def filter_ann_txt_files_just_maf(infile, cols_to_filter, freq_req, outfile_prefix, protein_changing_definitions, exonic_definitions, genename): ##filter rnaseq data for freq in freq_req: ##only 'rare' (<=1%) filtering_annotated.filter(working_dir, "and", infile, "1.temp", [cols_to_filter[0]], ['<='], [freq]) ##q>=30 and coverage >=5 filtering_annotated.filter(working_dir, "and", "1.temp", outfile_prefix + '.' + str(freq) + '.' + genename + ".xls", cols_to_filter[1], ['>=', '>='], [30,5])
def filter_rpt_c3h(file_prefix):
##remove if in rmsk, segdup
filtering_annotated.filter(working_dir, "and",
file_prefix + '.annotated.txt',
file_prefix + "11.temp", [11, 12], ['==', '=='],
['.', '.'])
##in c3hr mouse
filtering_annotated.filter(working_dir, "and", file_prefix + '11.temp',
file_prefix + ".c3h.xls", [25], ['!='], ['.'])
def filter_exonic_variants(infile, outfile):
col_exon = 6
exon_definition = ['exonic', 'splicing']
col_function = 9
syn_definition = 'synonymous SNV'
filtering_annotated.filter(working_dir, "or", infile, 'temp1.txt',
[col_exon, col_exon], ['==', '=='],
[exon_definition[0], exon_definition[1]])
##remove synonymous
filtering_annotated.filter(working_dir, "and", 'temp1.txt', outfile,
[col_function], ['!='], [syn_definition])
def filter_ann_file_3(file_prefix):
##remove if in rmsk, segdup
filtering_annotated.filter(working_dir, "and",
file_prefix + '.annotated.txt',
file_prefix + "21.temp", [11, 12], ['==', '=='],
['.', '.'])
##homozygous in all three mice
filtering_annotated.filter(working_dir, "or", file_prefix + '21.temp',
file_prefix + '.non_rpt.in_any.xls',
[16, 17, 18], ['!=', '!=', '!='],
['.', '.', '.'])
def filter_ann_file_2(file_prefix):
##remove if in rmsk, segdup, or b6
filtering_annotated.filter(working_dir, "and",
file_prefix + '.annotated.txt',
file_prefix + "11.temp", [11, 12, 15],
['==', '==', '=='], ['.', '.', '.'])
##homozygous in all three mice
filtering_annotated.filter(working_dir, "and", file_prefix + '11.temp',
file_prefix + '.non_rpt_b6.hom_in_all.xls',
[16, 17, 18], ['==', '==', '=='],
['hom', 'hom', 'hom'])
def filter_for_enu(file_prefix):
##remove if in rmsk, segdup, dbsnp, mgp, c3h
filtering_annotated.filter(working_dir, "and",
file_prefix + '.annotated.txt',
file_prefix + ".enu_rpts.xls",
[11, 12, 13, 14, 25],
['==', '==', '==', '==', '=='],
['.', '.', '.', '.', '.'])
##remove if in dbsnp, mgp, c3h
filtering_annotated.filter(working_dir, "and",
file_prefix + '.annotated.txt',
file_prefix + ".enu.xls", [13, 14, 25],
['==', '==', '=='], ['.', '.', '.'])
def filter_ann_txt_files(samples, cols_to_filter, freq_req, outfile_prefix):
##filter rnaseq data
for sample in samples:
for freq in freq_req:
##only 'rare' (<=1%)
filtering_annotated.filter(
working_dir, "and",
outfile_prefix + '.' + sample + '.annotated.txt', "1.temp",
[cols_to_filter[1]], ['<='], [freq])
##q>=30 and coverage >=5
filtering_annotated.filter(working_dir, "and", "1.temp", "2.temp",
cols_to_filter[4], ['>=', '>='],
[30, 5])
##exonic_variants in refGene
filtering_annotated.filter(
working_dir, "or", "2.temp",
outfile_prefix + '.' + str(freq) + "3.temp",
[cols_to_filter[0], cols_to_filter[0]], ['==', '=='],
[exonic_definitions[0], exonic_definitions[1]])
##get all protein changing
filtering_annotated.filter(
working_dir, "and",
outfile_prefix + '.' + str(freq) + "3.temp", outfile_prefix +
'.' + sample + '.' + str(freq) + ".protein_changing.xls",
make_list(cols_to_filter[2], non_protein_changing_definitions),
make_list('!=', non_protein_changing_definitions),
non_protein_changing_definitions)
def filter_ann_file_mt2(file_prefix): ##if not passed by mt2 filtering_annotated.filter(working_dir, "and", file_prefix + '.mt2.annotated.txt', file_prefix + '.mt2.passed.xls', [137], ['=='], ['PASS']) ##exonic_variants filtering_annotated.filter(working_dir, "or", file_prefix + '.mt2.annotated.txt' , file_prefix + "_1.temp", [6, 6], ['==','=='], [exon_definition[0],exon_definition[1]]) ##remove synonymous filtering_annotated.filter(working_dir, "and", file_prefix + "_1.temp", file_prefix + "_2.temp", [9], ['!='], ['synonymous SNV']) ##<10% in all gnomad filtering_annotated.filter(working_dir, "and", file_prefix + "_2.temp", file_prefix + ".mt2.exonic.rare.xls", [83,100,117], ['<=','<=','<='], [freq_req,freq_req,freq_req])
def filter_ann_txt_files(infile, cols_to_filter, freq_req, outfile_prefix, protein_changing_definitions, exonic_definitions, genename):
##filter rnaseq data
for freq in freq_req:
##only 'rare' (<=1%)
filtering_annotated.filter(working_dir, "and", infile, "1.temp", [cols_to_filter[0]], ['<='], [freq])
##q>=30 and coverage >=5
filtering_annotated.filter(working_dir, "and", "1.temp", "2.temp", cols_to_filter[1], ['>=', '>='], [30,5])
##exonic_variants in refGene
filtering_annotated.filter(working_dir, "or", "2.temp", outfile_prefix + '.' + str(freq) + ".exonic_temp.xls", [cols_to_filter[2], cols_to_filter[2]], ['==','=='], [exonic_definitions[0],exonic_definitions[1]])
##get all protein changing
filtering_annotated.filter(working_dir, "or", outfile_prefix + '.' + str(freq) + ".exonic_temp.xls", outfile_prefix + '.' + str(freq) + ".protein_changing." + genename + ".xls", make_list(cols_to_filter[3], protein_changing_definitions), make_list('==', protein_changing_definitions), protein_changing_definitions)
def filter_ann_file(file_prefix):
##remove if in rmsk, segdup
# filtering_annotated.filter(working_dir, "and", file_prefix + '.annotated.txt', file_prefix + "11.temp", [11,12], ['==','=='], ['.','.'])
##in chr17 region
# filtering_annotated.filter(working_dir, "and", file_prefix + '11.temp', file_prefix + '.chr17_20-30mb.xls', [1,3,3], ['==','>=','<='], ['chr17',20000000,30000000])
# filtering_annotated.filter(working_dir, "and", file_prefix + '11.temp', file_prefix + '.chr17_20-40mb.xls', [1,3,3], ['==','>=','<='], ['chr17',20000000,40000000])
# filtering_annotated.filter(working_dir, "and", file_prefix + '11.temp', file_prefix + '.chr17_20-60mb.xls', [1,3,3], ['==','>=','<='], ['chr17',20000000,60000000])
# filtering_annotated.filter(working_dir, "and", file_prefix + '11.temp', file_prefix + '.chr17.xls', [1], ['=='], ['chr17'])
# filtering_annotated.filter(working_dir, "and", file_prefix + '11.temp', file_prefix + '.chr17_20-30mb.xls', [1,3,3], ['==','>=','<='], ['chr17','20000000','30000000'])
##unique to ko
# filtering_annotated.filter(working_dir, "and", file_prefix + '.chr17_20-30mb.xls', file_prefix + '.chr17_20-30mb.ko_only.xls', [15], ['=='], ['.'])
# filtering_annotated.filter(working_dir, "and", file_prefix + '.chr17_20-40mb.xls', file_prefix + '.chr17_20-40mb.ko_only.xls', [15], ['=='], ['.'])
# filtering_annotated.filter(working_dir, "and", file_prefix + '.chr17_20-60mb.xls', file_prefix + '.chr17_20-60mb.ko_only.xls', [15], ['=='], ['.'])
# filtering_annotated.filter(working_dir, "and", file_prefix + '.chr17.xls', file_prefix + '.chr17.ko_only.xls', [15], ['=='], ['.'])
##in 129
filtering_annotated.filter(working_dir, "or",
file_prefix + '.chr17.ko_only.xls',
file_prefix + '.chr17.ko_only.129.xls',
[17, 18, 19], ['!=', '!=', '!='],
['.', '.', '.'])
def filter_ann_file_somatic_only(file_prefix, sample): file_prefix = file_prefix + '.' + sample ##remove if in control, i.e. not in TZ008 if sample == 'TZ001' or sample == 'TZ002': filtering_annotated.filter(working_dir, "and", file_prefix + '.annotated.txt', file_prefix + ".not_in_TZ008.xls", [153], ['=='], ['.']) ##remove if in control, i.e. not in TZ009 elif sample == 'TZ003': filtering_annotated.filter(working_dir, "and", file_prefix + '.annotated.txt', file_prefix + ".not_in_TZ009.xls", [154], ['=='], ['.']) ##remove if in control, i.e. not in TZ007 elif sample == 'TZ004' or sample == 'TZ005' or sample == 'TZ006': filtering_annotated.filter(working_dir, "and", file_prefix + '.annotated.txt', file_prefix + ".not_in_TZ007.xls", [152], ['=='], ['.']) else: print(sample, 'sample name not recognized')
def filter_rpt(file_prefix):
##remove if in rmsk, segdup
filtering_annotated.filter(working_dir, "and",
file_prefix + '.annotated.txt',
file_prefix + ".c15_vars.xls", [11, 12],
['==', '=='], ['.', '.'])
genome_and_window = homozygosity_mapping_ub26.make_windows(working_dir, genome_fai, ws, step_size).split('.')[0]
##make bed file from variants
homozygosity_mapping_ub26.make_bed_from_ann(working_dir, 'samtools', sample + '.hom_temp.txt', zygosity_col, info_col)
##hom and het count and hom percentage
homozygosity_mapping_ub26.count_and_percentage(working_dir, genome_and_window, sample + '.bed')
##naf
homozygosity_mapping_ub26.naf_in_window(working_dir, genome_and_window, sample + '.bed')
##total snp number
homozygosity_mapping_ub26.total_snp_in_window(working_dir, genome_and_window, sample + '.bed')
##combine bedgraphs for graphing in r
homozygosity_mapping_ub26.combine_bedgraphs_for_r(working_dir, sample, genome_and_window)
'''
# '''
##filter variants for candidates snps
affected_samples = ['M75', 'M77']
for sample in affected_samples:
##auts2
filtering_annotated.filter(working_dir, "and", sample + '.annotated.txt',
sample + "_1.temp", [1, 2, 3],
['==', '>=', '<='],
['chr5', 131437306, 132542649])
##keep if het in all sample
filtering_annotated.filter(working_dir, "and", sample + "_1.temp",
sample + ".auts2_het_in_all.xls",
[14, 15, 16, 17], ['==', '==', '==', '=='],
['het', 'het', 'het', 'het'])
# '''
# combine_fq_file(r1_files_to_combine, r2_files_to_combine, K541_combined_r1, K541_combined_r2) # align_with_bwa(fq_dict) variant_calling_samtools(fq_dict, mkdup_bam, st_vcf_suffix) convert_to_annovar(fq_dict, st_vcf_suffix + '.gz') run_table_annovar(fq_dict) multianno_to_annotated(fq_dict) ##filter variants for variants, homozygsity mapping then counts # ''' ##filter variants for candidates snps for sample in samples: ##exonic_variants filtering_annotated.filter(working_dir, "or", sample + '.annotated.txt' , sample + "_1.temp", [col_exon, col_exon], ['==','=='], [exon_definition[0],exon_definition[1]]) ##remove synonymous filtering_annotated.filter(working_dir, "and", sample + "_1.temp", sample + "_2.temp", [col_function], ['!='], [syn_definition]) ##remove if in dbsnp, sanger, or other mouse line filtering_annotated.filter(working_dir, "and", sample + "_2.temp", sample + "_3.temp", [13,14,15,16,17,18,19,20,21,22,23], ['==','==','==','==','==','==','==','==','==','==','=='], ['','','','','','','','','','','']) ##keep if hom filtering_annotated.filter(working_dir, "and", sample + "_3.temp", sample + '.hom_exonic_rare.xls', [zygosity_col], ['=='], ['hom']) ##filter variants by coverage and quality filtering_annotated.filter(working_dir, "and", sample + '.hom_exonic_rare.xls', sample + '.hom_exonic_rare_qual_filtered.xls', [cov_col,qual_col], ['>=','>='], [cov_definition,qual_definition]) # ''' # ''' ##homozygosity mapping # window_size = [100000,500000,1000000,2000000] window_size = [10000000]
##naf
homozygosity_mapping_cybertron.naf_in_window(working_dir, genome_and_window, sample + '.bed')
##total snp number
homozygosity_mapping_cybertron.total_snp_in_window(working_dir, genome_and_window, sample + '.bed')
##combine bedgraphs for graphing in r
homozygosity_mapping_cybertron.combine_bedgraphs_for_r(working_dir, sample, genome_and_window)
'''
## add not in affected mouse to c3Hh analysis
# '''
samples = ['mut_combined']
for ws in window_size:
for sample in samples:
##remove if in rmsk, segdup, not hom in unaffected mouse and keep hom if in c3h mouse
filtering_annotated.filter(working_dir, "and",
sample + '.annotated.txt',
sample + "21.temp", [11, 12], ['==', '=='],
['', ''])
filtering_annotated.filter(working_dir, "and", sample + '21.temp',
sample + "31.temp", [22], ['!='], ['hom'])
filtering_annotated.filter(working_dir, "and", sample + '31.temp',
sample + "41.temp", [26], ['=='], ['hom'])
##filter variants by coverage and quality
filtering_annotated.filter(working_dir, "and", sample + "41.temp",
sample + '.hom_temp.txt',
[cov_col, qual_col], ['>=', '>='],
[cov_definition, qual_definition])
##get hom vars
filtering_annotated.filter(working_dir, "and",
sample + ".hom_temp.txt",
sample + '.no_rpts_c3h_notunaff_hom.xls',
[zygosity_col], ['=='], ['hom'])
##hom and het count and hom percentage
homozygosity_mapping_cybertron.count_and_percentage(working_dir, genome_and_window, sample + '.bed')
##naf
homozygosity_mapping_cybertron.naf_in_window(working_dir, genome_and_window, sample + '.bed')
##total snp number
homozygosity_mapping_cybertron.total_snp_in_window(working_dir, genome_and_window, sample + '.bed')
##combine bedgraphs for graphing in r
homozygosity_mapping_cybertron.combine_bedgraphs_for_r(working_dir, sample, genome_and_window)
# '''
##filter variants for candidates snps
for sample in samples:
##remove if in dbsnp, sanger, or other mouse line
filtering_annotated.filter(working_dir, "and", sample + '.annotated.txt',
sample + "_23.temp", [11, 12, 13, 14, 15, 16],
['==', '==', '==', '==', '==', '=='],
['', '', '', '', '', ''])
##keep if hom
filtering_annotated.filter(working_dir, "and", sample + "_23.temp",
sample + "_24.temp", [zygosity_col], ['=='],
['hom'])
##filter variants by coverage and quality
filtering_annotated.filter(working_dir, "and", sample + "_24.temp",
sample + '.rare_qual_filtered.xls',
[cov_col, qual_col], ['>=', '>='],
[cov_definition, qual_definition])
##find C3H windows
##filter variants for counts
c3h_snp_suffix = '.c3h_filtered.xls'
c3h_snp_bed_suffix = '.c3h_filtered.bed'
filtering_annotated.filter(working_dir, "and", sample + '.hom_exonic_rare.xls', sample + '.hom_exonic_rare_qual_filtered.xls', [cov_col,qual_col], ['>=','>='], [cov_definition,qual_definition])
'''
# '''
##homozygosity mapping
##for 0917 analysis
window_size = [100000, 500000, 1000000, 2000000]
# window_size = [10000000]
step_size = 100000
fq_dict = ['timon_comb']
for ws in window_size:
for sample in fq_dict:
##remove if in dbsnp, sanger, other ped or rmsk
filtering_annotated.filter(
working_dir, "and", sample + '.annotated.txt', sample + "11.temp",
[11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], [
'==', '==', '==', '==', '==', '==', '==', '==', '==', '==',
'==', '==', '=='
], ['', '', '', '', '', '', '', '', '', '', '', '', ''])
# filtering_annotated.filter(working_dir, "and", sample + '.annotated.txt', sample + "11.temp", [11,12,23], ['==','==','=='], ['','',''])
##filter variants by coverage and quality
filtering_annotated.filter(working_dir, "and", sample + "11.temp",
sample + '.hom_temp.txt',
[cov_col, qual_col], ['>=', '>='],
[cov_definition, qual_definition])
#make bed file with windows and returns genome name and window size variable
genome_and_window = homozygosity_mapping_ub26.make_windows(
working_dir, genome_fai, ws, step_size).split('.')[0]
##make bed file from variants
def filter_ann_file_causal(file_prefix, sample): file_prefix = file_prefix + '.' + sample ##if not passed by gatk filtering_annotated.filter(working_dir, "and", file_prefix + '.annotated.txt', file_prefix + "_0.temp", [9], ['=='], ['PASS']) ##remove if in control, i.e. not in TZ008 if sample == 'TZ001' or sample == 'TZ002': filtering_annotated.filter(working_dir, "and", file_prefix + "_0.temp", file_prefix + ".not_in_TZ008.xls", [153], ['=='], ['.']) ##exonic_variants filtering_annotated.filter(working_dir, "or", file_prefix + ".not_in_TZ008.xls" , file_prefix + "_1.temp", [col_exon, col_exon], ['==','=='], [exon_definition[0],exon_definition[1]]) ##remove synonymous filtering_annotated.filter(working_dir, "and", file_prefix + "_1.temp", file_prefix + "_2.temp", [col_function], ['!='], [syn_definition]) ##<10% in all gnomad filtering_annotated.filter(working_dir, "and", file_prefix + "_2.temp", file_prefix + ".not_in_TZ008.exonic.rare.xls", af_cols, ['<=','<=','<='], [freq_req,freq_req,freq_req]) ##remove if in control, i.e. not in TZ009 elif sample == 'TZ003': filtering_annotated.filter(working_dir, "and", file_prefix + "_0.temp", file_prefix + ".not_in_TZ009.xls", [154], ['=='], ['.']) ##exonic_variants filtering_annotated.filter(working_dir, "or", file_prefix + ".not_in_TZ009.xls" , file_prefix + "_1.temp", [col_exon, col_exon], ['==','=='], [exon_definition[0],exon_definition[1]]) ##remove synonymous filtering_annotated.filter(working_dir, "and", file_prefix + "_1.temp", file_prefix + "_2.temp", [col_function], ['!='], [syn_definition]) ##<10% in all gnomad filtering_annotated.filter(working_dir, "and", file_prefix + "_2.temp", file_prefix + ".not_in_TZ009.exonic.rare.xls", af_cols, ['<=','<=','<='], [freq_req,freq_req,freq_req]) ##remove if in control, i.e. not in TZ007 elif sample == 'TZ004' or sample == 'TZ005' or sample == 'TZ006': filtering_annotated.filter(working_dir, "and", file_prefix + "_0.temp", file_prefix + ".not_in_TZ007.xls", [152], ['=='], ['.']) ##exonic_variants filtering_annotated.filter(working_dir, "or", file_prefix + ".not_in_TZ007.xls" , file_prefix + "_1.temp", [col_exon, col_exon], ['==','=='], [exon_definition[0],exon_definition[1]]) ##remove synonymous filtering_annotated.filter(working_dir, "and", file_prefix + "_1.temp", file_prefix + "_2.temp", [col_function], ['!='], [syn_definition]) ##<10% in all gnomad filtering_annotated.filter(working_dir, "and", file_prefix + "_2.temp", file_prefix + ".not_in_TZ007.exonic.rare.xls", af_cols, ['<=','<=','<='], [freq_req,freq_req,freq_req]) else: print(sample, 'sample name not recognized')
window_bed = genome_and_window + '.bed'
##all shared snps
out_bed = bed_to_graph.rsplit('.',
1)[0] + '.' + genome_and_window + '.bed'
##bedtools intersect
with open('temp.bed', "w") as naf_fh:
hom_bt_intersect = subprocess.Popen([
bedtools, 'intersect', '-a', window_bed, '-b', bed_to_graph,
'-c'
],
stdout=naf_fh)
hom_bt_intersect.wait()
##filter for region of interest
if '30mb' in bed_to_graph:
filtering_annotated.filter(working_dir, "and", 'temp.bed',
'temp2.bed', [1, 3, 3],
['==', '>', '<='],
['chr17', 20000000, 30000000])
elif '40mb' in bed_to_graph:
filtering_annotated.filter(working_dir, "and", 'temp.bed',
'temp2.bed', [1, 3, 3],
['==', '>', '<='],
['chr17', 20000000, 40000000])
elif '60mb' in bed_to_graph:
filtering_annotated.filter(working_dir, "and", 'temp.bed',
'temp2.bed', [1, 3, 3],
['==', '>', '<='],
['chr17', 20000000, 60000000])
else:
filtering_annotated.filter(working_dir, "and", 'temp.bed',
'temp2.bed', [1], ['=='], ['chr17'])
def filter_ann_txt_files(exac_txt, cases_txt, control_txt, esp6500_cols, case_controls_col, freq_req):
##remove duplicate vars from cases and controls and add count in first col
remove_dup_vars_and_add_count(cases_txt,'cases.nodup_temp.txt')
remove_dup_vars_and_add_count(control_txt,'controls.nodup_temp.txt')
##filter rnaseq data
for freq in freq_req:
for group in ['cases', 'controls']:
##only 'rare' (<=1%)
filtering_annotated.filter(working_dir, "and", group + '.nodup_temp.txt', group + '.' + str(freq) + ".1.temp", case_controls_col[1], make_list('<=', case_controls_col[1]), make_list(freq, case_controls_col[1]))
##q>=30 and coverage >=5
filtering_annotated.filter(working_dir, "and", group + '.' + str(freq) + ".1.temp", group + '.' + str(freq) + ".2.temp", case_ctl_qual_cov, ['>=', '>='], [30,5])
##exonic_variants in refGene
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".2.temp", group + '.' + str(freq) + ".exonic.xls", [case_controls_col[0], case_controls_col[0]], ['==','=='], [exonic_definitions[0],exonic_definitions[1]])
##get dispuptive
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".exonic.xls", group + '.' + str(freq) + ".disruptive.xls", make_list(case_controls_col[2], disruptive_definitions), make_list('==', disruptive_definitions), disruptive_definitions)
##get all protein changing
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".exonic.xls", group + '.' + str(freq) + ".protein_changing.xls", make_list(case_controls_col[2], protein_changing_definitions), make_list('==', protein_changing_definitions), protein_changing_definitions)
##get damaging - pp2_hdiv, pp2_hvar, cadd_phred, gerp - in all or in any
#get non synonymous snps
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".exonic.xls", group + '.' + str(freq) + ".3.temp", [case_controls_col[2], case_controls_col[2]], ['==', '=='], nosyn_definitions)
#get if all are positive
# filtering_annotated.filter(working_dir, "and", group + '.' + str(freq) + ".3.temp", group + '.' + str(freq) + ".4.temp", case_controls_col[3], make_list('>=', case_controls_col[3]), damaging_definitions)
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".3.temp", group + '.' + str(freq) + ".4a.temp", case_controls_col[3][:2], make_list('>=', case_controls_col[3][:2]), pp2_score)
filtering_annotated.filter(working_dir, "and", group + '.' + str(freq) + ".4a.temp", group + '.' + str(freq) + ".4b.temp", case_controls_col[3][2:], make_list('>=', case_controls_col[3][2:]), cadd_gerp_score)
remove_rows_with_no_data(group + '.' + str(freq) + ".4b.temp", group + '.' + str(freq) + ".damaging_all.xls", case_controls_col[3])
#get if any are positive
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".3.temp", group + '.' + str(freq) + ".5.temp", case_controls_col[3], make_list('>=', case_controls_col[3]), damaging_definitions)
remove_rows_with_no_data(group + '.' + str(freq) + ".5.temp", group + '.' + str(freq) + ".damaging_any.xls", case_controls_col[3])
##and exac
for group in ['exac3_1115']:
##only 'rare' (<=1%)
filtering_annotated.filter(working_dir, "and", exac_txt, group + '.' + str(freq) + ".1.temp", esp6500_rvis_col[1], make_list('<=', esp6500_rvis_col[1]), make_list(freq, esp6500_rvis_col[1]))
##exonic_variants in refGene
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".1.temp", group + '.' + str(freq) + ".exonic.xls", [esp6500_rvis_col[0], esp6500_rvis_col[0]], ['==','=='], [exonic_definitions[0],exonic_definitions[1]])
##get dispuptive
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".exonic.xls", group + '.' + str(freq) + ".disruptive.xls", make_list(esp6500_rvis_col[2], disruptive_definitions), make_list('==', disruptive_definitions), disruptive_definitions)
##get all protein changing
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".exonic.xls", group + '.' + str(freq) + ".protein_changing.xls", make_list(esp6500_rvis_col[2], protein_changing_definitions), make_list('==', protein_changing_definitions), protein_changing_definitions)
##get damaging - pp2_hdiv, pp2_hvar, cadd_phred, gerp - in all or in any
#get non synonymous snps
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".exonic.xls", group + '.' + str(freq) + ".3.temp", [esp6500_rvis_col[2], esp6500_rvis_col[2]], ['==', '=='], nosyn_definitions)
#get if all are positive
# filtering_annotated.filter(working_dir, "and", group + '.' + str(freq) + ".3.temp", group + '.' + str(freq) + ".4.temp", esp6500_rvis_col[3], make_list('>=', esp6500_rvis_col[3]), damaging_definitions)
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".3.temp", group + '.' + str(freq) + ".4a.temp", esp6500_rvis_col[3][:2], make_list('>=', esp6500_rvis_col[3][:2]), pp2_score)
filtering_annotated.filter(working_dir, "and", group + '.' + str(freq) + ".4a.temp", group + '.' + str(freq) + ".4b.temp", esp6500_rvis_col[3][2:], make_list('>=', esp6500_rvis_col[3][2:]), cadd_gerp_score)
remove_rows_with_no_data(group + '.' + str(freq) + ".4b.temp", group + '.' + str(freq) + ".damaging_all.xls", esp6500_rvis_col[3])
#get if any are positive
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".3.temp", group + '.' + str(freq) + ".5.temp", esp6500_rvis_col[3], make_list('>=', esp6500_rvis_col[3]), damaging_definitions)
remove_rows_with_no_data(group + '.' + str(freq) + ".5.temp", group + '.' + str(freq) + ".damaging_any.xls", esp6500_rvis_col[3])
def filter_ann_txt_files_just_exac(exac_txt, esp6500_cols, freq_req):
for freq in freq_req:
for group in [exac_unfiltered_prefix]:
# ##only 'rare' (<=1%)
# filtering_annotated.filter(working_dir, "and", exac_txt, group + '.' + str(freq) + ".1.temp", esp6500_rvis_col[1], make_list('<=', esp6500_rvis_col[1]), make_list(freq, esp6500_rvis_col[1]))
# ##exonic_variants in refGene
# filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".1.temp", group + '.' + str(freq) + ".exonic.xls", [esp6500_rvis_col[0], esp6500_rvis_col[0]], ['==','=='], [exonic_definitions[0],exonic_definitions[1]])
##only 'rare' (<=1%)
filtering_annotated.filter(working_dir, "and", exac_txt, group + '.' + str(freq) + ".1.temp", esp6500_rvis_col[1], make_list('<=', esp6500_rvis_col[1]), make_list(freq, esp6500_rvis_col[1]))
##exonic_variants in refGene
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".1.temp", group + '.' + str(freq) + ".exonic.xls", [esp6500_rvis_col[0], esp6500_rvis_col[0]], ['==','=='], [exonic_definitions[0],exonic_definitions[1]])
##get dispuptive
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".exonic.xls", group + '.' + str(freq) + ".disruptive.xls", make_list(esp6500_rvis_col[2], disruptive_definitions), make_list('==', disruptive_definitions), disruptive_definitions)
##get all protein changing
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".exonic.xls", group + '.' + str(freq) + ".protein_changing.xls", make_list(esp6500_rvis_col[2], protein_changing_definitions), make_list('==', protein_changing_definitions), protein_changing_definitions)
##get damaging - pp2_hdiv, pp2_hvar, cadd_phred, gerp - in all or in any
#get non synonymous snps
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".exonic.xls", group + '.' + str(freq) + ".3.temp", [esp6500_rvis_col[2], esp6500_rvis_col[2]], ['==', '=='], nosyn_definitions)
#get if all are positive
# filtering_annotated.filter(working_dir, "and", group + '.' + str(freq) + ".3.temp", group + '.' + str(freq) + ".4.temp", esp6500_rvis_col[3], make_list('>=', esp6500_rvis_col[3]), damaging_definitions)
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".3.temp", group + '.' + str(freq) + ".4a.temp", esp6500_rvis_col[3][:2], make_list('>=', esp6500_rvis_col[3][:2]), pp2_score)
filtering_annotated.filter(working_dir, "and", group + '.' + str(freq) + ".4a.temp", group + '.' + str(freq) + ".4b.temp", esp6500_rvis_col[3][2:], make_list('>=', esp6500_rvis_col[3][2:]), cadd_gerp_score)
remove_rows_with_no_data(group + '.' + str(freq) + ".4b.temp", group + '.' + str(freq) + ".damaging_all.xls", esp6500_rvis_col[3])
#get if any are positive
filtering_annotated.filter(working_dir, "or", group + '.' + str(freq) + ".3.temp", group + '.' + str(freq) + ".5.temp", esp6500_rvis_col[3], make_list('>=', esp6500_rvis_col[3]), damaging_definitions)
remove_rows_with_no_data(group + '.' + str(freq) + ".5.temp", group + '.' + str(freq) + ".damaging_any.xls", esp6500_rvis_col[3])
hom_bed_suffix = '.resc_hom.bed'
# window_size = [1000000, 500000, 100000]
# step_size = 100000
window_size = [100000]
step_size = 10000
zygosity_col = 27
cov_col = 29
cov_definition = 20
qual_col = 28
qual_definition = 30
working_dir = work_dir
genome_fai = fasta_fai
for sample in samples_to_annotate:
##remove if in repeat region or indel and cov/qual
filtering_annotated.filter(working_dir, "and", sample + '.annotated.xls',
sample + "21.temp", [16], ['=='], [''])
filtering_annotated.filter(working_dir, "and", sample + "21.temp",
sample + "22.temp", [4, 5], ['!=', '!='],
['-', '-'])
filtering_annotated.filter(working_dir, "and", sample + "22.temp",
sample + "23.temp", [cov_col, qual_col],
['>=', '>='], [cov_definition, qual_definition])
##keep if hom not hom in unrescued
filtering_annotated.filter(working_dir, "and", sample + "23.temp",
sample + "24.temp", [zygosity_col], ['=='],
['hom'])
##keep if not hom in unrescued
filtering_annotated.filter(working_dir, "and", sample + "24.temp",
sample + hom_snp_suffix, [20, 21, 22],
['!=', '!=', '!='], ['hom', 'hom', 'hom'])
