# HG changeset patch # User in_silico # Date 1528818921 14400 # Node ID 2ccaad5c7c010dd304203e738b070d987499aaa7 # Parent 953e716d7837d1ac72093ab6760e662f3437c3bd Uploaded diff -r 953e716d7837 -r 2ccaad5c7c01 cravat_submit/cravat_submit.py --- a/cravat_submit/cravat_submit.py Tue Jun 12 11:54:00 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,103 +0,0 @@ -import requests -import json -import time -import urllib -import sys -import csv - -input_filename = sys.argv[1] -input_select_bar = sys.argv[2] -output_filename = sys.argv[3] - -# 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 - -#plugs in params to given URL -submit = requests.post('http://staging.cravat.us/CRAVAT/rest/service/submit', files={'inputfile':open(input_filename)}, data={'email':'znylund@insilico.us.com', 'analyses': input_select_bar}) -#,'analysis':input_select_bar,'functionalannotation': "on"}) -#Makes the data a json dictionary, takes out only the job ID -jobid = json.loads(submit.text)['jobid'] -#out_file.write(jobid) -submitted = json.loads(submit.text)['status'] -#out_file.write('\t' + submitted) - -#loops until we find a status equal to Success, then breaks -while True: - check = requests.get('http://staging.cravat.us/CRAVAT/rest/service/status', params={'jobid': jobid}) - status = json.loads(check.text)['status'] - resultfileurl = json.loads(check.text)['resultfileurl'] - #out_file.write(str(status) + ', ') - if status == 'Success': - #out_file.write('\t' + resultfileurl) - break - else: - time.sleep(2) - -#out_file.write('\n') - -#creates three files -file_1 = time.strftime("%H:%M") + '_Z_Variant_Result.tsv' -file_2 = time.strftime("%H:%M") + '_Z_Additional_Details.tsv' -file_3 = time.strftime("%H:%M") + 'Combined_Variant_Results.tsv' - - -#Download the two results -urllib.urlretrieve("http://staging.cravat.us/CRAVAT/results/" + jobid + "/" + "Variant.Result.tsv", file_1) -urllib.urlretrieve("http://staging.cravat.us/CRAVAT/results/" + jobid + "/" + "Variant_Additional_Details.Result.tsv", file_2) - -headers = [] -duplicates = [] - -#opens the Variant Result file and the Variant Additional Details file as csv readers, then opens the output file (galaxy) as a writer -with open(file_1) as tsvin_1, open(file_2) as tsvin_2, open(output_filename, 'wb') as tsvout: - tsvreader_1 = csv.reader(tsvin_1, delimiter='\t') - tsvreader_2 = csv.reader(tsvin_2, delimiter='\t') - tsvout = csv.writer(tsvout, delimiter='\t') - -#loops through each row in the Variant Additional Details file - for row in tsvreader_2: - #sets row_2 equal to the same row in Variant Result file - row_2 = tsvreader_1.next() - #checks if row is empty or if the first term contains '#' - if row == [] or row[0][0] == '#': - continue - #checks if the row begins with input line - if row[0] == 'Input line': - #Goes through each value in the headers list in VAD - for value in row: - #Adds each value into headers - headers.append(value) - #Loops through the Keys in VR - for value in row_2: - #Checks if the value is already in headers - if value in headers: - continue - #else adds the header to headers - else: - headers.append(value) - - print headers - tsvout.writerow(headers) - - - else: - - cells = [] - #Goes through each value in the next list - for value in row: - #adds it to cells - cells.append(value) - #Goes through each value from the VR file after position 11 (After it is done repeating from VAD file) - for value in row_2[11:]: - #adds in the rest of the values to cells - cells.append(value) - - print cells - tsvout.writerow(cells) \ No newline at end of file diff -r 953e716d7837 -r 2ccaad5c7c01 cravat_submit/cravat_submit.xml --- a/cravat_submit/cravat_submit.xml Tue Jun 12 11:54:00 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,34 +0,0 @@ - - Submits, checks for, and retrieves data for cancer annotation - cravat_submit.py $input $dropdown $output - - - - - - - - - - - - - - - - - - - - - - - - - - - - This tool submits, checks for, and retrieves data for cancer annotation. - - - diff -r 953e716d7837 -r 2ccaad5c7c01 cravat_submit/vcf_converter.py --- a/cravat_submit/vcf_converter.py Tue Jun 12 11:54:00 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,243 +0,0 @@ -""" -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