Mercurial > repos > in_silico > cravat_score_and_annotate
changeset 4:953e716d7837 draft
Uploaded
| author | in_silico |
|---|---|
| date | Tue, 12 Jun 2018 11:54:00 -0400 |
| parents | 0ff2d655068c |
| children | 2ccaad5c7c01 |
| files | cravat_submit/cravat_submit.py cravat_submit/cravat_submit.xml cravat_submit/vcf_converter.py |
| diffstat | 3 files changed, 252 insertions(+), 42 deletions(-) [+] |
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--- a/cravat_submit/cravat_submit.py Tue Jun 12 11:05:00 2018 -0400 +++ b/cravat_submit/cravat_submit.py Tue Jun 12 11:54:00 2018 -0400 @@ -4,14 +4,18 @@ import urllib import sys import csv -import pdb input_filename = sys.argv[1] input_select_bar = sys.argv[2] output_filename = sys.argv[3] -#in_file = open('input_call.txt', "r") -#out_file = open('output_call.txt', "w") +# HACK: Input args corrections. +if input_select_bar == "None": + # The server represents an analyses of None as ""; however, submitting a blank string on command line throws off arg position + input_select_bar = "" + # The server represents the "Vest and Chasm" analyses as "VEST;CHASM; however, galaxy converts the semi-colon to an 'X'. Switch it back. +elif input_select_bar == "VESTXCHASM": + input_select_bar = "VEST;CHASM" write_header = True @@ -30,7 +34,6 @@ status = json.loads(check.text)['status'] resultfileurl = json.loads(check.text)['resultfileurl'] #out_file.write(str(status) + ', ') - pdb.set_trace() if status == 'Success': #out_file.write('\t' + resultfileurl) break @@ -97,40 +100,4 @@ cells.append(value) print cells - tsvout.writerow(cells) - - - - - - - - - - - - - - - - - - - - - - - -#a = 'col1\tcol2\tcol3' -#header_list = a.split('\t') - -#loop through the two results, when you first hit header you print out the headers in tabular form -#Print out each header only once -#Combine both headers into one output file -#loop through the rest of the data and assign each value to its assigned header -#combine this all into one output file - - - - - + tsvout.writerow(cells) \ No newline at end of file
--- a/cravat_submit/cravat_submit.xml Tue Jun 12 11:05:00 2018 -0400 +++ b/cravat_submit/cravat_submit.xml Tue Jun 12 11:54:00 2018 -0400 @@ -7,7 +7,7 @@ <param format="tabular" name="input" type="data" label="Source file"> </param> <param format="tabular" name="dropdown" type="select" label="Analysis Program"> - <option value="">None</option> + <option value="None">None</option> <option value="VEST">VEST</option> <option value="CHASM">CHASM</option> <option value="VEST;CHASM">VEST and CHASM</option>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/cravat_submit/vcf_converter.py Tue Jun 12 11:54:00 2018 -0400 @@ -0,0 +1,243 @@ +""" +A module originally obtained from the cravat package. Modified to use in the vcf +converter galaxy tool. + + +Register of changes made (Chris Jacoby): + 1) Changed imports as galaxy tool won't have access to complete cravat python package + 2) Defined BadFormatError in BaseConverted file, as I didn't have the BadFormatError module +""" + +from base_converter import BaseConverter, BadFormatError +import re + +class CravatConverter(BaseConverter): + + def __init__(self): + self.format_name = 'vcf' + self.samples = [] + self.var_counter = 0 + self.addl_cols = [{'name':'phred', + 'title':'Phred', + 'type':'string'}, + {'name':'filter', + 'title':'VCF filter', + 'type':'string'}, + {'name':'zygosity', + 'title':'Zygosity', + 'type':'string'}, + {'name':'alt_reads', + 'title':'Alternate reads', + 'type':'int'}, + {'name':'tot_reads', + 'title':'Total reads', + 'type':'int'}, + {'name':'af', + 'title':'Variant allele frequency', + 'type':'float'}] + + def check_format(self, f): + return f.readline().startswith('##fileformat=VCF') + + def setup(self, f): + + vcf_line_no = 0 + for line in f: + vcf_line_no += 1 + if len(line) < 6: + continue + if line[:6] == '#CHROM': + toks = re.split('\s+', line.rstrip()) + if len(toks) > 8: + self.samples = toks[9:] + break + + def convert_line(self, l): + if l.startswith('#'): return None + self.var_counter += 1 + toks = l.strip('\r\n').split('\t') + all_wdicts = [] + if len(toks) < 8: + raise BadFormatError('Wrong VCF format') + [chrom, pos, tag, ref, alts, qual, filter, info] = toks[:8] + if tag == '': + raise BadFormatError('ID column is blank') + elif tag == '.': + tag = 'VAR' + str(self.var_counter) + if chrom[:3] != 'chr': + chrom = 'chr' + chrom + alts = alts.split(',') + len_alts = len(alts) + if len(toks) == 8: + for altno in range(len_alts): + wdict = None + alt = alts[altno] + newpos, newref, newalt = self.extract_vcf_variant('+', pos, ref, alt) + wdict = {'tags':tag, + 'chrom':chrom, + 'pos':newpos, + 'ref_base':newref, + 'alt_base':newalt, + 'sample_id':'no_sample', + 'phred': qual, + 'filter': filter} + all_wdicts.append(wdict) + elif len(toks) > 8: + sample_datas = toks[9:] + genotype_fields = {} + genotype_field_no = 0 + for genotype_field in toks[8].split(':'): + genotype_fields[genotype_field] = genotype_field_no + genotype_field_no += 1 + if not ('GT' in genotype_fields): + raise BadFormatError('No GT Field') + gt_field_no = genotype_fields['GT'] + for sample_no in range(len(sample_datas)): + sample = self.samples[sample_no] + sample_data = sample_datas[sample_no].split(':') + gts = {} + for gt in sample_data[gt_field_no].replace('/', '|').split('|'): + if gt == '.': + continue + else: + gts[int(gt)] = True + for gt in sorted(gts.keys()): + wdict = None + if gt == 0: + continue + else: + alt = alts[gt - 1] + newpos, newref, newalt = self.extract_vcf_variant('+', pos, ref, alt) + zyg = self.homo_hetro(sample_data[gt_field_no]) + depth, alt_reads, af = self.extract_read_info(sample_data, gt, gts, genotype_fields) + + wdict = {'tags':tag, + 'chrom':chrom, + 'pos':newpos, + 'ref_base':newref, + 'alt_base':newalt, + 'sample_id':sample, + 'phred': qual, + 'filter': filter, + 'zygosity': zyg, + 'tot_reads': depth, + 'alt_reads': alt_reads, + 'af': af, + } + all_wdicts.append(wdict) + return all_wdicts + + #The vcf genotype string has a call for each allele separated by '\' or '/' + #If the call is the same for all allels, return 'hom' otherwise 'het' + def homo_hetro(self, gt_str): + if '.' in gt_str: + return ''; + + gts = gt_str.strip().replace('/', '|').split('|') + for gt in gts: + if gt != gts[0]: + return 'het' + return 'hom' + + #Extract read depth, allele count, and allele frequency from optional VCR information + def extract_read_info (self, sample_data, gt, gts, genotype_fields): + depth = '' + alt_reads = '' + ref_reads = '' + af = '' + + #AD contains 2 values usually ref count and alt count unless there are + #multiple alts then it will have alt 1 then alt 2. + if 'AD' in genotype_fields and genotype_fields['AD'] <= len(sample_data): + if 0 in gts.keys(): + #if part of the genotype is reference, then AD will have #ref reads, #alt reads + ref_reads = sample_data[genotype_fields['AD']].split(',')[0] + alt_reads = sample_data[genotype_fields['AD']].split(',')[1] + elif gt == max(gts.keys()): + #if geontype has multiple alt bases, then AD will have #alt1 reads, #alt2 reads + alt_reads = sample_data[genotype_fields['AD']].split(',')[1] + else: + alt_reads = sample_data[genotype_fields['AD']].split(',')[0] + + if 'DP' in genotype_fields and genotype_fields['DP'] <= len(sample_data): + depth = sample_data[genotype_fields['DP']] + elif alt_reads != '' and ref_reads != '': + #if DP is not present but we have alt and ref reads count, dp = ref+alt + depth = int(alt_reads) + int(ref_reads) + + if 'AF' in genotype_fields and genotype_fields['AF'] <= len(sample_data): + af = float(sample_data[genotype_fields['AF']] ) + elif depth != '' and alt_reads != '': + #if AF not specified, calc it from alt and ref reads + af = float(alt_reads) / float(depth) + + return depth, alt_reads, af + + def extract_vcf_variant (self, strand, pos, ref, alt): + + reflen = len(ref) + altlen = len(alt) + + # Returns without change if same single nucleotide for ref and alt. + if reflen == 1 and altlen == 1 and ref == alt: + return pos, ref, alt + + # Trimming from the start and then the end of the sequence + # where the sequences overlap with the same nucleotides + new_ref2, new_alt2, new_pos = \ + self.trimming_vcf_input(ref, alt, pos, strand) + + if new_ref2 == '': + new_ref2 = '-' + if new_alt2 == '': + new_alt2 = '-' + + return new_pos, new_ref2, new_alt2 + + # This function looks at the ref and alt sequences and removes + # where the overlapping sequences contain the same nucleotide. + # This trims from the end first but does not remove the first nucleotide + # because based on the format of VCF input the + # first nucleotide of the ref and alt sequence occur + # at the position specified. + # End removed first, not the first nucleotide + # Front removed and position changed + def trimming_vcf_input(self, ref, alt, pos, strand): + pos = int(pos) + reflen = len(ref) + altlen = len(alt) + minlen = min(reflen, altlen) + new_ref = ref + new_alt = alt + new_pos = pos + # Trims from the end. Except don't remove the first nucleotide. + # 1:6530968 CTCA -> GTCTCA becomes C -> GTC. + for nt_pos in range(0, minlen - 1): + if ref[reflen - nt_pos - 1] == alt[altlen - nt_pos - 1]: + new_ref = ref[:reflen - nt_pos - 1] + new_alt = alt[:altlen - nt_pos - 1] + else: + break + new_ref_len = len(new_ref) + new_alt_len = len(new_alt) + minlen = min(new_ref_len, new_alt_len) + new_ref2 = new_ref + new_alt2 = new_alt + # Trims from the start. 1:6530968 G -> GT becomes 1:6530969 - -> T. + for nt_pos in range(0, minlen): + if new_ref[nt_pos] == new_alt[nt_pos]: + if strand == '+': + new_pos += 1 + elif strand == '-': + new_pos -= 1 + new_ref2 = new_ref[nt_pos + 1:] + new_alt2 = new_alt[nt_pos + 1:] + else: + new_ref2 = new_ref[nt_pos:] + new_alt2 = new_alt[nt_pos:] + break + return new_ref2, new_alt2, new_pos + + +if __name__ == "__main__": + c = CravatConverter() \ No newline at end of file