--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pal_filter.py	Wed Feb 24 08:25:17 2016 -0500
@@ -0,0 +1,500 @@
+#!/usr/bin/python -tt
+#
+# pal_filter
+# https://github.com/graemefox/pal_filter
+#
+################################################################################
+# Graeme Fox - 15/02/2016 - graeme.fox@manchester.ac.uk
+# Tested on 64-bit Ubuntu, with Python 2.7
+#
+################################################################################
+# PROGRAM DESCRIPTION
+#
+# Program to pick optimum loci from the output of pal_finder_v0.02.04
+#
+# This program can be used to filter output from pal_finder and choose the
+# 'optimum' loci.
+#
+# For the paper referncing this workflow, see Griffiths et al.
+# (unpublished as of 15/02/2016) (sarah.griffiths-5@postgrad.manchester.ac.uk)
+#
+################################################################################
+#
+# This program also contains a quality-check method to improve the rate of PCR
+# success. For this QC method, paired end reads are assembled using
+# PANDAseq so you must have PANDAseq installed.
+#
+# For the paper referencing this assembly-QC method see Fox et al.
+# (unpublished as of 15/02/2016) (graeme.fox@manchester.ac.uk)
+#
+# For best results in PCR for marker development, I suggest enabling all the
+# filter options AND the assembly based QC
+#
+################################################################################
+# REQUIREMENTS
+#
+# Must have Biopython installed (www.biopython.org).
+#
+# If you with to perform the assembly QC step, you must have:
+# PandaSeq (https://github.com/neufeld/pandaseq)
+# PandaSeq must be in your $PATH / able to run from anywhere
+################################################################################
+# REQUIRED OPTIONS
+#
+# -i forward_paired_ends.fastQ
+# -j reverse_paired_ends.fastQ
+# -p pal_finder output - the "(microsatellites with read IDs and
+# primer pairs)" file
+#
+# By default the program does nothing....
+#
+# NON-REQUIRED OPTIONS
+#
+# -assembly:  turn on the pandaseq assembly QC step
+# -primers:  filter microsatellite loci to just those which
+# have primers designed
+#
+# -occurrences:  filter microsatellite loci to those with primers
+# which appear only once in the dataset
+#
+# -rankmotifs:  filter microsatellite loci to just those with perfect motifs.
+# Rank the output by size of motif (largest first)
+#
+###########################################################
+# For repeat analysis, the following extra non-required options may be useful:
+#
+# Since PandaSeq Assembly, and fastq -> fasta conversion are slow, do them the
+# first time, generate the files and then skip either, or both steps with
+# the following:
+#
+# -a:  skip assembly step
+# -c:  skip fastq -> fasta conversion step
+#
+# Just make sure to keep the assembled/converted files in the correct directory
+# with the correct filename(s)
+###########################################################
+#
+# EXAMPLE USAGE:
+#
+# pal_filtery.py -i R1.fastq -j R2.fastq
+# -p pal_finder_output.tabular -primers -occurrences -rankmotifs -assembly
+#
+###########################################################
+import Bio, subprocess, argparse, csv, os, re, time
+from Bio import SeqIO
+
+# Get values for all the required and optional arguments
+
+parser = argparse.ArgumentParser(description='pal_filter')
+parser.add_argument('-i','--input1', help='Forward paired-end fastq file', \
+                    required=True)
+
+parser.add_argument('-j','--input2', help='Reverse paired-end fastq file', \
+                    required=True)
+
+parser.add_argument('-p','--pal_finder', help='Output from pal_finder ', \
+                    required=True)
+
+parser.add_argument('-assembly','--assembly_QC', help='Perform the PandaSeq \
+                    based QC', nargs='?', const=1, type=int, required=False)
+
+parser.add_argument('-a','--skip_assembly', help='If the assembly has already \
+                    been run, skip it with -a', nargs='?', const=1, \
+                    type=int, required=False)
+
+parser.add_argument('-c','--skip_conversion', help='If the fastq to fasta \
+                    conversion has already been run, skip it with -c', \
+                    nargs='?', const=1, type=int, required=False)
+
+parser.add_argument('-primers','--filter_by_primer_column', help='Filter \
+                    pal_finder output to just those loci which have primers \
+                    designed', nargs='?', const=1, type=int, required=False)
+
+parser.add_argument('-occurrences','--filter_by_occurrences_column', \
+                    help='Filter pal_finder output to just loci with primers \
+                    which only occur once in the dataset', nargs='?', \
+                    const=1, type=int, required=False)
+
+parser.add_argument('-rankmotifs','--filter_and_rank_by_motif_size', \
+                    help='Filter pal_finder output to just loci which are a \
+                    perfect repeat unit. Also, rank the loci by motif size \
+                    (largest first)', nargs='?', const=1, type=int, \
+                    required=False)
+
+args = vars(parser.parse_args())
+
+# parse arguments
+
+R1_input = args['input1'];
+R2_input = args['input2'];
+pal_finder_output = args['pal_finder'];
+perform_assembly = args['assembly_QC']
+skip_assembly = args['skip_assembly'];
+skip_conversion = args['skip_conversion'];
+filter_primers = args['filter_by_primer_column'];
+filter_occurrences = args['filter_by_occurrences_column'];
+filter_rank_motifs = args['filter_and_rank_by_motif_size'];
+
+# set default values for arguments
+if perform_assembly is None:
+    perform_assembly = 0
+if skip_assembly is None:
+    skip_assembly = 0
+if skip_conversion is None:
+    skip_conversion = 0
+if filter_primers is None:
+    filter_primers = 0
+if filter_occurrences is None:
+    filter_occurrences = 0
+if filter_rank_motifs is None:
+    filter_rank_motifs = 0
+
+############################################################
+# Function List                                            #
+############################################################
+
+# Reverse complement a sequence
+
+def ReverseComplement1(seq):
+    seq_dict = {'A':'T','T':'A','G':'C','C':'G'}
+    return "".join([seq_dict[base] for base in reversed(seq)])
+
+# Convert a .fastq to a .fasta, filter to just lines we want
+# and strip MiSeq barcodes
+
+def fastq_to_fasta(file):
+    file_name = os.path.splitext(os.path.basename(file))[0]
+    with open(file_name + "_filtered.fasta", "w") as out:
+        for record in SeqIO.parse(file, "fastq"):
+            ID = str(record.id)
+            SEQ = str(record.seq)
+            if ID in wanted:
+                out.write(">" + ID + "\n" + SEQ + "\n")
+
+# strip the miseq barcodes from a fasta file
+
+def strip_barcodes(file):
+    file_name = os.path.splitext(os.path.basename(file))[0]
+    with open(file_name + "_adapters_removed.fasta", "w") as out:
+        for record in SeqIO.parse(file, "fasta"):
+            match = re.search(r'\S*:', record.id)
+            if match:
+                correct = match.group().rstrip(":")
+            else:
+                correct = str(record.id)
+            SEQ = str(record.seq)
+            if correct in wanted:
+                out.write(">" + correct + "\n" + SEQ + "\n")
+
+############################################################
+# MAIN PROGRAM                                             #
+############################################################
+print "\n~~~~~~~~~~"
+print "pal_filter"
+print "~~~~~~~~~~\n"
+time.sleep(1)
+print "\"Find the optimum loci in your pal_finder output and increase "\
+                    "the rate of successful microsatellite marker development\""
+print "\nSee Griffiths et al. (currently unpublished) for more details......"
+time.sleep(2)
+
+if (perform_assembly == 0 and filter_primers == 0 and filter_occurrences == 0 \
+                    and filter_rank_motifs == 0):
+    print "\nNo optional arguments supplied."
+    print "\nBy default this program does nothing."
+    print "\nNo files produced and no modifications made."
+    print "\nFinished.\n"
+    exit()
+
+else:
+    print "\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
+    print "Checking supplied filtering parameters:"
+    print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
+    time.sleep(2)
+    if filter_primers == 1:
+        print "-primers flag supplied."
+        print "Filtering pal_finder output on the \"Primers found (1=y,0=n)\"" \
+                    " column."
+        print "Only rows where primers have successfully been designed will"\
+                    " pass the filter.\n"
+        time.sleep(2)
+    if filter_occurrences == 1:
+        print "-occurrences flag supplied."
+        print "Filtering pal_finder output on the \"Occurences of Forward" \
+                    " Primer in Reads\" and \"Occurences of Reverse Primer" \
+                    " in Reads\" columns."
+        print "Only rows where both primers occur only a single time in the"\
+                    " reads pass the filter.\n"
+        time.sleep(2)
+    if filter_rank_motifs == 1:
+        print "-rankmotifs flag supplied."
+        print "Filtering pal_finder output on the \"Motifs(bases)\" column to" \
+                    " just those with perfect repeats."
+        print "Only rows containing 'perfect' repeats will pass the filter."
+        print "Also, ranking output by size of motif (largest first).\n"
+        time.sleep(2)
+# index the raw fastq files so that the sequences can be pulled out and
+# added to the filtered output file
+
+# Indexing input sequence files
+print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
+print "Indexing FastQ files....."
+print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
+R1fastq_sequences_index = SeqIO.index(R1_input,'fastq')
+R2fastq_sequences_index = SeqIO.index(R2_input,'fastq')
+print "Indexing complete."
+
+# create a set to hold the filtered output
+wanted_lines = set()
+
+# get lines from the pal_finder output which meet filter settings
+
+# read the pal_finder output file into a csv reader
+with open (pal_finder_output) as csvfile_infile:
+    csv_f = csv.reader(csvfile_infile, delimiter='\t')
+    header =  csv_f.next()
+    with open(os.path.splitext(os.path.basename(pal_finder_output))[0] + \
+                    ".filtered", 'w') as csvfile_outfile:
+# write the header line for the output file
+        csvfile_outfile.write('\t'.join(header) + "\tR1_Sequence_ID\t"
+                              "R1_Sequence\tR2_Sequence_ID\tR2_Sequence\n")
+        for row in csv_f:
+# get the sequence ID
+            seq_ID = row[0]
+# get the raw sequence reads and convert to a format that can
+# go into a tsv file
+            R1_sequence = R1fastq_sequences_index[seq_ID].format("fasta").\
+                    replace("\n","\t",1).replace("\n","")
+            R2_sequence = R2fastq_sequences_index[seq_ID].format("fasta").\
+                    replace("\n","\t",1).replace("\n","")
+            seq_info = "\t" + R1_sequence + "\t" + R2_sequence + "\n"
+# navigate through all different combinations of filter options
+# if the primer filter is switched on
+            if filter_primers == 1:
+# check the occurrences of primers field
+                if row[5] == "1":
+# if filter occurrences of primers is switched on
+                    if filter_occurrences == 1:
+# check the occurrences of primers field
+                        if (row[15] == "1" and row[16] == "1"):
+# if rank by motif is switched on
+                            if filter_rank_motifs == 1:
+# check for perfect motifs
+                                if row[1].count('(') == 1:
+# all 3 filter switched on
+# write line out to output
+                                    csvfile_outfile.write('\t'.join(row) + \
+                                                        seq_info)
+
+                            else:
+                                csvfile_outfile.write('\t'.join(row) + seq_info)
+                    elif filter_rank_motifs == 1:
+                        if row[1].count('(') == 1:
+                            csvfile_outfile.write('\t'.join(row) + seq_info)
+                    else:
+                        csvfile_outfile.write('\t'.join(row) + seq_info)
+            elif filter_occurrences == 1:
+                if (row[15] == "1" and row[16] == "1"):
+                    if filter_rank_motifs == 1:
+                        if row[1].count('(') == 1:
+                            csvfile_outfile.write('\t'.join(row) + seq_info)
+                    else:
+                        csvfile_outfile.write('\t'.join(row) + seq_info)
+            elif filter_rank_motifs == 1:
+                if row[1].count('(') == 1:
+                    csvfile_outfile.write('\t'.join(row) + seq_info)
+            else:
+                csvfile_outfile.write('\t'.join(row) + seq_info)
+
+# if filter_rank_motifs is active, order the file by the size of the motif
+if filter_rank_motifs == 1:
+    rank_motif = []
+    ranked_list = []
+# read in the non-ordered file and add every entry to rank_motif list
+    with open(os.path.splitext(os.path.basename(pal_finder_output))[0] + \
+                        ".filtered") as csvfile_ranksize:
+        csv_rank = csv.reader(csvfile_ranksize, delimiter='\t')
+        header = csv_rank.next()
+        for line in csv_rank:
+            rank_motif.append(line)
+
+# open the file ready to write the ordered list
+    with open(os.path.splitext(os.path.basename(pal_finder_output))[0] + \
+                        ".filtered", 'w') as rank_outfile:
+        rankwriter = csv.writer(rank_outfile, delimiter='\t', \
+                        lineterminator='\n')
+        rankwriter.writerow(header)
+        count = 2
+        while count < 10:
+            for row in rank_motif:
+# count size of motif
+                motif = re.search(r'[ATCG]*',row[1])
+                if motif:
+                    the_motif = motif.group()
+# rank it and write into ranked_list
+                    if len(the_motif) == count:
+                        ranked_list.insert(0, row)
+            count = count +  1
+# write out the ordered list, into the .filtered file
+        for row in ranked_list:
+            rankwriter.writerow(row)
+
+print "\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
+print "Checking assembly flags supplied:"
+print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
+if perform_assembly == 0:
+    print "Assembly flag not supplied. Not performing assembly QC.\n"
+if perform_assembly == 1:
+    print "-assembly flag supplied: Performing PandaSeq assembly quality checks."
+    print "See Fox et al. (currently unpublished) for full details on the"\
+                        " quality-check process.\n"
+    time.sleep(5)
+
+# Get readID, F primers, R primers and motifs from filtered pal_finder output
+    seqIDs = []
+    motif = []
+    F_primers = []
+    R_primers = []
+    with open(os.path.splitext(os.path.basename(pal_finder_output))[0] + \
+                    ".filtered") as input_csv:
+        pal_finder_csv = csv.reader(input_csv, delimiter='\t')
+        header = pal_finder_csv.next()
+        for row in pal_finder_csv:
+            seqIDs.append(row[0])
+            motif.append(row[1])
+            F_primers.append(row[7])
+            R_primers.append(row[9])
+
+# Get a list of just the unique IDs we want
+    wanted = set()
+    for line in seqIDs:
+        wanted.add(line)
+
+# Assemble the paired end reads into overlapping contigs using PandaSeq
+# (can be skipped with the -a flag if assembly has already been run
+# and the appropriate files are in the same directory as the script,
+# and named "Assembly.fasta" and "Assembly_adapters_removed.fasta")
+#
+# The first time you riun the script you MUST not enable the -a flag.t
+# but you are able to skip the assembly in subsequent analysis using the
+# -a flag.
+
+    if skip_assembly == 0:
+        pandaseq_command = 'pandaseq -A pear -f ' + R1_input + ' -r ' + \
+                        R2_input + ' -o 25 -t 0.95 -w Assembly.fasta'
+        subprocess.call(pandaseq_command, shell=True)
+        strip_barcodes("Assembly.fasta")
+        print "\nPaired end reads been assembled into overlapping reads."
+        print "\nFor future analysis, you can skip this assembly step using \
+                        the -a flag, provided that the assembly.fasta file is \
+                        intact and in the same location."
+    else:
+        print "\n(Skipping the assembly step as you provided the -a flag)"
+
+# Fastq files need to be converted to fasta. The first time you run the script
+# you MUST not enable the -c flag, but you are able to skip the conversion
+# later using the -c flag. Make sure the fasta files are in the same location
+#and do not change the filenames
+
+    if skip_conversion ==0:
+        fastq_to_fasta(R1_input)
+        fastq_to_fasta(R2_input)
+        print "\nThe input fastq files have been converted to the fasta format."
+        print "\nFor any future analysis, you can skip this conversion step \
+                        using the -c flag, provided that the fasta files \
+                        are intact and in the same location."
+    else:
+        print "\n(Skipping the fastq -> fasta conversion as you provided the" \
+                        " -c flag).\n"
+
+# get the files and everything else we will need
+
+# Assembled fasta file
+    assembly_file = "Assembly_adapters_removed.fasta"
+# filtered R1 reads
+    R1_fasta = os.path.splitext(os.path.basename(R1_input))[0] + \
+                        "_filtered.fasta"
+# filtered R2 reads
+    R2_fasta = os.path.splitext(os.path.basename(R2_input))[0] + \
+                        "_filtered.fasta"
+
+    outputfilename = os.path.splitext(os.path.basename(R1_input))[0]
+
+# parse the files with SeqIO
+    assembly_sequences = SeqIO.parse(assembly_file,'fasta')
+    R1fasta_sequences = SeqIO.parse(R1_fasta,'fasta')
+
+# create some empty lists to hold the ID tags we are interested in
+    assembly_IDs = []
+    fasta_IDs = []
+
+# populate the above lists with sequence IDs
+    for sequence in assembly_sequences:
+        assembly_IDs.append(sequence.id)
+    for sequence in R1fasta_sequences:
+        fasta_IDs.append(sequence.id)
+
+# Index the assembly fasta file
+    assembly_sequences_index = SeqIO.index(assembly_file,'fasta')
+    R1fasta_sequences_index = SeqIO.index(R1_fasta,'fasta')
+    R2fasta_sequences_index = SeqIO.index(R2_fasta,'fasta')
+
+# prepare the output file
+    with open (outputfilename + "_pal_filter_assembly_output.txt", 'w') \
+                    as outputfile:
+# write the headers for the output file
+        outputfile.write("readPairID\t Forward Primer\t F Primer Position in "
+                         "Assembled Read\t Reverse Primer\t R Primer Position in "
+                         "Assembled Read\t Motifs(bases)\t Assembled Read ID\t "
+                         "Assembled Read Sequence\t Raw Forward Read ID\t Raw "
+                         "Forward Read Sequence\t Raw Reverse Read ID\t Raw Reverse "
+                         "Read Sequence\n")
+
+# cycle through parameters from the pal_finder output
+        for x, y, z, a in zip(seqIDs, F_primers, R_primers, motif):
+            if str(x) in assembly_IDs:
+            # get the raw sequences ready to go into the output file
+                assembly_seq = (assembly_sequences_index.get_raw(x).decode())
+# fasta entries need to be converted to single line so sit nicely in the output
+                assembly_output = assembly_seq.replace("\n","\t")
+                R1_fasta_seq = (R1fasta_sequences_index.get_raw(x).decode())
+                R1_output = R1_fasta_seq.replace("\n","\t",1).replace("\n","")
+                R2_fasta_seq = (R2fasta_sequences_index.get_raw(x).decode())
+                R2_output = R2_fasta_seq.replace("\n","\t",1).replace("\n","")
+                assembly_no_id = '\n'.join(assembly_seq.split('\n')[1:])
+
+# check that both primer sequences can be seen in the assembled contig
+                if y or ReverseComplement1(y) in assembly_no_id and z or \
+                                    ReverseComplement1(z) in assembly_no_id:
+                    if y in assembly_no_id:
+# get the positions of the primers in the assembly to predict fragment length
+                        F_position = assembly_no_id.index(y)+len(y)+1
+                    if ReverseComplement1(y) in assembly_no_id:
+                        F_position = assembly_no_id.index(ReverseComplement1(y)\
+                                            )+len(ReverseComplement1(y))+1
+                    if z in assembly_no_id:
+                        R_position = assembly_no_id.index(z)+1
+                    if ReverseComplement1(z) in assembly_no_id:
+                        R_position = assembly_no_id.index(ReverseComplement1(z)\
+                                            )+1
+
+# write everything out into the output file
+                    output = (str(x) + "\t" + y + "\t" + str(F_position) \
+                                        + "\t" + (z) + "\t" + str(R_position) \
+                                        + "\t" + a + "\t" + assembly_output \
+                                        + R1_output + "\t" + R2_output + "\n")
+                    outputfile.write(output)
+        print "\nPANDAseq quality check complete."
+        print "Results from PANDAseq quality check (and filtering, if any" \
+                                " any filters enabled) written to output file" \
+                                " ending \"_pal_filter_assembly_output.txt\".\n\n"
+
+    print "Filtering of pal_finder results complete."
+    print "Filtered results written to output file ending \".filtered\"."
+    print "\nFinished\n"
+else:
+    if (skip_assembly == 1 or skip_conversion == 1):
+        print "\nERROR: You cannot supply the -a flag or the -c flag without \
+                    also supplying the -assembly flag.\n"
+
+        print "\nProgram Finished\n"