diff --git a/scripts/NanoCaller.py b/NanoCaller old mode 100644 new mode 100755 similarity index 94% rename from scripts/NanoCaller.py rename to NanoCaller index 4aef3e7..fbccdda --- a/scripts/NanoCaller.py +++ b/NanoCaller @@ -1,3 +1,5 @@ +#!/usr/bin/env python + from warnings import simplefilter simplefilter(action='ignore', category=FutureWarning) @@ -5,10 +7,11 @@ import multiprocessing as mp from intervaltree import Interval, IntervalTree from subprocess import PIPE, Popen -from utils import * +from nanocaller_src.utils import * + def run(args): - import snpCaller, indelCaller + from nanocaller_src import snpCaller, indelCaller pool = mp.Pool(processes=args.cpu) @@ -47,7 +50,7 @@ def run(args): dirname = os.path.dirname(__file__) if args.exclude_bed in ['hg38', 'hg19', 'mm10', 'mm39']: - args.exclude_bed=os.path.join(dirname, 'release_data/bed_files/%s_centro_telo.bed.gz' %args.exclude_bed) + args.exclude_bed=os.path.join(dirname, 'nanocaller_src/release_data/bed_files/%s_centro_telo.bed.gz' %args.exclude_bed) if args.include_bed: tbx = pysam.TabixFile(args.include_bed) @@ -106,7 +109,7 @@ def run(args): in_dict={'chrom':args.chrom, 'start':start, 'end':end, 'sam_path':sam_path, 'fasta_path':args.ref, \ 'mincov':args.mincov, 'maxcov':args.maxcov, 'min_allele_freq':args.min_allele_freq, 'min_nbr_sites':args.min_nbr_sites, \ 'threshold':threshold, 'snp_model':args.snp_model,'indel_model':args.indel_model, 'cpu':args.cpu, 'vcf_path':args.output,'prefix':args.prefix,'sample':args.sample, 'seq':args.sequencing, \ - 'del_t':args.del_threshold,'ins_t':args.ins_threshold,'supplementary':args.supplementary, 'include_bed':args.include_bed\ + 'del_t':args.del_threshold,'ins_t':args.ins_threshold,'impute_indel_phase':args.impute_indel_phase, 'supplementary':args.supplementary, 'include_bed':args.include_bed\ , 'exclude_bed':args.exclude_bed,'win_size':args.win_size,'small_win_size':args.small_win_size} ind_time=time.time() indel_vcf=indelCaller.test_model(in_dict, pool) @@ -141,15 +144,15 @@ def run(args): t=time.time() - preset_dict={'ont':{'sequencing':'ont', 'snp_model':'ONT-HG002', 'indel_model':'ONT-HG002', 'neighbor_threshold':'0.4,0.6', 'ins_threshold':0.4,'del_threshold':0.6, 'enable_whatshap':False}, + preset_dict={'ont':{'sequencing':'ont', 'snp_model':'ONT-HG002', 'indel_model':'ONT-HG002', 'neighbor_threshold':'0.4,0.6', 'ins_threshold':0.4,'del_threshold':0.6, 'enable_whatshap':False, 'impute_indel_phase':False}, - 'ul_ont': {'sequencing':'ul_ont', 'snp_model':'ONT-HG002', 'indel_model':'ONT-HG002', 'neighbor_threshold':'0.4,0.6', 'ins_threshold':0.4,'del_threshold':0.6, 'enable_whatshap':False}, + 'ul_ont': {'sequencing':'ul_ont', 'snp_model':'ONT-HG002', 'indel_model':'ONT-HG002', 'neighbor_threshold':'0.4,0.6', 'ins_threshold':0.4,'del_threshold':0.6, 'enable_whatshap':False, 'impute_indel_phase':False}, - 'ul_ont_extreme':{'sequencing':'ul_ont_extreme', 'snp_model':'ONT-HG002', 'indel_model':'ONT-HG002', 'neighbor_threshold':'0.4,0.6', 'ins_threshold':0.4,'del_threshold':0.6, 'enable_whatshap':False}, + 'ul_ont_extreme':{'sequencing':'ul_ont_extreme', 'snp_model':'ONT-HG002', 'indel_model':'ONT-HG002', 'neighbor_threshold':'0.4,0.6', 'ins_threshold':0.4,'del_threshold':0.6, 'enable_whatshap':False, 'impute_indel_phase':False}, - 'ccs':{'sequencing':'pacbio', 'snp_model': 'CCS-HG002', 'indel_model':'CCS-HG002', 'neighbor_threshold':'0.3,0.7', 'ins_threshold':0.4,'del_threshold':0.4, 'enable_whatshap':True}, + 'ccs':{'sequencing':'pacbio', 'snp_model': 'CCS-HG002', 'indel_model':'CCS-HG002', 'neighbor_threshold':'0.3,0.7', 'ins_threshold':0.4,'del_threshold':0.4, 'enable_whatshap':True, 'impute_indel_phase':True}, - 'clr':{'sequencing':'pacbio', 'snp_model':'CLR-HG002', 'indel_model':'ONT-HG002', 'neighbor_threshold':'0.3,0.6', 'ins_threshold':0.6,'del_threshold':0.6, 'win_size':10, 'small_win_size':2, 'enable_whatshap':True} + 'clr':{'sequencing':'pacbio', 'snp_model':'CLR-HG002', 'indel_model':'ONT-HG002', 'neighbor_threshold':'0.3,0.6', 'ins_threshold':0.6,'del_threshold':0.6, 'win_size':10, 'small_win_size':2, 'enable_whatshap':True, 'impute_indel_phase':False} } flag_dict={"seq":"sequencing", "p":"preset", "o":"output", "sup":"supplementary","nbr_t":"neighbor_threshold","ins_t":"ins_threshold", "del_t":"del_threshold"} @@ -212,6 +215,8 @@ def run(args): indel_group.add_argument("-win_size", "--win_size", help="Size of the sliding window in which the number of indels is counted to determine indel candidate site. Only indels longer than 2bp are counted in this window. Larger window size can increase recall, but use a maximum of 50 only", type=int, default=40) indel_group.add_argument("-small_win_size", "--small_win_size", help="Size of the sliding window in which indel frequency is determined for small indels", type=int, default=4) + indel_group.add_argument('-impute_indel_phase','--impute_indel_phase', help='Infer read phase by rudimentary allele clustering if the no or insufficient phasing information is available, can be useful for datasets without SNPs or regions with poor phasing quality', default=False, action='store_true') + #phasing phase_group.add_argument('-phase_bam','--phase_bam', help='Phase bam files if snps mode is selected. This will phase bam file without indel calling.', default=False, action='store_true') diff --git a/scripts/NanoCaller_WGS.py b/NanoCaller_WGS old mode 100644 new mode 100755 similarity index 99% rename from scripts/NanoCaller_WGS.py rename to NanoCaller_WGS index 6364c2e..1c9abc0 --- a/scripts/NanoCaller_WGS.py +++ b/NanoCaller_WGS @@ -1,3 +1,5 @@ +#!/usr/bin/env python + from warnings import simplefilter simplefilter(action='ignore', category=FutureWarning) @@ -5,7 +7,7 @@ import multiprocessing as mp from intervaltree import Interval, IntervalTree from subprocess import PIPE, Popen -from utils import * +from nanocaller_src.utils import * if __name__ == '__main__': diff --git a/scripts/generate_SNP_pileups.py b/nanocaller_src/generate_SNP_pileups.py similarity index 100% rename from scripts/generate_SNP_pileups.py rename to nanocaller_src/generate_SNP_pileups.py diff --git a/scripts/generate_indel_pileups.py b/nanocaller_src/generate_indel_pileups.py similarity index 84% rename from scripts/generate_indel_pileups.py rename to nanocaller_src/generate_indel_pileups.py index 5097dc4..12bbb70 100644 --- a/scripts/generate_indel_pileups.py +++ b/nanocaller_src/generate_indel_pileups.py @@ -188,9 +188,11 @@ def ex_bed(tree, pos): ref_dict={j:s.upper() if s in 'AGTC' else '' for j,s in zip(range(max(1,start-200),end+400+1),fastafile.fetch(chrom,max(1,start-200)-1,end+400)) } + chrom_length=fastafile.get_reference_length(chrom) + hap_dict={1:[],2:[]} - for pread in samfile.fetch(chrom, max(0,dct['start']-100), dct['end']+100): + for pread in samfile.fetch(chrom, max(0,dct['start']-100000), dct['end']+1000): if pread.has_tag('HP'): hap_dict[pread.get_tag('HP')].append(pread.qname) @@ -214,6 +216,8 @@ def ex_bed(tree, pos): position_queue_small=collections.deque(small_size*[{}], small_size) variants={} + extra_variants={} + max_range={0:max(10,window_size),1:10} count=0 @@ -221,9 +225,6 @@ def ex_bed(tree, pos): for pcol in samfile.pileup(chrom,max(0,start-1),end,min_base_quality=0,\ flag_filter=flag,truncate=True): v_pos=pcol.pos+1 - if count>200: - print('%s: Skipping region %s:%d-%d due to poor alignment.' %(str(datetime.datetime.now()), chrom, v_pos, end), flush =True) - break if in_bed(include_intervals, v_pos) and not ex_bed(exclude_intervals, v_pos): read_names=pcol.get_query_names() @@ -257,8 +258,10 @@ def ex_bed(tree, pos): ins_queue_small_0.append(ins_set_small_0) ins_queue_small_1.append(ins_set_small_1) - - if v_pos>prev and len_seq_0>=mincov and len_seq_1>=mincov: + if v_pos<=prev: + continue + + if len_seq_0>=mincov and len_seq_1>=mincov: del_freq_0=len(set.union(*del_queue_0))/len_seq_0 if len_seq_0>0 else 0 ins_freq_0=len(set.union(*ins_queue_0))/len_seq_0 if len_seq_0>0 else 0 @@ -282,16 +285,15 @@ def ex_bed(tree, pos): prev=v_pos+10 variants[max(1,v_pos-10)]=1 count+=1 + - elif dct['seq']=='pacbio' and len_seq_tot >=2*mincov: + elif dct['impute_indel_phase'] and len_seq_tot >=2*mincov: seq_v2=[x.upper() for x in pcol.get_query_sequences( mark_matches=False, mark_ends=False, add_indels=True)] seq=[x[:2] for x in seq_v2] seq_tot=''.join(seq) - del_freq_tot=(seq_tot.count('-')+seq_tot.count('*'))/len_seq_tot if len_seq_tot>0 else 0 ins_freq_tot=seq_tot.count('+')/len_seq_tot if len_seq_tot>0 else 0 - if (del_t<=del_freq_tot or ins_t<=ins_freq_tot): groups={} for s,n in zip(seq_v2,read_names): @@ -300,67 +302,73 @@ def ex_bed(tree, pos): groups[s].append(n) counts=sorted([(x,len(groups[x])) for x in groups],key=lambda x:x[1],reverse=True) + if counts[0][1]<=0.8*len_seq_tot: - read_names_0=groups[counts[0][0]] - read_names_1=groups[counts[1][0]] + read_names_0=set(groups[counts[0][0]]) + read_names_1=set(groups[counts[1][0]]) if counts[1][1]>=mincov else set(read_names)-read_names_0 else: read_names_0=groups[counts[0][0]][:counts[0][1]//2] read_names_1=groups[counts[0][0]][counts[0][1]//2:] - if len(read_names_0)>=mincov and len(read_names_1)>=mincov: prev=v_pos+10 variants[max(1,v_pos-10)]=1 + extra_variants[max(1,v_pos-10)]=(read_names_0,read_names_1) count+=1 for pcol in samfile.pileup(chrom,max(0,start-10-window_size),end,min_base_quality=0, flag_filter=flag,truncate=True): v_pos=pcol.pos+1 - - if v_pos in variants: + if v_pos in extra_variants: + read_names=pcol.get_query_names() + read_names_0, read_names_1 =extra_variants[v_pos] + + elif v_pos in variants: read_names=pcol.get_query_names() + read_names_0=set(read_names) & hap_reads_0 read_names_1=set(read_names) & hap_reads_1 - - - d={'hap0':{},'hap1':{}} - d_tot={} - - ref=''.join([ref_dict[p] for p in range(v_pos-window_before,min(end,v_pos+window_after+1))]) - - for pread in pcol.pileups: - dt=pread.alignment.query_sequence[max(0,pread.query_position_or_next-window_before):pread.query_position_or_next+window_after] - d_tot[pread.alignment.qname]=dt - - if pread.alignment.qname in read_names_0: - d['hap0'][pread.alignment.qname]=dt + else: + continue - elif pread.alignment.qname in read_names_1: - d['hap1'][pread.alignment.qname]=dt - - - seq_list=d['hap0'] - flag0,_, data_0,alt_0,ref_seq_0=msa(seq_list,ref,v_pos,2,dct['maxcov']) + d={'hap0':{},'hap1':{}} + d_tot={} + + ref=''.join([ref_dict[p] for p in range(v_pos-window_before,min(chrom_length,v_pos+window_after+1))]) + + + for pread in pcol.pileups: + dt=pread.alignment.query_sequence[max(0,pread.query_position_or_next-window_before):pread.query_position_or_next+window_after] + d_tot[pread.alignment.qname]=dt + + if pread.alignment.qname in read_names_0: + d['hap0'][pread.alignment.qname]=dt + + elif pread.alignment.qname in read_names_1: + d['hap1'][pread.alignment.qname]=dt + + + seq_list=d['hap0'] + flag0,_, data_0,alt_0,ref_seq_0=msa(seq_list,ref,v_pos,2,dct['maxcov']) + + seq_list=d['hap1'] + flag1,_, data_1,alt_1,ref_seq_1=msa(seq_list,ref,v_pos,2,dct['maxcov']) + + seq_list = d_tot + flag_total,indel_flag_total,data_total,alt_total,ref_seq_total=msa(seq_list,ref,v_pos,dct['mincov'],dct['maxcov']) + + if flag0 and flag1 and flag_total: + output_pos.append(v_pos) + output_data_0.append(data_0) + output_data_1.append(data_1) + output_data_total.append(data_total) + + tp=max_range[variants[v_pos]] + + alleles.append([allele_prediction(alt_0, ref_seq_0, max_range[variants[v_pos]]),\ + allele_prediction(alt_1, ref_seq_1, max_range[variants[v_pos]]), \ + allele_prediction(alt_total, ref_seq_total, max_range[variants[v_pos]])]) - seq_list=d['hap1'] - flag1,_, data_1,alt_1,ref_seq_1=msa(seq_list,ref,v_pos,2,dct['maxcov']) - - seq_list = d_tot - flag_total,indel_flag_total,data_total,alt_total,ref_seq_total=msa(seq_list,ref,v_pos,dct['mincov'],dct['maxcov']) - - if flag0 and flag1 and flag_total: - output_pos.append(v_pos) - output_data_0.append(data_0) - output_data_1.append(data_1) - output_data_total.append(data_total) - - tp=max_range[variants[v_pos]] - - alleles.append([allele_prediction(alt_0, ref_seq_0, max_range[variants[v_pos]]),\ - allele_prediction(alt_1, ref_seq_1, max_range[variants[v_pos]]), \ - allele_prediction(alt_total, ref_seq_total, max_range[variants[v_pos]])]) - - if len(output_pos)==0: return (output_pos,output_data_0,output_data_1,output_data_total,alleles) diff --git a/scripts/indelCaller.py b/nanocaller_src/indelCaller.py similarity index 97% rename from scripts/indelCaller.py rename to nanocaller_src/indelCaller.py index c45b567..60857d1 100644 --- a/scripts/indelCaller.py +++ b/nanocaller_src/indelCaller.py @@ -5,10 +5,10 @@ import numpy as np import multiprocessing as mp import tensorflow as tf -from model_architect_indel import * +from .model_architect_indel import * from Bio import pairwise2 -from generate_indel_pileups import get_indel_testing_candidates -from utils import * +from .generate_indel_pileups import get_indel_testing_candidates +from .utils import * if type(tf.contrib) != type(tf): tf.contrib._warning = None config = tf.compat.v1.ConfigProto() @@ -136,8 +136,7 @@ def test_model(params,pool): q=-100*np.log10(1e-6+batch_prob_all[j,0]) allele0_data, allele1_data,allele_total_data= batch_alleles_seq[j] - if batch_pred_all[j]==1: - if allele_total_data[0]: + if batch_pred_all[j]==1 and allele_total_data[0]: gq=-100*np.log10(1+1e-6-batch_prob_all[j,1]) s='%s\t%d\t.\t%s\t%s\t%.2f\tPASS\t.\tGT:GQ\t1/1:%.2f\n' %(chrom, batch_pos[j], allele_total_data[0], allele_total_data[1], q, gq) f.write(s) diff --git a/scripts/model_architect.py b/nanocaller_src/model_architect.py similarity index 100% rename from scripts/model_architect.py rename to nanocaller_src/model_architect.py diff --git a/scripts/model_architect_indel.py b/nanocaller_src/model_architect_indel.py similarity index 100% rename from scripts/model_architect_indel.py rename to nanocaller_src/model_architect_indel.py diff --git a/scripts/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.coverage b/nanocaller_src/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.coverage similarity index 100% rename from scripts/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.coverage rename to nanocaller_src/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.coverage diff --git a/scripts/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.data-00000-of-00001 b/nanocaller_src/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.data-00000-of-00001 similarity index 100% rename from scripts/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.data-00000-of-00001 rename to nanocaller_src/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.data-00000-of-00001 diff --git a/scripts/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.index b/nanocaller_src/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.index similarity index 100% rename from scripts/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.index rename to nanocaller_src/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.index diff --git a/scripts/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.meta b/nanocaller_src/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.meta similarity index 100% rename from scripts/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.meta rename to nanocaller_src/release_data/ONT_models/SNPs/HG001_guppy2.3.8_giab-3.3.2/model-100.meta diff --git a/scripts/release_data/ONT_models/SNPs/HG001_guppy2.3.8_guppy4.2.2_giab-3.3.2/model-100.coverage b/nanocaller_src/release_data/ONT_models/SNPs/HG001_guppy2.3.8_guppy4.2.2_giab-3.3.2/model-100.coverage similarity index 100% rename from scripts/release_data/ONT_models/SNPs/HG001_guppy2.3.8_guppy4.2.2_giab-3.3.2/model-100.coverage rename to nanocaller_src/release_data/ONT_models/SNPs/HG001_guppy2.3.8_guppy4.2.2_giab-3.3.2/model-100.coverage diff --git a/scripts/release_data/ONT_models/SNPs/HG001_guppy2.3.8_guppy4.2.2_giab-3.3.2/model-100.data-00000-of-00001 b/nanocaller_src/release_data/ONT_models/SNPs/HG001_guppy2.3.8_guppy4.2.2_giab-3.3.2/model-100.data-00000-of-00001 similarity index 100% rename from scripts/release_data/ONT_models/SNPs/HG001_guppy2.3.8_guppy4.2.2_giab-3.3.2/model-100.data-00000-of-00001 rename to nanocaller_src/release_data/ONT_models/SNPs/HG001_guppy2.3.8_guppy4.2.2_giab-3.3.2/model-100.data-00000-of-00001 diff --git a/scripts/release_data/ONT_models/SNPs/HG001_guppy2.3.8_guppy4.2.2_giab-3.3.2/model-100.index b/nanocaller_src/release_data/ONT_models/SNPs/HG001_guppy2.3.8_guppy4.2.2_giab-3.3.2/model-100.index similarity index 100% rename from scripts/release_data/ONT_models/SNPs/HG001_guppy2.3.8_guppy4.2.2_giab-3.3.2/model-100.index rename to nanocaller_src/release_data/ONT_models/SNPs/HG001_guppy2.3.8_guppy4.2.2_giab-3.3.2/model-100.index diff --git 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--git a/scripts/release_data/hifi_models/SNPs/HG002_giab-4.2.1/model-100.data-00000-of-00001 b/nanocaller_src/release_data/hifi_models/SNPs/HG002_giab-4.2.1/model-100.data-00000-of-00001 similarity index 100% rename from scripts/release_data/hifi_models/SNPs/HG002_giab-4.2.1/model-100.data-00000-of-00001 rename to nanocaller_src/release_data/hifi_models/SNPs/HG002_giab-4.2.1/model-100.data-00000-of-00001 diff --git a/scripts/release_data/hifi_models/SNPs/HG002_giab-4.2.1/model-100.index b/nanocaller_src/release_data/hifi_models/SNPs/HG002_giab-4.2.1/model-100.index similarity index 100% rename from scripts/release_data/hifi_models/SNPs/HG002_giab-4.2.1/model-100.index rename to nanocaller_src/release_data/hifi_models/SNPs/HG002_giab-4.2.1/model-100.index diff --git a/scripts/release_data/hifi_models/SNPs/HG002_giab-4.2.1/model-100.meta b/nanocaller_src/release_data/hifi_models/SNPs/HG002_giab-4.2.1/model-100.meta similarity index 100% rename from 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a/scripts/release_data/hifi_models/indels/NanoCaller3_beta/model-25.index b/nanocaller_src/release_data/hifi_models/indels/NanoCaller3_beta/model-25.index similarity index 100% rename from scripts/release_data/hifi_models/indels/NanoCaller3_beta/model-25.index rename to nanocaller_src/release_data/hifi_models/indels/NanoCaller3_beta/model-25.index diff --git a/scripts/release_data/hifi_models/indels/NanoCaller3_beta/model-25.meta b/nanocaller_src/release_data/hifi_models/indels/NanoCaller3_beta/model-25.meta similarity index 100% rename from scripts/release_data/hifi_models/indels/NanoCaller3_beta/model-25.meta rename to nanocaller_src/release_data/hifi_models/indels/NanoCaller3_beta/model-25.meta diff --git a/scripts/snpCaller.py b/nanocaller_src/snpCaller.py similarity index 98% rename from scripts/snpCaller.py rename to nanocaller_src/snpCaller.py index 091e869..c475b52 100644 --- a/scripts/snpCaller.py +++ b/nanocaller_src/snpCaller.py @@ -5,10 +5,10 @@ import numpy as np import multiprocessing as mp import tensorflow as tf -from model_architect import * -from generate_SNP_pileups import get_snp_testing_candidates +from .model_architect import * +from .generate_SNP_pileups import get_snp_testing_candidates from intervaltree import Interval, IntervalTree -from utils import * +from .utils import * if type(tf.contrib) != type(tf): tf.contrib._warning = None config = tf.ConfigProto() diff --git a/scripts/utils.py b/nanocaller_src/utils.py similarity index 100% rename from scripts/utils.py rename to nanocaller_src/utils.py