--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/LICENSE	Sun Jan 29 16:25:48 2023 +0000
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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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>.

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/MolD.xml	Sun Jan 29 16:25:48 2023 +0000
@@ -0,0 +1,155 @@
+<tool id="MolD" name="MolD" version="0.1.0+galaxy0" python_template_version="3.5" profile="21.05">
+    <description>DNA diagnoses for pre-defined taxa</description>
+    <requirements>
+    </requirements>
+    <command detect_errors="exit_code"><![CDATA[
+      echo -e "INPUT_FILE=$input1 \n
+               OUTPUT_FILE=$output1 \n
+               qTAXA= 
+               #if $pattern !="*" and not $pattern_merged 
+                P:$pattern \n
+               #elif $pattern_merged
+                 P+:$pattern  \n
+               #elif $morethanN > 0
+                  >$morethanN \n
+               #else
+                 $select_taxa.taxa_select   \n
+               #end if
+                Taxon_rank=$rank \n
+                Gaps_as_chars=$gaps \n
+                Cutoff=$adv_opt.cutoff \n
+                NumberN=$adv_opt.numn \n
+                Number_of_iterations=$adv_opt.numi \n
+                MaxLen1=$adv_opt.maxlen1 \n
+                MaxLen2=$adv_opt.maxlen2 \n
+               #if $adv_opt.iref=="NO"
+                 Iref=$adv_opt.iref \n
+               #elif $adv_opt.iref_mode
+                 Iref=$adv_opt.iref, in  \n
+               #else
+                 Iref=$adv_opt.iref, ex  \n
+               #end if
+                Pdiff=$adv_opt.pdiff \n
+                PrSeq=$adv_opt.PrSeq \n
+                NMaxSeq=$adv_opt.nmaxseq \n
+                Scoring=$adv_opt.scoring \n" >params.txt &&
+
+       $__tool_directory__/MolD_v1.4 -i params.txt >$output2
+      
+    ]]></command>
+
+    <inputs>
+      <param  type="data" name="input1" label="Source file" format="fasta" />
+      <section name="select_taxa" title="See taxa for selection">
+        <param name="taxa_select" type="select" display="checkboxes" multiple="true" checked="true" label="Choose taxa to diagnose">
+          <options from_dataset="input1" startswith=">" >
+             <column name="name" index="0"/>
+             <column name="value" index="0"/>
+             <filter type="multiple_splitter" column="0" separator="|"/>
+             <filter column="0" type="regexp" value=">" keep="false" />
+              <filter column="0" type="sort_by" />
+              <filter type="unique_value" name="unique_taxon" column="0"/>
+           </options>
+        </param>
+      </section>
+      <param name="morethanN" type="integer" label="N of sequences" value="0" help="Only taxa with more than N sequences are to be diagnosed (overrides selection!)" />
+      <param name="pattern"  type="text" label="Pattern" value="*" help="Only taxa with certain pattern in the taxon name are to be diagnosed (overrides selection!)" />
+      <param name="pattern_merged"  type="boolean" checked="false" label="Merge"  help="Merge taxa with above pattern before diagnosis"/>
+     <param name="rank"  type="boolean" checked="true" label="Taxon Rank" truevalue="1" falsevalue="2" help="Set taxon rank: if species true(1), if above species false(2)"/>
+      <param name="gaps"  type="boolean" label="Gaps as chars"  help="Code gaps as characters: yes or no"/>
+        <section name="adv_opt" title="Advanced parameters for pDNC recovery">
+          <param name="cutoff"  type="text" label="Cutoff" value="100" help="Set number of the informative positions to be considered (default 100) or use '> + integer' to set desired cutoff value (for example '> 1')" />
+         <param name="numn" type="integer" label="Number N" value="5" help="Set how many ambiguously called nucleotides are allowed (default 5)" />
+          <param name="numi" type="integer" label="Number i" value="10000" help="Set number recursions of MolD (default 10000)" />
+         <param name="maxlen1" type="integer" label="MaxLen1" value="12" help="Set maximum length for the raw pDNCs (defailt 12)" />
+         <param name="maxlen2" type="integer" label="MaxLen2" value="7" help="Set maximum length for the refined pDNCs (default 7)" />
+         <param name="iref"  type="text" label="Index reference" value="NO" help="Set a sequence to be used as a reference for site indexing (default NO)" />
+         <param name="iref_mode"  type="boolean" checked="true" label="Index mode"  help="Yes, if seqence will be used for diagnoses calculations or No if seq is to be used for indexin only"/>
+         <param name="pdiff"   type="integer" optional="true" label="PDiff" value="" help="Set percent difference between original and modified sequence (default 1 for species-level taxa, 3 for for supraspecific taxa)" />    
+         <param name="PrSeq" type="float" optional="true" label="PrSeq" value="" help="Set proportion of sequences in the dataset to be modified (default 0.5 for species-level, and 0.1 for supraspecific taxa)." />
+         <param name="nmaxseq" type="integer" label="NMaxSeq" value="10" help="Set max number of sequences per taxon to modify (default 10)" />
+         <param  type="select" name="scoring" label="Scoring" help="Set threshold of sDNC rating(default moderate 75)">
+           <option value="lousy">Lousy (66)</option>
+           <option value="moderate" selected="true">Moderate (75)</option>
+           <option value="stringent" >Stringent (90)</option>
+           <option value="very stringent">Very stringent (95)</option>
+         </param>
+      </section>
+    </inputs>
+
+    <outputs>
+      <data name="output1" label="MolD: ${os.path.splitext($input1.name)[0]}.html" format="html"/>
+      <data name="output2" label="MolD: ${os.path.splitext($input1.name)[0]}.txt" format="txt"/>
+    </outputs>
+    <tests>
+      <test>
+          <param name="input1" value="Example_input_alignment_Pontohedyle_COI.fas"/>
+          <param name="taxa_select" value="milaschewitchii,neridae"/>
+          <output name="output2">
+            <assert_contents>
+              <has_text text="milaschewitchii"/>
+            </assert_contents> 
+          </output>
+      </test>
+  </tests>
+  
+    <help><![CDATA[
+**About MolD**
+
+*A program to identify robust sets of diagnostic sites for pre-defined taxa from DNA sequences, using a tree independent algorithm to retrieve diagnostic nucleotide 
+characters.*
+
+*This Galaxy version implements MolD 1.4, developed 2022.* 
+*MolD was written in Python by Alexander Fedosov.*
+*The Galaxy wrapper was developed by F. Fischell in the framework of the iTaxoTools project in 2022/2023. Only tested for fasta alignment files. Transition and seed parameters are not currently supported. Tamura-Nei distance calculation is either really slow or broken, we recommend not using it.*
+
+Standalone executables with graphical user interface of the newest version of MolD are available for Windows and Mac from:
+
+http://itaxotools.org/
+
+https://github.com/iTaxoTools/iTaxoTools-Executables/releases
+
+For detailed instructions on the use of MolD and interpretation of results, see the iTaxoTools manual available at:
+
+http://itaxotools.org/
+
+
+
+**MolD 1.4**
+
+This program is tailored for seamlessly recovering DNA-based diagnoses from large DNA datasets and is capable of identifying diagnostic combinations of nucleotides (DNCs) in addition to single (pure) diagnostic sites, enabling users to base DNA diagnoses on a minimal number of diagnostic sites necessary for a reliable differentiation of taxa, rather than providing a long list of such sites. 
+The MolD algorithm assembles DNA diagnoses that fulfill pre-defined criteria of reliability, which is achieved by repeatedly scoring diagnostic nucleotide combinations against datasets of in silico mutated sequences. 
+Version 
+
+Version 1.4 has several improved functionalities, in particular the output files produced are better documented and more easy to interpret for unexperienced users. 
+
+*Note: several new features of the version 1.4 od MolD are not yet implemented in the Galaxy version; in particular the option to output lists of pairwise diagnostic sites among nucleotides is missing:*
+
+
+**Input files**
+
+Input is a fasta file of aligned sequence files (for best performance of the program, without missing data and indels)  where each entry starts with the identifier line, and one or more lines of nucleotide sequence. Identifier line starts with ‘>’ and must contain two parts, separated by a pipe (‘|’) symbol. The first part is a free-style sequence identifier. The names of the taxa to be diagnosed correspond to the second element, i.e., the taxon identifier: >SequenceID####|taxon. The taxon identifier corresponds to the name of the species, genus etc to which you assign this sequence. 
+For more details, see the iTaxoTools manual and example files for MolD at http://itaxotools.org/
+
+**Settings**
+
+1. Specify the taxon rank of your query taxa. In most cases these will correspond to species or species-level OTUs. If your specified taxa instead correspond to supraspecific units such as  genera or species groups, it is useful to change the setting accordingly to "supraspecific taxa" (in the parameter file: 1 for species, 2 for supraspecific taxa). The specified taxon rank will influence the maximum divergence allowed when simulating sequences in the last step in MolD. 
+
+2. Specify if gaps should be coded as characters or as missing data. If specifying “Yes”, dashes (‘-‘) in the alignment are transformed into ‘D’ characters and these treated as independent character states. If specifying “No” dashes are treated as missing data (‘N’). 
+
+3. For advanced settings, see the iTaxoTools manual at http://itaxotools.org/
+
+**Output**
+The  output file summarizes the main output of the MolD algorithm. After recapitulating the main parameter settings, it provides for each of the taxa selected for the analysis, and basoic information for each taxon. Most importantly, the file includes for each taxon a final "robust Diagnostic Nucleotide Combination" (rDNC) as well as a diagnosis text that can be copy-pasted into the respective section of a research paper for the purpose of diagnosing a taxon. Note that the rDNC is a diagnostic combination of positions, i.e., only the combination of the listed positions will provide a robust diagnosis (while each position on its own might or might not be universally diagnostic).
+
+
+    ]]></help>
+      <citations>
+        <citation type="doi">
+          10.1101/838151
+        </citation>
+        <citation type="doi">
+            10.11646/megataxa.6.2.1 
+        </citation>
+      </citations>
+</tool>

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/MolD_parameter_file.txt	Sun Jan 29 16:25:48 2023 +0000
@@ -0,0 +1,73 @@
+#############SET INPUT AND OUTPUT FILES (INCLUDING COMPLETE PATH)
+INPUT_FILE=D:\MOLD3\Pontohedyle_COI.fas
+OUTPUT_FILE=D:\MOLD3\Pontohedyle_COI.txt
+
+# Code gaps as characters: 'yes' or 'no'
+Gaps_as_chars=no
+
+#############SET TAXON PARAMETERS (NO DEFAULTS, no parameters entered will lead to an error).
+# Set list of the focus taxa (enter a comma separated list)
+# to combine several taxa in the list and diagnosed pooled records as a single taxon, put '+' between the pooled taxa names
+# to diagnose all taxa, enter 'ALL'
+# to diagnose all taxa with more than N sequences available (where N is a natural number), enter '>N'
+# to diagnose only taxa, with certain pattern in the taxon name, enter 'P:pattern'
+# to pool all records with with certain pattern in the taxon name together, and diagnose as single taxon, enter 'P+:pattern'
+# to obtain sets of diagnostic sites for taxa pairs, enter:
+#   allVSall - for all possible pairwise comparisons in the dataset
+#   taxon1VSall - for pairwise comparisons of taxon1 with all other taxa in the dataset
+#   taxon1VStaxon2 - for pairwise comparison of taxa taxon 1 and taxon2
+
+#qTAXA=all, allVSall
+qTAXA= neridae, wiggi, verrucosa, neridae+wiggi+verrucosa, neridaeVSwiggiVSverrucosa
+#qTAXA= neridaeVSwiggiVSverrucosa
+
+# Set taxon rank: if species - 1, if above species - 2.
+
+Taxon_rank=1
+
+#############SET ADVANCED PARAMETERS FOR pDNC RECOVERY.
+# If you don't want to set them, don't enter anything after '='
+# Set number of the informative positions to be considered, integer (default 100)
+# By entering '>0' all informative positions will be selected.
+
+Cutoff=>1
+
+# Set how many ambiguously called nucleotides are allowed (default 25).
+
+NumberN=100
+# Set number recursions of MolD (default 10000).
+
+Number_of_iterations=10000
+
+# Set maximum length for the raw pDNCs (defailt 12).
+
+MaxLen1=12
+
+# Set maximum length for the refined pDNCs (default 7).
+
+MaxLen2=5
+
+# Set a sequence to be used as a reference for site indexing (1st position will correspond to the beginning of this sequence.
+# Default - indexing starts from the beginning of the alignment. So, alignment is expected to be trimmed on the left to match the beginning of the reference sequence (if the reference is provided).
+# Leave as is, or enter sequence <identifier>,in (if seq will be used for diagnoses calculations), or <identifier>,ex if seq is to be used for indexin only.
+
+Iref=NO
+
+############ SET PARAMETERS OF ARTIFICIAL DATASETS (only sDNSs).
+# Set percent difference between original and modified sequence (default 1 for species-level taxa, 3 for for supraspecific taxa).
+
+Pdiff=1
+
+# Set max number of sequences per taxon to modify (default 10).
+
+NMaxSeq=10
+
+# Set rDNC scoring threshold (default stringent).
+# 100 artificial datasets are created to score the sDNC. If the sDNC remains diagnostic in  requested (defined by value of threshold),
+# or higher number of artificial datasets in two consequtive runs, then sDNC is output. The threshold values are like:
+# lousy: 66
+# moderate: 75
+# stringent: 90
+# very_stringent: 95
+
+Scoring=moderate

Binary file MolD_v1.4 has changed

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/MolD_v1.4.py	Sun Jan 29 16:25:48 2023 +0000
@@ -0,0 +1,740 @@
+"""
+This script compiles rDNC-based DNA diagnoses for a pre-defined taxa in a dataset. This is the MAIN WORKING VERSION v1.4
+This version already implements the new functionalities:
+- automatic trimming of the alignment to match the median sequence length (should seq len distribution and provided NumberN settings require that)
+- expanded qCLADEs setting
+- diagnoses in pairwise comparisons of taxa.
+- selection of a reference sequence for indexing DNCs
+
+THIS version LACKS SPART compatibility
+
+"""
+import os, sys
+import random
+import argparse
+from io import StringIO
+######################################################################## FUNCTIONS
+#***STEP 1 - SORTING ENTRIES BY CLADE AND IDENTIFYING NUCLEOTIDE POSITIONS SHARED WITHIN CLADE
+def Step1(raw_records):
+    Clades=[]
+    for i in range(len(raw_records)):
+        Clade=raw_records[i][1]
+        if Clade not in Clades:
+            Clades.append(Clade)
+    clade_sorted_seqs = {}
+    for letter in Clades:
+        clade_sorted_seqs[letter]=[]
+        for i in range(len(raw_records)):
+            if raw_records[i][1]==letter:
+                clade_sorted_seqs[letter].append(raw_records[i][2])
+    shared_positions={}
+    for key in clade_sorted_seqs:
+        sh_pos=[]
+        for i in range(len(clade_sorted_seqs[key][0])):
+            shared_nucleotide = True
+            csm = clade_sorted_seqs[key][0][i] #candidate shared nucleotide
+            for j in range(1, len(clade_sorted_seqs[key])):
+                if clade_sorted_seqs[key][j][i] != csm:
+                    shared_nucleotide = False
+                    break
+            if shared_nucleotide == True and csm != 'N':
+                sh_pos.append(i)
+        shared_positions[key]=sh_pos
+    return Clades, clade_sorted_seqs, shared_positions
+
+#***STEP 2 COMPILING COMPARISON LISTS FOR CLADES AND IDENTIFYING VARIABLE POSITIONS AND N PRIORITY POSITIONS WITH LARGEST CUTOFFS
+def C_VP_PP(clade_sorted_seqs, clade, shared_positions, CUTOFF):# complist_variable_positions_priority_positions; Arguments: dictionary, string, dictionary
+    CShN={}#a dictionary keys - clade shared positions, values - nucleotides at those positions
+    for pos in shared_positions[clade]:
+        CShN[pos] = clade_sorted_seqs[clade][0][pos]#creates a dictionary shared position : nucleotide
+    complist=[]
+    for key in clade_sorted_seqs:
+        if key != clade:
+            complist = complist + clade_sorted_seqs[key]#creates a list of all other sequences for comparison
+    cutoffs = {}
+    pures = []####! newline
+    for key in CShN:
+        newcomplist = []
+        for k in complist:
+            if k[key] == CShN[key]:
+                newcomplist.append(k)
+            else: continue
+        cutoffs[key] = len(complist) - len(newcomplist)
+        if len(newcomplist) == 0:####! newline
+            pures.append(key)####! newline
+    CPP = []
+    for key in sorted(cutoffs, key = cutoffs.get, reverse = True):
+        CPP.append(key)
+    if CUTOFF[0] == '>':#VERYNEW
+        Clade_priority_positions = {pos:CShN[pos] for pos in CPP if cutoffs[pos] > int(CUTOFF[1:])}#VERYNEW
+    else:#VERYNEW
+        Clade_priority_positions = {}
+        for position in CPP[:int(CUTOFF)]:#Here you define how many of the clade shared combinations are used in subsequent search
+            Clade_priority_positions[position] = CShN[position]
+    return complist, Clade_priority_positions, cutoffs, pures####! pures added
+
+#***STEPS 3 RANDOM SEARCH ACROSS PRIORITY POSITIONS TO FIND RAW DIAGNOSTIC COMBINATIONS AND TO SUBSEQUENTLY REFINE THEM
+def random_position(somelist, checklist):#gives a random index (integer) of the specified range, and returns indexed somelist element if it is not present in the checklist 
+    while True:
+        i = random.randint(0, len(somelist) - 1)
+        if somelist[i] not in checklist:
+            return somelist[i]
+            break
+        else:
+            continue
+
+def step_reduction_complist(clade, complist, CPP, checked_ind):#checks randomly selected positions of CladeSharedNucleotides with sequences of other clades, until a diagnostic combination of nucleotides for a selected clade is found.
+    if len(complist) == 0:
+        return checked_ind
+    elif len(checked_ind) == len(CPP):
+        return checked_ind
+    else:
+        newcomplist = []
+        pos = random_position(list(CPP.keys()), checked_ind)
+        for j in complist:
+            if j[pos] == CPP[pos] or j[pos] == 'N':#VERYNEW
+                newcomplist.append(j)
+            else: continue
+        new_checked_ind = checked_ind + [pos]
+        return step_reduction_complist(clade, newcomplist, CPP, new_checked_ind)
+
+def ConditionD(newcomb, complist, CPP):#The function checks the 'Condition D' - i.e. whither any given combination of nucleotide positions is diagnostic for the selected clade
+    ContD = False
+    for i in newcomb:
+        newcomplist = []
+        for m in complist:
+            if m[i] == CPP[i]:
+                newcomplist.append(m)
+            else: continue
+        complist = newcomplist
+    if len(complist) == 0:
+        ContD = True
+    return ContD
+
+def RemoveRedundantPositions(raw_comb, complist, CPP):# The function removes positions from the raw combinations one by one, and then checks whether new combination fulfills the condition D, thus recursively reducing the diagnostic combination.
+    red_possible = False
+    for j in raw_comb:
+        newcomb = [k for k in raw_comb if k != j]
+        if ConditionD(newcomb, complist, CPP) == True:
+            red_possible = True
+            return RemoveRedundantPositions(newcomb, complist, CPP)
+        else: pass
+    if red_possible == False:
+        return raw_comb
+
+#PUTS EVERYTHING TOGETHER - 20000 ROUNDS OF RANDOM SEARCH FOLLOWED BY REFINING OF 500 SHORTEST COMBINATIONS
+def Diagnostic_combinations(qCLADE, complist, CPP, n1, maxlen1, maxlen2):
+    Achecked_ind = []
+    bestlists = []
+    n = n1
+    while n>0:#STEP3 proposes raw diagnostic combinations
+        raw_comb = step_reduction_complist(qCLADE, complist, CPP, Achecked_ind)
+        if len(raw_comb) <= maxlen1:
+            refined_comb = RemoveRedundantPositions(raw_comb, complist, CPP)
+            if len(refined_comb) <= maxlen2 and sorted(refined_comb) not in bestlists:
+                bestlists.append(sorted(refined_comb))
+        n=n-1
+    bestlists.sort(key=len)
+    return bestlists
+
+#***STEP 4 ANALYSIS OF OUTPUT rDNCs
+def IndependentKey(diagnostic_combinations):#PRESENTLY NOT INVOLVED - returns independent diagnostic nucleotide combinations, and identifies key nucleotide positions
+    independent_combinations = []
+    selected_positions = []
+    for i in range(len(diagnostic_combinations)):
+        if len(selected_positions) == 0:
+            for j in range(0, i):
+                if len(set(diagnostic_combinations[i]) & set(diagnostic_combinations[j])) == 0 and len(set(diagnostic_combinations[i]) & set(selected_positions)) == 0:
+                    independent_combinations.append(diagnostic_combinations[i])
+                    independent_combinations.append(diagnostic_combinations[j])
+                    for k in range(len(diagnostic_combinations[i])):
+                        selected_positions.append(diagnostic_combinations[i][k])
+                    for l in range(len(diagnostic_combinations[j])):
+                        selected_positions.append(diagnostic_combinations[j][l])
+        else:
+            if len(set(diagnostic_combinations[i]) & set(selected_positions)) == 0:
+                independent_combinations.append(diagnostic_combinations[i])
+                for k in range(len(diagnostic_combinations[i])):
+                    selected_positions.append(diagnostic_combinations[i][k])
+    independent_combinations.sort(key=len)
+    key_positions = []
+    for pos in diagnostic_combinations[0]:
+        KP = True
+        for combination in diagnostic_combinations[1:]:
+            if pos not in combination:
+                KP = False
+                break
+            else: continue
+        if KP == True:
+            key_positions.append(pos)
+    return independent_combinations, key_positions
+
+#SPECIFIC FUNCTIONS FOR THE rDNCs
+def PositionArrays(Motifs):#VERYNEW ALL FUNCTION NEW
+    PositionArrays = []
+    VarPosList = []
+    for i in range(len(Motifs[0])):
+        Const = True
+        array = [Motifs[0][i]]
+        for j in range(len(Motifs[1:])):
+            if Motifs[j][i] != 'N':
+                if Motifs[j][i] != array[-1]:
+                    Const = False
+                array.append(Motifs[j][i])
+        PositionArrays.append(array)
+        if Const == False:
+            VarPosList.append(i)
+    return PositionArrays, VarPosList
+
+def random_sequence_new(SEQ, PositionArrays, VarPosList, Pdiff):#VERYNEW FUNCTION REVISED
+    #print(["ROUND", len(SEQ)*Pdiff/100, round(len(SEQ)*Pdiff/100)])
+    n = round(len(SEQ)*Pdiff/100)
+    N = random.sample(list(range(1, n)), 1)[0]
+    PosToChange = random.sample([p for p in VarPosList if SEQ[p] != 'D'], N)#this is a new definition to keep alignment gaps unchanged
+    NEWSEQ = ''
+    for i in range(len(SEQ)):
+        if i not in PosToChange:
+            NEWSEQ = NEWSEQ + SEQ[i]
+        else:
+            newarray = [j for j in PositionArrays[i] if j != SEQ[i]]
+            newbase = random.sample(newarray, 1)[0]
+            NEWSEQ = NEWSEQ + newbase
+    return NEWSEQ
+
+def GenerateBarcode_new(Diagnostic_combinations, length):#VERYNEW FUNCTION REVISED - This function calculates diagnostic combinations and assembles a barcode of desired length for a query taxon. First all single position DNCs are added, then based on the frequency of a nucleotide position in the DNCs of the 2 positions, and then based on the frequency of a position in longer DNCs
+    len1 = []
+    len2 = []
+    lenmore = []
+    for comb in Diagnostic_combinations:
+        if len(comb) == len(Diagnostic_combinations[0]):
+            for i in comb:
+                len1.append(i)
+        elif len(comb) == len(Diagnostic_combinations[0])+1:
+            for j in comb:
+                len2.append(j)
+        else:
+            for k in comb:
+                lenmore.append(k)
+    if len(Diagnostic_combinations[0]) == 1:
+        Setin = len1
+    else:
+        Setin = []
+        for pos in sorted(len1, key=len1.count, reverse = True):
+            if not pos in Setin:
+                Setin.append(pos)
+    for pos1 in sorted(len2, key=len2.count, reverse = True):
+        if not pos1 in Setin:
+            Setin.append(pos1)
+    for pos2 in sorted(lenmore, key=lenmore.count, reverse = True):
+        if not pos2 in Setin:
+            Setin.append(pos2)
+    return Setin[:length]
+
+def Screwed_dataset_new(raw_records, nseq_per_clade_to_screw, PositionArrays, VarPosList, Percent_difference, Taxon, Cutoff):#VERYNEW FUNCTION REVISED
+    clades=[]
+    for i in range(len(raw_records)):
+        Clade=raw_records[i][1]
+        if Clade not in clades:
+            clades.append(Clade)
+    clade_sorted_seqs = {}
+    for letter in clades:
+        clade_sorted_seqs[letter]=[]
+        for i in range(len(raw_records)):
+            if raw_records[i][1]==letter:
+                clade_sorted_seqs[letter].append(raw_records[i][2])
+    
+    for clade in clades:
+        seqlist = clade_sorted_seqs[clade]
+        newseqs = []
+        if len(seqlist) > nseq_per_clade_to_screw:
+            iSTS = random.sample(list(range(len(seqlist))), nseq_per_clade_to_screw)
+            for k in range(len(seqlist)):
+                if k in iSTS:
+                    newseq = random_sequence_new(seqlist[k], PositionArrays, VarPosList, Percent_difference)
+                else:
+                    newseq = seqlist[k]
+                newseqs.append(newseq)
+        elif len(clade_sorted_seqs[clade]) == nseq_per_clade_to_screw:
+            for k in range(len(seqlist)):
+                newseq = random_sequence_new(seqlist[k], PositionArrays, VarPosList, Percent_difference)
+                newseqs.append(newseq)
+        else:
+            for i in range(nseq_per_clade_to_screw):
+                seq = random.sample(seqlist, 1)[0]
+                newseq = random_sequence_new(seq, PositionArrays, VarPosList, Percent_difference)
+                newseqs.append(newseq)
+        clade_sorted_seqs[clade] = newseqs
+    
+    shared_positions={}
+    for key in clade_sorted_seqs:
+        sh_pos=[]
+        for i in range(len(clade_sorted_seqs[key][0])):
+            shared_nucleotide = True
+            csm = clade_sorted_seqs[key][0][i] #candidate shared nucleotide
+            for j in range(1, len(clade_sorted_seqs[key])):
+                if clade_sorted_seqs[key][j][i] != csm:
+                    shared_nucleotide = False
+                    break
+            if shared_nucleotide == True and csm != 'N':
+                sh_pos.append(i)
+        shared_positions[key]=sh_pos
+    
+    x,y,z,pures = C_VP_PP(clade_sorted_seqs, Taxon, shared_positions, Cutoff)#STEP2####
+    return x, y
+
+#NEWFUNCYIONS
+def medianSeqLen(listofseqs):#OCT2022
+    seqlens = [i.count('A')+i.count('C')+i.count('G')+i.count('T') for i in listofseqs]
+    medlen = sorted(seqlens)[int(len(seqlens)/2)]
+    medseq = listofseqs[seqlens.index(medlen)]
+    start = min([medseq.find('A'),medseq.find('C'),medseq.find('G'),medseq.count('T')])
+    if not 'N' in medseq[start:]:
+        end = len(medseq)
+    else:
+        for i in range(start, len(medseq), 1):
+            if medseq[i:].count('N') == len(medseq[i:]):
+                end = i
+                break
+    return medlen, start, end
+
+def getAllPairs(taxalist):
+    uniquetaxapairs = []
+    stl = sorted(list(set(taxalist)))
+    for i in range(len(stl)):
+        for j in range(i+1, len(stl)):
+            uniquetaxapairs.append([stl[i],stl[j]])
+    return uniquetaxapairs
+        
+
+################################################READ IN PARAMETER FILE AND DATA FILE
+
+def get_args(): #arguments needed to give to this script
+    parser = argparse.ArgumentParser(description="run MolD")
+    required = parser.add_argument_group("required arguments")
+    required.add_argument("-i", help="textfile with parameters of the analysis", required=True)
+    return parser.parse_args()
+
+def mainprocessing(gapsaschars=None, taxalist=None, taxonrank=None, cutoff=None, numnucl=None, numiter=None, maxlenraw=None, maxlenrefined=None, iref=None, pdiff=None, nmax=None, thresh=None, tmpfname=None, origfname=None):
+    ParDict = {}
+    if not(all([gapsaschars, taxalist, taxonrank, cutoff, numnucl, numiter, maxlenraw, maxlenrefined, iref, pdiff, nmax, thresh, tmpfname])):
+        args = get_args()
+        with open(args.i) as params:
+            for line in params:
+                line = line.strip()
+                if line.startswith('#'):
+                    pass
+                else:
+                    if len(line.split('=')) == 2 and len(line.split('=')[1]) != 0:
+                        ParDict[line.split('=')[0]] = line.split('=')[1].replace(' ', '')#VERYNEW
+    else:
+        ParDict['Gaps_as_chars'] = gapsaschars
+        ParDict['qTAXA'] = taxalist
+        ParDict['Taxon_rank'] = taxonrank
+        ParDict['INPUT_FILE'] = tmpfname
+        ParDict['ORIG_FNAME'] = origfname# I do not understand this ORIG_FNAME, it throws an error with the command line
+        ParDict['Cutoff'] = cutoff
+        ParDict['NumberN'] = numnucl
+        ParDict['Number_of_iterations'] = numiter
+        ParDict['MaxLen1'] = maxlenraw
+        ParDict['MaxLen2'] = maxlenrefined
+        ParDict['Iref'] = iref
+        ParDict['Pdiff'] = pdiff
+        #ParDict['PrSeq'] = prseq
+        ParDict['NMaxSeq'] = nmax
+        ParDict['Scoring'] = thresh
+        ParDict['OUTPUT_FILE'] = "str"        
+    print(ParDict)
+    ############################################# #VERYNEW HOW GAPS ARE TREATED
+    #REQUIRES A NEW FIELD IN THE GUI
+    if ParDict['Gaps_as_chars'] == 'yes':
+        gaps2D = True#VERYNEW
+    else:#VERYNEW
+        gaps2D = False#VERYNEW
+    
+    ############################################ DATA FILE
+    checkread = open(ParDict['INPUT_FILE'], 'r')
+    firstline = checkread.readline()
+    checkread.close()
+    imported=[]#set up a new list with species and identifiers
+    if '>' in firstline:
+        f = open(ParDict['INPUT_FILE'], 'r') 
+        for line in f:#VERYNEW - THE DATA READING FROM ALIGNMENT FILE IS ALL REVISED UNTIL f.close()
+            line=line.rstrip()
+            if line.startswith('>'):
+                data = line[1:].split('|')
+                if len(data) != 2:
+                    print('Check number of entries in', data[0])
+                    #break
+                data.append('')
+                imported.append(data)
+            else:
+                if gaps2D == True:#
+                    DNA = line.upper().replace('-', 'D').replace('R', 'N').replace('Y', 'N').replace('S','N').replace('W','N').replace('M','N').replace('K','N')#VERYNEW
+                else:
+                    DNA = line.upper().replace('-', 'N').replace('R', 'N').replace('Y', 'N').replace('S','N').replace('W','N').replace('M','N').replace('K','N')#VERYNEW
+                imported[-1][-1] = imported[-1][-1]+DNA
+        f.close()
+    else:
+        f = open(ParDict['INPUT_FILE'], 'r') 
+        for line in f:#VERYNEW - THE DATA READING FROM ALIGNMENT FILE IS ALL REVISED UNTIL f.close()
+            line=line.rstrip()
+            data = line.split('\t')
+            if len(data) != 3:
+                print('Check number of entries in', data[0])
+            ID = data[0]
+            thetax = data[1]
+            if gaps2D == True:#
+                DNA = data[2].upper().replace('-', 'D').replace('R', 'N').replace('Y', 'N').replace('S','N').replace('W','N').replace('M','N').replace('K','N')#VERYNEW
+            else:
+                DNA = data[2].upper().replace('-', 'N').replace('R', 'N').replace('Y', 'N').replace('S','N').replace('W','N').replace('M','N').replace('K','N')#VERYNEW
+            imported.append([ID, thetax, DNA])
+        f.close()
+    
+    if 'NumberN' in list(ParDict.keys()):#How many ambiguously called nucleotides are allowed
+        NumberN = int(ParDict['NumberN'])
+    else:
+        NumberN = 5
+    
+    if len(set([len(i[2]) for i in imported])) != 1:
+        print('Alignment contains sequences of different lengths:', set([len(i[2]) for i in imported]))
+    else:
+        mlen, sstart, send = medianSeqLen([i[2] for i in imported])#OCT2022 - start
+        if mlen + NumberN < len(imported[0][2]):
+            Slice = True
+            FragmentLen = mlen
+            corr = sstart
+        else:
+            Slice = False
+            FragmentLen = len(imported[0][2])
+            sstart = 0
+            send = FragmentLen+1
+            corr = 0
+    raw_records=[]
+    for i in imported:
+        if ParDict['Iref'] != 'NO' and ParDict['Iref'].split(',')[0] == i[0] and ParDict['Iref'].split(',')[1] in ['ex', 'excl', 'out']:
+            continue
+        else:
+            if i[2][sstart:send].count('N') < NumberN and len(i[2][sstart:send]) == FragmentLen:
+                newi = [i[0], i[1], i[2][sstart:send]]
+                raw_records.append(newi)#OCT2022 - end
+    print('\n########################## PARAMETERS ######################\n')#VERYNEW
+    #print('input file:', ParDict['ORIG_FNAME']) #Outcommented ORIG_FNAME
+    print('input file:', ParDict['INPUT_FILE']) #Replacement of the line above
+    print('Coding gaps as characters:', gaps2D)
+    print('Maximum undetermined nucleotides allowed:', NumberN)
+    print('Length of the alignment:', len(imported[0][2]),'->', FragmentLen)
+    print('Indexing reference:', ParDict['Iref'].replace('NO', 'Not set').replace('in', 'included').replace('ex', 'excluded'))
+    print('Read in', len(raw_records), 'sequences')
+    
+    PosArrays, VarPosList = PositionArrays([i[2] for i in raw_records])#VERYNEW
+   
+    #############################################READ IN OTHER ANALYSIS PARAMETERS
+    ##OCT2022 - start
+    withplus = []
+    P2 = []
+    shift = True
+    if ParDict['qTAXA'][0] == '>':#THIS OPTION DIAGNOSES ALL TAXA WITH MORE THAN USER-DEFINED NUMBER OF SEQUENCES AVAILABLE
+        NumSeq = int(ParDict['qTAXA'][1:])
+        Taxarecords = [i[1] for i in raw_records]
+        qCLADEs = []
+        for j in set(Taxarecords):
+            if Taxarecords.count(j) >= NumSeq:
+                qCLADEs.append(j)
+    elif ParDict['qTAXA'].startswith('P:'):#THIS OPTION DIAGNOSES ALL TAXA CONTAINING A USER-DEFINED PATTERN IN THE NAME
+        pattern = ParDict['qTAXA'].split(':')[1]
+        Taxarecords = [i[1] for i in raw_records]
+        qCLADEs = []
+        for j in set(Taxarecords):
+            if pattern in j:
+                qCLADEs.append(j)
+    elif ParDict['qTAXA'].startswith('P+:'):#THIS OPTION POOLS ALL TAXA CONTAINING A USER-DEFINED PATTERN IN THE NAME IN ONE TAXON AND DIAGNOSES IT
+        pattern = ParDict['qTAXA'].split(':')[1]
+        Taxarecords = set([i[1] for i in raw_records if pattern in i[1]])
+        spp = '+'.join(sorted(list(Taxarecords)))
+        qCLADEs = [spp]
+        nrecords = []
+        for rec in raw_records:
+            if rec[1] in Taxarecords:
+                nrecords.append([rec[0], spp, rec[2]])
+            else:
+                nrecords.append(rec)
+        raw_records = nrecords
+    else:#THIS OPTION DIAGNOSES ALL TAXA FROM A USER-DEFINED LIST; TAXA MAY BE COMBINED BY USING '+'
+        qCLADEs = []
+        allrecs = ParDict['qTAXA'].split(',')
+        for item in allrecs:
+            if item in ['ALL', 'All', 'all']:#THIS OPTION DIAGNOSES ALL TAXA IN THE DATASET
+                qCLADEs = list(set([i[1] for i in raw_records]))
+            elif item in [i[1] for i in raw_records]:
+                qCLADEs.append(item)
+            elif '+' in item:
+                withplus.append(item)
+            elif 'VS' in item:
+                P2.append(item.split('VS'))
+            else:
+                print('UNRECOGNIZED TAXON', item)
+    #OCT2022 - end
+    print('query taxa:', len(qCLADEs+withplus), '-', str(sorted(qCLADEs)+sorted(withplus)).replace('[','').replace(']','').replace("'", ''))#1.3
+    
+    if 'Cutoff' in list(ParDict.keys()):#CUTOFF Number of the informative positions to be considered, default 100
+        Cutoff = ParDict['Cutoff']#VERYNEW
+    else:
+        Cutoff = 100
+    print('Cutoff set as:', Cutoff)
+    if 'Number_of_iterations' in list(ParDict.keys()):#Number iterations of MolD
+        N1 = int(ParDict['Number_of_iterations'])
+    else:
+        N1 = 10000
+    print('Number iterations of MolD set as:', N1)
+    
+    if 'MaxLen1' in list(ParDict.keys()):#Maximum length for the raw mDNCs
+        MaxLen1 = int(ParDict['MaxLen1'])
+    else:
+        MaxLen1 = 12
+    print('Maximum length of raw mDNCs set as:', MaxLen1)
+    
+    if 'MaxLen2' in list(ParDict.keys()):#Maximum length for the refined mDNCs
+        MaxLen2 = int(ParDict['MaxLen2'])
+    else:
+        MaxLen2 = 7
+    print('Maximum length of refined mDNCs set as:', MaxLen2)
+    
+    if 'Pdiff' in list(ParDict.keys()):#Percent difference
+        Percent_difference = float(ParDict['Pdiff'])
+    else:
+        if int(ParDict['Taxon_rank']) == 1:#read in taxon rank to configure Pdiff parameter of artificial dataset
+            Percent_difference = 1
+        else:
+            Percent_difference = 5
+    print('simulated sequences up to', Percent_difference, 'percent divergent from original ones')
+    
+    if 'NMaxSeq' in list(ParDict.keys()):#Maximum number of sequences per taxon to be modified
+        Seq_per_clade_to_screw = int(ParDict['NMaxSeq'])
+    else:
+        Seq_per_clade_to_screw = 10####!changed value
+    print('Maximum number of sequences modified per clade', Seq_per_clade_to_screw)
+    
+    if 'Scoring' in list(ParDict.keys()):
+        if ParDict['Scoring'] == 'lousy':
+            threshold = 66####!changed value
+        elif ParDict['Scoring'] == 'moderate':
+            threshold = 75####!changed value
+        elif ParDict['Scoring'] == 'stringent':
+            threshold = 90####!changed value
+        elif ParDict['Scoring'] == 'very_stringent':
+            threshold = 95####!changed value
+        else:
+            threshold = 75####!changed value
+    else:
+        threshold = 75####!changed value
+    #print(ParDict['Scoring'], 'scoring of the rDNCs; threshold in two consequtive runs:', threshold)
+    print('scoring of the rDNCs; threshold in two consequtive runs:', threshold)
+    #OCT2022 - start
+    if corr > 1 and len(ParDict['Iref'].split(',')) == 2:
+        print('\nNOTE: The alignment was trimmed automatically to match median sequences length. The analysed slice starts from the site',str(sstart+1),'and ends on the site',str(send+1),'. The site indexing in the DNCs as in the provided reference.')
+    if corr > 1 and ParDict['Iref'] == 'NO':
+        print('\nNOTE: The alignment was trimmed automatically to match median sequences length. The analysed slice starts from the site',str(sstart+1),'and ends on the site',str(send+1),'. The site indexing in the rDNC as in the untrimmed alignment.')
+    #OCT2022 - end
+    thephrase = 'The DNA diagnosis for the taxon'
+    ###################################################IMPLEMENTATION
+    #Setting up a new class just for the convenient output formatting
+    class SortedDisplayDict(dict):#this is only to get a likable formatting of the barcode
+        def __str__(self):
+            return "[" + ", ".join("%r: %r" % (key, self[key]) for key in sorted(self)) + "]"
+    
+    class SortedDisplayDictVerbose(dict):#this is only to get a likable formatting of the barcode
+        def __str__(self):
+            return ", ".join("%r %r" % (self[key],'in the site '+str(key)) for key in sorted(self)).replace("'", '').replace("A", "'A'").replace("C", "'C'").replace("G", "'G'").replace("T", "'T'")+'.'
+    
+    #Calling functions and outputing results
+    if ParDict['OUTPUT_FILE'] == "str":
+        g = StringIO()
+    else:
+        g = open(ParDict['OUTPUT_FILE'], "w")#Initiating output file
+    #VERYNEW
+    print('<h4>########################## PARAMETERS ######################</h4>', file=g)
+    #print("<p>", 'input file:', ParDict['ORIG_FNAME'], "</p>", file=g) #outcommented ORIG_FNAME
+    print("<p>", 'input file:', ParDict['INPUT_FILE'], "</p>", file=g)
+    print("<p>", 'Coding gaps as characters:', gaps2D, "</p>", file=g)
+    print("<p>", 'Maximum undetermined nucleotides allowed:', NumberN, "</p>", file=g)
+    print("<p>", 'Length of the alignment:', len(imported[0][2]),'->', FragmentLen, "</p>", file=g)
+    print("<p>", 'Indexing reference:', ParDict['Iref'].replace('NO', 'Not set').replace('in', 'included').replace('ex', 'excluded'), "</p>", file=g)#OCT2022
+    print("<p>", 'Read in', len(raw_records), 'sequences', "</p>", file=g)
+    print("<p>", 'query taxa:', len(qCLADEs+withplus), '-', str(sorted(qCLADEs)+sorted(withplus)).replace('[','').replace(']','').replace("'", ''), "</p>", file=g)#1.3
+    print("<p>", 'Cutoff set as:', Cutoff, "</p>", file=g)
+    print("<p>", 'Number iterations of MolD set as:', N1, "</p>", file=g)
+    print("<p>", 'Maximum length of raw mDNCs set as:', MaxLen1, "</p>", file=g)
+    print("<p>", 'Maximum length of refined mDNCs set as:', MaxLen2, "</p>", file=g)
+    print("<p>", 'simulated sequences up to', Percent_difference, 'percent divergent from original ones', "</p>", file=g)
+    print("<p>", 'Maximum number of sequences modified per clade', Seq_per_clade_to_screw, "</p>", file=g)
+    #print("<p>", ParDict['Scoring'], 'scoring of the rDNCs; threshold in two consequtive runs:', threshold, "</p>", file=g)
+    print("<p>", 'scoring of the rDNCs; threshold in two consequtive runs:', threshold, "</p>", file=g)
+    if corr > 1 and len(ParDict['Iref'].split(',')) == 2:
+        print('<h4>NOTE: The alignment was trimmed automatically to match median sequences length. The analysed slice starts from the site',str(sstart+1),'and ends on the site',str(send+1),'. The site indexing in the DNCs as in the provided reference.</h4>', file=g)
+    if corr > 1 and ParDict['Iref'] == 'NO':
+        print('<h4>NOTE: The alignment was trimmed automatically to match median sequences length. The analysed slice starts from the site',str(sstart+1),'and ends on the site',str(send+1),'. The site indexing in the rDNC as in the untrimmed alignment.</h4>', file=g)
+
+    print('<h4>########################### RESULTS ##########################</h4>', file=g)
+    
+    for qCLADE in sorted(qCLADEs) + sorted(withplus):#OCT2022
+        if '+' in qCLADE:
+            if shift == True:
+                old_records = raw_records
+                shift == False
+            else:
+                raw_records = old_records
+            spp = qCLADE.split('+')
+            nrecords = []
+            for rec in raw_records:
+                if rec[1] in spp:
+                    nrecords.append([rec[0], qCLADE, rec[2]])
+                else:
+                    nrecords.append(rec)
+            raw_records = nrecords
+        print('\n**************', qCLADE, '**************')
+        print('<h4>**************', qCLADE, '**************</h4>', file=g)
+        Clades, clade_sorted_seqs, shared_positions = Step1(raw_records)#STEP1
+        x,y,z,pures = C_VP_PP(clade_sorted_seqs, qCLADE, shared_positions, Cutoff)#STEP2 ####! added pures
+        newy = {key:y[key] for key in y if not key in pures} ####! newline
+        print('Sequences analyzed:', len(clade_sorted_seqs[qCLADE]))
+        print("<p>",'Sequences analyzed:', len(clade_sorted_seqs[qCLADE]), "</p>", file=g)
+        ND_combinations = [[item] for item in pures] ####! before ND_combinations were initiated as an empty list
+        print('single nucleotide mDNCs:', len(pures), '-', str(SortedDisplayDict({pos+corr : y[pos-1] for pos in [i+1 for i in pures]}))[1:-1])#OCT2022
+        print("<p>",'single nucleotide mDNCs*:',len(pures), '-', str(SortedDisplayDict({pos+corr : y[pos-1] for pos in [i+1 for i in pures]}))[1:-1], "</p>", file=g)#OCT2022
+        N = 1 ####!
+        while N > 0:#STEP3
+            try:
+                q = Diagnostic_combinations(qCLADE, x, newy, N1, MaxLen1, MaxLen2) ####! newy instead of y
+            except IndexError:
+                print(N, 'IndexError')
+                continue
+            for comb in q:
+                if not comb in ND_combinations:
+                    ND_combinations.append(comb)
+            N-=1
+        ND_combinations.sort(key=len)
+        #################################### mDNC output
+        try:
+            Nind, KeyPos = IndependentKey(ND_combinations)#STEP4
+        except IndexError:
+            print('no mDNCs recovered for', qCLADE)#VERYNEW
+            print("<p>", 'no mDNCs recovered for', "</p>", qCLADE, file=g)#VERYNEW
+            continue
+        Allpos = []#Create list of all positions involved in mDNCs
+        for comb in ND_combinations:
+            for pos in comb:
+                if not pos in Allpos:
+                    Allpos.append(pos)
+        print('\nmDNCs retrieved:', str(len(ND_combinations)) + '; Sites involved:', str(len(Allpos)) + '; Independent mDNCs:', len(Nind))#VERYNEW
+        print("<p>", 'mDNCs* retrieved:', str(len(ND_combinations)) + '; Sites involved:', str(len(Allpos)) + '; Independent mDNCs**:', len(Nind), "</p>", file=g)#VERYNEW
+        print('Shortest retrieved mDNC:', SortedDisplayDict({pos+corr : y[pos-1] for pos in [i+1 for i in ND_combinations[0]]}), '\n')#OCT2022
+        print("<p>",'Shortest retrieved mDNC*:', SortedDisplayDict({pos+corr : y[pos-1] for pos in [i+1 for i in ND_combinations[0]]}), "</p>", file=g)#OCT2022
+        ######################################################## rDNC output
+        Barcode_scores = []#Initiate a list for rDNC scores
+        npos = len(ND_combinations[0])
+        BestBarcode = 'none'####! newline
+        while npos <= min([10, len(Allpos)]):#in this loop the positions are added one-by-one to a rDNC and the rDNC is then rated on the artificially generated datasets
+            Barcode = GenerateBarcode_new(ND_combinations, npos)#Initiate a rDNC
+            Barcode_score = 0#Initiate a score to rate a rDNC
+            N = 100
+            while N > 0:
+                NComplist, NCPP = Screwed_dataset_new(raw_records, Seq_per_clade_to_screw, PosArrays, VarPosList, Percent_difference, qCLADE, Cutoff)#Create an artificial dataset VERYNEW
+                NBarcode = [i for i in Barcode if i in list(NCPP.keys())]
+                if len(Barcode) - len(NBarcode) <= 1 and ConditionD(NBarcode, NComplist, NCPP) == True:####! new condition (first) added
+                    Barcode_score +=1
+                N -=1
+            print(npos, 'rDNC_score (100):', [k+corr+1 for k in Barcode], '-', Barcode_score)#VERYNEW
+            print("<p>", npos, 'rDNC_score (100):', [k+corr+1 for k in Barcode], '-', Barcode_score, "</p>", file=g)#OCT2022
+            if Barcode_score >= threshold and len(Barcode_scores) == 1: ###1.3
+                BestBarcode = Barcode###1.3
+            if Barcode_score >= threshold and len(Barcode_scores) > 1 and Barcode_score >= max(Barcode_scores): ###1.3
+                BestBarcode = Barcode####!newline
+            Barcode_scores.append(Barcode_score)
+            if len(Barcode_scores) >= 2 and Barcode_scores[-1] >= threshold and Barcode_scores[-2] >= threshold:#Check whether the rDNC fulfills robustnes criteria 85:85:85
+                print('Final rDNC:', SortedDisplayDict({pos+corr : y[pos-1] for pos in [i+1 for i in BestBarcode]}))#OCT2022
+                print("<p>",'Final rDNC***:', SortedDisplayDict({pos+corr : y[pos-1] for pos in [i+1 for i in BestBarcode]}), "</p>", file=g)#OCT2022
+                print('\n',thephrase, qCLADE, 'is:', SortedDisplayDictVerbose({pos+corr : y[pos-1] for pos in [i+1 for i in BestBarcode]}))#OCT2022
+                print("<p>", thephrase, qCLADE, 'is:', SortedDisplayDictVerbose({pos+corr : y[pos-1] for pos in [i+1 for i in BestBarcode]}), "</p>", file=g)#OCT2022
+                break
+            else:# VERY NEW FROM HERE ONWARDS
+                npos += 1
+                if npos > min([10, len(Allpos)]):
+                    if BestBarcode != 'none':
+                        print('The highest scoring rDNC for taxon', qCLADE, 'is:', SortedDisplayDictVerbose({pos+corr : y[pos-1] for pos in [i+1 for i in BestBarcode]}))#OCT2022
+                        print("<p>", 'The highest scoring rDNC*** for taxon', qCLADE, 'is:', SortedDisplayDictVerbose({pos+corr : y[pos-1] for pos in [i+1 for i in BestBarcode]}), "</p>", file=g)#OCT2022
+                    else:
+                        print('No sufficiently robust DNA diagnosis for taxon', qCLADE, 'was retrieved')
+                        print("<p>", 'No sufficiently robust DNA diagnosis for taxon', qCLADE, 'was retrieved', "</p>", file=g)
+    #OCT2022 - start
+    print("<h4>", '################################# EXPLANATIONS ####################################', "</h4>", file=g)
+    print("<p>", '  * mDNC -(=minimal Diagnostic nucleotide combination) is a combination of nucleotides at specified sites of the alignment,', "</p>", file=g)
+    print("<p>", '    unique for a query taxon. Therefore it is sufficient to differentiate a query taxon from all reference taxa in a dataset.', "</p>", file=g)
+    print("<p>", '    Because it comprises minimal necessary number of nucleotide sites to differentiate a query, any mutation in the mDNC in'  "</p>", file=g)
+    print("<p>", '    single specimen of a query taxon will automatically disqualify it as a diagnostic combination.', "</p>", file=g)
+    print("<p>",  "</p>", file=g)
+    print("<p>", ' ** two or more mDNCs are INDEPENDENT if they constitute non-overlapping sets of nucleotide sites.', "</p>", file=g)
+    print("<p>",  "</p>", file=g)
+    print("<p>", '*** rDNC -(=robust/redundant Diagnostic nucleotide combination) is a combination of nucleotides at specified sites of the alignment,', "</p>", file=g)
+    print("<p>", '    unique for a query taxon and (likewise mDNC) sufficient to differentiate a query taxon from all reference taxa in a dataset.', "</p>", file=g)
+    print("<p>", '    However, rDNC comprises more than a minimal necessary number of diagnostic sites, and therefore is robust to single nucleotide', "</p>", file=g)
+    print("<p>", '    replacements. Even if a mutation arises in one of the rDNC sites, the remaining ones will (with high probability) remain sufficient ', "</p>", file=g)
+    print("<p>", '    to diagnose the query taxon', "</p>", file=g)
+    print("<h4>", '    Final diagnosis corresponds to rDNC', "</h4>", file=g)
+    #OCT2022 - end
+    
+    if ParDict['OUTPUT_FILE'] == "str":
+        contents = g.getvalue()
+        os.unlink(ParDict['INPUT_FILE'])
+    else:
+        contents = None
+    g.close()
+    #OCT2022 - start
+    if len(P2) != 0:
+        ext = '.'+ParDict['OUTPUT_FILE'].split('.')[-1]
+        h = open(ParDict['OUTPUT_FILE'].replace(ext, '_pairwise'+ext), "w")#Initiating output file
+        if len(withplus) != 0:
+            raw_records = old_records
+        taxain = [i[1] for i in raw_records]
+        tpairs = []
+        for alist in P2:
+            if alist.count('all')+alist.count('All')+alist.count('ALL') == 2:
+                tpairs = getAllPairs(taxain)
+            elif alist.count('all')+alist.count('All')+alist.count('ALL') == 1:
+                thetax = [i for i in taxain if i in alist][0]
+                print(thetax)
+                for atax in sorted(list(set(taxain))):
+                    if atax != thetax:
+                        tpairs.append([thetax, atax])
+            else:
+                for apair in getAllPairs(alist):
+                    tpairs.append(apair)
+        for apair in tpairs:
+            t1 = apair[0]
+            t2 = apair[1]
+            p2records = [i for i in raw_records if i[1] in [t1, t2]]
+            print('\n**************', t1, 'VS', t2,'**************')
+            print('<h4>**************', t1, 'VS', t2, '**************</h4>', file=h)
+            C2, css2, sp2 = Step1(p2records)#STEP1
+            x2,y2,z2,pures2 = C_VP_PP(css2, t1, sp2, '>0')#STEP2 ####! added pures
+            Pairphrase = 'Each of the following '+ str(len(pures2))+' sites is invariant across sequences of '+ t1+ ' and differentiates it from '+ t2+': '
+            counterPures = {}
+            for site in pures2:
+                counterPures[site] = "'or'".join(list(set([thing[site] for thing in css2[t2] if thing[site] != 'N'])))
+                Pairphrase = Pairphrase + str(site+corr)+" ('"+str(y2[site])+"' vs '"+str(counterPures[site])+"'), "
+            print(Pairphrase[:-2])
+            print("<p>",Pairphrase[:-2],'</h4>', file=h)#OCT2022
+            x2r,y2r,z2r,pures2r = C_VP_PP(css2, t2, sp2, '>0')#STEP2 ####! added pures
+            Pairphraser = 'Each of the following '+ str(len(pures2r))+' sites is invariant across sequences of '+ t2+ ' and differentiates it from '+ t1+': '
+            counterPuresr = {}
+            for site in pures2r:
+                counterPuresr[site] = "'or'".join(list(set([thing[site] for thing in css2[t1] if thing[site] != 'N'])))
+                Pairphraser = Pairphraser + str(site+corr)+" ('"+str(y2r[site])+"' vs '"+str(counterPuresr[site])+"'), "
+            print(Pairphraser[:-2])
+            print("<p>",Pairphraser[:-2],'</h4>', file=h)#OCT2022
+        h.close()
+            
+    #OCT2022 - end
+    return contents, qCLADEs
+if __name__ == "__main__":
+    c, q = mainprocessing()
+  
\ No newline at end of file

Binary file MolD_v1_3_manual.pdf has changed

Binary file MolD_v1_4_manual_new_in_red.docx has changed

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/README.md	Sun Jan 29 16:25:48 2023 +0000
@@ -0,0 +1,3 @@
+# MolD
+Original scripts and manual of the program MolD with manual and example files
+Please see the newest release - version 1.3

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/api.py	Sun Jan 29 16:25:48 2023 +0000
@@ -0,0 +1,295 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from flask import Flask, make_response, request, current_app, send_file, url_for
+from flask_restful import Resource, Api, reqparse, abort
+from flask.json import jsonify
+from flasgger import Swagger
+from flask_cors import CORS, cross_origin
+from flask_restful.utils import cors
+import logging
+import base64
+import uuid
+import tempfile
+import os
+import html2text
+
+# for emails
+import smtplib
+from email.message import EmailMessage
+from email.mime.multipart import MIMEMultipart
+from email.mime.text import MIMEText
+from email.mime.image import MIMEImage
+from celery import Celery
+from celery.schedules import crontab
+
+import json
+
+#from MolD_sDNC import SortedDisplayDict
+#from MolD_sDNC import Step1
+#from MolD_sDNC import C_VP_PP
+#from MolD_sDNC import random_position
+#from MolD_sDNC import step_reduction_complist
+#from MolD_sDNC import ConditionD
+#from MolD_sDNC import RemoveRedundantPositions
+#from MolD_sDNC import Diagnostic_combinations
+#from MolD_sDNC import IndependentKey
+#from MolD_sDNC import random_sequence2
+#from MolD_sDNC import GenerateBarcode2
+#from MolD_sDNC import Screwed_dataset31
+from MolD_sDNCFASTA import mainprocessing
+
+
+logging.basicConfig(format='%(levelname)s: %(asctime)s - %(message)s',
+                    level=logging.DEBUG, datefmt='%d.%m.%Y %I:%M:%S %p')
+
+app = Flask(__name__)
+app.config.from_envvar('APPSETTINGS')
+API_VERSION = app.config.get('API_VERSION', 1)
+
+cors = CORS(app, resources={r"/*": {"origins": "*"}}, methods=['GET', 'POST', 'PATCH', 'DELETE', 'HEAD', 'OPTIONS'])
+api = Api(app, prefix=f"/api/v{API_VERSION}")
+
+gunicorn_logger = logging.getLogger('gunicorn.error')
+app.logger.handlers = gunicorn_logger.handlers
+app.logger.setLevel(gunicorn_logger.level)
+
+REDIS_HOST = app.config.get('REDIS_HOST', 'localhost')
+REDIS_PORT = app.config.get('REDIS_PORT', 6379)
+REDIS_DB = app.config.get('REDIS_DB', 0)
+
+MAILUSER = app.config.get('MAILUSER')
+MAILPASS = app.config.get('MAILPASS')
+
+app.config['SWAGGER'] = {
+    'uiversion': 3
+}
+swtemplate = {
+    "info": {
+        "title": "MoID API",
+        "description": "MoID API is the online version of a tree independent algorithm to retrieve diagnostic nucleotide characters from monolocus datasets. BioRxiv. DOI: 10.1101/838151",
+        "version": f"{API_VERSION}",
+    },
+    "schemes": [
+        "https"
+    ]
+}
+
+swagger_config = {
+    "headers": [
+    ],
+    "specs": [
+        {
+            "endpoint": 'apidescr',
+            "route": '/apidescr.json',
+            "rule_filter": lambda rule: True,
+            "model_filter": lambda tag: True,
+        }
+    ],
+    "static_url_path": "/flasgger_static",
+    "swagger_ui": True,
+    "specs_route": "/docs/",
+    'uiversion': 3
+}
+
+swagger = Swagger(app, config=swagger_config, template=swtemplate)
+
+SEND_EMAILS = True
+
+app.config['CELERY_BROKER_URL'] = 'redis://{}:{}/{}'.format(REDIS_HOST, REDIS_PORT, REDIS_DB)
+app.config['CELERY_RESULT_BACKEND'] = 'redis://{}:{}/{}'.format(REDIS_HOST, REDIS_PORT, REDIS_DB)
+
+def make_celery(app):
+    celery = Celery(app.import_name, broker=app.config['CELERY_BROKER_URL'])
+    celery.conf.update(app.config)
+    TaskBase = celery.Task
+    class ContextTask(TaskBase):
+        abstract = True
+        def __call__(self, *args, **kwargs):
+            with app.app_context():
+                return TaskBase.__call__(self, *args, **kwargs)
+    celery.Task = ContextTask
+    return celery
+
+celery = make_celery(app)
+
+#@celery.on_after_configure.connect
+#def setup_periodic_tasks(sender, **kwargs):
+#    sender.add_periodic_task(
+#        crontab(minute=0, hour='*/3'),
+#        check_pending_notifications.s(),
+#    )
+
+
+@celery.task
+def send_email_notification(email, status, parameters, taxalist, orig_filename):
+    print("Sending email")
+    sender = MAILUSER
+    taxa = ", ".join(taxalist)
+    print(taxalist)
+    taxa1 = "-".join([t.replace(" ", "_") for t in taxalist])
+    msg = MIMEMultipart('related')
+    msg['Subject'] = f'MolD results: {taxa}'
+    msg['From'] = sender
+    msg['To'] = email
+
+    # TODO: add three txt files:
+    # 
+    
+    email_body = """\
+<html>
+    <head></head>
+       <body>
+
+    Results:
+     {}
+
+    ***************
+
+    <p>Citation: <b>Fedosov A.E.</b>, Achaz G., Puillandre N. 2019. Revisiting use of DNA characters in taxonomy with MolD – a tree independent algorithm to retrieve diagnostic nucleotide characters from monolocus datasets. BioRxiv, published online on 11.11.2019. DOI: 10.1101/838151 </p>
+       </body>
+</html>
+    """.format(status)
+    
+    plain_text_results = html2text.html2text(status)
+    
+    message_text = MIMEText(email_body, 'html')
+    msg.attach(message_text)
+
+    text_attachment = f"""
+Results: 
+{plain_text_results}
+
+***************
+
+Citation: Fedosov A.E., Achaz G., Puillandre N. 2019. Revisiting use of DNA characters in taxonomy with MolD – a tree independent algorithm to retrieve diagnostic nucleotide characters from monolocus datasets. BioRxiv, published online on 11.11.2019. DOI: 10.1101/838151
+    """
+    
+    mail_attachment = MIMEText(text_attachment)
+    fname = f"{orig_filename}.results.txt"
+    mail_attachment.add_header('Content-Disposition', 'attachment', filename=fname)
+    msg.attach(mail_attachment)
+    
+    print("mail ready to be sent")
+    s = smtplib.SMTP('smtp.gmail.com', 587)
+    s.ehlo()
+    s.starttls()
+    s.login(MAILUSER, MAILPASS)
+    s.sendmail(sender, email, msg.as_string())
+    s.quit()
+    print("mail sent")
+
+
+@celery.task
+def process_data(email, gapsaschars, taxalist, taxonrank, cutoff, numnucl, numiter, maxlenraw, maxlenrefined, pdiff, nmax, thresh, tmpfname, orig_fname):
+    print("Processing data")
+
+    results, qclades = mainprocessing(gapsaschars, taxalist, taxonrank, cutoff, numnucl, numiter, maxlenraw, maxlenrefined, pdiff, nmax, thresh, tmpfname, orig_fname)
+
+    parameters = f"""
+    List of focus taxa: {taxalist}
+    Taxon rank: {taxonrank}
+    Cutoff: {cutoff}
+    NNNNN...: {numnucl}
+    Num iterations: {numiter}
+    Max length for the raw mDNCs: {maxlenraw}
+    Max length for the refined mDNCs: {maxlenrefined}
+    Pdiff: {pdiff}
+    NMaxSeq: {nmax}
+    Threshold of rDNC rating: {thresh}
+    """
+    
+    send_email_notification.delay(email, results, parameters, qclades, orig_fname)
+    
+@app.errorhandler(404)
+def not_found(error):
+    return make_response(jsonify({'error': 'Not found'}), 404)
+
+
+class DataAPI(Resource):
+    def __init__(self):
+        self.method_decorators = []
+
+    def options(self, *args, **kwargs):
+        return jsonify([])
+
+
+    #@token_required
+    @cross_origin()
+    def post(self):
+        """
+        POST data to analyse
+        ---
+        parameters:
+         - in: formData
+           name: file
+           type: file
+           required: true
+           description: Data file
+         - in: formData
+           name: tags
+           type: array
+           required: false
+           description: A list of image tags to link with the image
+           items:
+             type: string
+             description: Tag text
+         - in: formData
+           name: width
+           type: integer
+           required: false
+           description: Image width
+         - in: formData
+           name: height
+           type: integer
+           required: false
+           description: Image height
+
+        responses:
+          200:
+            description: Data processing
+          400:
+            description: Bad request
+        """
+        app.logger.debug(request.files)
+        app.logger.debug(request.values)
+        email = request.values.get('email', None)
+        gapsaschars = request.values.get('gapsaschars', "no")
+        taxalist = request.values.get('taxalist', "ALL")
+        taxalist = taxalist.replace(" ", '')
+        taxonrank = int(request.values.get('taxonrank', 2))
+        cutoff = request.values.get('cutoff', ">0")
+        numnucl = int(request.values.get('numnucl', 5))
+        numiter = int(request.values.get('numites', 10000))
+        maxlenraw = int(request.values.get('maxlenraw', 12))
+        maxlenrefined = int(request.values.get('maxlenrefined', 7))
+        pdiff = int(request.values.get('pdiff', 1))
+        #prseq = float(request.values.get('prseq', 0.1))
+        nmax = int(request.values.get('nmax', 20))
+        thresh = int(request.values.get('thresh', 85))
+        
+        if not all([email, gapsaschars, taxalist, taxonrank, cutoff, numnucl, numiter, maxlenraw, maxlenrefined, pdiff, nmax, thresh]):
+            return make_response(jsonify({'error': 'Parameter missing'}), 400)
+            
+        if not request.files:
+            return make_response(jsonify({'error': 'No file provided'}), 400)
+
+        
+        data_file = [request.files.get(f) for f in request.files][-1]
+
+        thumb_height = request.values.get('height', None)
+        tmpdname = "/tmp"
+        tmpfname = str(uuid.uuid4())
+        tmp_upl_file = os.path.join(tmpdname, tmpfname)
+        data_file.save(tmp_upl_file)
+        app.logger.debug(tmp_upl_file)
+        process_data.delay(email, gapsaschars, taxalist, taxonrank, cutoff, numnucl, numiter, maxlenraw, maxlenrefined, pdiff, nmax, thresh, tmp_upl_file, data_file.filename)
+
+        return jsonify("OK"), 200
+
+
+api.add_resource(DataAPI, '/data', endpoint='processdata')
+
+if __name__ == '__main__':
+    app.debug = True
+    app.run(host='0.0.0.0')

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/datatypes_conf.xml	Sun Jan 29 16:25:48 2023 +0000
@@ -0,0 +1,6 @@
+<?xml version="1.0"?>
+<datatypes>
+  <registration>
+    <datatype extension="spart" type="galaxy.datatypes.data:Text" subclass="true" display_in_upload="true"/>
+  </registration>
+</datatypes>
\ No newline at end of file

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Example_input_alignment_Pontohedyle_COI.fas	Sun Jan 29 16:25:48 2023 +0000
@@ -0,0 +1,54 @@
+>ZSM20071381|milaschewitchii
+NNNTCTATATCTTGTTTTTGGAGTCTGGTGCGGTTTAGTGGGTACAGGGCTTTCCCTTCTTATCCGGTTTGAGCTAGGCACGTCCTCCGTCTTCATAGACGAGCACTTTTATAATGTTATTGTGACGGCCCATGCATTTGTTATAATTTTTTTTATGGTCATGCCACTAATAATTGGTGGTTTTGGGAATTGAATGGTTCCCCTGTTAATTGGGGCCCCCGACATAAGGTTTCCTCGAATAAATAACATAAGATTTTGGTTGCTTCCCCCTAGGTTTGTTCTTTTGTTGAGCTCAAGATTAGTTGAGGGGGGTGCGGGTACAGGGTGAACTGTCTATCCGCCCCTAAGCGGGTCTGTTGCGCACGGAGGAGCCTCGGTAGACTTAGTTATTTTTTCACTACATTTGGCGGGAATGTCTTCTATTCTTGGGGCTATCAACTTCATTACAACAATCTTTAATATGCGGTCCCCCGGGCTAACAATGGAACGGCTGAGGTTGTTTGTTTGGTCTGTTCTAGTCACGGCTTTTTTACTTCTGCTAAGACTGCCTGTGCTAGCAGGGGCTATCACAATGCTTTTAACTGATCGTAACTTTAACACTAGTTTTTTTGACCCGGCCGGAGGGGGGGACCCTATTTTATACCAACACCTTTTC
+>ZSM20080925|milaschewitchii
+NNNTCTATATCTTGTTTTTGGAGTCTGGTGCGGTTTAGTAGGTACAGGGCTTTCCCTTCTTATCCGGTTTGAGCTAGGCACGTCCTCCGTGTTCATAGACGAGCACTTTTATAATGTTATTGTGACGGCCCATGCATTTGTTATAATTTTTTTTATGGTCATGCCACTAATAATTGGTGGTTTTGGGAATTGAATGGTTCCCCTGTTAATTGGGGCCCCCGACATAAGGTTTCCTCGGATAAACAACATAAGGTTTTGGTTGCTTCCCCCTAGGTTTGTTCTTTTGTTGAGCTCAAGATTAGTTGAGGGGGGTGCGGGTACAGGGTGAACTGTCTATCCGCCCCTAAGCGGGTCTGTTGCGCACGGAGGAGCCTCGGTAGACTTGGTTATTTTTTCACTACATTTGGCGGGAATGTCTTCTATTCTTGGGGCTATCAACTTCATTACAACAATCTTTAATATGCGGTCCCCCGGGCTAACAATGGAGCGGCTGAGGTTGTTTGTTTGGTCTGTTCTAGTCACGGCTTTTTTACTTCTGCTAAGATTGCCTGTGCTAGCAGGGGCTATCACAATGCTTTTAACTGATCGTAACTTTAACACTAGTTTTTTTGACCCGGCCGGAGGGGGGGACCCTATTTTATACCAACACCTTTTC
+>ZSM20080953|milaschewitchii
+AACTCTATATCTTGTTTTTGGAGTCTGGTGCGGTTTAGTGGGTACAGGGCTTTCTCTTCTTATCCGGTTTGAGCTAGGCACGTCCTCCGTCTTCATAGACGAGCACTTTTATAATGTTATTGTAACGGCCCATGCATTTGTTATAATTTTTTTTATGGTCATGCCACTAATAATTGGCGGTTTTGGTAATTGAATGGTTCCCCTGTTAATTGGGGCCCCCGACATAAGGTTTCCTCGAATAAATAACATAAGGTTTTGGTTGCTTCCCCCTAGGTTTGTTCTTTTGTTAAGCTCAAGATTAGTTGAGGGGGGCGCGGGTACAGGGTGAACTGTCTATCCGCCCCTAAGCGGGTCTGTTGCGCACGGAGGAGCCTCGGTAGACTTAGTTATTTTTTCACTACATTTGGCGGGAATGTCTTCTATTCTTGGGGCTATCAACTTCATTACAACAATCTTTAATATACGGTCCCCCGGGCTAACAATGGAACGGCTGAGGTTGTTTGTTTGGTCTGTTCTAGTCACGGCTTTTTTACTTCTGCTAAGATTGCCCGTGCTAGCAGGGGCTATCACAATGCTTTTAACTGATCGTAACTTTAACACTAGTTTTTTTGACCCGGCCGGAGGGGGGGACCCTATTTTATACCAACACCTTTTC
+>SICBC2010KJ01B09|brasilensis
+CACGTTGTACATTATCTTTGGGGTTTGATGCGGTTTGGTCGGTGCGGGGTTGTCTTTGTTGATTCGGGTAGAGTTGGGGACTTCTAGGGTGTTAACCGACCCTCACTTCTACAATGTGGTTGTGACTGCCCATGCTTTCGTTATGATTTTTTTTATGGTTATACCTGTCTTGATCGGGGGTTTTGGTAACTGAATGATCCCGCTTCTGATTGGGGCACCTGATATGGCTTTTCCTCGGTTAAATAACCTAAGGTTTTGGCTGCTCCCGCCTGCGTTTATTTTACTTATAAGCTCGGTTTTGGTAGAAGGCGGAGCGGGGACAGGTTGAACCTTGTATCCCCCTCTGAGCG---CGGAGGGGCATTCAGGATTTTCAGTTGATTTAGCCATTTTTTCTCTGCACTTGGCAGGGGTCTCTTCTATCTTAGGGGCAGTTAATTTTATTACCACTATTTGGAATATGCGGGCCCCGGGGGTTACTTGAGAGCGGCTGAATCTTTTTGTCTGGTCTTTACTTATTACAGCGTTATTGTTGCTGTTGTCACTGCCTGTGCTAGCTGGTGCTCTTACAATGTTGTTAACTGATCGGAATTTTAATACTACTTTTTTTGATCCGGCGGGGGGCGGGGACCCTGTTTTATACCAACACCTGTTC
+>SICBC2010KJ01C09|brasilensis
+CACGTTGTACATTATCTTTGGGGTTTGATGTGGTTTGGTCGGTGCGGGGTTGTCTTTGTTGATTCGGGTAGAGTTGGGGACTTCTAGAGTGTTAACCGACCCTCACTTCTACAATGTGGTTGTGACTGCCCATGCTTTCGTTATGATTTTTTTTATGGTTATACCTGTCTTGATTGGGGGTTTTGGTAACTGAATGATCCCGCTTCTGATTGGGGCACCTGATATGGCTTTTCCTCGGTTAAATAATCTAAGGTTTTGGCTGCTCCCTCCCGCGTTTATTTTACTTATAAGCTCGGTTCTGGTAGAAGGCGGAGCGGGGACAGGTTGAACCTTGTATCCCCCTCTGAGCG---CGGAGGGTCATTCAGGGTTTTCAGTTGATTTAGCCATTTTTTCTCTGCACTTGGCAGGGGTTTCTTCTATTTTAGGGGCAGTTAACTTTATTACCACTATTTGGAATATGCGGGCCCCGGGGGTTACTTGAGAGCGGCTGAATCTTTTTGTCTGGTCTTTACTTATTACAGCGTTATTGCTGCTGTTGTCACTGCCTGTGCTAGCTGGTGCTCTTACAATGTTGTTAACTGATCGGAATTTTAATACTACTTTTTTTGATCCGGCGGGGGGCGGGGACCCTGTTTTATACCAACACCTGTTC
+>ZSM20110723|brasilensis
+CACGTTGTACATTATCTTTGGGGTTTGATGCGGTTTGGTCGGTGCGGGGTTGTCTTTGTTGATTCGGGTAGAGTTGGGGGCTTCTAGGGTGTTAACCGACCCTCACTTCTACAATGTGGTTGTGACTGCCCATGCTTTCGTTATGATTTTTTTTATGGTTATACCTGTCTTGATCGGGGGGTTTGGTAACTGAATGATCCCGCTTCTGATTGGGGCACCTGATATGGCTTTTCCTCGGTTAAATAACCTAAGGTTTTGGCTGCTCCCTCCCGCGTTTATTTTACTTATAAGCTCGGTTCTGGTAGAAGGCGGAGCGGGGACAGGTTGAACCTTGTATCCCCCTCTGAGCG---CGGAGGGTCATTCAGGGTTTTCAGTTGATTTAGCCATTTTTTCTCTGCACTTGGCAGGGGTCTCTTCTATTTTAGGGGCAGTTAACTTTATTACCACTATTTGGAATATGCGGGCCCCGGGGGTTACTTGAGAGCGGCTGAATCTTTTTGTCTGGTCTTTACTTATTACAGCGTTATTGTTGCTGTTGTCACTGCCTGTGCTAGCTGGTGCTCTTACAATGTTGTTAACTGATCGGAATTTTAATACTACTTTTTTTGATCCGGCGGGGGGCGGGGACCCTGTTTTATACCAACACTTGTTC
+>ZSM20110722|brasilensis
+TACGCTATACATTATCTTTGGGGTTTGATGCGGCTTGGTCGGTGCGGGGTTGTCTTTGTTGATTCGGGTGGAGTTAGGGACTTCTGGGGTGTTAACCGACCCACACTTCTACAATGTGGTAGTGACTGCCCATGCTTTCGTCATGATTTTTTTTATGGTTATACCCGTCTTGATCGGGGGTTTCGGTAATTGAATGATCCCGCTCCTGATTGGGGCACCTGATATGGCCTTTCCCCGGTTAAATAATTTAAGGTTTTGGCTGCTTCCTCCTGCGTTTATTTTACTTATAAGCTCGGTTTTAGTAGAAGGCGGTGCGGGGACAGGCTGAACTTTATACCCCCCCTTGAGCG---CGGAGGGTCACTCAGGGTTTTCAGTTGATTTAGCTATTTTTTCTCTGCACTTGGCGGGGGTCTCATCTATTCTAGGGGCGGTTAACTTCATCACAACTATCTGGAATATGCGGGCCCCAGGGGTTACTTGAGAGCGGTTGAATCTTTTTGTGTGGTCTTTACTCATTACAGCGTTATTGTTGTTGTTGTCACTACCAGTACTGGCTGGTGCTCTTACAATGTTGTTAACTGATCGGAATTTTAATACTACTTTTTTTGATCCAGCGGGGGGTGGGGACCCAGTTTTATATCAACATCTGTTC
+>ZSM20071820|verrucosa
+GACCTTGTATATAGTATTTGGTGTGTTAGCTGGGTTGGTGGGAACTGGCTTATCCTTGTTAATTCGTTTTGAGTTGGGAACAGCATCTGTTTTTATGGATGAGCATTTTTATAATGTAGTTGTCACTGCGCATGCCTTTGTTATGATTTTTTTTATAGTTATGCCTCTTATAATTGGGGGGTTTGGAAACTGAATAGTTCCTTTACTTATTGGTGCCCCCGATATAAGTTTTCCTCGAATAAATAATATAAGATTCTGATTATTGCCGCCCTCATTTATTTTACTTTTATGCTCTGCTATGGTAGAAGGAGGAGCTGGCACTGGGTGAACTGTCTATCCTCCGTTAAGAGGTCCTATTGCCCATGGCGGATCTTCTGTTGACTTAGTAATTTTTTCTTTACATCTGGCTGGGATATCTTCAATCTTGGGAGCTATTAATTTTATTACTACCATTTTTAATATACGGTCTCCTGGGATGACTATAGAGCGCTTGAACTTATTTGTTTGATCCGTATTAGTAACTGCCTTTTTGCTTTTACTTAGACTTCCTGTTCTTGCTGGGGCTATTACAATGCTTTTAACAGATCGAAACTTTAATACTAGGTTTTTTGATCCTGCCGGGGGTGGGGATCCAATTTTATATCAACACTTGTTC
+>ZSM20080176|verrucosa
+GACTTTATATATAGTGTTTGGTGTGTGAGCTGGGTTGGTGGGAACTGGCTTATCCTTGTTAATTCGTTTTGAGTTGGGAACAGCATCTGTTTTTATGGATGAGCATTTTTATAATGTAGTTGTCACTGCACATGCCTTTGTCATGATTTTTTTTATAGTTATGCCTCTTATAATTGGGGGGTTCGGAAATTGAATAGTTCCTTTGCTTATTGGTGCTCCCGATATAAGCTTTCCTCGAATAAACAATATAAGATTCTGATTATTGCCGCCCTCATTTATTTTACTTTTATGCTCTGCTATGGTAGAAGGAGGGGCTGGGACTGGGTGAACTGTTTACCCTCCGTTAAGAGGTCCTATTGCCCATGGCAGGTCTTCTGTTGATTTAGTAATTTTTTCTTTACATCTGGCTGGAATGTCTTCAATCTTGGGAGCTATTAATTTTATTACTACCATTTTTAATATACGGTCTCCTGGGATGACTATAGAACGCTTGAATTTATTTGTTTGATCTGTATTAGTGACTGCCTTTTTGCTTTTACTCAGACTTCCTGTTCTTGCTGGGGCTATTACAATGCTTTTAACAGATCGAAACTTTAATACTAGGTTTTTTGATCCTGCTGGGGGCGGGGATCCAATTTTATACCAACATTTGTTT
+>ZSM20071135|verrucosa
+AACTTTATATATAGTATTTGGTGTGTGAGCTGGGTTGGTGGGAACTGGCTTATCCTTGTTAATTCGTTTTGAGTTGGGAACGGCATCTGTTTTTATGGATGAACATTTTTATAATGTAGTTGTCACTGCGCATGCCTTTGTTATAATTTTTTTTATAGTTATGCCTCTTATAATTGGGGGGTTTGGAAACTGAATAGTTCCTTTACTTATTGGTGCTCCCGATATAAGCTTTCCTCGAATAAATAATATAAGATTCTGATTATTACCGCCATCATTTATTTTACTTTTATGCTCTGCTATGGTAGAAGGAGGAGCTGGGACTGGGTGAACCGTCTATCCTCCGTTAAGAGGTCCTATTGCCCATGGCGGATCTTCTGTTGACTTAGTAATTTTTTCTTTACATCTGGCTGGGATATCTTCAATCTTGGGAGCTATTAATTTTATTACTACCATTTTTAATATACGGTCTCCTGGGATAAATATAGAACGTTTAAACTTATTTGTTTGATCCGTATTAGTGACTGCCTTTTTGCTTTTACTTAGACTTCCTGTTCTTGCTGGGGCCATTACAATGCTTTTAACAGATCGAAACTTTAATACTAGGTTTTTTGATCCTGCTGGGGGTGGGGATCCAATTTTATATCAACATTTGTTC
+>ZSM20100388|verrucosa
+GACTTTGTATATAGTGTTTGGTGTGTGAGCTGGGTTGGTGGGAACTGGTTTATCCTTGTTAGTTCGTTTTGAGCTGGGAACAGCATCTGTTTTTATGGATGAGCATTTTTATAACGTAGTTGTCACTGCGCATGCCTTTGTTATGATTTTTTTTATAGTTATGCCTCTTATAATTGGAGGGTTCGGAAACTGAATAGTTCCTTTACTTATTGGTGCTCCCGATATAAGCTTTCCTCGAATAAACAATATAAGATTCTGATTATTGCCGCCATCATTTATTTTACTTTTATGTTCTGCTATGGTAGAAGGAGGAGCTGGGACTGGGTGAACTGTCTATCCTCCGTTAAGAGGTCCTATTGCCCATGGCGGATCTTCTGTTGACTTAGTGATTTTTTCTTTACATCTGGCTGGGATATCTTCAATCTTGGGAGCTATTAATTTTATTACTACCATTTTTAATATACGGTCTCCTGGGATGACTATAGAACGCTTGAACTTATTTGTTTGATCTGTATTAGTAACTGCCTTTTTGCTTTTACTTAGACTTCCTGTTCTTGCTGGGGCCATTACAATGCTTTTAACAGATCGAAACTTTAATACTAGGTTTTTTGATCCTGCTGGGGGCGGGGATCCAATTTTATACCAACATTTGTTC
+>ZSM20100389|verrucosa
+GACTTTGTATATAGTGTTTGGTGTGTGAGCTGGGTTGGTGGGAACTGGTTTATCCTTGTTAGTTCGTTTTGAGCTGGGAACAGCATCTGTTTTTATGGATGAGCATTTTTATAACGTAGTTGTCACTGCGCATGCCTTTGTTATGATTTTTTTTATAGTTATGCCTCTTATAATTGGAGGGTTCGGAAACTGAATAGTTCCTTTACTTATTGGTGCTCCCGATATAAGCTTTCCTCGAATAAACAATATAAGATTCTGATTATTGCCGCCATCATTTATTTTACTTTTATGTTCTGCTATGGTAGAAGGAGGAGCTGGGACTGGGTGAACTGTCTATCCTCCGTTAAGAGGTCCTATTGCCCATGGCGGATCTTCTGTTGACTTAGTGATTTTTTCTTTACATCTGGCTGGGATATCTTCAATCTTGGGAGCTATTAATTTTATTACTACCATTTTTAATATACGGTCTCCTGGGATGACTATAGAACGCTTGAACTTATTTGTTTGATCTGTATTAGTAACTGCCTTTTTGCTTTTACTTAGACTTCCTGTTCTTGCTGGGGCCATTACAATGCTTTTAACAGATCGAAACTTTAATACTAGGTTTTTTGATCCTGCTGGGGGCGGGGATCCAATTTTATACCAACATTTGTTC
+>ZSM20100390|verrucosa
+GACTTTGTATATAGTGTTTGGTGTGTGAGCTGGGTTGGTGGGAACTGGTTTATCCTTGTTAGTTCGTTTTGAGCTGGGAACAGCATCTGTTTTTATGGATGAGCATTTTTATAACGTAGTTGTCACTGCGCATGCCTTTGTTATGATTTTTTTTATAGTTATGCCTCTTATAATTGGAGGGTTCGGAAACTGAATAGTTCCTTTACTTATTGGTGCTCCCGATATAAGCTTTCCTCGAATAAACAATATAAGATTCTGATTATTGCCGCCATCATTTATTTTACTTTTATGTTCTGCTATGGTAGAAGGAGGAGCTGGGACTGGGTGAACTGTCTATCCTCCGTTAAGAGGTCCTATTGCCCATGGCGGATCTTCTGTTGACTTAGTGATTTTTTCTTTACATCTGGCTGGGATATCTTCAATCTTGGGAGCTATTAATTTTATTACTACCATTTTTAATATACGGTCTCCTGGGATGACTATAGAACGCTTGAACTTATTTGTTTGATCTGTATTAGTAACTGCCTTTTTGCTTTTACTTAGACTTCCTGTTCTTGCTGGGGCCATTACAATGCTTTTAACAGATCGAAACTTTAATACTAGGTTTTTTGATCCTGCTGGGGGCGGGGATCCAATTTTATACCAACATTTGTTC
+>ZSM20100391|verrucosa
+GACCTTATATATAGTATTTGGTGTGTGAGCTGGGTTGGTGGGAACTGGCCTATCCTTGTTGATTCGTTTTGAGTTGGGAACAGCATCTGTTTTTATAGATGAGCATTTTTATAATGTAGTTGTCACTGCGCATGCCTTTGTTATGATTTTTTTTATAGTTATGCCTCTTATAATTGGGGGTTTTGGAAACTGAATAGTTCCTTTACTTATTGGTGCTCCCGATATAAGCTTTCCTCGAATAAATAATATAAGATTCTGATTATTGCCGCCATCATTTATTTTACTTTTATGCTCTGCTATGGTAGAAGGAGGAGCTGGGACTGGGTGAACTGTCTATCCTCCGTTAAGAGGTCCTATCGCCCATGGCGGATCTTCTGTTGACTTAGTAATTTTTTCTTTACATCTGGCTGGGATATCTTCAATCTTGGGAGCTATTAATTTTATTACTACCATTTTTAATATGCGGTCTCCTGGGATGACTATAGAACGCTTGAATTTATTTGTTTGATCCGTATTAGTGACTGCCTTTTTGCTTTTACTTAGACTTCCTGTTCTTGCTGGGGCCATTACAATGCTTTTAACAGATCGAAACTTTAATACTAGGTTTTTTGATCCTGCTGGAGGTGGGGATCCAATTTTATATCAACATTTGTTC
+>ZSM20081013|kepii
+GACTTTGTATATAGTATTTGGTGTATGAGCTGGGTTAGTAGGAACAGGACTTTCTTTGTTAATCCGATTTGAGTTAGGTACAGCATCAGTGTTTATAGATGAGCATTTTTATAATGTCATTGTCACAGCGCATGCCTTTGTAATAATCTTTTTCATAGTAATGCCTCTAATGATTGGGGGTTTTGGGAATTGGATGGTTCCTTTATTAATTGGAGCTCCGGATATAAGGTTTCCTCGAATAAATAATATGAGATTTTGGCTGCTTCCCCCCTCATTTCTGTTACTGTTAAGGTCAGTTATGGTAGAAGGGGGAGCAGGCACAGGTTGGACGGTATACCCTCCTTTGAGGGGCCCAATTGCTCATGGTGGTTCTTCTGTTGATTTGGTAATTTTCTCCTTGCATCTAGCTGGGATGTCTTCTATTTTAGGGGCTATTAACTTTATTACAACTATTTATAACATGCGTTCTCCAGGTATAACAATAGAACGTTTAGATTTATTTGTTTGGTCCGTTCTAGTTACTGCGTTTTTATTACTTTTAAGTCTTCCCGTTCTAGCAGGGGCCATTACCATGCTTTTAACGGATCGGAATTTTAACACTAGCTTTTTTGATCCGGCTGGAGGGGGAGATCCAATTCTATACCAGCATTTGTTT
+>ZSM20090197|joni
+CACTTTGTATATAGTGTTTGGTGTTTGAGCAGGTCTTGTGGGTACAGGTCTGTCTTTATTAATTCGTTTCGAACTAGGAACAGCGTCTGTCTTCATAGACGAGCATTTCTATAATGTTGTTGTCACGGCTCATGCTTTTGTAATAATTTTTTTTATAGTGATGCCTTTAATAATTGGGGGTTTTGGAAATTGAATGGTTCCTTTACTAATCGGGGCTCCTGATATAAGTTTTCCTCGTATAAACAATATAAGTTTTTGGCTACTTCCTCCCTCTTTTGTTTTGTTGTTATGCTCAGCGATAGTAGAAGGAGGCGCTGGAACTGGTTGAACAGTATACCCTCCTCTTAGAGGTCCCGTTGGTCATGGAGGCTCTTCTGTAGATTTGGTGATTTTTTCTCTTCATTTGGCAGGGATATCTTCTATTTTGGGGGCTATCAATTTTATTACTACGATTTTCAATATACGGTCTCCAGGGATAACTATGGAACGATTGAATTTATTTGTCTGATCAGTTTTAGTTACCGCATTTCTTTTATTATTAAGACTTCCAGTCCTAGCTGGGGCAATTACTATGCTTCTTACAGATCGGAACTTTAATACAAGGTTCTTTGATCCAGCCGGGGGTGGGGACCCTATTTTATATCAACACTTGTTC
+>SICBC2010KJ01D05|joni
+CACTTTGTATATAGTGTTTGGTGTTTGGGCAGGTCTTGTGGGTACAGGTCTCTCTTTATTAATTCGTTTCGAACTAGGGACAGCGTCTGTCTTCATAGACGAGCATTTCTATAATGTTGTTGTCACGGCTCATGCTTTTGTAATAATTTTTTTTATAGTGATGCCTTTAATAATTGGAGGTTTTGGTAATTGAATGGTTCCTTTATTAATCGGGGCTCCTGATATAAGTTTTCCTCGTATAAACAATATGAGTTTTTGGCTACTTCCTCCCTCTTTTGTTTTGTTGTTATGCTCAGCGATAGTAGAAGGAGGCGCTGGAACTGGTTGAACAGTATACCCTCCTCTTAGAGGTCCGGTTGGTCATGGGGGTTCTTCTGTAGATTTAGTGATTTTTTCTCTTCATTTGGCAGGAATATCTTCTATTTTAGGGGCTATTAATTTTATTACTACGATTTTCAACATACGGTCTCCAGGGATAACTATGGAGCGATTGAATTTATTTGTCTGATCAGTTTTAGTTACCGCATTTCTTTTATTATTAAGACTTCCAGTCTTAGCTGGGGCAATTACTATGCTTCTTACAGATCGGAACTTTAATACAAGGTTCTTTGATCCGGCCGGGGGTGGGGACCCTATTTTATATCAACACTTGTTC
+>SICBC2010KJ01C08|joni
+CACTTTGTATATAGTGTTTGGTGTTTGAGCAGGTCTTGTGGGTACAGGTCTGTCTTTATTAATTCGTTTTGAACTAGGAACAGCGTCTGTCTTCATAGACGAGCATTTCTACAATGTTGTTGTCACGGCTCATGCTTTTGTAATAATTTTTTTTATAGTGATGCCTTTAATAATTGGGGGTTTTGGTAACTGAATGGTTCCTTTACTAATCGGGGCTCCTGATATAAGTTTTCCTCGTATAAATAATATAAGTTTTTGGCTACTTCCTCCCTCTTTTGTTTTGTTGTTATGCTCAGCGATAGTAGAAGGAGGCGCTGGAACTGGTTGAACAGTATATCCCCCTCTTAGAGGTCCGGTCGGTCATGGAGGTTCCTCTGTAGATTTGGTGATTTTTTCTCTTCATTTGGCAGGGATATCTTCTATTTTAGGGGCTATTAATTTTATCACTACGATTTTCAACATACGGTCTCCAGGAATAACTATGGAACGATTGAATTTGTTTGTCTGATCAGTTTTAGTTACCGCATTTCTTTTATTATTAAGACTTCCAGTTCTAGCCGGGGCAATTACTATGCTTCTTACAGATCGGAACTTTAATACAAGGTTCTTTGATCCAGCCGGGGGTGGGGACCCTATTTTATATCAACACTTGTTC
+>AMC476062001|neridae
+AACTTTGTACATGGTTTTTGGAGTTTGGGCTGGTCTTGTGGGGACCGGCTTGTCTTTATTAATTCGATTTGAGTTAGGGACGGCAAGAGTTTTTATGGATGAACACTTTTATAATGTGATTGTGACGGCTCATGCTTTTGTTATAATTTTCTTTATGGTTATACCCTTGATGATTGGAGGGTTTGGGAATTGAATAGTCCCTCTGTTAATTGGGGCCCCAGACATAAGGTTTCCACGTATAAACAATATAAGTTTCTGACTACTACCTCCTTCGTTCTTGCTTCTTCTTTGTTCTGCAATGGTTGAAGGAGGAGCTGGAACAGGTTGAACTGTTTACCCTCCTCTTAGTGGACCTATTGCGCATGGTGGGTCTTCTGTTGACTTGGTAATCTTTTCGTTACACTTGGCTGGTATATCTTCCATTTTAGGAGCTATTAACTTTATTACAACTATCTTCAACATACGATCCCCAGGAATGTCTATGGAGCGACTGAATTTATTTGTATGATCAGTTTTAGTTACGGCTTTTTTATTATTATTGAGTTTACCTGTCCTTGCTGGTGCCATTACAATGCTGTTGACTGATCGGAATTTTAATACCAGCTTTTTTGACCCTGCCGGAGGGGGGGATCCTATTTTGTATCAACATCTTTTC
+>ZSM20100595|wiggi
+TACTTTATACATGATTTTTGGGGTATGATGTGGCCTGGTAGGGACTGGTCTATCCCTATTAATTCGTTTCGAACTGGGAACTGCTACAGTTTTTATAGATGAGCACTTTTACAATGTTGTTGTAACCGCTCATGCTTTTGTAATAATTTTTTTTATGGTTATGCCTCTTATGATTGGGGGTTTTGGAAACTGAATAGTTCCTCTACTGATTGGAGCTCCTGATATAAGATTTCCTCGTATGAACAACATAAGTTTTTGACTATTGCCTCCTTCTTTTATTCTTTTGCTGTGCTCTGCTATGGTTGAGGGGGGAGCAGGGACTGGATGGACAGTTTATCCACCTCTTAGAGGCCCAATTGCCCATGGAGGTTCTTCTGTTGACTTAGTTATTTTTTCCCTTCATTTGGCAGGGATGTCTTCTATTTTAGGGGCAATTAATTTTATCACAACTATTTTTAATATACGATCTCCGGGTATAAGAATGGAACGTTTAAATTTGTTTGTTTGATCAGTATTAGTGACTGCCTTTTTGCTTTTACTAAGTTTACCTGTCTTGGCTGGTGCTATCACCATGCTTTTAACCGATCGAAATTTCAACACTAGCTTCTTTGATCCGGCAGGAGGGGGGGATCCTATTTTGTATCAACATCTATTT
+>ZSM20100596|wiggi
+TACTTTATACATGATTTTTGGGGTATGATGTGGCCTGGTAGGGACTGGTCTATCCTTATTAATTCGTTTCGAACTAGGAACTGCTACAGTTTTTATAGATGAGCACTTTTACAATGTTGTTGTAACCGCTCATGCTTTTGTAATAATTTTTTTTATGGTTATGCCTCTTATGATTGGGGGTTTCGGAAACTGAATAGTTCCTCTACTGATTGGAGCTCCTGATATAAGATTTCCTCGCATAAACAACATAAGTTTTTGACTGTTGCCTCCTTCTTTTATTCTTTTGCTGTGTTCCGCTATGGTTGAGGGGGGAGCAGGGACTGGATGGACAGTTTATCCGCCTCTTAGAGGCCCAATTGCCCATGGAGGTTCTTCTGTTGACTTAGTTATTTTTTCCCTTCATTTGGCAGGGATGTCTTCTATTTTAGGGGCAATTAATTTTATTACAACTATTTTTAATATACGATCTCCGGGTATAAGAATGGAACGTTTAAATTTGTTTGTTTGATCAGTATTAGTGACTGCCTTTTTGCTTTTACTAAGTTTACCTGTCTTGGCTGGTGCTATTACCATGCTTTTAACCGATCGAAATTTCAACACTAGCTTCTTTGATCCGGCAGGAGGGGGGGATCCTATTTTGTATCAACATCTATTC
+>ZSM20100597|wiggi
+TACTTTATACATGATTTTTGGGGTGTGATGTGGCCTGGTAGGGACTGGTCTATCCCTATTAATTCGTTTCGAACTAGGAACTGCTACAGTTTTTATAGATGAGCACTTTTACAATGTTGTTGTAACCGCTCATGCTTTTGTAATAATTTTTTTTATGGTTATGCCTCTTATGATTGGGGGTTTCGGAAACTGAATAGTTCCTCTACTGATTGGAGCTCCTGACATAAGATTTCCTCGTATAAACAACATAAGTTTTTGACTGTTGCCTCCTTCTTTTATTCTTTTGCTGTGTTCCGCTATGGTTGAGGGGGGAGCAGGGACTGGATGGACAGTTTATCCGCCTCTTAGAGGCCCAATTGCCCATGGAGGTTCTTCTGTTGACTTAGTTATTTTTTCCCTTCATTTGGCAGGGATGTCTTCTATTTTAGGGGCAATTAATTTTATCACAACTATTTTTAATATACGATCTCCGGGTATAAGAATGGAACGTTTAAATTTGTTTGTTTGATCAGTATTAGTGACTGCCTTTTTGCTTTTACTAAGTTTACCTGTCTTGGCTGGTGCTATTACCATGCTTTTAACCGATCGAAATTTCAACACTAGCTTCTTTGATCCGGCAGGAGGGGGGGATCCTATTTTGTATCAACATCTATTC
+>ZSM20100603|wiggi
+TACTTTATACATGATTTTTGGGGTATGATGTGGCCTGGTAGGGACTGGTCTATCCCTATTAATTCGTTTCGAACTAGGAACTGCTACAGTTTTTATAGATGAGCACTTTTACAATGTTGTTGTAACCGCTCATGCTTTTGTAATAATTTTTTTTATGGTTATGCCTCTTATGATTGGGGGTTTCGGAAACTGAATAGTTCCTCTACTGATTGGAGCTCCTGATATAAGATTTCCTCGTATAAACAACATAAGTTTTTGACTGTTGCCTCCTTCTTTTATTCTTTTGCTGTGTTCCGCTATGGTTGAGGGGGGAGCAGGGACTGGATGGACAGTTTATCCGCCTCTTAGAGGCCCAATTGCCCATGGAGGTTCTTCTGTTGACTTAGTTATTTTTTCCCTTCATTTGGCAGGGATGTCTTCTATTTTAGGGGCAATTAATTTTATCACAACTATTTTTAATATACGATCTCCAGGTATAAGAATGGAACGTTTAAATTTGTTTGTTTGATCAGTATTAGTGACTGCCTTTTTGCTTTTACTAAGTTTACCTGTCTTGGCTGGTGCTATTACCATGCTTTTAACCGATCGAAATTTCAACACTAGCTTCTTTGATCCGGCAGGAGGGGGGGATCCTATTTTGTATCAACATCTATTC
+>ZSM20100592|wenzli
+AACTTTATACATAATTTTTGGTGTTTGGTGTGGGTTAGTTGGAACTGGGCTTTCTCTGCTTATTCGATTTGAGCTAGGAACTGCCTCTGTCTTAATAGATGAACATTTTTATAATGTGATTGTTACAGCTCATGCATTTGTCATAATTTTTTTCATAGTTATACCCTTAATAATTGGAGGATTTGGGAATTGAATAGTTCCATTATTAATTGGAGCTGTGGATATAAGCTTTCCACGTATAAATAATATAAGATTTTGATTGCTTCCCCCTTCCTTTATTTTTCTACTGTGTTCATCTATAATTGAAGGAGGTGCTGGAACTGGGTGAACAGTATATCCTCCTCTGAGGGGTCCTATTGCTCATGCTGGGTCTTCAGTCGATCTTGTAATTTTTTCTTTACACTTGGCAGGGATATCTTCTATTTTAGGTGCTATTAATTTTATTACTACTATTTTTAATATACGATCTCCTGGGGTAGGAATAGAACGTCTAAATTTGTTTGTTTGATCTGTATTAGTAACAGCTTTTCTTTTACTTCTAAGACTTCCTGTTTTAGCAGGAGCTATTACTATGCTATTAACTGATCGTAATATTAATACAACATTCTTTGACCCCGCAGGAGGAGGTGACCCTATTTTATACCAACATTTGTTT
+>ZSM20081014|wenzli
+AACTTTATATATAATTTTTGGTGTTTGATGTGGATTAGTTGGAACTGGGCTTTCATTACTCATTCGATTTGAGTTAGGGACTGCTTCCGTTTTAATAGACGAGCACTTTTATAATGTGATTGTAACTGCTCATGCATTCGTAATAATTTTTTTTATGGTTATACCCCTAATAATTGGAGGATTTGGAAATTGAATAGTACCTTTATTAATTGGTGCCGTCGATATAAGGTTTCCCCGTATAAATAATATAAGATTCTGGTTACTTCCTCCATCATTTATCTTTCTTCTATGCTCTTCTATAGTCGAAGGAGGGGCTGGGACAGGTTGAACAGTATATCCTCCTTTAAGAGGATCTATTGCTCATGCTGGATCTTCAGTAGATCTAGTAATTTTTTCTCTACATTTAGCAGGTATGTCTTCTATTCTTGGTGCAATTAATTTTATTACTACTATTTTTAATATGCGGTCTCCAGGAATTACCCTAGAACGCTTAAATTTGTTCGTTTGGTCGGTATTGGTAACAGCTTTTCTGTTACTTTTAAGATTACCTGTTTTAGCTGGAGCAATTACTATGTTGTTAACTGATCGTAACATTAATACGACTTTCTTTGATCCTGCAGGAGGAGGGGATCCTATTTTATACCAACACTTATTT
+>ZSM20100379|wenzli
+AACTTTATATATAATTTTTGGTGTTTGATGTGGATTAGTTGGAACTGGGCTTTCATTACTCATTCGATTTGAGTTAGGGACTGCTTCCGTTTTAATAGACGAGCACTTTTATAATGTGATTGTAACTGCTCATGCATTCGTAATAATTTTTTTTATGGTTATACCCCTAATAATTGGAGGATTTGGAAATTGAATAGTACCTTTGTTAATTGGTGCCGTCGATATAAGGTTTCCTCGTATAAATAATATAAGATTCTGGTTACTTCCTCCATCATTTATCTTTCTTCTATGCTCTTCTATAGTCGAAGGAGGAGCTGGGACAGGTTGAACAGTATATCCTCCTTTAAGAGGATCTATTGCTCATGCTGGATCTTCAGTAGATCTAGTAATTTTTTCTCTACACTTAGCAGGTATGTCTTCTATTCTTGGTGCAATTAATTTTATTACTACTATTTTTAATATGCGGTCTCCAGGAATCACCCTAGAACGCTTAAATTTGTTCGTTTGATCGGTATTGGTAACAGCTTTTTTGTTACTTTTAAGATTACCTGTTTTAGCTGGAGCAATTACTATGTTGTTAACTGATCGTAACATTAATACGACTTTCTTTGATCCTGCAGGAGGAGGGGATCCTATTTTATACCAACATTTATTT
+>ZSM20071133|peteryalli
+AACCTTATACATGTTGTTTGGAATTTGGTGCGGATTAGTTGGGACAGCTTTGTCACTGCTGATTCGGATTGAGCTCGGCGTGACTTCTGTGTTTTCAGATAGTCACTTTTACAATGTTATTGTTACTGCGCATGCTTTCACTATAATTTTTTTTATGGTTATGCCAATTATAATTGGCGGGTTCGGCAATTGAATGGTCCCCTTGCTTCTCGGTGCCCCTGATATGAGATTCCCTCGAATAAACAATCTAAGATTTTGATTACTACCTCCATCTTTTCTACTTCTCCTGTGTAGAAGTATAGTAGAAGGAGGCGCAGGGACTGGGTGAACAGTTTACCCCCCTCTTAGTGGTCCAACAGGACACAGAAGTTCATCAGTCGACTTAGTAATTTTCTCCCTTCATTTAGCTGGGGTTTCTTCTATTTTAGGGGCCATTAATTTTATTACGACTATTTACAATATACGAATTCCCGGAGTTACAATAGATCGGCTGAACTTATTTTGTTGGTCGATTTTGGTGACAGCGTTTCTTCTCTTACTGAGTCTTCCAGTTCTCGCCGGAGCTATTACTATACTTTTGACTGACCGGAACTTCAATACTAGATTTTTTGATCCAGCTGGGGGAGGCGACCCCATTTTATACCAGCATTTATTT

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Pontohedyle_COI.fas	Sun Jan 29 16:25:48 2023 +0000
@@ -0,0 +1,54 @@
+>ZSM20071381|milaschewitchii
+NNNTCTATATCTTGTTTTTGGAGTCTGGTGCGGTTTAGTGGGTACAGGGCTTTCCCTTCTTATCCGGTTTGAGCTAGGCACGTCCTCCGTCTTCATAGACGAGCACTTTTATAATGTTATTGTGACGGCCCATGCATTTGTTATAATTTTTTTTATGGTCATGCCACTAATAATTGGTGGTTTTGGGAATTGAATGGTTCCCCTGTTAATTGGGGCCCCCGACATAAGGTTTCCTCGAATAAATAACATAAGATTTTGGTTGCTTCCCCCTAGGTTTGTTCTTTTGTTGAGCTCAAGATTAGTTGAGGGGGGTGCGGGTACAGGGTGAACTGTCTATCCGCCCCTAAGCGGGTCTGTTGCGCACGGAGGAGCCTCGGTAGACTTAGTTATTTTTTCACTACATTTGGCGGGAATGTCTTCTATTCTTGGGGCTATCAACTTCATTACAACAATCTTTAATATGCGGTCCCCCGGGCTAACAATGGAACGGCTGAGGTTGTTTGTTTGGTCTGTTCTAGTCACGGCTTTTTTACTTCTGCTAAGACTGCCTGTGCTAGCAGGGGCTATCACAATGCTTTTAACTGATCGTAACTTTAACACTAGTTTTTTTGACCCGGCCGGAGGGGGGGACCCTATTTTATACCAACACCTTTTC
+>ZSM20080925|milaschewitchii
+NNNTCTATATCTTGTTTTTGGAGTCTGGTGCGGTTTAGTAGGTACAGGGCTTTCCCTTCTTATCCGGTTTGAGCTAGGCACGTCCTCCGTGTTCATAGACGAGCACTTTTATAATGTTATTGTGACGGCCCATGCATTTGTTATAATTTTTTTTATGGTCATGCCACTAATAATTGGTGGTTTTGGGAATTGAATGGTTCCCCTGTTAATTGGGGCCCCCGACATAAGGTTTCCTCGGATAAACAACATAAGGTTTTGGTTGCTTCCCCCTAGGTTTGTTCTTTTGTTGAGCTCAAGATTAGTTGAGGGGGGTGCGGGTACAGGGTGAACTGTCTATCCGCCCCTAAGCGGGTCTGTTGCGCACGGAGGAGCCTCGGTAGACTTGGTTATTTTTTCACTACATTTGGCGGGAATGTCTTCTATTCTTGGGGCTATCAACTTCATTACAACAATCTTTAATATGCGGTCCCCCGGGCTAACAATGGAGCGGCTGAGGTTGTTTGTTTGGTCTGTTCTAGTCACGGCTTTTTTACTTCTGCTAAGATTGCCTGTGCTAGCAGGGGCTATCACAATGCTTTTAACTGATCGTAACTTTAACACTAGTTTTTTTGACCCGGCCGGAGGGGGGGACCCTATTTTATACCAACACCTTTTC
+>ZSM20080953|milaschewitchii
+AACTCTATATCTTGTTTTTGGAGTCTGGTGCGGTTTAGTGGGTACAGGGCTTTCTCTTCTTATCCGGTTTGAGCTAGGCACGTCCTCCGTCTTCATAGACGAGCACTTTTATAATGTTATTGTAACGGCCCATGCATTTGTTATAATTTTTTTTATGGTCATGCCACTAATAATTGGCGGTTTTGGTAATTGAATGGTTCCCCTGTTAATTGGGGCCCCCGACATAAGGTTTCCTCGAATAAATAACATAAGGTTTTGGTTGCTTCCCCCTAGGTTTGTTCTTTTGTTAAGCTCAAGATTAGTTGAGGGGGGCGCGGGTACAGGGTGAACTGTCTATCCGCCCCTAAGCGGGTCTGTTGCGCACGGAGGAGCCTCGGTAGACTTAGTTATTTTTTCACTACATTTGGCGGGAATGTCTTCTATTCTTGGGGCTATCAACTTCATTACAACAATCTTTAATATACGGTCCCCCGGGCTAACAATGGAACGGCTGAGGTTGTTTGTTTGGTCTGTTCTAGTCACGGCTTTTTTACTTCTGCTAAGATTGCCCGTGCTAGCAGGGGCTATCACAATGCTTTTAACTGATCGTAACTTTAACACTAGTTTTTTTGACCCGGCCGGAGGGGGGGACCCTATTTTATACCAACACCTTTTC
+>SICBC2010KJ01B09|brasilensis
+CACGTTGTACATTATCTTTGGGGTTTGATGCGGTTTGGTCGGTGCGGGGTTGTCTTTGTTGATTCGGGTAGAGTTGGGGACTTCTAGGGTGTTAACCGACCCTCACTTCTACAATGTGGTTGTGACTGCCCATGCTTTCGTTATGATTTTTTTTATGGTTATACCTGTCTTGATCGGGGGTTTTGGTAACTGAATGATCCCGCTTCTGATTGGGGCACCTGATATGGCTTTTCCTCGGTTAAATAACCTAAGGTTTTGGCTGCTCCCGCCTGCGTTTATTTTACTTATAAGCTCGGTTTTGGTAGAAGGCGGAGCGGGGACAGGTTGAACCTTGTATCCCCCTCTGAGCG---CGGAGGGGCATTCAGGATTTTCAGTTGATTTAGCCATTTTTTCTCTGCACTTGGCAGGGGTCTCTTCTATCTTAGGGGCAGTTAATTTTATTACCACTATTTGGAATATGCGGGCCCCGGGGGTTACTTGAGAGCGGCTGAATCTTTTTGTCTGGTCTTTACTTATTACAGCGTTATTGTTGCTGTTGTCACTGCCTGTGCTAGCTGGTGCTCTTACAATGTTGTTAACTGATCGGAATTTTAATACTACTTTTTTTGATCCGGCGGGGGGCGGGGACCCTGTTTTATACCAACACCTGTTC
+>SICBC2010KJ01C09|brasilensis
+CACGTTGTACATTATCTTTGGGGTTTGATGTGGTTTGGTCGGTGCGGGGTTGTCTTTGTTGATTCGGGTAGAGTTGGGGACTTCTAGAGTGTTAACCGACCCTCACTTCTACAATGTGGTTGTGACTGCCCATGCTTTCGTTATGATTTTTTTTATGGTTATACCTGTCTTGATTGGGGGTTTTGGTAACTGAATGATCCCGCTTCTGATTGGGGCACCTGATATGGCTTTTCCTCGGTTAAATAATCTAAGGTTTTGGCTGCTCCCTCCCGCGTTTATTTTACTTATAAGCTCGGTTCTGGTAGAAGGCGGAGCGGGGACAGGTTGAACCTTGTATCCCCCTCTGAGCG---CGGAGGGTCATTCAGGGTTTTCAGTTGATTTAGCCATTTTTTCTCTGCACTTGGCAGGGGTTTCTTCTATTTTAGGGGCAGTTAACTTTATTACCACTATTTGGAATATGCGGGCCCCGGGGGTTACTTGAGAGCGGCTGAATCTTTTTGTCTGGTCTTTACTTATTACAGCGTTATTGCTGCTGTTGTCACTGCCTGTGCTAGCTGGTGCTCTTACAATGTTGTTAACTGATCGGAATTTTAATACTACTTTTTTTGATCCGGCGGGGGGCGGGGACCCTGTTTTATACCAACACCTGTTC
+>ZSM20110723|brasilensis
+CACGTTGTACATTATCTTTGGGGTTTGATGCGGTTTGGTCGGTGCGGGGTTGTCTTTGTTGATTCGGGTAGAGTTGGGGGCTTCTAGGGTGTTAACCGACCCTCACTTCTACAATGTGGTTGTGACTGCCCATGCTTTCGTTATGATTTTTTTTATGGTTATACCTGTCTTGATCGGGGGGTTTGGTAACTGAATGATCCCGCTTCTGATTGGGGCACCTGATATGGCTTTTCCTCGGTTAAATAACCTAAGGTTTTGGCTGCTCCCTCCCGCGTTTATTTTACTTATAAGCTCGGTTCTGGTAGAAGGCGGAGCGGGGACAGGTTGAACCTTGTATCCCCCTCTGAGCG---CGGAGGGTCATTCAGGGTTTTCAGTTGATTTAGCCATTTTTTCTCTGCACTTGGCAGGGGTCTCTTCTATTTTAGGGGCAGTTAACTTTATTACCACTATTTGGAATATGCGGGCCCCGGGGGTTACTTGAGAGCGGCTGAATCTTTTTGTCTGGTCTTTACTTATTACAGCGTTATTGTTGCTGTTGTCACTGCCTGTGCTAGCTGGTGCTCTTACAATGTTGTTAACTGATCGGAATTTTAATACTACTTTTTTTGATCCGGCGGGGGGCGGGGACCCTGTTTTATACCAACACTTGTTC
+>ZSM20110722|brasilensis
+TACGCTATACATTATCTTTGGGGTTTGATGCGGCTTGGTCGGTGCGGGGTTGTCTTTGTTGATTCGGGTGGAGTTAGGGACTTCTGGGGTGTTAACCGACCCACACTTCTACAATGTGGTAGTGACTGCCCATGCTTTCGTCATGATTTTTTTTATGGTTATACCCGTCTTGATCGGGGGTTTCGGTAATTGAATGATCCCGCTCCTGATTGGGGCACCTGATATGGCCTTTCCCCGGTTAAATAATTTAAGGTTTTGGCTGCTTCCTCCTGCGTTTATTTTACTTATAAGCTCGGTTTTAGTAGAAGGCGGTGCGGGGACAGGCTGAACTTTATACCCCCCCTTGAGCG---CGGAGGGTCACTCAGGGTTTTCAGTTGATTTAGCTATTTTTTCTCTGCACTTGGCGGGGGTCTCATCTATTCTAGGGGCGGTTAACTTCATCACAACTATCTGGAATATGCGGGCCCCAGGGGTTACTTGAGAGCGGTTGAATCTTTTTGTGTGGTCTTTACTCATTACAGCGTTATTGTTGTTGTTGTCACTACCAGTACTGGCTGGTGCTCTTACAATGTTGTTAACTGATCGGAATTTTAATACTACTTTTTTTGATCCAGCGGGGGGTGGGGACCCAGTTTTATATCAACATCTGTTC
+>ZSM20071820|verrucosa
+GACCTTGTATATAGTATTTGGTGTGTTAGCTGGGTTGGTGGGAACTGGCTTATCCTTGTTAATTCGTTTTGAGTTGGGAACAGCATCTGTTTTTATGGATGAGCATTTTTATAATGTAGTTGTCACTGCGCATGCCTTTGTTATGATTTTTTTTATAGTTATGCCTCTTATAATTGGGGGGTTTGGAAACTGAATAGTTCCTTTACTTATTGGTGCCCCCGATATAAGTTTTCCTCGAATAAATAATATAAGATTCTGATTATTGCCGCCCTCATTTATTTTACTTTTATGCTCTGCTATGGTAGAAGGAGGAGCTGGCACTGGGTGAACTGTCTATCCTCCGTTAAGAGGTCCTATTGCCCATGGCGGATCTTCTGTTGACTTAGTAATTTTTTCTTTACATCTGGCTGGGATATCTTCAATCTTGGGAGCTATTAATTTTATTACTACCATTTTTAATATACGGTCTCCTGGGATGACTATAGAGCGCTTGAACTTATTTGTTTGATCCGTATTAGTAACTGCCTTTTTGCTTTTACTTAGACTTCCTGTTCTTGCTGGGGCTATTACAATGCTTTTAACAGATCGAAACTTTAATACTAGGTTTTTTGATCCTGCCGGGGGTGGGGATCCAATTTTATATCAACACTTGTTC
+>ZSM20080176|verrucosa
+GACTTTATATATAGTGTTTGGTGTGTGAGCTGGGTTGGTGGGAACTGGCTTATCCTTGTTAATTCGTTTTGAGTTGGGAACAGCATCTGTTTTTATGGATGAGCATTTTTATAATGTAGTTGTCACTGCACATGCCTTTGTCATGATTTTTTTTATAGTTATGCCTCTTATAATTGGGGGGTTCGGAAATTGAATAGTTCCTTTGCTTATTGGTGCTCCCGATATAAGCTTTCCTCGAATAAACAATATAAGATTCTGATTATTGCCGCCCTCATTTATTTTACTTTTATGCTCTGCTATGGTAGAAGGAGGGGCTGGGACTGGGTGAACTGTTTACCCTCCGTTAAGAGGTCCTATTGCCCATGGCAGGTCTTCTGTTGATTTAGTAATTTTTTCTTTACATCTGGCTGGAATGTCTTCAATCTTGGGAGCTATTAATTTTATTACTACCATTTTTAATATACGGTCTCCTGGGATGACTATAGAACGCTTGAATTTATTTGTTTGATCTGTATTAGTGACTGCCTTTTTGCTTTTACTCAGACTTCCTGTTCTTGCTGGGGCTATTACAATGCTTTTAACAGATCGAAACTTTAATACTAGGTTTTTTGATCCTGCTGGGGGCGGGGATCCAATTTTATACCAACATTTGTTT
+>ZSM20071135|verrucosa
+AACTTTATATATAGTATTTGGTGTGTGAGCTGGGTTGGTGGGAACTGGCTTATCCTTGTTAATTCGTTTTGAGTTGGGAACGGCATCTGTTTTTATGGATGAACATTTTTATAATGTAGTTGTCACTGCGCATGCCTTTGTTATAATTTTTTTTATAGTTATGCCTCTTATAATTGGGGGGTTTGGAAACTGAATAGTTCCTTTACTTATTGGTGCTCCCGATATAAGCTTTCCTCGAATAAATAATATAAGATTCTGATTATTACCGCCATCATTTATTTTACTTTTATGCTCTGCTATGGTAGAAGGAGGAGCTGGGACTGGGTGAACCGTCTATCCTCCGTTAAGAGGTCCTATTGCCCATGGCGGATCTTCTGTTGACTTAGTAATTTTTTCTTTACATCTGGCTGGGATATCTTCAATCTTGGGAGCTATTAATTTTATTACTACCATTTTTAATATACGGTCTCCTGGGATAAATATAGAACGTTTAAACTTATTTGTTTGATCCGTATTAGTGACTGCCTTTTTGCTTTTACTTAGACTTCCTGTTCTTGCTGGGGCCATTACAATGCTTTTAACAGATCGAAACTTTAATACTAGGTTTTTTGATCCTGCTGGGGGTGGGGATCCAATTTTATATCAACATTTGTTC
+>ZSM20100388|verrucosa
+GACTTTGTATATAGTGTTTGGTGTGTGAGCTGGGTTGGTGGGAACTGGTTTATCCTTGTTAGTTCGTTTTGAGCTGGGAACAGCATCTGTTTTTATGGATGAGCATTTTTATAACGTAGTTGTCACTGCGCATGCCTTTGTTATGATTTTTTTTATAGTTATGCCTCTTATAATTGGAGGGTTCGGAAACTGAATAGTTCCTTTACTTATTGGTGCTCCCGATATAAGCTTTCCTCGAATAAACAATATAAGATTCTGATTATTGCCGCCATCATTTATTTTACTTTTATGTTCTGCTATGGTAGAAGGAGGAGCTGGGACTGGGTGAACTGTCTATCCTCCGTTAAGAGGTCCTATTGCCCATGGCGGATCTTCTGTTGACTTAGTGATTTTTTCTTTACATCTGGCTGGGATATCTTCAATCTTGGGAGCTATTAATTTTATTACTACCATTTTTAATATACGGTCTCCTGGGATGACTATAGAACGCTTGAACTTATTTGTTTGATCTGTATTAGTAACTGCCTTTTTGCTTTTACTTAGACTTCCTGTTCTTGCTGGGGCCATTACAATGCTTTTAACAGATCGAAACTTTAATACTAGGTTTTTTGATCCTGCTGGGGGCGGGGATCCAATTTTATACCAACATTTGTTC
+>ZSM20100389|verrucosa
+GACTTTGTATATAGTGTTTGGTGTGTGAGCTGGGTTGGTGGGAACTGGTTTATCCTTGTTAGTTCGTTTTGAGCTGGGAACAGCATCTGTTTTTATGGATGAGCATTTTTATAACGTAGTTGTCACTGCGCATGCCTTTGTTATGATTTTTTTTATAGTTATGCCTCTTATAATTGGAGGGTTCGGAAACTGAATAGTTCCTTTACTTATTGGTGCTCCCGATATAAGCTTTCCTCGAATAAACAATATAAGATTCTGATTATTGCCGCCATCATTTATTTTACTTTTATGTTCTGCTATGGTAGAAGGAGGAGCTGGGACTGGGTGAACTGTCTATCCTCCGTTAAGAGGTCCTATTGCCCATGGCGGATCTTCTGTTGACTTAGTGATTTTTTCTTTACATCTGGCTGGGATATCTTCAATCTTGGGAGCTATTAATTTTATTACTACCATTTTTAATATACGGTCTCCTGGGATGACTATAGAACGCTTGAACTTATTTGTTTGATCTGTATTAGTAACTGCCTTTTTGCTTTTACTTAGACTTCCTGTTCTTGCTGGGGCCATTACAATGCTTTTAACAGATCGAAACTTTAATACTAGGTTTTTTGATCCTGCTGGGGGCGGGGATCCAATTTTATACCAACATTTGTTC
+>ZSM20100390|verrucosa
+GACTTTGTATATAGTGTTTGGTGTGTGAGCTGGGTTGGTGGGAACTGGTTTATCCTTGTTAGTTCGTTTTGAGCTGGGAACAGCATCTGTTTTTATGGATGAGCATTTTTATAACGTAGTTGTCACTGCGCATGCCTTTGTTATGATTTTTTTTATAGTTATGCCTCTTATAATTGGAGGGTTCGGAAACTGAATAGTTCCTTTACTTATTGGTGCTCCCGATATAAGCTTTCCTCGAATAAACAATATAAGATTCTGATTATTGCCGCCATCATTTATTTTACTTTTATGTTCTGCTATGGTAGAAGGAGGAGCTGGGACTGGGTGAACTGTCTATCCTCCGTTAAGAGGTCCTATTGCCCATGGCGGATCTTCTGTTGACTTAGTGATTTTTTCTTTACATCTGGCTGGGATATCTTCAATCTTGGGAGCTATTAATTTTATTACTACCATTTTTAATATACGGTCTCCTGGGATGACTATAGAACGCTTGAACTTATTTGTTTGATCTGTATTAGTAACTGCCTTTTTGCTTTTACTTAGACTTCCTGTTCTTGCTGGGGCCATTACAATGCTTTTAACAGATCGAAACTTTAATACTAGGTTTTTTGATCCTGCTGGGGGCGGGGATCCAATTTTATACCAACATTTGTTC
+>ZSM20100391|verrucosa
+GACCTTATATATAGTATTTGGTGTGTGAGCTGGGTTGGTGGGAACTGGCCTATCCTTGTTGATTCGTTTTGAGTTGGGAACAGCATCTGTTTTTATAGATGAGCATTTTTATAATGTAGTTGTCACTGCGCATGCCTTTGTTATGATTTTTTTTATAGTTATGCCTCTTATAATTGGGGGTTTTGGAAACTGAATAGTTCCTTTACTTATTGGTGCTCCCGATATAAGCTTTCCTCGAATAAATAATATAAGATTCTGATTATTGCCGCCATCATTTATTTTACTTTTATGCTCTGCTATGGTAGAAGGAGGAGCTGGGACTGGGTGAACTGTCTATCCTCCGTTAAGAGGTCCTATCGCCCATGGCGGATCTTCTGTTGACTTAGTAATTTTTTCTTTACATCTGGCTGGGATATCTTCAATCTTGGGAGCTATTAATTTTATTACTACCATTTTTAATATGCGGTCTCCTGGGATGACTATAGAACGCTTGAATTTATTTGTTTGATCCGTATTAGTGACTGCCTTTTTGCTTTTACTTAGACTTCCTGTTCTTGCTGGGGCCATTACAATGCTTTTAACAGATCGAAACTTTAATACTAGGTTTTTTGATCCTGCTGGAGGTGGGGATCCAATTTTATATCAACATTTGTTC
+>ZSM20081013|kepii
+GACTTTGTATATAGTATTTGGTGTATGAGCTGGGTTAGTAGGAACAGGACTTTCTTTGTTAATCCGATTTGAGTTAGGTACAGCATCAGTGTTTATAGATGAGCATTTTTATAATGTCATTGTCACAGCGCATGCCTTTGTAATAATCTTTTTCATAGTAATGCCTCTAATGATTGGGGGTTTTGGGAATTGGATGGTTCCTTTATTAATTGGAGCTCCGGATATAAGGTTTCCTCGAATAAATAATATGAGATTTTGGCTGCTTCCCCCCTCATTTCTGTTACTGTTAAGGTCAGTTATGGTAGAAGGGGGAGCAGGCACAGGTTGGACGGTATACCCTCCTTTGAGGGGCCCAATTGCTCATGGTGGTTCTTCTGTTGATTTGGTAATTTTCTCCTTGCATCTAGCTGGGATGTCTTCTATTTTAGGGGCTATTAACTTTATTACAACTATTTATAACATGCGTTCTCCAGGTATAACAATAGAACGTTTAGATTTATTTGTTTGGTCCGTTCTAGTTACTGCGTTTTTATTACTTTTAAGTCTTCCCGTTCTAGCAGGGGCCATTACCATGCTTTTAACGGATCGGAATTTTAACACTAGCTTTTTTGATCCGGCTGGAGGGGGAGATCCAATTCTATACCAGCATTTGTTT
+>ZSM20090197|joni
+CACTTTGTATATAGTGTTTGGTGTTTGAGCAGGTCTTGTGGGTACAGGTCTGTCTTTATTAATTCGTTTCGAACTAGGAACAGCGTCTGTCTTCATAGACGAGCATTTCTATAATGTTGTTGTCACGGCTCATGCTTTTGTAATAATTTTTTTTATAGTGATGCCTTTAATAATTGGGGGTTTTGGAAATTGAATGGTTCCTTTACTAATCGGGGCTCCTGATATAAGTTTTCCTCGTATAAACAATATAAGTTTTTGGCTACTTCCTCCCTCTTTTGTTTTGTTGTTATGCTCAGCGATAGTAGAAGGAGGCGCTGGAACTGGTTGAACAGTATACCCTCCTCTTAGAGGTCCCGTTGGTCATGGAGGCTCTTCTGTAGATTTGGTGATTTTTTCTCTTCATTTGGCAGGGATATCTTCTATTTTGGGGGCTATCAATTTTATTACTACGATTTTCAATATACGGTCTCCAGGGATAACTATGGAACGATTGAATTTATTTGTCTGATCAGTTTTAGTTACCGCATTTCTTTTATTATTAAGACTTCCAGTCCTAGCTGGGGCAATTACTATGCTTCTTACAGATCGGAACTTTAATACAAGGTTCTTTGATCCAGCCGGGGGTGGGGACCCTATTTTATATCAACACTTGTTC
+>SICBC2010KJ01D05|joni
+CACTTTGTATATAGTGTTTGGTGTTTGGGCAGGTCTTGTGGGTACAGGTCTCTCTTTATTAATTCGTTTCGAACTAGGGACAGCGTCTGTCTTCATAGACGAGCATTTCTATAATGTTGTTGTCACGGCTCATGCTTTTGTAATAATTTTTTTTATAGTGATGCCTTTAATAATTGGAGGTTTTGGTAATTGAATGGTTCCTTTATTAATCGGGGCTCCTGATATAAGTTTTCCTCGTATAAACAATATGAGTTTTTGGCTACTTCCTCCCTCTTTTGTTTTGTTGTTATGCTCAGCGATAGTAGAAGGAGGCGCTGGAACTGGTTGAACAGTATACCCTCCTCTTAGAGGTCCGGTTGGTCATGGGGGTTCTTCTGTAGATTTAGTGATTTTTTCTCTTCATTTGGCAGGAATATCTTCTATTTTAGGGGCTATTAATTTTATTACTACGATTTTCAACATACGGTCTCCAGGGATAACTATGGAGCGATTGAATTTATTTGTCTGATCAGTTTTAGTTACCGCATTTCTTTTATTATTAAGACTTCCAGTCTTAGCTGGGGCAATTACTATGCTTCTTACAGATCGGAACTTTAATACAAGGTTCTTTGATCCGGCCGGGGGTGGGGACCCTATTTTATATCAACACTTGTTC
+>SICBC2010KJ01C08|joni
+CACTTTGTATATAGTGTTTGGTGTTTGAGCAGGTCTTGTGGGTACAGGTCTGTCTTTATTAATTCGTTTTGAACTAGGAACAGCGTCTGTCTTCATAGACGAGCATTTCTACAATGTTGTTGTCACGGCTCATGCTTTTGTAATAATTTTTTTTATAGTGATGCCTTTAATAATTGGGGGTTTTGGTAACTGAATGGTTCCTTTACTAATCGGGGCTCCTGATATAAGTTTTCCTCGTATAAATAATATAAGTTTTTGGCTACTTCCTCCCTCTTTTGTTTTGTTGTTATGCTCAGCGATAGTAGAAGGAGGCGCTGGAACTGGTTGAACAGTATATCCCCCTCTTAGAGGTCCGGTCGGTCATGGAGGTTCCTCTGTAGATTTGGTGATTTTTTCTCTTCATTTGGCAGGGATATCTTCTATTTTAGGGGCTATTAATTTTATCACTACGATTTTCAACATACGGTCTCCAGGAATAACTATGGAACGATTGAATTTGTTTGTCTGATCAGTTTTAGTTACCGCATTTCTTTTATTATTAAGACTTCCAGTTCTAGCCGGGGCAATTACTATGCTTCTTACAGATCGGAACTTTAATACAAGGTTCTTTGATCCAGCCGGGGGTGGGGACCCTATTTTATATCAACACTTGTTC
+>AMC476062001|neridae
+AACTTTGTACATGGTTTTTGGAGTTTGGGCTGGTCTTGTGGGGACCGGCTTGTCTTTATTAATTCGATTTGAGTTAGGGACGGCAAGAGTTTTTATGGATGAACACTTTTATAATGTGATTGTGACGGCTCATGCTTTTGTTATAATTTTCTTTATGGTTATACCCTTGATGATTGGAGGGTTTGGGAATTGAATAGTCCCTCTGTTAATTGGGGCCCCAGACATAAGGTTTCCACGTATAAACAATATAAGTTTCTGACTACTACCTCCTTCGTTCTTGCTTCTTCTTTGTTCTGCAATGGTTGAAGGAGGAGCTGGAACAGGTTGAACTGTTTACCCTCCTCTTAGTGGACCTATTGCGCATGGTGGGTCTTCTGTTGACTTGGTAATCTTTTCGTTACACTTGGCTGGTATATCTTCCATTTTAGGAGCTATTAACTTTATTACAACTATCTTCAACATACGATCCCCAGGAATGTCTATGGAGCGACTGAATTTATTTGTATGATCAGTTTTAGTTACGGCTTTTTTATTATTATTGAGTTTACCTGTCCTTGCTGGTGCCATTACAATGCTGTTGACTGATCGGAATTTTAATACCAGCTTTTTTGACCCTGCCGGAGGGGGGGATCCTATTTTGTATCAACATCTTTTC
+>ZSM20100595|wiggi
+TACTTTATACATGATTTTTGGGGTATGATGTGGCCTGGTAGGGACTGGTCTATCCCTATTAATTCGTTTCGAACTGGGAACTGCTACAGTTTTTATAGATGAGCACTTTTACAATGTTGTTGTAACCGCTCATGCTTTTGTAATAATTTTTTTTATGGTTATGCCTCTTATGATTGGGGGTTTTGGAAACTGAATAGTTCCTCTACTGATTGGAGCTCCTGATATAAGATTTCCTCGTATGAACAACATAAGTTTTTGACTATTGCCTCCTTCTTTTATTCTTTTGCTGTGCTCTGCTATGGTTGAGGGGGGAGCAGGGACTGGATGGACAGTTTATCCACCTCTTAGAGGCCCAATTGCCCATGGAGGTTCTTCTGTTGACTTAGTTATTTTTTCCCTTCATTTGGCAGGGATGTCTTCTATTTTAGGGGCAATTAATTTTATCACAACTATTTTTAATATACGATCTCCGGGTATAAGAATGGAACGTTTAAATTTGTTTGTTTGATCAGTATTAGTGACTGCCTTTTTGCTTTTACTAAGTTTACCTGTCTTGGCTGGTGCTATCACCATGCTTTTAACCGATCGAAATTTCAACACTAGCTTCTTTGATCCGGCAGGAGGGGGGGATCCTATTTTGTATCAACATCTATTT
+>ZSM20100596|wiggi
+TACTTTATACATGATTTTTGGGGTATGATGTGGCCTGGTAGGGACTGGTCTATCCTTATTAATTCGTTTCGAACTAGGAACTGCTACAGTTTTTATAGATGAGCACTTTTACAATGTTGTTGTAACCGCTCATGCTTTTGTAATAATTTTTTTTATGGTTATGCCTCTTATGATTGGGGGTTTCGGAAACTGAATAGTTCCTCTACTGATTGGAGCTCCTGATATAAGATTTCCTCGCATAAACAACATAAGTTTTTGACTGTTGCCTCCTTCTTTTATTCTTTTGCTGTGTTCCGCTATGGTTGAGGGGGGAGCAGGGACTGGATGGACAGTTTATCCGCCTCTTAGAGGCCCAATTGCCCATGGAGGTTCTTCTGTTGACTTAGTTATTTTTTCCCTTCATTTGGCAGGGATGTCTTCTATTTTAGGGGCAATTAATTTTATTACAACTATTTTTAATATACGATCTCCGGGTATAAGAATGGAACGTTTAAATTTGTTTGTTTGATCAGTATTAGTGACTGCCTTTTTGCTTTTACTAAGTTTACCTGTCTTGGCTGGTGCTATTACCATGCTTTTAACCGATCGAAATTTCAACACTAGCTTCTTTGATCCGGCAGGAGGGGGGGATCCTATTTTGTATCAACATCTATTC
+>ZSM20100597|wiggi
+TACTTTATACATGATTTTTGGGGTGTGATGTGGCCTGGTAGGGACTGGTCTATCCCTATTAATTCGTTTCGAACTAGGAACTGCTACAGTTTTTATAGATGAGCACTTTTACAATGTTGTTGTAACCGCTCATGCTTTTGTAATAATTTTTTTTATGGTTATGCCTCTTATGATTGGGGGTTTCGGAAACTGAATAGTTCCTCTACTGATTGGAGCTCCTGACATAAGATTTCCTCGTATAAACAACATAAGTTTTTGACTGTTGCCTCCTTCTTTTATTCTTTTGCTGTGTTCCGCTATGGTTGAGGGGGGAGCAGGGACTGGATGGACAGTTTATCCGCCTCTTAGAGGCCCAATTGCCCATGGAGGTTCTTCTGTTGACTTAGTTATTTTTTCCCTTCATTTGGCAGGGATGTCTTCTATTTTAGGGGCAATTAATTTTATCACAACTATTTTTAATATACGATCTCCGGGTATAAGAATGGAACGTTTAAATTTGTTTGTTTGATCAGTATTAGTGACTGCCTTTTTGCTTTTACTAAGTTTACCTGTCTTGGCTGGTGCTATTACCATGCTTTTAACCGATCGAAATTTCAACACTAGCTTCTTTGATCCGGCAGGAGGGGGGGATCCTATTTTGTATCAACATCTATTC
+>ZSM20100603|wiggi
+TACTTTATACATGATTTTTGGGGTATGATGTGGCCTGGTAGGGACTGGTCTATCCCTATTAATTCGTTTCGAACTAGGAACTGCTACAGTTTTTATAGATGAGCACTTTTACAATGTTGTTGTAACCGCTCATGCTTTTGTAATAATTTTTTTTATGGTTATGCCTCTTATGATTGGGGGTTTCGGAAACTGAATAGTTCCTCTACTGATTGGAGCTCCTGATATAAGATTTCCTCGTATAAACAACATAAGTTTTTGACTGTTGCCTCCTTCTTTTATTCTTTTGCTGTGTTCCGCTATGGTTGAGGGGGGAGCAGGGACTGGATGGACAGTTTATCCGCCTCTTAGAGGCCCAATTGCCCATGGAGGTTCTTCTGTTGACTTAGTTATTTTTTCCCTTCATTTGGCAGGGATGTCTTCTATTTTAGGGGCAATTAATTTTATCACAACTATTTTTAATATACGATCTCCAGGTATAAGAATGGAACGTTTAAATTTGTTTGTTTGATCAGTATTAGTGACTGCCTTTTTGCTTTTACTAAGTTTACCTGTCTTGGCTGGTGCTATTACCATGCTTTTAACCGATCGAAATTTCAACACTAGCTTCTTTGATCCGGCAGGAGGGGGGGATCCTATTTTGTATCAACATCTATTC
+>ZSM20100592|wenzli
+AACTTTATACATAATTTTTGGTGTTTGGTGTGGGTTAGTTGGAACTGGGCTTTCTCTGCTTATTCGATTTGAGCTAGGAACTGCCTCTGTCTTAATAGATGAACATTTTTATAATGTGATTGTTACAGCTCATGCATTTGTCATAATTTTTTTCATAGTTATACCCTTAATAATTGGAGGATTTGGGAATTGAATAGTTCCATTATTAATTGGAGCTGTGGATATAAGCTTTCCACGTATAAATAATATAAGATTTTGATTGCTTCCCCCTTCCTTTATTTTTCTACTGTGTTCATCTATAATTGAAGGAGGTGCTGGAACTGGGTGAACAGTATATCCTCCTCTGAGGGGTCCTATTGCTCATGCTGGGTCTTCAGTCGATCTTGTAATTTTTTCTTTACACTTGGCAGGGATATCTTCTATTTTAGGTGCTATTAATTTTATTACTACTATTTTTAATATACGATCTCCTGGGGTAGGAATAGAACGTCTAAATTTGTTTGTTTGATCTGTATTAGTAACAGCTTTTCTTTTACTTCTAAGACTTCCTGTTTTAGCAGGAGCTATTACTATGCTATTAACTGATCGTAATATTAATACAACATTCTTTGACCCCGCAGGAGGAGGTGACCCTATTTTATACCAACATTTGTTT
+>ZSM20081014|wenzli
+AACTTTATATATAATTTTTGGTGTTTGATGTGGATTAGTTGGAACTGGGCTTTCATTACTCATTCGATTTGAGTTAGGGACTGCTTCCGTTTTAATAGACGAGCACTTTTATAATGTGATTGTAACTGCTCATGCATTCGTAATAATTTTTTTTATGGTTATACCCCTAATAATTGGAGGATTTGGAAATTGAATAGTACCTTTATTAATTGGTGCCGTCGATATAAGGTTTCCCCGTATAAATAATATAAGATTCTGGTTACTTCCTCCATCATTTATCTTTCTTCTATGCTCTTCTATAGTCGAAGGAGGGGCTGGGACAGGTTGAACAGTATATCCTCCTTTAAGAGGATCTATTGCTCATGCTGGATCTTCAGTAGATCTAGTAATTTTTTCTCTACATTTAGCAGGTATGTCTTCTATTCTTGGTGCAATTAATTTTATTACTACTATTTTTAATATGCGGTCTCCAGGAATTACCCTAGAACGCTTAAATTTGTTCGTTTGGTCGGTATTGGTAACAGCTTTTCTGTTACTTTTAAGATTACCTGTTTTAGCTGGAGCAATTACTATGTTGTTAACTGATCGTAACATTAATACGACTTTCTTTGATCCTGCAGGAGGAGGGGATCCTATTTTATACCAACACTTATTT
+>ZSM20100379|wenzli
+AACTTTATATATAATTTTTGGTGTTTGATGTGGATTAGTTGGAACTGGGCTTTCATTACTCATTCGATTTGAGTTAGGGACTGCTTCCGTTTTAATAGACGAGCACTTTTATAATGTGATTGTAACTGCTCATGCATTCGTAATAATTTTTTTTATGGTTATACCCCTAATAATTGGAGGATTTGGAAATTGAATAGTACCTTTGTTAATTGGTGCCGTCGATATAAGGTTTCCTCGTATAAATAATATAAGATTCTGGTTACTTCCTCCATCATTTATCTTTCTTCTATGCTCTTCTATAGTCGAAGGAGGAGCTGGGACAGGTTGAACAGTATATCCTCCTTTAAGAGGATCTATTGCTCATGCTGGATCTTCAGTAGATCTAGTAATTTTTTCTCTACACTTAGCAGGTATGTCTTCTATTCTTGGTGCAATTAATTTTATTACTACTATTTTTAATATGCGGTCTCCAGGAATCACCCTAGAACGCTTAAATTTGTTCGTTTGATCGGTATTGGTAACAGCTTTTTTGTTACTTTTAAGATTACCTGTTTTAGCTGGAGCAATTACTATGTTGTTAACTGATCGTAACATTAATACGACTTTCTTTGATCCTGCAGGAGGAGGGGATCCTATTTTATACCAACATTTATTT
+>ZSM20071133|peteryalli
+AACCTTATACATGTTGTTTGGAATTTGGTGCGGATTAGTTGGGACAGCTTTGTCACTGCTGATTCGGATTGAGCTCGGCGTGACTTCTGTGTTTTCAGATAGTCACTTTTACAATGTTATTGTTACTGCGCATGCTTTCACTATAATTTTTTTTATGGTTATGCCAATTATAATTGGCGGGTTCGGCAATTGAATGGTCCCCTTGCTTCTCGGTGCCCCTGATATGAGATTCCCTCGAATAAACAATCTAAGATTTTGATTACTACCTCCATCTTTTCTACTTCTCCTGTGTAGAAGTATAGTAGAAGGAGGCGCAGGGACTGGGTGAACAGTTTACCCCCCTCTTAGTGGTCCAACAGGACACAGAAGTTCATCAGTCGACTTAGTAATTTTCTCCCTTCATTTAGCTGGGGTTTCTTCTATTTTAGGGGCCATTAATTTTATTACGACTATTTACAATATACGAATTCCCGGAGTTACAATAGATCGGCTGAACTTATTTTGTTGGTCGATTTTGGTGACAGCGTTTCTTCTCTTACTGAGTCTTCCAGTTCTCGCCGGAGCTATTACTATACTTTTGACTGACCGGAACTTCAATACTAGATTTTTTGATCCAGCTGGGGGAGGCGACCCCATTTTATACCAGCATTTATTT

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Pontohedyle_COI_all.out	Sun Jan 29 16:25:48 2023 +0000
@@ -0,0 +1,69 @@
+<h4>########################## PARAMETERS ######################</h4>
+<p> input file: D:\MOLD3\Pontohedyle_COI.fas </p>
+<p> Coding gaps as characters: False </p>
+<p> Maximum undetermined nucleotides allowed: 100 </p>
+<p> Length of the alignment: 655 -> 655 </p>
+<p> Indexing reference: Not set </p>
+<p> Read in 27 sequences </p>
+<p> query taxa: 4 - neridae, verrucosa, wiggi, neridae+wiggi+verrucosa </p>
+<p> Cutoff set as: >1 </p>
+<p> Number iterations of MolD set as: 10000 </p>
+<p> Maximum length of raw mDNCs set as: 12 </p>
+<p> Maximum length of refined mDNCs set as: 5 </p>
+<p> simulated sequences up to 1.0 percent divergent from original ones </p>
+<p> Maximum number of sequences modified per clade 10 </p>
+<p> scoring of the rDNCs; threshold in two consequtive runs: 75 </p>
+<h4>########################### RESULTS ##########################</h4>
+<h4>************** neridae **************</h4>
+<p> Sequences analyzed: 1 </p>
+<p> single nucleotide mDNCs*: 13 - 46: 'C', 151: 'C', 169: 'G', 220: 'A', 277: 'C', 278: 'T', 289: 'T', 391: 'C', 397: 'G', 421: 'C', 479: 'T', 505: 'A', 601: 'C' </p>
+<p> mDNCs* retrieved: 7071; Sites involved: 299; Independent mDNCs**: 105 </p>
+<p> Shortest retrieved mDNC*: [46: 'C'] </p>
+<p> 1 rDNC_score (100): [46] - 86 </p>
+<p> 2 rDNC_score (100): [46, 151] - 93 </p>
+<p> Final rDNC***: [46: 'C', 151: 'C'] </p>
+<p> The DNA diagnosis for the taxon neridae is: 'C' in the site 46, 'C' in the site 151. </p>
+<h4>************** verrucosa **************</h4>
+<p> Sequences analyzed: 7 </p>
+<p> single nucleotide mDNCs*: 5 - 118: 'A', 343: 'G', 367: 'C', 421: 'A', 451: 'C' </p>
+<p> mDNCs* retrieved: 5964; Sites involved: 239; Independent mDNCs**: 69 </p>
+<p> Shortest retrieved mDNC*: [118: 'A'] </p>
+<p> 1 rDNC_score (100): [118] - 65 </p>
+<p> 2 rDNC_score (100): [118, 343] - 94 </p>
+<p> 3 rDNC_score (100): [118, 343, 367] - 97 </p>
+<p> Final rDNC***: [118: 'A', 343: 'G', 367: 'C'] </p>
+<p> The DNA diagnosis for the taxon verrucosa is: 'A' in the site 118, 'G' in the site 343, 'C' in the site 367. </p>
+<h4>************** wiggi **************</h4>
+<p> Sequences analyzed: 4 </p>
+<p> single nucleotide mDNCs*: 3 - 127: 'C', 325: 'A', 583: 'C' </p>
+<p> mDNCs* retrieved: 6841; Sites involved: 281; Independent mDNCs**: 86 </p>
+<p> Shortest retrieved mDNC*: [127: 'C'] </p>
+<p> 1 rDNC_score (100): [127] - 80 </p>
+<p> 2 rDNC_score (100): [127, 325] - 94 </p>
+<p> Final rDNC***: [127: 'C', 325: 'A'] </p>
+<p> The DNA diagnosis for the taxon wiggi is: 'C' in the site 127, 'A' in the site 325. </p>
+<h4>************** neridae+wiggi+verrucosa **************</h4>
+<p> Sequences analyzed: 12 </p>
+<p> single nucleotide mDNCs*: 0 -  </p>
+<p> mDNCs* retrieved: 3251; Sites involved: 96; Independent mDNCs**: 17 </p>
+<p> Shortest retrieved mDNC*: [59: 'T', 91: 'T'] </p>
+<p> 2 rDNC_score (100): [91, 196] - 0 </p>
+<p> 3 rDNC_score (100): [91, 196, 259] - 68 </p>
+<p> 4 rDNC_score (100): [91, 196, 259, 94] - 90 </p>
+<p> 5 rDNC_score (100): [91, 196, 259, 94, 538] - 92 </p>
+<p> Final rDNC***: [91: 'T', 94: 'T', 196: 'A', 259: 'A', 538: 'A'] </p>
+<p> The DNA diagnosis for the taxon neridae+wiggi+verrucosa is: 'T' in the site 91, 'T' in the site 94, 'A' in the site 196, 'A' in the site 259, 'A' in the site 538. </p>
+<h4> ################################# EXPLANATIONS #################################### </h4>
+<p>   * mDNC -(=minimal Diagnostic nucleotide combination) is a combination of nucleotides at specified sites of the alignment, </p>
+<p>     unique for a query taxon. Therefore it is sufficient to differentiate a query taxon from all reference taxa in a dataset. </p>
+<p>     Because it comprises minimal necessary number of nucleotide sites to differentiate a query, any mutation in the mDNC in</p>
+<p>     single specimen of a query taxon will automatically disqualify it as a diagnostic combination. </p>
+<p> </p>
+<p>  ** two or more mDNCs are INDEPENDENT if they constitute non-overlapping sets of nucleotide sites. </p>
+<p> </p>
+<p> *** rDNC -(=robust/redundant Diagnostic nucleotide combination) is a combination of nucleotides at specified sites of the alignment, </p>
+<p>     unique for a query taxon and (likewise mDNC) sufficient to differentiate a query taxon from all reference taxa in a dataset. </p>
+<p>     However, rDNC comprises more than a minimal necessary number of diagnostic sites, and therefore is robust to single nucleotide </p>
+<p>     replacements. Even if a mutation arises in one of the rDNC sites, the remaining ones will (with high probability) remain sufficient  </p>
+<p>     to diagnose the query taxon </p>
+<h4>     Final diagnosis corresponds to rDNC </h4>

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Pontohedyle_COI_all_pairwise.out	Sun Jan 29 16:25:48 2023 +0000
@@ -0,0 +1,9 @@
+<h4>************** neridae VS verrucosa **************</h4>
+<p> Each of the following 122 sites is invariant across sequences of neridae and differentiates it from verrucosa: 9 ('C' vs 'T'), 12 ('G' vs 'A'), 15 ('T' vs 'A'or'G'), 21 ('A' vs 'T'), 24 ('T' vs 'G'), 27 ('G' vs 'A'), 33 ('T' vs 'G'), 34 ('C' vs 'T'), 36 ('T' vs 'G'), 42 ('G' vs 'A'), 45 ('C' vs 'T'), 51 ('G' vs 'A'), 54 ('T' vs 'C'), 57 ('A' vs 'G'), 66 ('A' vs 'T'), 75 ('A' vs 'G'), 78 ('G' vs 'A'), 85 ('A' vs 'T'), 86 ('G' vs 'C'), 87 ('A' vs 'T'), 105 ('C' vs 'T'), 117 ('G' vs 'A'), 118 ('A' vs 'G'), 123 ('G' vs 'C'), 126 ('G' vs 'T'), 129 ('T' vs 'A'or'G'), 135 ('T' vs 'C'), 150 ('C' vs 'T'), 156 ('G' vs 'A'), 162 ('A' vs 'G'), 165 ('C' vs 'T'), 166 ('T' vs 'C'), 168 ('G' vs 'T'), 171 ('G' vs 'A'), 186 ('G' vs 'A'), 198 ('C' vs 'T'), 202 ('C' vs 'T'), 205 ('T' vs 'C'), 207 ('A' vs 'T'), 213 ('G' vs 'T'), 219 ('A' vs 'C'), 222 ('C' vs 'T'), 228 ('G' vs 'T'or'C'), 234 ('A' vs 'T'), 237 ('T' vs 'A'), 252 ('T' vs 'A'), 259 ('C' vs 'T'), 262 ('C' vs 'T'), 267 ('T' vs 'G'), 270 ('T' vs 'A'or'C'), 273 ('G' vs 'A'), 276 ('C' vs 'T'), 277 ('T' vs 'A'), 279 ('G' vs 'T'), 280 ('C' vs 'T'), 282 ('T' vs 'A'), 286 ('C' vs 'T'), 288 ('T' vs 'A'), 297 ('A' vs 'T'), 303 ('T' vs 'A'), 318 ('A' vs 'G'or'C'), 321 ('A' vs 'T'), 324 ('T' vs 'G'), 342 ('T' vs 'G'), 343 ('C' vs 'T'), 345 ('T' vs 'A'), 348 ('T' vs 'A'), 351 ('A' vs 'T'), 360 ('G' vs 'C'), 366 ('T' vs 'C'), 384 ('G' vs 'A'), 390 ('C' vs 'T'), 396 ('G' vs 'T'), 402 ('C' vs 'T'), 403 ('T' vs 'C'), 411 ('T' vs 'A'or'G'), 420 ('C' vs 'A'), 423 ('T' vs 'C'), 426 ('A' vs 'G'), 438 ('C' vs 'T'), 447 ('A' vs 'T'), 450 ('T' vs 'C'), 453 ('C' vs 'T'), 456 ('C' vs 'T'), 459 ('C' vs 'T'), 465 ('A' vs 'G'), 468 ('C' vs 'T'), 471 ('A' vs 'T'), 474 ('A' vs 'G'), 478 ('T' vs 'A'), 483 ('G' vs 'A'), 489 ('A' vs 'T'or'C'), 490 ('C' vs 'T'), 504 ('A' vs 'T'), 510 ('A' vs 'T'or'C'), 513 ('T' vs 'A'), 519 ('T' vs 'A'or'G'), 522 ('G' vs 'T'), 525 ('T' vs 'C'), 531 ('A' vs 'G'), 532 ('T' vs 'C'), 534 ('A' vs 'T'), 538 ('T' vs 'C'), 540 ('G' vs 'T'or'C'), 543 ('T' vs 'A'), 544 ('T' vs 'C'), 546 ('A' vs 'T'), 552 ('C' vs 'T'), 561 ('T' vs 'G'), 576 ('G' vs 'T'), 579 ('G' vs 'A'), 582 ('T' vs 'A'), 588 ('G' vs 'A'), 591 ('T' vs 'C'), 600 ('C' vs 'T'), 603 ('C' vs 'G'), 612 ('C' vs 'T'), 624 ('G' vs 'T'or'C'), 633 ('T' vs 'A'), 639 ('G' vs 'A'), 649 ('C' vs 'T'), 651 ('T' vs 'G') </h4>
+<p> Each of the following 111 sites is invariant across sequences of verrucosa and differentiates it from neridae: 9 ('T' vs 'C'), 12 ('A' vs 'G'), 21 ('T' vs 'A'), 24 ('G' vs 'T'), 27 ('A' vs 'G'), 33 ('G' vs 'T'), 34 ('T' vs 'C'), 36 ('G' vs 'T'), 42 ('A' vs 'G'), 45 ('T' vs 'C'), 51 ('A' vs 'G'), 54 ('C' vs 'T'), 57 ('G' vs 'A'), 66 ('T' vs 'A'), 75 ('G' vs 'A'), 78 ('A' vs 'G'), 85 ('T' vs 'A'), 86 ('C' vs 'G'), 87 ('T' vs 'A'), 105 ('T' vs 'C'), 117 ('A' vs 'G'), 118 ('G' vs 'A'), 123 ('C' vs 'G'), 126 ('T' vs 'G'), 135 ('C' vs 'T'), 150 ('T' vs 'C'), 156 ('A' vs 'G'), 162 ('G' vs 'A'), 165 ('T' vs 'C'), 166 ('C' vs 'T'), 168 ('T' vs 'G'), 171 ('A' vs 'G'), 186 ('A' vs 'G'), 198 ('T' vs 'C'), 202 ('T' vs 'C'), 205 ('C' vs 'T'), 207 ('T' vs 'A'), 213 ('T' vs 'G'), 219 ('C' vs 'A'), 222 ('T' vs 'C'), 234 ('T' vs 'A'), 237 ('A' vs 'T'), 252 ('A' vs 'T'), 259 ('T' vs 'C'), 262 ('T' vs 'C'), 267 ('G' vs 'T'), 273 ('A' vs 'G'), 276 ('T' vs 'C'), 277 ('A' vs 'T'), 279 ('T' vs 'G'), 280 ('T' vs 'C'), 282 ('A' vs 'T'), 286 ('T' vs 'C'), 288 ('A' vs 'T'), 297 ('T' vs 'A'), 303 ('A' vs 'T'), 321 ('T' vs 'A'), 324 ('G' vs 'T'), 342 ('G' vs 'T'), 343 ('T' vs 'C'), 345 ('A' vs 'T'), 348 ('A' vs 'T'), 351 ('T' vs 'A'), 360 ('C' vs 'G'), 366 ('C' vs 'T'), 384 ('A' vs 'G'), 390 ('T' vs 'C'), 396 ('T' vs 'G'), 402 ('T' vs 'C'), 403 ('C' vs 'T'), 420 ('A' vs 'C'), 423 ('C' vs 'T'), 426 ('G' vs 'A'), 438 ('T' vs 'C'), 447 ('T' vs 'A'), 450 ('C' vs 'T'), 453 ('T' vs 'C'), 456 ('T' vs 'C'), 459 ('T' vs 'C'), 465 ('G' vs 'A'), 468 ('T' vs 'C'), 471 ('T' vs 'A'), 474 ('G' vs 'A'), 478 ('A' vs 'T'), 483 ('A' vs 'G'), 490 ('T' vs 'C'), 504 ('T' vs 'A'), 513 ('A' vs 'T'), 522 ('T' vs 'G'), 525 ('C' vs 'T'), 531 ('G' vs 'A'), 532 ('C' vs 'T'), 534 ('T' vs 'A'), 538 ('C' vs 'T'), 543 ('A' vs 'T'), 544 ('C' vs 'T'), 546 ('T' vs 'A'), 552 ('T' vs 'C'), 561 ('G' vs 'T'), 576 ('T' vs 'G'), 579 ('A' vs 'G'), 582 ('A' vs 'T'), 588 ('A' vs 'G'), 591 ('C' vs 'T'), 600 ('T' vs 'C'), 603 ('G' vs 'C'), 612 ('T' vs 'C'), 633 ('A' vs 'T'), 639 ('A' vs 'G'), 649 ('T' vs 'C'), 651 ('G' vs 'T') </h4>
+<h4>************** neridae VS wiggi **************</h4>
+<p> Each of the following 132 sites is invariant across sequences of neridae and differentiates it from wiggi: 0 ('A' vs 'T'), 6 ('G' vs 'A'), 13 ('G' vs 'A'), 21 ('A' vs 'G'), 24 ('T' vs 'A'or'G'), 27 ('G' vs 'A'), 28 ('G' vs 'T'), 29 ('C' vs 'G'), 33 ('T' vs 'C'), 36 ('T' vs 'G'), 39 ('G' vs 'A'), 45 ('C' vs 'T'), 48 ('C' vs 'T'), 49 ('T' vs 'C'), 51 ('G' vs 'A'), 54 ('T' vs 'C'), 66 ('A' vs 'T'), 69 ('T' vs 'C'), 72 ('G' vs 'A'), 73 ('T' vs 'C'), 78 ('G' vs 'A'), 81 ('G' vs 'T'), 84 ('A' vs 'T'), 86 ('G' vs 'C'), 96 ('G' vs 'A'), 102 ('A' vs 'G'), 111 ('T' vs 'C'), 117 ('G' vs 'T'), 118 ('A' vs 'G'), 123 ('G' vs 'A'), 126 ('G' vs 'C'), 141 ('T' vs 'A'), 150 ('C' vs 'T'), 162 ('A' vs 'G'), 165 ('C' vs 'T'), 166 ('T' vs 'C'), 168 ('G' vs 'T'), 177 ('A' vs 'G'), 180 ('G' vs 'T'), 186 ('G' vs 'A'), 189 ('T' vs 'C'), 198 ('C' vs 'T'), 204 ('G' vs 'A'), 205 ('T' vs 'C'), 207 ('A' vs 'G'), 213 ('G' vs 'A'), 216 ('C' vs 'T'), 219 ('A' vs 'T'), 228 ('G' vs 'A'), 234 ('A' vs 'T'), 246 ('T' vs 'C'), 255 ('C' vs 'T'), 262 ('C' vs 'T'), 264 ('A' vs 'G'), 273 ('G' vs 'T'), 276 ('C' vs 'T'), 277 ('T' vs 'A'), 279 ('G' vs 'T'), 283 ('C' vs 'T'), 285 ('T' vs 'G'), 288 ('T' vs 'G'), 297 ('A' vs 'T'), 306 ('A' vs 'G'), 309 ('A' vs 'G'), 315 ('T' vs 'A'), 318 ('A' vs 'G'), 321 ('A' vs 'T'), 324 ('T' vs 'A'), 327 ('A' vs 'G'), 330 ('T' vs 'A'), 336 ('C' vs 'T'), 339 ('T' vs 'A'or'G'), 348 ('T' vs 'A'), 351 ('A' vs 'C'), 354 ('T' vs 'A'), 360 ('G' vs 'C'), 366 ('T' vs 'A'), 369 ('G' vs 'T'), 384 ('G' vs 'A'), 387 ('A' vs 'T'), 390 ('C' vs 'T'), 396 ('G' vs 'C'), 397 ('T' vs 'C'), 399 ('A' vs 'T'), 402 ('C' vs 'T'), 408 ('T' vs 'A'), 411 ('T' vs 'G'), 414 ('A' vs 'G'), 420 ('C' vs 'T'), 429 ('A' vs 'G'), 432 ('T' vs 'A'), 438 ('C' vs 'T'), 453 ('C' vs 'T'), 456 ('C' vs 'T'), 459 ('C' vs 'T'), 468 ('C' vs 'T'), 474 ('A' vs 'T'), 477 ('G' vs 'A'), 478 ('T' vs 'A'), 479 ('C' vs 'G'), 480 ('T' vs 'A'), 486 ('G' vs 'A'), 489 ('A' vs 'T'), 490 ('C' vs 'T'), 492 ('G' vs 'A'), 498 ('A' vs 'G'), 504 ('A' vs 'T'), 513 ('T' vs 'A'), 519 ('T' vs 'G'), 522 ('G' vs 'T'), 525 ('T' vs 'C'), 531 ('A' vs 'G'), 532 ('T' vs 'C'), 534 ('A' vs 'T'), 538 ('T' vs 'C'), 540 ('G' vs 'A'), 553 ('C' vs 'T'), 555 ('T' vs 'G'), 564 ('C' vs 'T'), 570 ('A' vs 'C'), 576 ('G' vs 'T'), 579 ('G' vs 'A'), 582 ('T' vs 'C'), 588 ('G' vs 'A'), 594 ('T' vs 'C'), 597 ('T' vs 'C'), 600 ('C' vs 'T'), 606 ('T' vs 'C'), 612 ('C' vs 'T'), 615 ('T' vs 'G'), 618 ('C' vs 'A'), 651 ('T' vs 'A') </h4>
+<p> Each of the following 130 sites is invariant across sequences of wiggi and differentiates it from neridae: 0 ('T' vs 'A'), 6 ('A' vs 'G'), 13 ('A' vs 'G'), 21 ('G' vs 'A'), 27 ('A' vs 'G'), 28 ('T' vs 'G'), 29 ('G' vs 'C'), 33 ('C' vs 'T'), 36 ('G' vs 'T'), 39 ('A' vs 'G'), 45 ('T' vs 'C'), 48 ('T' vs 'C'), 49 ('C' vs 'T'), 51 ('A' vs 'G'), 54 ('C' vs 'T'), 66 ('T' vs 'A'), 69 ('C' vs 'T'), 72 ('A' vs 'G'), 73 ('C' vs 'T'), 78 ('A' vs 'G'), 81 ('T' vs 'G'), 84 ('T' vs 'A'), 86 ('C' vs 'G'), 96 ('A' vs 'G'), 102 ('G' vs 'A'), 111 ('C' vs 'T'), 117 ('T' vs 'G'), 118 ('G' vs 'A'), 123 ('A' vs 'G'), 126 ('C' vs 'G'), 141 ('A' vs 'T'), 150 ('T' vs 'C'), 162 ('G' vs 'A'), 165 ('T' vs 'C'), 166 ('C' vs 'T'), 168 ('T' vs 'G'), 177 ('G' vs 'A'), 180 ('T' vs 'G'), 186 ('A' vs 'G'), 189 ('C' vs 'T'), 198 ('T' vs 'C'), 204 ('A' vs 'G'), 205 ('C' vs 'T'), 207 ('G' vs 'A'), 213 ('A' vs 'G'), 216 ('T' vs 'C'), 219 ('T' vs 'A'), 228 ('A' vs 'G'), 234 ('T' vs 'A'), 246 ('C' vs 'T'), 255 ('T' vs 'C'), 262 ('T' vs 'C'), 264 ('G' vs 'A'), 273 ('T' vs 'G'), 276 ('T' vs 'C'), 277 ('A' vs 'T'), 279 ('T' vs 'G'), 283 ('T' vs 'C'), 285 ('G' vs 'T'), 288 ('G' vs 'T'), 297 ('T' vs 'A'), 306 ('G' vs 'A'), 309 ('G' vs 'A'), 315 ('A' vs 'T'), 318 ('G' vs 'A'), 321 ('T' vs 'A'), 324 ('A' vs 'T'), 327 ('G' vs 'A'), 330 ('A' vs 'T'), 336 ('T' vs 'C'), 348 ('A' vs 'T'), 351 ('C' vs 'A'), 354 ('A' vs 'T'), 360 ('C' vs 'G'), 366 ('A' vs 'T'), 369 ('T' vs 'G'), 384 ('A' vs 'G'), 387 ('T' vs 'A'), 390 ('T' vs 'C'), 396 ('C' vs 'G'), 397 ('C' vs 'T'), 399 ('T' vs 'A'), 402 ('T' vs 'C'), 408 ('A' vs 'T'), 411 ('G' vs 'T'), 414 ('G' vs 'A'), 420 ('T' vs 'C'), 429 ('G' vs 'A'), 432 ('A' vs 'T'), 438 ('T' vs 'C'), 453 ('T' vs 'C'), 456 ('T' vs 'C'), 459 ('T' vs 'C'), 468 ('T' vs 'C'), 474 ('T' vs 'A'), 477 ('A' vs 'G'), 478 ('A' vs 'T'), 479 ('G' vs 'C'), 480 ('A' vs 'T'), 486 ('A' vs 'G'), 489 ('T' vs 'A'), 490 ('T' vs 'C'), 492 ('A' vs 'G'), 498 ('G' vs 'A'), 504 ('T' vs 'A'), 513 ('A' vs 'T'), 519 ('G' vs 'T'), 522 ('T' vs 'G'), 525 ('C' vs 'T'), 531 ('G' vs 'A'), 532 ('C' vs 'T'), 534 ('T' vs 'A'), 538 ('C' vs 'T'), 540 ('A' vs 'G'), 553 ('T' vs 'C'), 555 ('G' vs 'T'), 564 ('T' vs 'C'), 570 ('C' vs 'A'), 576 ('T' vs 'G'), 579 ('A' vs 'G'), 582 ('C' vs 'T'), 588 ('A' vs 'G'), 594 ('C' vs 'T'), 597 ('C' vs 'T'), 600 ('T' vs 'C'), 606 ('C' vs 'T'), 612 ('T' vs 'C'), 615 ('G' vs 'T'), 618 ('A' vs 'C'), 651 ('A' vs 'T') </h4>
+<h4>************** verrucosa VS wiggi **************</h4>
+<p> Each of the following 91 sites is invariant across sequences of verrucosa and differentiates it from wiggi: 9 ('T' vs 'C'), 12 ('A' vs 'G'), 13 ('G' vs 'A'), 21 ('T' vs 'G'), 28 ('G' vs 'T'), 29 ('C' vs 'G'), 33 ('G' vs 'C'), 34 ('T' vs 'C'), 39 ('G' vs 'A'), 42 ('A' vs 'G'), 57 ('G' vs 'A'), 69 ('T' vs 'C'), 72 ('G' vs 'A'), 84 ('A' vs 'T'), 85 ('T' vs 'A'), 87 ('T' vs 'A'), 105 ('T' vs 'C'), 111 ('T' vs 'C'), 117 ('A' vs 'T'), 123 ('C' vs 'A'), 126 ('T' vs 'C'), 135 ('C' vs 'T'), 156 ('A' vs 'G'), 171 ('A' vs 'G'), 202 ('T' vs 'C'), 207 ('T' vs 'G'), 213 ('T' vs 'A'), 219 ('C' vs 'T'), 237 ('A' vs 'T'or'C'), 246 ('T' vs 'C'), 252 ('A' vs 'T'), 255 ('C' vs 'T'), 259 ('T' vs 'C'), 267 ('G' vs 'T'), 273 ('A' vs 'T'), 280 ('T' vs 'C'), 282 ('A' vs 'T'), 283 ('C' vs 'T'), 285 ('T' vs 'G'), 286 ('T' vs 'C'), 288 ('A' vs 'G'), 303 ('A' vs 'T'), 306 ('A' vs 'G'), 309 ('A' vs 'G'), 315 ('T' vs 'A'), 324 ('G' vs 'A'), 327 ('A' vs 'G'), 339 ('T' vs 'A'or'G'), 342 ('G' vs 'T'), 343 ('T' vs 'C'), 345 ('A' vs 'T'), 351 ('T' vs 'C'), 354 ('T' vs 'A'), 366 ('C' vs 'A'), 396 ('T' vs 'C'), 397 ('T' vs 'C'), 399 ('A' vs 'T'), 403 ('C' vs 'T'), 408 ('T' vs 'A'), 420 ('A' vs 'T'), 423 ('C' vs 'T'), 426 ('G' vs 'A'), 429 ('A' vs 'G'), 432 ('T' vs 'A'), 447 ('T' vs 'A'), 450 ('C' vs 'T'), 465 ('G' vs 'A'), 471 ('T' vs 'A'or'G'), 474 ('G' vs 'T'), 480 ('T' vs 'A'), 483 ('A' vs 'G'), 498 ('A' vs 'G'), 543 ('A' vs 'T'), 544 ('C' vs 'T'), 546 ('T' vs 'A'), 552 ('T' vs 'C'), 553 ('C' vs 'T'), 555 ('T' vs 'G'), 561 ('G' vs 'T'), 570 ('A' vs 'C'), 582 ('A' vs 'C'), 591 ('C' vs 'T'), 594 ('T' vs 'C'), 597 ('T' vs 'C'), 603 ('G' vs 'C'), 606 ('T' vs 'C'), 615 ('T' vs 'G'), 633 ('A' vs 'T'), 639 ('A' vs 'G'), 649 ('T' vs 'C'), 651 ('G' vs 'A') </h4>
+<p> Each of the following 103 sites is invariant across sequences of wiggi and differentiates it from verrucosa: 0 ('T' vs 'A'or'G'), 9 ('C' vs 'T'), 12 ('G' vs 'A'), 13 ('A' vs 'G'), 15 ('T' vs 'A'or'G'), 21 ('G' vs 'T'), 28 ('T' vs 'G'), 29 ('G' vs 'C'), 33 ('C' vs 'G'), 34 ('C' vs 'T'), 39 ('A' vs 'G'), 42 ('G' vs 'A'), 57 ('A' vs 'G'), 69 ('C' vs 'T'), 72 ('A' vs 'G'), 81 ('T' vs 'A'or'G'), 84 ('T' vs 'A'), 85 ('A' vs 'T'), 87 ('A' vs 'T'), 105 ('C' vs 'T'), 111 ('C' vs 'T'), 117 ('T' vs 'A'), 123 ('A' vs 'C'), 126 ('C' vs 'T'), 129 ('T' vs 'A'or'G'), 135 ('T' vs 'C'), 141 ('A' vs 'T'or'C'), 156 ('G' vs 'A'), 171 ('G' vs 'A'), 202 ('C' vs 'T'), 207 ('G' vs 'T'), 213 ('A' vs 'T'), 219 ('T' vs 'C'), 228 ('A' vs 'T'or'C'), 246 ('C' vs 'T'), 252 ('T' vs 'A'), 255 ('T' vs 'C'), 259 ('C' vs 'T'), 267 ('T' vs 'G'), 270 ('T' vs 'A'or'C'), 273 ('T' vs 'A'), 280 ('C' vs 'T'), 282 ('T' vs 'A'), 283 ('T' vs 'C'), 285 ('G' vs 'T'), 286 ('C' vs 'T'), 288 ('G' vs 'A'), 303 ('T' vs 'A'), 306 ('G' vs 'A'), 309 ('G' vs 'A'), 315 ('A' vs 'T'), 324 ('A' vs 'G'), 327 ('G' vs 'A'), 330 ('A' vs 'T'or'C'), 342 ('T' vs 'G'), 343 ('C' vs 'T'), 345 ('T' vs 'A'), 351 ('C' vs 'T'), 354 ('A' vs 'T'), 366 ('A' vs 'C'), 369 ('T' vs 'A'or'G'), 387 ('T' vs 'A'or'G'), 396 ('C' vs 'T'), 397 ('C' vs 'T'), 399 ('T' vs 'A'), 403 ('T' vs 'C'), 408 ('A' vs 'T'), 420 ('T' vs 'A'), 423 ('T' vs 'C'), 426 ('A' vs 'G'), 429 ('G' vs 'A'), 432 ('A' vs 'T'), 447 ('A' vs 'T'), 450 ('T' vs 'C'), 465 ('A' vs 'G'), 474 ('T' vs 'G'), 479 ('G' vs 'A'or'C'), 480 ('A' vs 'T'), 483 ('G' vs 'A'), 498 ('G' vs 'A'), 510 ('A' vs 'T'or'C'), 540 ('A' vs 'T'or'C'), 543 ('T' vs 'A'), 544 ('T' vs 'C'), 546 ('A' vs 'T'), 552 ('C' vs 'T'), 553 ('T' vs 'C'), 555 ('G' vs 'T'), 561 ('T' vs 'G'), 570 ('C' vs 'A'), 582 ('C' vs 'A'), 591 ('T' vs 'C'), 594 ('C' vs 'T'), 597 ('C' vs 'T'), 603 ('C' vs 'G'), 606 ('C' vs 'T'), 615 ('G' vs 'T'), 618 ('A' vs 'T'or'C'), 624 ('G' vs 'T'or'C'), 633 ('T' vs 'A'), 639 ('G' vs 'A'), 649 ('C' vs 'T'), 651 ('A' vs 'G') </h4>

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/testout	Sun Jan 29 16:25:48 2023 +0000
@@ -0,0 +1,50 @@
+<h4>########################## PARAMETERS ######################</h4>
+<p> input file: test-data/Pontohedyle_COI.fas </p>
+<p> Coding gaps as characters: False </p>
+<p> Maximum undetermined nucleotides allowed: 5 </p>
+<p> Length of the alignment: 655 -> 655 </p>
+<p> Indexing reference: Not set </p>
+<p> Read in 27 sequences </p>
+<p> query taxa: 2 - brasilensis, joni </p>
+<p> Cutoff set as: 100 </p>
+<p> Number iterations of MolD set as: 10000 </p>
+<p> Maximum length of raw mDNCs set as: 12 </p>
+<p> Maximum length of refined mDNCs set as: 7 </p>
+<p> simulated sequences up to 1 percent divergent from original ones </p>
+<p> Maximum number of sequences modified per clade 10 </p>
+<p> scoring of the rDNCs; threshold in two consequtive runs: 75 </p>
+<h4>########################### RESULTS ##########################</h4>
+<h4>************** brasilensis **************</h4>
+<p> Sequences analyzed: 4 </p>
+<p> single nucleotide mDNCs*: 45 - 4: 'G', 16: 'C', 40: 'C', 44: 'G', 46: 'G', 68: 'G', 97: 'C', 101: 'C', 102: 'C', 167: 'G', 169: 'C', 170: 'T', 197: 'A', 202: 'G', 217: 'A', 227: 'G', 228: 'C', 239: 'T', 272: 'G', 287: 'A', 295: 'G', 310: 'C', 332: 'T', 357: 'A', 358: 'G', 365: 'T', 372: 'T', 387: 'C', 434: 'G', 456: 'G', 457: 'G', 467: 'G', 482: 'T', 483: 'G', 497: 'C', 499: 'T', 512: 'T', 518: 'A', 529: 'A', 535: 'G', 542: 'T', 543: 'C', 566: 'C', 619: 'G', 635: 'G' </p>
+<p> mDNCs* retrieved: 1048; Sites involved: 100; Independent mDNCs**: 71 </p>
+<p> Shortest retrieved mDNC*: [4: 'G'] </p>
+<p> 1 rDNC_score (100): [4] - 52 </p>
+<p> 2 rDNC_score (100): [4, 16] - 86 </p>
+<p> 3 rDNC_score (100): [4, 16, 40] - 93 </p>
+<p> Final rDNC***: [4: 'G', 16: 'C', 40: 'C'] </p>
+<p> The DNA diagnosis for the taxon brasilensis is: 'G' in the site 4, 'C' in the site 16, 'C' in the site 40. </p>
+<h4>************** joni **************</h4>
+<p> Sequences analyzed: 3 </p>
+<p> single nucleotide mDNCs*: 10 - 31: 'A', 85: 'G', 160: 'G', 283: 'G', 298: 'G', 451: 'G', 523: 'C', 526: 'A', 578: 'C', 580: 'T' </p>
+<p> mDNCs* retrieved: 2662; Sites involved: 100; Independent mDNCs**: 50 </p>
+<p> Shortest retrieved mDNC*: [31: 'A'] </p>
+<p> 1 rDNC_score (100): [31] - 65 </p>
+<p> 2 rDNC_score (100): [31, 85] - 97 </p>
+<p> 3 rDNC_score (100): [31, 85, 160] - 99 </p>
+<p> Final rDNC***: [31: 'A', 85: 'G', 160: 'G'] </p>
+<p> The DNA diagnosis for the taxon joni is: 'A' in the site 31, 'G' in the site 85, 'G' in the site 160. </p>
+<h4> ################################# EXPLANATIONS #################################### </h4>
+<p>   * mDNC -(=minimal Diagnostic nucleotide combination) is a combination of nucleotides at specified sites of the alignment, </p>
+<p>     unique for a query taxon. Therefore it is sufficient to differentiate a query taxon from all reference taxa in a dataset. </p>
+<p>     Because it comprises minimal necessary number of nucleotide sites to differentiate a query, any mutation in the mDNC in</p>
+<p>     single specimen of a query taxon will automatically disqualify it as a diagnostic combination. </p>
+<p> </p>
+<p>  ** two or more mDNCs are INDEPENDENT if they constitute non-overlapping sets of nucleotide sites. </p>
+<p> </p>
+<p> *** rDNC -(=robust/redundant Diagnostic nucleotide combination) is a combination of nucleotides at specified sites of the alignment, </p>
+<p>     unique for a query taxon and (likewise mDNC) sufficient to differentiate a query taxon from all reference taxa in a dataset. </p>
+<p>     However, rDNC comprises more than a minimal necessary number of diagnostic sites, and therefore is robust to single nucleotide </p>
+<p>     replacements. Even if a mutation arises in one of the rDNC sites, the remaining ones will (with high probability) remain sufficient  </p>
+<p>     to diagnose the query taxon </p>
+<h4>     Final diagnosis corresponds to rDNC </h4>

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/uwsgi.ini	Sun Jan 29 16:25:48 2023 +0000
@@ -0,0 +1,19 @@
+[uwsgi]
+project = mold-dev
+module = wsgi:app
+master = True
+#http-socket = localhost:9300
+socket-timeout = 3000
+buffer-size = 65535
+protocol = http
+processes = 2
+vacuum = true
+chdir = /home/annndrey/work/git/MolD_release
+home = /home/annndrey/work/git/MolD_release/venv
+#logto = mold.log
+#cheaper-algo = spare
+#cheaper = 2
+#cheaper-initial = 2
+workers = 1
+#cheaper-step = 1
+wsgi-disable-file-wrapper = true
\ No newline at end of file

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/wsgi.py	Sun Jan 29 16:25:48 2023 +0000
@@ -0,0 +1,5 @@
+from api import app
+
+if __name__ == "__main__":
+    app.run()
+