGAAATCTAAAGATGAAGTATTCAGCAAGTTCAAGGAATTCAAAGCCT
-TGATAGAGAATCTTTCTGAAAGGAAGATTAAAATACTCAGATCAGATAATGGAGGAGCAT
-ATACCTCAAAAGAGTTTGTGAACTTCTGCAGAGATGTTGGGATCAACAAGGAACTCACTA
-CTCCCTATAATCCTCAACAAAATGGTGTAGCTGAAAGGAAGAACAGAACAATTATGGAAG
-TAGTAAAGACCATGATCCATGATCAAGATCTTCCTATGTGCTTATGGGCAGAAGCAGCAA
-TGGCAGTTGTTTATGTGCAGAACCGATTATCCCATAGCGCACTTGGGTTCAAGACCCCGG
-AAGAGATGTTCACCGGAAAGAAGCCAGAGGTAAGCCATCTCAAAATATTTGGCTGCCCAG
-TCTTCATACACATTCCGAAAGACAAAAGAAACAAGTTGGAACCCTCCGGAAAGAAGGGAA
-TATTTGTGGGATACTGTGAGGTCTCCAAGGCCTTCAGAATCTACATACCAGGTCACTACC
-ACATTGAGATCAGCAGGGACGTGACCTTTGATGAAGAAGCAGCACTTAAGAAATCAAGAA
-GATGCCATCTTGAAGAAGTATATGAAGAGGAACCCGTAATTCCCAGGATTGCAAAATCCG
-TAAGGGAAGTTCCCAGAGCTGCAAAACCAAGGAGGGAAGTCGTAACTTCCCCTAATGAGG
-AAACTCCTGAAGACCATAATATAACAGAAATTCAAGAACCTCTTCAAATGACCTTCTCCC
-ATAAAAGAAAGCCTACTTGGGCAAGAGAGCTTATTCAAGATGGAGAGAAGTATGGTGTTC
-CAAAAGGAACCTCGAGACAGGTGAAAACCCCAAAGCCATTCTCCAGTTACACGGCTTTGA
-TGTGTGATCTCCTAGATGAGGAGACTACCTGCTTTGAAGAAGCCATTCAAAAGAAGGAAT
-GGGCAGATGCCATGACAGAGGAATACCAATCCATAATAAAGAACGATGTATGGCAAATAG
-TTCCCAAACCAAAAAGTAAGGATATGGTATCATCAAAATGGCTCTTCAAAATAAAACATG
-CTGCTGATGGAAGTATTGAGAAATATAAAGCAAGATTTGTCCCTCGTGGCTTTTCCCAGA
-AAGAAGGCATTGACTATGAAGAGACATTCGCTCCTGTAGCTAGATACACTTCGATTAGAA
-CCATCATAGCTCTTGCAGCTAAGATGAAATGGAAGTTGCACCAGATGTACGTGAAGACAA
-CTTTCCTGAATGGTGTTATCGAAGAGGAAGTGTATATAGAGCAACCCCAAGGATTTGAGG
-TTGAAGACAGGAAGTCTCATGTCTGCAGATTAAAGAAAGCCTTATACAGATTGAAGCAAG
-CTCCTAGAGCTTGGTATGGCCGTATAGACAGTTTCTTGACAAGCTTGGGCTTTACCAAGA
-GTAAAGTTGATTCAAATCTCTACTTCAAAATTATGAACGATGAGCCAGTAATACTATTGT
-TGTATGTGGATGACTTATTCTTAACTAGAGAAGAAAAGATCATCACCGAATGCACTATTT
-CCTAGGTCTTGAGGTATGGCAGAGTCCAGAGAGGATCTTCCTTAACCAAGGGAAGTACAC
-AGTCGAAATCTTGAAGAGATTCGACATGTTGGAATGCAAGCCCATGAATACACCCATGGA
-AGTGAAGCTGAAGTTGTTGGTCGATACTTCATCAGACTTGATAGATGCCACACTGTACAG
-ACAGATTATTGGATTGCTAATGTACTTGACGAACACCAGACCAAACATTTGTTTTGCCGT
-GAACACCTTGAGTCAGTTTCTGGTAGAACCTAGACATGTTCACCTAGTGGTTGCAAAACA
-TGTGATGAGGTACCTTAAAGATACATTAGATTATGGTCTCAGTTATGACGGAGATCACAA
-TTTCACATTGAGTGGATACACTGATTCAGATTGGGCAGGAAGCGTTGCTGACAGAAAAAG
-CACTTCCGGATGCTGTTTTAGTTTGGGATCGGCCATGATCTCGTGGCAGAGTAGGAAGCA
-ATCAAGTGTTGCTCTCAGCATAGCGGAAGCAGAGTACATAGTTGCATGTTCTGCTAGCTG
-CGAAGCTATATGGCTTCGAAAGTTGCTGATCGGTCTATTGACCTAGAAATGGAAGCAACC
-ACGATCCTATGTGACAACCAGAGCTGCATAAAGATGANNNNNNNAGAATCCTGTATTCCA
-TGACAGGTCGAAGCACATAGAGATTCGCTATCATTACATCCGTGACATGGTGCAGAGAGG
-AGCCTTGAAGCTCCAGTACATTAGCACGGATGAACAGGTTGCTGACGTGTTGACCAAACC
-TCTATCTCACGTAAAGTTTGAACACTTTCGAGATAAGCTTGGTATAGTCCAAAAGGACCC
-TCCCTGAAAGGGGGAGTATATATTTGTTCCTTGCTTCTGACCTCGTTGTTAGACGTAAAG
-CAAGATGAGTCATTGCTTCTGACCTTGCTGTTAGACGTAAAGCATGACGCTGTGTGTTCC
-TTGCTTCTGACCTCGCTGTTAGACGTAAAGCAAGATGATTCATTGCTTATGACCTCGTTG
-TTAGACGTAAAGCAAGATGAGTCATTGCTTCTAACCTCGTTGTTAGACGTAAAGCAAGAT
-GAGTCATTGCTTCTGACCTCGTTGTTAGACGTAAAGCATGACGATGTGTGTTCCTTGCTT
-CTGACCTCATTGTTAGACGTAAAGCAAGATGATTCCTCGCTTTTGACCTCGTTGTTAGAC
-GTAAAGCGAGATGTTGATGTAAAGCAAGAATACCCTTCTTGTTAAGGGAGAGTGTTGAAA
-TAATGTAACTGCAAAGAGTATTCTTATTATTATTGATATATTATTCATGGGATCAATCCC
-CATTGTGGATTGATCATGTCGATTATTGTATTAGGACCAAATCCCCTTTTGCGGATTGGT
-CAACAGACAATTGTAATAGGCAGAATAAGAAGTCGGCCCCAACAGTTATTATTAAGACCT
-AGAAATATAATAAAGGTGGGTTTACCCACCCAGAGTGAGAAGTCGGCCCTGCCAGTAAAG
-ACACTTCACACGTGCCAAACTTGTATATATAGAGAGGCCAATGCCTCACTTTGACACACA
-ACTCACTTGCTCATTCTGAGAGGCAAGTTTGGATTTACCAGTTGGAGAGAGAGAGAAGAG
-TGGTGCATCTTTTGGAGGAGGACTTTCTATATTGGTTTGAGTTATCTCTTGACATTGTCT
-TGGCAATCTATAATAAGGAAGTTTTACTAGGTTTTCTTCCCCTAGAGAGTTTCCCTGGAT
-AAATGTTGTGTCTCTTGTGAATGTGTATTTCTCAGTGATCTTGTTCTTATGTTGTTGTTA
-TCTATTGAAATGCAAAAAACTATAATAATGGCATAAACATTAACAAAAAC
->Wicker_Bianca_AF521177-1
-CTCTATGTTAGGAATTAATTAAATGTATTAATTAATGGGATAATCCCTGCGTTGCCGGTT
-GCCTTGTTTTAGCAACCGTTCCTTGTAAACCGCCTCTGTAACAGGGGTATAAATACCCAC
-ATCTTCAATCAATGAAAACACTGTTCCATCATTCTGTCACTTTCACTACTTTACACGTTA
-TCAGCGCTCGCTCTGCCGAGAGCGACAGAGAGCCAAGAGAAAGGTGAGAATGGCAGGGAG
-AGATTTGTCCTCGCTTGCTTTTGCTTTCGCACGTTTCCTGCAGAAAGAAGACAGCCGCTT
-CACTCGCACGCAGCGCCACCGCATTGCACGTCGCCGTCAGGGTCTTGGTGCAAGGAATGG
-CCCTCGCCGCCGTTCCTTCACCAGGAACCACCGCCAGAACCGGAGTGGCCCTCATCGCCG
-CTTCTTCGCCGGGAACCACCGCCAGAACCGCCGCCGTGGGGCCATCAGCGTCGTTCCTCG
-AAGAAGATGTGATGTTCCCCTGTGAACTTGCGCCGCCGCCTCTTCCTCCATACTGCATGC
-AGCACGGATTCGGGCCATGCCCTGCGCGCACGGATGGCCACGTCCCGGAGCTGCCATCCC
-CGACGCCGGCAGCGGCAGCACACACCGGCCCCGTCCCGGATCTGCCATCTCCGACACCGG
-ATCGAGAGGAACGAGGCGGTTATCCCACCATCCCCATCTTGAACATCCAGATCAAGGTTG
-AAGAAGAGGGGATGGAGGCCATGGGCTCGTCGTCGTCGCTCCGCCCTCCATCACCGGCGA
-TACCTCCTCCTCCTCCGGCTCCGCCCCTTCCGCTGACGCCACCGCCGGAAGCTCGCCGGA
-TCCTGTGCTAGTTCGCCGCTGCTATGGCGCAGAACCGGGCGGCTCTTCGTGGCGCGTGGT
-CGCCGGACGCCCTGGGCTTCGCCGGCGCACCGGAGGCAAGCAGCAGCGGGAGTAGCCGGG
-CTGCGCTGCGGGGCCAGCCCCGTTTTCCTTGAAGCAGCCATGGGGAGAGGAGGCTGGAGT
-TGTGAGGTGTTTGGGTCTGCATCGGGAAGAAGATGGGAAACAGGAGAGAAGTGGCTGTGT
-GGCCTCGGCTTGCTGCCTTGCTCTCTCCCTTATCCATTTCTCCAACCGGTATATGGTCCA
-ACACAGGCCACGTCCACTCCAAACTCCAAGCCACGTTTTCCTCTCCAAAAGGCCTACAAG
-CCACGTTTCCTCTTCAAAAGGCATACAGGCCAGGAAATAAAGTGACTATTAGCGTTGTGT
-TTTAATCATAGTACTACTATGCTGCAATTACTATATTGCACTGCAGATTGTTCCTTTTTA
-TTTCAGTGTGATATGTTATTGTATTAAGAACCTCTCCTTATCATATTCACTCGGTATTGT
-ACTGTAGTATGAATACTTAGATTGTATATTGTCTAAAATAAAGTCTCAGATTATGTGTAC
-ATATTCTATATAATTGCCTCAGCAATTTTCATTTTTTTAGAATATGTAGTTTTCAAGTAC
-AATTTTTTTTTAATTCAAATTTTTGGGATCACAAGGTAGATGAGGCATCTACTGCATTTT
-GCAGATTATCCTCATCGCTGAAAGATCTACATTTTGCCTTCGTTATTTTGGCAAAATGAC
-TATCTTCGAGTGCTCTCCCGAATATGCGAATTAATGTGACTACCTCAAATTTTTACAAAT
-CACAAGGTAATTGTGTGATCTACTGCATAATTTGCAGATTATCCACTCTTGCTGAGAGGT
-CTACATCTAGTCTTTTGACAAGATGACTACCTTCAAAGTGTTTTTGTAAAATATAGTATG
-ACACAAGGTAATGGACTACGAACATCTACTGGTCAAAACCAGACTATGTTCTCCACTACT
-ATGAGATCTACACCCCAACGGTGAATATTTTCGAAGTGTCATATACATAAAATGAGGTCT
-ACACATAAAGCATCAGCTATACTTGAAAATTTTGCTTCCTAAAGCATTTGTTGTAACTTA
-TATTTTACTCTCATAGTAAAACTAAATTATGCATGAAAATACCATGCAGGAAATGGCTGG
-TGTCATGCATCGTGAATTTCATGAACTTGAACTAGATGGTAGTATCTATTTACCATGGGC
-TATGGATGCAAAGATAGCCCTCGAAAGCCGTAAACTCGGTTTCACTATCACCACACCCGT
-TGAAGGCGCCGGGAGAAATCCCCACGGCAGCCAAATATAGAGCTTTAAGTTTCTTAAGGC
-ACCATCTCCATTCAGACTTAAAGTCGGAATATTTGATGGAAGAAGATCCACTGGTTTTGT
-GGAACTCTCTCAAGGAGAGATATGACCAGCAAAGAGCAGTCATGCTGCCAGAAGCACAGA
-GAGAATGGTCCCTCATAAGGTTCCAAGACTTCAAATCTGTGGCAGCATATAACTCTGCAG
-TTCACAAGGTTAACTCAAAACTAAGGTTTTGCAATCAAGAAATTTCTGAAGAAGACCTTA
-TTGAGAAGACCTTGTGCACTTTCCACCCCTCGATGAGGGTACTACAATAGAAGTACCGTC
-AACAAAAATACAAAAAGTATTCTGAGCTCATATACACTTTACTTCAGGCTGAGAAACATG
-ATGAACTTCTCATGAAGAATCATCAGACACGCCCAACGGGCTCAATGCCACTCCCTGAAG
-CACATGCTAACACTCAGTTTACTAAAAAGTATGGGGGCAACAAAAAGAATTTCAAAAAAT
-TCAATGGAAAGTGGAAAAGGAACAACAAGCAGAAAAGCTTTGGTCAATCTAAAGGTAAAG
-GTCACTTCAAGAAAAATGACCGAAACACCAACTCCGAGATTTGTCAAAGATGTGGATGTA
-CTAACCATCGTACTAGTAAATGCCGAACTCCCAAGCATCTAGCGGATTTATACATCAAAT
-CCACTGGGAAAGGCAAACAAGTTCATGGAAAAGCCGAAGCTCACTTCAACGCCTTACAAC
-AAGAAGAAAACCTTGAAGCTAGCACTTCTCAAAGCGCTCCTAAGGAAACAAGGCAGAAGG
-ATGAAGATCTTCTTGATGTTGGCAACATGATGGTGGAATACACCCAAGACGCATAGAGAT
-CTTATATAATCTCCACATCACTCATTTGATGTTATCAACTATTTTATTGTAATAATGGAT
-GTTATAATTTGTATAACTCTAAGTAGAATAAAAGTCCTACGGACCAATCGTATTATGTAA
-TTCAAACATAATGTCATATTTATATTTGTAACTGTAATATGAATACTTTGTATGTATATA
-TTTATATGTGAGTAATATGAATAAAGTTTCATCCTAAAATATTCTTTTACTCTATATAGA
-TTTTACGGGAAAATAATCCAATGGAAGAAGAATTATGTTTGGTGGACAGTTGTACCAGTA
-ACACAATATTAAGGGAAATAAAATATTTCCAAACTCTAACTAAGAGAAAAGGGAATATTA
-TGACCATTGACAGTCGCGATGCGTCGATTATTGGTTCAGGACGAGCCACACTCGTTCTAC
-CTATGGGTAGTACAATTGCAATTGAGGATGCACTATTGTATCCTCAATCTACACGCACTC
-TTCTAAGTTTCAAAGACCTCAGATCAAATAACTTCCATTTGGAAACAATATCTGAAAATA
-ATTTTGAACATTTGCTTTTAACAAAGAGTAATGGGTCTGAGAAACAAATTCTTGAGAAAT
-TCCCCTCACTTTCATCTGGATTATATTACAGTTATATAAAACCCGTACCACATGTTGCGT
-ACAAAATAATTTTTCAAGATCTTGATAAATTCAAGACTTGGCATGATCGCCTAGGTCATC
-CTGGCGTAGGGATGATGACAAAAATTATTGACAATTCTATTGGTCACAGCCTTCCAACTA
-TCAATTTCTCAAAATTATCAGATTTTGTGTGCACCGCATGTGCAACTGGAAAATTAATTA
-TAAAACCATCTTATCTTAAAGTTAAAAATGAGTCATTAAATTTTCTTGAACGCATTCAAG
-GAGATATATGTGGTCCAATTCAAGCACTATCAGGACCTTTTAGATATTTCATGGTGCTCA
-TATATGCATCTACTAGATGGTCACATGTGTGTCTATTGTCCACACGAAATCATGCTTTTT
-CCCAGCTTATTGATCAAATTATCAAATTAAGAGCAAATCATCCTAAAAATAGGATAAAAA
-CAATTCGAATGGATAATGCCGCTGAATTTTCTTCACGTGCATTCAATGACTATTGCATGG
-CTATGGGCATTCATTTAGAACATTTTGTGCCTTATGTTCATACTCAAAATGGTTTGGCTG
-AATCTCTCATCAAAAGAGTAAAATTAGTTGCTCGACCACTATTACAGAATTGTAATTTAC
-CAGCATCATGTTGGGCACATGCGGTATTACACGCCGCAGATCTGATACAAATCAGACCAA
-CTGCATATCATACAACCTCCCCGCTACAACTAGTACGTAGCACTCAGCCAAGTATTTCCC
-ATCTACGAAAATTCGGTTGCGCAGTATACGTACCGATATCACCACCGCAGCGTACATCCA
-TGGGCCCCCACAGAAAACTAGGGATCTATGTGGGTTATAACTCTCCGTCAATAATAAAAT
-ATCTTGAACCTCTTACAGGGGACCTGTTTACTGCCCGCTACGCTGATTCAATTTTTGATG
-AGGACCATTTTCAGGCATTAGGGGGAGAATCAAACCACAAAGAATGCCAGGAAATAGATT
-GGAATGTAACAGGCATTCAGTCCTTAGATCCACGTACTAAAGAATCTGAAACTGAAGTTC
-AGAGGATCATAGATTTGCAACATATTGCAAATAATCTGCCAGATGCATTTACTGACCATA
-AAGGTGTCACTAAATCACATATTCCCGCTGTTAATGCACCAGAACGAGTGGAGGTACCAA
-CTAAAACCACTCAAACCACAAATGAGAGTAAGAGGGGGAGAAATCTGGTTAGTCGGAATA
-TAGCTTCTCAAAAGCCTCCGCGGAAACAGAGGAAATCAAATCCTCTACCAGTAAATGCAA
-TTCAACCTCAAGTTGAAGGACACCAACCAGATGCTCAACATCTTGAACCTAGCATAAATG
-CGCATAAAAACATAATTGCTGGGACATCGGGACACCATGGTTCTATTGTTGTGGGAAATC
-ACATAGAGTCTGAAGGTATAAAAGAAATTTCCATAAACTATACAGATTCAGGAGAATCAT
-ATAATAGAGAGACTCCAATTGTCGACATATATTTCGCCTCTAAAATTGCTGAAACCCTTC
-AAGTGGATCCAGAACCAAAGACCGTCAGGGAGTGCCTCAAGCGTCCTGATTGGCCTAAAT
-GGAAGGAAGCAATTGAGGCAGAAGTGCGCTCGCTCAACAAAAGAGAGGTATTTTCCTCGG
-TAATACCTACTCCTCATAATGTATTCCCTGTTGGAGCAAAATGGGTTTTTGTTCGAAAAA
-GGAATGAAAACAATGAGGTGGTGAGATACAAAGCGAGGCTTGTAGCACAAGGGTTCACGC
-AGAGGCCCGACATCGATTACGATGATACATACTCTCCTGTAATGAGTGGAATAACGTTTC
-GATACTTAATATCTTTGGCAGTACAAATGAATTTATCTATGCAGTTGATGGATGTAGTGA
-CAACATACTTATATGGGTCACTCAAATCGGACATATATATGAAAGTCCCTGAATGACTTA
-AAATGTCGAATCCAAAAGAAAATCGCAACGCATATTGTGTAAAATTACAAAAGTCACTAT
-ATGGCTTAAAACAATCGGGTAGAATGTGGTATAACCGATTGAGTGAGTTCCTTATTCAAA
-AAGGCTACTCAAATAATGATGATTGCCCTTGTGTATTGATAAAGAAATCCTCAAATGGAT
-TTTGCATCATCTCAGTGTACGTTGATGACCTCAATATCATGGGAAGTACACCTGATATCG
-AAGAAGCACACAATCATCTAATGGCGAATTTGAGATGAAAGATTTGGGAAAGACCAAATT
-CTGCTTAGGCTTACAGCTTGAGCATCTTCCCTCGGGAATTTTAGTATACCAACCTGCATA
-TATTCAAAAGGTTTTGGAAAATTTTAATATGGATAAATCATATCCAACCAAAACACCCAT
-GGTTGTCAGATCCCTTGATATGAATAAAGATCCTTTTAGACCTCGGGATGATGACGAAGA
-GATATTAGGACCTGAGTTCCCGTATCTCAGTGCCATTGGTGCGTTAATATACCTTGCAAA
-TTGCACCAGGCGTGATATTGCATTTACAGTGAATTTACTAGTTAGACATAGCGTTGCTTC
-ATCGTAACGTCATTGGACGGGAGTAAATAATATCCTTAGATATTTACATGGCACAAAGGA
-TCTTGGCTTATTCTATCAGATAAACCAAGATATGACTATGGTANGATATACTGATNGCTG
-CTATCTATCTGATCCTCACAATGTCAGGTCACAAACAGGTTTCGTTTTCTTATATGGTGG
-AACTGCTTTTTCATGGAAGTCAACAAAACAGACTCTCCTAGCAACCTCCACTAATCATTC
-CTGAACTTGTTGCATTTTTTGAAGCATCTTAAGATTGTGTATGGCTTCGCAGGATGATTA
-ACCCTATTCAAACTTCATGTGGTGTTGGTTCATTAGGATCACCAACTATTATATATGAAG
-ATAATGCAGCCTCGCCATTGTCTCAAAATGCAAATGTGGTTTATGTTAGAAAGTAATATC
-CCCACACCTATATTCTTCCTAAGGTTATTTTAATCCTCAGTGCATTACAGAAGGGATGGA
-GAAATTTGATATTTTCCCAAATTAAATCATGTGCCAATTTAGCAGATTTGTTCCCCAAGT
-TTTTTCCAAATTCAACGTTCCAGAAATCCATTCATGGAAATTGGTATAGAGATGATTCCC
-GAGATTTGCAAAGTTCAGGGGGAGAAATCTCCCTGAAAATATACCCGTTTAATTATCATC
-AGGTAATGAATATTGTACTCTTTTCCTTTATGAGTTTTTCCAACAGGGTTTCTCATATAA
-GGTTTTTAACGAGACAATTAAATACAAGTATTGATGCATGCCATATCATATTTCTCCTTA
-TATTTTTCCTACTAGGTTTTAAAGGAGTTTTTTATGGCACATCTCATTGCACTCTTTTCA
-TTATGAGTTTTTTTGACATTTTCTCTCATAATGTTTTTAATGAAGCCATATCTTATCAAT
-GATCATATATCATACTTTCTATTTTCCCTATCGGGGTTTTAAAGGAAGTACTCAAGACAT
-ATATTGTTCTCTAAACTCAAAAATGAGTTTTATCCCTATATAAAGGTTTTCTCAAATGAG
-TTATCATGAGGCAATAATCATTATATGTTGCACAATTTTTTCCTTATTATTTTTCCACTG
-GGTTTAAAGGAGTTTTAGCAACATATCTACACTATTGTCCTTATATTTTTTCCACAGGGT
-TTTTGGAGGAGACTTTAAAGATTATACAACGACTTTTCAAGATGAAGATGAGGAACATTC
-TTAAAGAGAAAAATTTACAAGGATTATTATTTATCAAGATGATGCACATTTACACAGACA
-AGCATGGATTAGGGAGAGTGTTAGGAATTAATTAAATGTATTAATTAATGGGATAATCCC
-TGCGTTGCCGGTTGCCTTGTTTTAGCAACCGTTCCTTGTAAACCGCCTCTGTAACAAGGG
-TATAAATACCCACATCTTCAATCAATGAAAACACTGTTCCATCATTCTGTCACTTTTACT
-ACTTTACACTCTA