def align(work_dir, sample_name, l_fpath, r_fpath, bwa, smb, bwa_prefix, dedup=True, threads=1): info('Running bwa to align reads...') bam_fpath = make_bam_fpath(work_dir) if can_reuse(bam_fpath, [l_fpath, r_fpath]): return bam_fpath tmp_dirpath = join(work_dir, 'sambamba_tmp_dir') safe_mkdir(tmp_dirpath) bwa_cmdline = ( '{bwa} mem -t {threads} -v 2 {bwa_prefix} {l_fpath} {r_fpath} | ' + '{smb} view /dev/stdin -t {threads} -f bam -S -o - | ' + '{smb} sort /dev/stdin -t {threads} --tmpdir {tmp_dirpath} -o {bam_fpath}' ).format(**locals()) run(bwa_cmdline, output_fpath=bam_fpath, stdout_to_outputfile=False) if dedup: dedup_bam_fpath = add_suffix(bam_fpath, 'dedup') dedup_cmdl = '{smb} markdup -t {threads} {bam_fpath} {dedup_bam_fpath}'.format( **locals()) run(dedup_cmdl, output_fpath=dedup_bam_fpath, stdout_to_outputfile=False) verify_bam(dedup_bam_fpath) os.rename(dedup_bam_fpath, bam_fpath) sambamba.index_bam(bam_fpath) # samtools view -b -S -u - | # sambamba sort -N -t 8 -m 682M --tmpdir /Molly/saveliev/cancer-dream-syn3/work/align/syn3-normal/split/tx/tmpwdXndE/syn3-normal-sort-1_20000000-sorttmp-full # -o /Molly/saveliev/cancer-dream-syn3/work/align/syn3-normal/split/tx/tmpwdXndE/syn3-normal-sort-1_20000000.bam # /dev/stdin # if dedup: # info() # info('Calling SamBlaster to mark duplicates') # markdup_sam_fpath = markdup_sam(sam_fpath, samblaster) # if markdup_sam_fpath: # sam_fpath = markdup_sam_fpath # info() # info('Converting to BAM') # cmdline = sambamba.get_executable() + ' view -t {threads} -S -f bam {sam_fpath}'.format(**locals()) # run(cmdline, output_fpath=bam_fpath, reuse=cfg.reuse_intermediate) # # info() # info('Sorting BAM') # prefix = splitext(sorted_bam_fpath)[0] # cmdline = sambamba.get_executable() + ' sort -t {threads} {bam_fpath} -o {sorted_bam_fpath}'.format(**locals()) # run(cmdline, output_fpath=sorted_bam_fpath, stdout_to_outputfile=False, reuse=cfg.reuse_intermediate) return bam_fpath
def run_qualimap(work_dir, output_dir, output_fpaths, bam_fpath, genome, bed_fpath=None, threads=1): info('Analysing ' + bam_fpath) safe_mkdir(dirname(output_dir)) safe_mkdir(output_dir) mem_cmdl = '' mem_m = get_qualimap_max_mem(bam_fpath) mem = str(int(mem_m)) + 'M' mem_cmdl = '--java-mem-size=' + mem cmdline = (find_executable() + ' bamqc --skip-duplicated -nt {threads} {mem_cmdl} -nr 5000 ' '-bam {bam_fpath} -outdir {output_dir}') if genome.startswith('hg') or genome.startswith('GRCh'): cmdline += ' -gd HUMAN' if genome.startswith('mm'): cmdline += ' -gd MOUSE' if bed_fpath: cmdline += ' -gff {bed_fpath}' debug('Using amplicons/capture panel ' + bed_fpath) cmdline = cmdline.format(**locals()) if not all( can_reuse(fp, [bam_fpath, bed_fpath] if bed_fpath else [bam_fpath]) for fp in output_fpaths): for fp in output_fpaths: if isfile(fp): os.remove(fp) try: run(cmdline, env_vars=dict(DISPLAY=None)) except subprocess.CalledProcessError as e: if 'The alignment file is unsorted.' in e.output: info() info('BAM file is unsorted; trying to sort and rerun QualiMap') sorted_bam_fpath = sort_bam(bam_fpath) cmdline = cmdline.replace(bam_fpath, sorted_bam_fpath) run(cmdline, env_vars=dict(DISPLAY=None)) if not all( verify_file( fp, cmp_f=[bam_fpath, bed_fpath] if bed_fpath else [bam_fpath]) for fp in output_fpaths): critical('Some of the QualiMap results were not generated') return output_dir
def set_up_dirs(proc_name, output_dir=None, work_dir=None, log_dir=None): """ Creates output_dir, work_dir, and sets up log """ output_dir = safe_mkdir( adjust_path(output_dir or join(os.getcwd(), proc_name)), 'output_dir') debug('Saving results into ' + output_dir) work_dir = safe_mkdir(work_dir or join(output_dir, 'work'), 'working directory') info('Using work directory ' + work_dir) log_fpath = set_up_log(log_dir or safe_mkdir(join(work_dir, 'log')), proc_name + '.log') return output_dir, work_dir, log_fpath
def align(work_dir, sample_name, l_fpath, r_fpath, bwa, smb, bwa_prefix, dedup=True, threads=1): info('Running bwa to align reads...') bam_fpath = make_bam_fpath(work_dir) if can_reuse(bam_fpath, [l_fpath, r_fpath]): return bam_fpath tmp_dirpath = join(work_dir, 'sambamba_tmp_dir') safe_mkdir(tmp_dirpath) bwa_cmdline = ('{bwa} mem -t {threads} -v 2 {bwa_prefix} {l_fpath} {r_fpath} | ' + '{smb} view /dev/stdin -t {threads} -f bam -S -o - | ' + '{smb} sort /dev/stdin -t {threads} --tmpdir {tmp_dirpath} -o {bam_fpath}').format(**locals()) run(bwa_cmdline, output_fpath=bam_fpath, stdout_to_outputfile=False) if dedup: dedup_bam_fpath = add_suffix(bam_fpath, 'dedup') dedup_cmdl = '{smb} markdup -t {threads} {bam_fpath} {dedup_bam_fpath}'.format(**locals()) run(dedup_cmdl, output_fpath=dedup_bam_fpath, stdout_to_outputfile=False) verify_bam(dedup_bam_fpath) os.rename(dedup_bam_fpath, bam_fpath) sambamba.index_bam(bam_fpath) # samtools view -b -S -u - | # sambamba sort -N -t 8 -m 682M --tmpdir /Molly/saveliev/cancer-dream-syn3/work/align/syn3-normal/split/tx/tmpwdXndE/syn3-normal-sort-1_20000000-sorttmp-full # -o /Molly/saveliev/cancer-dream-syn3/work/align/syn3-normal/split/tx/tmpwdXndE/syn3-normal-sort-1_20000000.bam # /dev/stdin # if dedup: # info() # info('Calling SamBlaster to mark duplicates') # markdup_sam_fpath = markdup_sam(sam_fpath, samblaster) # if markdup_sam_fpath: # sam_fpath = markdup_sam_fpath # info() # info('Converting to BAM') # cmdline = sambamba.get_executable() + ' view -t {threads} -S -f bam {sam_fpath}'.format(**locals()) # run(cmdline, output_fpath=bam_fpath, reuse=cfg.reuse_intermediate) # # info() # info('Sorting BAM') # prefix = splitext(sorted_bam_fpath)[0] # cmdline = sambamba.get_executable() + ' sort -t {threads} {bam_fpath} -o {sorted_bam_fpath}'.format(**locals()) # run(cmdline, output_fpath=sorted_bam_fpath, stdout_to_outputfile=False, reuse=cfg.reuse_intermediate) return bam_fpath
def run_multisample_qualimap(output_dir, work_dir, samples, targqc_full_report): """ 1. Generates Qualimap2 plots and put into plots_dirpath 2. Adds records to targqc_full_report.plots """ plots_dirpath = join(output_dir, 'plots') individual_report_fpaths = [s.qualimap_html_fpath for s in samples] if isdir(plots_dirpath) and not any( not can_reuse(join(plots_dirpath, f), individual_report_fpaths) for f in listdir(plots_dirpath) if not f.startswith('.')): debug('Qualimap miltisample plots exist - ' + plots_dirpath + ', reusing...') else: # Qualimap2 run for multi-sample plots if len([s.qualimap_html_fpath for s in samples if s.qualimap_html_fpath]) > 0: if find_executable() is not None: # and get_qualimap_type(find_executable()) == 'full': qualimap_output_dir = join(work_dir, 'qualimap_multi_bamqc') _correct_qualimap_genome_results(samples) _correct_qualimap_insert_size_histogram(samples) safe_mkdir(qualimap_output_dir) rows = [] for sample in samples: if sample.qualimap_html_fpath: rows += [[sample.name, sample.qualimap_html_fpath]] data_fpath = write_tsv_rows(([], rows), join(qualimap_output_dir, 'qualimap_results_by_sample.tsv')) qualimap_plots_dirpath = join(qualimap_output_dir, 'images_multisampleBamQcReport') cmdline = find_executable() + ' multi-bamqc --data {data_fpath} -outdir {qualimap_output_dir}'.format(**locals()) run(cmdline, env_vars=dict(DISPLAY=None), checks=[lambda _1, _2: verify_dir(qualimap_output_dir)], reuse=cfg.reuse_intermediate) if not verify_dir(qualimap_plots_dirpath): warn('Warning: Qualimap for multi-sample analysis failed to finish. TargQC will not contain plots.') return None else: if exists(plots_dirpath): shutil.rmtree(plots_dirpath) shutil.move(qualimap_plots_dirpath, plots_dirpath) else: warn('Warning: Qualimap for multi-sample analysis was not found. TargQC will not contain plots.') return None targqc_full_report.plots = [] for plot_fpath in listdir(plots_dirpath): plot_fpath = join(plots_dirpath, plot_fpath) if verify_file(plot_fpath) and plot_fpath.endswith('.png'): targqc_full_report.plots.append(relpath(plot_fpath, output_dir))
def determine_sex(work_dir, bam_fpath, avg_depth, genome, target_bed=None): debug() debug('Determining sex') pybedtools.set_tempdir(safe_mkdir(join(work_dir, 'pybedtools_tmp'))) male_bed = None for k in chry_key_regions_by_genome: if k in genome: male_bed = BedTool(chry_key_regions_by_genome.get(k)) break if not male_bed: warn('Warning: no male key regions for ' + genome + ', cannot identify sex') return None male_area_size = get_total_bed_size(male_bed) debug('Male region total size: ' + str(male_area_size)) if target_bed: target_male_bed = join(work_dir, 'male.bed') with file_transaction(work_dir, target_male_bed) as tx: BedTool(target_bed).intersect(male_bed).merge().saveas(tx) target_male_area_size = get_total_bed_size(target_male_bed) if target_male_area_size == 0: debug('The male non-PAR region does not overlap with the capture target - cannot determine sex.') return None male_bed = target_male_bed else: debug('WGS, determining sex based on chrY key regions coverage.') info('Detecting sex by comparing the Y chromosome key regions coverage and average coverage depth.') if not bam_fpath: critical('BAM file is required.') index_bam(bam_fpath) chry_mean_coverage = _calc_mean_coverage(work_dir, male_bed, bam_fpath, 1) debug('Y key regions average depth: ' + str(chry_mean_coverage)) avg_depth = float(avg_depth) debug('Sample average depth: ' + str(avg_depth)) if avg_depth < AVG_DEPTH_THRESHOLD_TO_DETERMINE_SEX: debug('Sample average depth is too low (less than ' + str(AVG_DEPTH_THRESHOLD_TO_DETERMINE_SEX) + ') - cannot determine sex') return None if chry_mean_coverage == 0: debug('Y depth is 0 - it\s female') sex = 'F' else: factor = avg_depth / chry_mean_coverage debug('Sample depth / Y depth = ' + str(factor)) if factor > FEMALE_Y_COVERAGE_FACTOR: # if mean target coverage much higher than chrY coverage debug('Sample depth is more than ' + str(FEMALE_Y_COVERAGE_FACTOR) + ' times higher than Y depth - it\s female') sex = 'F' else: debug('Sample depth is not more than ' + str(FEMALE_Y_COVERAGE_FACTOR) + ' times higher than Y depth - it\s male') sex = 'M' debug('Sex is ' + sex) debug() return sex
def parallel_view(n_samples, parallel_cfg, work_dir): prev_dir = os.getcwd() os.chdir(safe_mkdir(work_dir)) view = get_parallel_view(n_samples, parallel_cfg) os.chdir(prev_dir) try: yield view finally: view.stop()
def get_gender(genome, bam_fpath, bed_fpath, sample, avg_depth): gender = None chrom_lengths = ref.get_chrom_lengths(genome) chrom_names = [chrom for chrom, length in chrom_lengths] if 'Y' in chrom_names or 'chrY' in chrom_names: gender = determine_sex(sample.work_dir, bam_fpath, avg_depth, genome, bed_fpath) if gender: with open(join(safe_mkdir(sample.dirpath), 'gender.txt'), 'w') as f: f.write(gender[0].upper()) return gender
def run_qualimap(work_dir, output_dir, output_fpaths, bam_fpath, genome, bed_fpath=None, threads=1): info('Analysing ' + bam_fpath) safe_mkdir(dirname(output_dir)) safe_mkdir(output_dir) mem_cmdl = '' mem_m = get_qualimap_max_mem(bam_fpath) mem = str(int(mem_m)) + 'M' mem_cmdl = '--java-mem-size=' + mem cmdline = (find_executable() + ' bamqc --skip-duplicated -nt {threads} {mem_cmdl} -nr 5000 ' '-bam {bam_fpath} -outdir {output_dir}') if genome.startswith('hg') or genome.startswith('GRCh'): cmdline += ' -gd HUMAN' if genome.startswith('mm'): cmdline += ' -gd MOUSE' if bed_fpath: cmdline += ' -gff {bed_fpath}' debug('Using amplicons/capture panel ' + bed_fpath) cmdline = cmdline.format(**locals()) if not all(can_reuse(fp, [bam_fpath, bed_fpath] if bed_fpath else [bam_fpath]) for fp in output_fpaths): for fp in output_fpaths: if isfile(fp): os.remove(fp) try: run(cmdline, env_vars=dict(DISPLAY=None)) except subprocess.CalledProcessError as e: if 'The alignment file is unsorted.' in e.output: info() info('BAM file is unsorted; trying to sort and rerun QualiMap') sorted_bam_fpath = sort_bam(bam_fpath) cmdline = cmdline.replace(bam_fpath, sorted_bam_fpath) run(cmdline, env_vars=dict(DISPLAY=None)) if not all(verify_file(fp, cmp_f=[bam_fpath, bed_fpath] if bed_fpath else [bam_fpath]) for fp in output_fpaths): critical('Some of the QualiMap results were not generated') return output_dir
def get_gender(genome, bam_fpath, bed_fpath, sample, avg_depth): gender = None chrom_lengths = ref.get_chrom_lengths(genome) chrom_names = [chrom for chrom, length in chrom_lengths] if 'Y' in chrom_names or 'chrY' in chrom_names: gender = determine_sex(sample.work_dir, bam_fpath, avg_depth, genome, bed_fpath) if gender: with open(join(safe_mkdir(sample.dirpath), 'gender.txt'), 'w') as f: f.write(gender[0].upper()) return gender
def clean_bed(bed_fpath, work_dir): clean_fpath = intermediate_fname(work_dir, bed_fpath, 'clean') if not can_reuse(clean_fpath, bed_fpath): pybedtools.set_tempdir(safe_mkdir(join(work_dir, 'pybedtools_tmp'))) bed = BedTool(bed_fpath) bed = bed.filter(lambda x: x.chrom and not any( x.chrom.startswith(e) for e in ['#', ' ', 'track', 'browser'])) bed = bed.remove_invalid() with file_transaction(work_dir, clean_fpath) as tx_out_file: bed.saveas(tx_out_file) verify_bed(clean_fpath, is_critical=True) debug('Saved clean BED file into ' + clean_fpath) return clean_fpath
def run_multisample_qualimap(output_dir, work_dir, samples, targqc_full_report): """ 1. Generates Qualimap2 plots and put into plots_dirpath 2. Adds records to targqc_full_report.plots """ plots_dirpath = join(output_dir, 'plots') individual_report_fpaths = [s.qualimap_html_fpath for s in samples] if isdir(plots_dirpath) and not any( not can_reuse(join(plots_dirpath, f), individual_report_fpaths) for f in listdir(plots_dirpath) if not f.startswith('.')): debug('Qualimap miltisample plots exist - ' + plots_dirpath + ', reusing...') else: # Qualimap2 run for multi-sample plots if len( [s.qualimap_html_fpath for s in samples if s.qualimap_html_fpath]) > 0: if find_executable( ) is not None: # and get_qualimap_type(find_executable()) == 'full': qualimap_output_dir = join(work_dir, 'qualimap_multi_bamqc') _correct_qualimap_genome_results(samples) _correct_qualimap_insert_size_histogram(samples) safe_mkdir(qualimap_output_dir) rows = [] for sample in samples: if sample.qualimap_html_fpath: rows += [[sample.name, sample.qualimap_html_fpath]] data_fpath = write_tsv_rows( ([], rows), join(qualimap_output_dir, 'qualimap_results_by_sample.tsv')) qualimap_plots_dirpath = join(qualimap_output_dir, 'images_multisampleBamQcReport') cmdline = find_executable( ) + ' multi-bamqc --data {data_fpath} -outdir {qualimap_output_dir}'.format( **locals()) run(cmdline, env_vars=dict(DISPLAY=None), checks=[lambda _1, _2: verify_dir(qualimap_output_dir)], reuse=cfg.reuse_intermediate) if not verify_dir(qualimap_plots_dirpath): warn( 'Warning: Qualimap for multi-sample analysis failed to finish. TargQC will not contain plots.' ) return None else: if exists(plots_dirpath): shutil.rmtree(plots_dirpath) shutil.move(qualimap_plots_dirpath, plots_dirpath) else: warn( 'Warning: Qualimap for multi-sample analysis was not found. TargQC will not contain plots.' ) return None targqc_full_report.plots = [] for plot_fpath in listdir(plots_dirpath): plot_fpath = join(plots_dirpath, plot_fpath) if verify_file(plot_fpath) and plot_fpath.endswith('.png'): targqc_full_report.plots.append(relpath(plot_fpath, output_dir))
def _annotate(bed, ref_bed, chr_order, fai_fpath, work_dir, ori_col_num, high_confidence=False, reannotate=False, is_debug=False, **kwargs): # if genome: # genome_fpath = cut(fai_fpath, 2, output_fpath=intermediate_fname(work_dir, fai_fpath, 'cut2')) # intersection = bed.intersect(ref_bed, sorted=True, wao=True, g='<(cut -f1,2 ' + fai_fpath + ')') # intersection = bed.intersect(ref_bed, sorted=True, wao=True, genome=genome.split('-')[0]) # else: intersection_bed = None intersection_fpath = None pybedtools.set_tempdir(safe_mkdir(join(work_dir, 'bedtools'))) if is_debug: intersection_fpath = join(work_dir, 'intersection.bed') if isfile(intersection_fpath): info('Loading from ' + intersection_fpath) intersection_bed = BedTool(intersection_fpath) if not intersection_bed: if count_bed_cols(fai_fpath) == 2: debug('Fai fields size is 2 ' + fai_fpath) intersection_bed = bed.intersect(ref_bed, wao=True, g=fai_fpath) else: debug('Fai fields is ' + str(count_bed_cols(fai_fpath)) + ', not 2') intersection_bed = bed.intersect(ref_bed, wao=True) if is_debug and not isfile(intersection_fpath): intersection_bed.saveas(intersection_fpath) debug('Saved intersection to ' + intersection_fpath) total_annotated = 0 total_uniq_annotated = 0 total_off_target = 0 met = set() overlaps_by_tx_by_gene_by_loc = OrderedDefaultDict(lambda: OrderedDefaultDict(lambda: defaultdict(list))) # off_targets = list() expected_fields_num = ori_col_num + len(ebl.BedCols.cols[:-4]) + 1 for i, intersection_fields in enumerate(intersection_bed): inters_fields_list = list(intersection_fields) if len(inters_fields_list) < expected_fields_num: critical(f'Cannot parse the reference BED file - unexpected number of lines ' '({len(inters_fields_list} in {inters_fields_list}' + ' (less than {expected_fields_num})') a_chr, a_start, a_end = intersection_fields[:3] a_extra_columns = intersection_fields[3:ori_col_num] overlap_fields = [None for _ in ebl.BedCols.cols] overlap_fields[:len(intersection_fields[ori_col_num:-1])] = intersection_fields[ori_col_num:-1] keep_gene_column = not reannotate a_gene = None if keep_gene_column: a_gene = a_extra_columns[0] e_chr = overlap_fields[0] overlap_size = int(intersection_fields[-1]) assert e_chr == '.' or a_chr == e_chr, f'Error on line {i}: chromosomes don\'t match ({a_chr} vs {e_chr}). Line: {intersection_fields}' # fs = [None for _ in ebl.BedCols.cols] # fs[:3] = [a_chr, a_start, a_end] reg = (a_chr, int(a_start), int(a_end), tuple(a_extra_columns)) if e_chr == '.': total_off_target += 1 # off_targets.append(fs) overlaps_by_tx_by_gene_by_loc[reg][a_gene] = OrderedDefaultDict(list) else: # fs[3:-1] = db_feature_fields[3:-1] total_annotated += 1 if (a_chr, a_start, a_end) not in met: total_uniq_annotated += 1 met.add((a_chr, a_start, a_end)) e_gene = overlap_fields[ebl.BedCols.GENE] if keep_gene_column and e_gene != a_gene: overlaps_by_tx_by_gene_by_loc[reg][a_gene] = OrderedDefaultDict(list) else: transcript_id = overlap_fields[ebl.BedCols.ENSEMBL_ID] overlaps_by_tx_by_gene_by_loc[reg][e_gene][transcript_id].append((overlap_fields, overlap_size)) info(' Total annotated regions: ' + str(total_annotated)) info(' Total unique annotated regions: ' + str(total_uniq_annotated)) info(' Total off target regions: ' + str(total_off_target)) info('Resolving ambiguities...') annotated = _resolve_ambiguities(overlaps_by_tx_by_gene_by_loc, chr_order, **kwargs) return annotated
def proc_fastq(samples, parall_view, work_dir, bwa_prefix, downsample_to, num_pairs_by_sample=None, dedup=True): num_pairs_by_sample = num_pairs_by_sample or dict() if downsample_to: # Read pairs counts debug() if all(s.name in num_pairs_by_sample for s in samples): debug('Using read pairs counts extracted from FastQC reports') elif all(can_reuse(make_pair_counts_fpath(join(work_dir, s.name)), s.l_fpath) for s in samples): debug('Reusing pairs counts, reading from files') num_pairs_by_sample = {s.name: int(open(make_pair_counts_fpath(join(work_dir, s.name))).read().strip()) for s in samples} else: info('Counting read pairs') num_pairs = parall_view.run(count_read_pairs, [[s.name, safe_mkdir(join(work_dir, s.name)), s.l_fpath] for s in samples]) num_pairs_by_sample = {s.name: pairs_count for s, pairs_count in zip(samples, num_pairs)} # Downsampling debug() if all(can_reuse(make_downsampled_fpath(join(work_dir, s.name), s.l_fpath), s.l_fpath) and can_reuse(make_downsampled_fpath(join(work_dir, s.name), s.r_fpath), s.r_fpath) for s in samples): debug('Reusing downsampled FastQ') for s in samples: s.l_fpath = make_downsampled_fpath(join(work_dir, s.name), s.l_fpath) s.r_fpath = make_downsampled_fpath(join(work_dir, s.name), s.r_fpath) else: if isinstance(downsample_to, float): info('Downsampling FastQ to ' + str(float(downsample_to)) + ' fraction of reads') else: info('Downsampling FastQ to ' + str(int(downsample_to)) + ' read pairs') fastq_pairs = parall_view.run(downsample, [[join(work_dir, s.name), s.name, s.l_fpath, s.r_fpath, downsample_to, num_pairs_by_sample.get(s.name)] for s in samples]) for s, (l_r, r_r) in zip(samples, fastq_pairs): s.l_fpath = l_r s.r_fpath = r_r else: info('Skipping downsampling') debug() if all(can_reuse(make_bam_fpath(join(work_dir, s.name)), [s.l_fpath, s.r_fpath]) for s in samples): debug('All downsampled BAM exists, reusing') for s in samples: s.bam = make_bam_fpath(join(work_dir, s.name)) else: bwa = which('bwa') if not isfile(bwa): critical('BWA not found under ' + bwa) smb = sambamba.get_executable() if not (bwa and smb): if not bwa: err('Error: bwa is required for the alignment pipeline') if not smb: err('Error: sambamba is required for the alignment pipeline') critical('Tools required for alignment not found') info('Aligning reads to the reference') bam_fpaths = parall_view.run(align, [[join(work_dir, s.name), s.name, s.l_fpath, s.r_fpath, bwa, smb, bwa_prefix, dedup, parall_view.cores_per_job] for s in samples]) bam_fpaths = [verify_bam(b) for b in bam_fpaths] if len(bam_fpaths) < len(samples): critical('Some samples were not aligned successfully.') for bam, s in zip(bam_fpaths, samples): s.bam = bam return num_pairs_by_sample
def annotate(input_bed_fpath, output_fpath, work_dir, genome=None, reannotate=True, high_confidence=False, only_canonical=False, coding_only=False, short=False, extended=False, is_debug=False, **kwargs): debug('Getting features from storage') features_bed = ebl.get_all_features(genome) if features_bed is None: critical('Genome ' + genome + ' is not supported. Supported: ' + ', '.join(ebl.SUPPORTED_GENOMES)) if genome: fai_fpath = reference_data.get_fai(genome) chr_order = reference_data.get_chrom_order(genome) else: fai_fpath = None chr_order = bed_chrom_order(input_bed_fpath) input_bed_fpath = sort_bed(input_bed_fpath, work_dir=work_dir, chr_order=chr_order, genome=genome) ori_bed = BedTool(input_bed_fpath) ori_col_num = ori_bed.field_count() reannotate = reannotate or ori_col_num == 3 pybedtools.set_tempdir(safe_mkdir(join(work_dir, 'bedtools'))) ori_bed = BedTool(input_bed_fpath) # if reannotate: # bed = BedTool(input_bed_fpath).cut([0, 1, 2]) # keep_gene_column = False # else: # if col_num > 4: # bed = BedTool(input_bed_fpath).cut([0, 1, 2, 3]) # keep_gene_column = True # features_bed = features_bed.saveas() # cols = features_bed.field_count() # if cols < 12: # features_bed = features_bed.each(lambda f: f + ['.']*(12-cols)) if high_confidence: features_bed = features_bed.filter(ebl.high_confidence_filter) if only_canonical: features_bed = features_bed.filter(ebl.get_only_canonical_filter(genome)) if coding_only: features_bed = features_bed.filter(ebl.protein_coding_filter) # unique_tx_by_gene = find_best_tx_by_gene(features_bed) info('Extracting features from Ensembl GTF') features_bed = features_bed.filter(lambda x: x[ebl.BedCols.FEATURE] in ['exon', 'CDS', 'stop_codon', 'transcript']) # x[ebl.BedCols.ENSEMBL_ID] == unique_tx_by_gene[x[ebl.BedCols.GENE]]) info('Overlapping regions with Ensembl data') if is_debug: ori_bed = ori_bed.saveas(join(work_dir, 'bed.bed')) debug(f'Saved regions to {ori_bed.fn}') features_bed = features_bed.saveas(join(work_dir, 'features.bed')) debug(f'Saved features to {features_bed.fn}') annotated = _annotate(ori_bed, features_bed, chr_order, fai_fpath, work_dir, ori_col_num, high_confidence=False, reannotate=reannotate, is_debug=is_debug, **kwargs) full_header = [ebl.BedCols.names[i] for i in ebl.BedCols.cols] add_ori_extra_fields = ori_col_num > 3 if not reannotate and ori_col_num == 4: add_ori_extra_fields = False # no need to report the original gene field if we are not re-annotating info('Saving annotated regions...') total = 0 with file_transaction(work_dir, output_fpath) as tx: with open(tx, 'w') as out: header = full_header[:6] if short: header = full_header[:4] if extended: header = full_header[:-1] if add_ori_extra_fields: header.append(full_header[-1]) if extended: out.write('## ' + ebl.BedCols.names[ebl.BedCols.TX_OVERLAP_PERCENTAGE] + ': part of region overlapping with transcripts\n') out.write('## ' + ebl.BedCols.names[ebl.BedCols.EXON_OVERLAPS_PERCENTAGE] + ': part of region overlapping with exons\n') out.write('## ' + ebl.BedCols.names[ebl.BedCols.CDS_OVERLAPS_PERCENTAGE] + ': part of region overlapping with protein coding regions\n') out.write('\t'.join(header) + '\n') for full_fields in annotated: fields = full_fields[:6] if short: fields = full_fields[:4] if extended: fields = full_fields[:-1] if add_ori_extra_fields: fields.append(full_fields[-1]) out.write('\t'.join(map(_format_field, fields)) + '\n') total += 1 debug('Saved ' + str(total) + ' total annotated regions') return output_fpath
def annotate(input_bed_fpath, output_fpath, work_dir, genome=None, reannotate=True, high_confidence=False, only_canonical=False, coding_only=False, short=False, extended=False, is_debug=False, **kwargs): debug('Getting features from storage') features_bed = ebl.get_all_features(genome) if features_bed is None: critical('Genome ' + genome + ' is not supported. Supported: ' + ', '.join(ebl.SUPPORTED_GENOMES)) if genome: fai_fpath = reference_data.get_fai(genome) chr_order = reference_data.get_chrom_order(genome) else: fai_fpath = None chr_order = bed_chrom_order(input_bed_fpath) input_bed_fpath = sort_bed(input_bed_fpath, work_dir=work_dir, chr_order=chr_order, genome=genome) ori_bed = BedTool(input_bed_fpath) ori_col_num = ori_bed.field_count() reannotate = reannotate or ori_col_num == 3 pybedtools.set_tempdir(safe_mkdir(join(work_dir, 'bedtools'))) ori_bed = BedTool(input_bed_fpath) # if reannotate: # bed = BedTool(input_bed_fpath).cut([0, 1, 2]) # keep_gene_column = False # else: # if col_num > 4: # bed = BedTool(input_bed_fpath).cut([0, 1, 2, 3]) # keep_gene_column = True # features_bed = features_bed.saveas() # cols = features_bed.field_count() # if cols < 12: # features_bed = features_bed.each(lambda f: f + ['.']*(12-cols)) if high_confidence: features_bed = features_bed.filter(ebl.high_confidence_filter) if only_canonical: features_bed = features_bed.filter( ebl.get_only_canonical_filter(genome)) if coding_only: features_bed = features_bed.filter(ebl.protein_coding_filter) # unique_tx_by_gene = find_best_tx_by_gene(features_bed) info('Extracting features from Ensembl GTF') features_bed = features_bed.filter(lambda x: x[ ebl.BedCols.FEATURE] in ['exon', 'CDS', 'stop_codon', 'transcript']) # x[ebl.BedCols.ENSEMBL_ID] == unique_tx_by_gene[x[ebl.BedCols.GENE]]) info('Overlapping regions with Ensembl data') if is_debug: ori_bed = ori_bed.saveas(join(work_dir, 'bed.bed')) debug(f'Saved regions to {ori_bed.fn}') features_bed = features_bed.saveas(join(work_dir, 'features.bed')) debug(f'Saved features to {features_bed.fn}') annotated = _annotate(ori_bed, features_bed, chr_order, fai_fpath, work_dir, ori_col_num, high_confidence=False, reannotate=reannotate, is_debug=is_debug, **kwargs) full_header = [ebl.BedCols.names[i] for i in ebl.BedCols.cols] add_ori_extra_fields = ori_col_num > 3 if not reannotate and ori_col_num == 4: add_ori_extra_fields = False # no need to report the original gene field if we are not re-annotating info('Saving annotated regions...') total = 0 with file_transaction(work_dir, output_fpath) as tx: with open(tx, 'w') as out: header = full_header[:6] if short: header = full_header[:4] if extended: header = full_header[:-1] if add_ori_extra_fields: header.append(full_header[-1]) if extended: out.write( '## ' + ebl.BedCols.names[ebl.BedCols.TX_OVERLAP_PERCENTAGE] + ': part of region overlapping with transcripts\n') out.write( '## ' + ebl.BedCols.names[ebl.BedCols.EXON_OVERLAPS_PERCENTAGE] + ': part of region overlapping with exons\n') out.write( '## ' + ebl.BedCols.names[ebl.BedCols.CDS_OVERLAPS_PERCENTAGE] + ': part of region overlapping with protein coding regions\n' ) out.write('\t'.join(header) + '\n') for full_fields in annotated: fields = full_fields[:6] if short: fields = full_fields[:4] if extended: fields = full_fields[:-1] if add_ori_extra_fields: fields.append(full_fields[-1]) out.write('\t'.join(map(_format_field, fields)) + '\n') total += 1 debug('Saved ' + str(total) + ' total annotated regions') return output_fpath
def start_targqc( work_dir, output_dir, samples, target_bed_fpath, parallel_cfg, bwa_prefix, fai_fpath=None, genome=config.genome, depth_threshs=config.depth_thresholds, downsample_to=config.downsample_fraction, padding=config.padding, dedup=config.dedup, num_pairs_by_sample=None, reannotate=config.reannotate, ): d = get_description() info('*' * len(d)) info(d) info('*' * len(d)) info() fai_fpath = fai_fpath or ref.get_fai(genome) target = Target(work_dir, output_dir, fai_fpath, padding=padding, bed_fpath=target_bed_fpath, reannotate=reannotate, genome=genome, is_debug=logger.is_debug) fastq_samples = [ s for s in samples if not s.bam and s.l_fpath and s.r_fpath ] from targqc.utilz.parallel import parallel_view if fastq_samples: if not bwa_prefix: critical('--bwa-prefix is required when running from fastq') with parallel_view(len(fastq_samples), parallel_cfg, join(work_dir, 'sge_fastq')) as view: num_pairs_by_sample = proc_fastq(fastq_samples, view, work_dir, bwa_prefix, downsample_to, num_pairs_by_sample, dedup=dedup) info() for s in samples: if s.bam: info(s.name + ': using alignment ' + s.bam) with parallel_view(len(samples), parallel_cfg, join(work_dir, 'sge_bam')) as view: info('Sorting BAMs...') sorted_bams = view.run( sort_bam, [[s.bam, safe_mkdir(join(work_dir, s.name))] for s in samples]) for s, sorted_bam in zip(samples, sorted_bams): s.bam = sorted_bam if all(can_reuse(s.bam + '.bai', s.bam) for s in samples): debug('BAM indexes exists') else: info('Indexing BAMs...') view.run(index_bam, [[s.bam] for s in samples]) info('Making general reports...') make_general_reports(view, samples, target, genome, depth_threshs, padding, num_pairs_by_sample, is_debug=logger.is_debug, reannotate=reannotate, fai_fpath=fai_fpath) info() info('*' * 70) tsv_fpath, html_fpath = make_tarqc_html_report(output_dir, work_dir, samples, bed_fpath=target_bed_fpath) info('TargQC summary saved in: ') info(' ' + html_fpath) info(' ' + tsv_fpath) info() with parallel_view(len(samples), parallel_cfg, join(work_dir, 'sge_bam')) as view: info('Making region-level reports...') make_region_reports(view, work_dir, samples, target, genome, depth_threshs) info() info('*' * 70) tsv_region_rep_fpath = combined_regional_reports(work_dir, output_dir, samples) info() info('*' * 70) info('TargQC summary saved in: ') info(' ' + html_fpath) info(' ' + tsv_fpath) info('Per-region coverage statistics saved into:') info(' ' + tsv_region_rep_fpath) return html_fpath
def proc_fastq(samples, parall_view, work_dir, bwa_prefix, downsample_to, num_pairs_by_sample=None, dedup=True): num_pairs_by_sample = num_pairs_by_sample or dict() if downsample_to: # Read pairs counts debug() if all(s.name in num_pairs_by_sample for s in samples): debug('Using read pairs counts extracted from FastQC reports') elif all( can_reuse(make_pair_counts_fpath(join(work_dir, s.name)), s.l_fpath) for s in samples): debug('Reusing pairs counts, reading from files') num_pairs_by_sample = { s.name: int( open(make_pair_counts_fpath(join(work_dir, s.name))).read().strip()) for s in samples } else: info('Counting read pairs') num_pairs = parall_view.run( count_read_pairs, [[s.name, safe_mkdir(join(work_dir, s.name)), s.l_fpath] for s in samples]) num_pairs_by_sample = { s.name: pairs_count for s, pairs_count in zip(samples, num_pairs) } # Downsampling debug() if all( can_reuse( make_downsampled_fpath(join(work_dir, s.name), s.l_fpath), s.l_fpath) and can_reuse( make_downsampled_fpath(join(work_dir, s.name), s.r_fpath), s.r_fpath) for s in samples): debug('Reusing downsampled FastQ') for s in samples: s.l_fpath = make_downsampled_fpath(join(work_dir, s.name), s.l_fpath) s.r_fpath = make_downsampled_fpath(join(work_dir, s.name), s.r_fpath) else: if isinstance(downsample_to, float): info('Downsampling FastQ to ' + str(float(downsample_to)) + ' fraction of reads') else: info('Downsampling FastQ to ' + str(int(downsample_to)) + ' read pairs') fastq_pairs = parall_view.run(downsample, [[ join(work_dir, s.name), s.name, s.l_fpath, s.r_fpath, downsample_to, num_pairs_by_sample.get(s.name) ] for s in samples]) for s, (l_r, r_r) in zip(samples, fastq_pairs): s.l_fpath = l_r s.r_fpath = r_r else: info('Skipping downsampling') debug() if all( can_reuse(make_bam_fpath(join(work_dir, s.name)), [s.l_fpath, s.r_fpath]) for s in samples): debug('All downsampled BAM exists, reusing') for s in samples: s.bam = make_bam_fpath(join(work_dir, s.name)) else: bwa = which('bwa') if not isfile(bwa): critical('BWA not found under ' + bwa) smb = sambamba.get_executable() if not (bwa and smb): if not bwa: err('Error: bwa is required for the alignment pipeline') if not smb: err('Error: sambamba is required for the alignment pipeline') critical('Tools required for alignment not found') info('Aligning reads to the reference') bam_fpaths = parall_view.run(align, [[ join(work_dir, s.name), s.name, s.l_fpath, s.r_fpath, bwa, smb, bwa_prefix, dedup, parall_view.cores_per_job ] for s in samples]) bam_fpaths = [verify_bam(b) for b in bam_fpaths] if len(bam_fpaths) < len(samples): critical('Some samples were not aligned successfully.') for bam, s in zip(bam_fpaths, samples): s.bam = bam return num_pairs_by_sample
def _annotate(bed, ref_bed, chr_order, fai_fpath, work_dir, ori_col_num, high_confidence=False, reannotate=False, is_debug=False, **kwargs): # if genome: # genome_fpath = cut(fai_fpath, 2, output_fpath=intermediate_fname(work_dir, fai_fpath, 'cut2')) # intersection = bed.intersect(ref_bed, sorted=True, wao=True, g='<(cut -f1,2 ' + fai_fpath + ')') # intersection = bed.intersect(ref_bed, sorted=True, wao=True, genome=genome.split('-')[0]) # else: intersection_bed = None intersection_fpath = None pybedtools.set_tempdir(safe_mkdir(join(work_dir, 'bedtools'))) if is_debug: intersection_fpath = join(work_dir, 'intersection.bed') if isfile(intersection_fpath): info('Loading from ' + intersection_fpath) intersection_bed = BedTool(intersection_fpath) if not intersection_bed: if count_bed_cols(fai_fpath) == 2: debug('Fai fields size is 2 ' + fai_fpath) intersection_bed = bed.intersect(ref_bed, wao=True, g=fai_fpath) else: debug('Fai fields is ' + str(count_bed_cols(fai_fpath)) + ', not 2') intersection_bed = bed.intersect(ref_bed, wao=True) if is_debug and not isfile(intersection_fpath): intersection_bed.saveas(intersection_fpath) debug('Saved intersection to ' + intersection_fpath) total_annotated = 0 total_uniq_annotated = 0 total_off_target = 0 met = set() overlaps_by_tx_by_gene_by_loc = OrderedDefaultDict( lambda: OrderedDefaultDict(lambda: defaultdict(list))) # off_targets = list() expected_fields_num = ori_col_num + len(ebl.BedCols.cols[:-4]) + 1 for i, intersection_fields in enumerate(intersection_bed): inters_fields_list = list(intersection_fields) if len(inters_fields_list) < expected_fields_num: critical( f'Cannot parse the reference BED file - unexpected number of lines ' '({len(inters_fields_list} in {inters_fields_list}' + ' (less than {expected_fields_num})') a_chr, a_start, a_end = intersection_fields[:3] a_extra_columns = intersection_fields[3:ori_col_num] overlap_fields = [None for _ in ebl.BedCols.cols] overlap_fields[:len(intersection_fields[ori_col_num:-1] )] = intersection_fields[ori_col_num:-1] keep_gene_column = not reannotate a_gene = None if keep_gene_column: a_gene = a_extra_columns[0] e_chr = overlap_fields[0] overlap_size = int(intersection_fields[-1]) assert e_chr == '.' or a_chr == e_chr, f'Error on line {i}: chromosomes don\'t match ({a_chr} vs {e_chr}). Line: {intersection_fields}' # fs = [None for _ in ebl.BedCols.cols] # fs[:3] = [a_chr, a_start, a_end] reg = (a_chr, int(a_start), int(a_end), tuple(a_extra_columns)) if e_chr == '.': total_off_target += 1 # off_targets.append(fs) overlaps_by_tx_by_gene_by_loc[reg][a_gene] = OrderedDefaultDict( list) else: # fs[3:-1] = db_feature_fields[3:-1] total_annotated += 1 if (a_chr, a_start, a_end) not in met: total_uniq_annotated += 1 met.add((a_chr, a_start, a_end)) e_gene = overlap_fields[ebl.BedCols.GENE] if keep_gene_column and e_gene != a_gene: overlaps_by_tx_by_gene_by_loc[reg][ a_gene] = OrderedDefaultDict(list) else: transcript_id = overlap_fields[ebl.BedCols.ENSEMBL_ID] overlaps_by_tx_by_gene_by_loc[reg][e_gene][ transcript_id].append((overlap_fields, overlap_size)) info(' Total annotated regions: ' + str(total_annotated)) info(' Total unique annotated regions: ' + str(total_uniq_annotated)) info(' Total off target regions: ' + str(total_off_target)) info('Resolving ambiguities...') annotated = _resolve_ambiguities(overlaps_by_tx_by_gene_by_loc, chr_order, **kwargs) return annotated
def determine_sex(work_dir, bam_fpath, avg_depth, genome, target_bed=None): debug() debug('Determining sex') pybedtools.set_tempdir(safe_mkdir(join(work_dir, 'pybedtools_tmp'))) male_bed = None for k in chry_key_regions_by_genome: if k in genome: male_bed = BedTool(chry_key_regions_by_genome.get(k)) break if not male_bed: warn('Warning: no male key regions for ' + genome + ', cannot identify sex') return None male_area_size = get_total_bed_size(male_bed) debug('Male region total size: ' + str(male_area_size)) if target_bed: target_male_bed = join(work_dir, 'male.bed') with file_transaction(work_dir, target_male_bed) as tx: BedTool(target_bed).intersect(male_bed).merge().saveas(tx) target_male_area_size = get_total_bed_size(target_male_bed) if target_male_area_size == 0: debug( 'The male non-PAR region does not overlap with the capture target - cannot determine sex.' ) return None male_bed = target_male_bed else: debug('WGS, determining sex based on chrY key regions coverage.') info( 'Detecting sex by comparing the Y chromosome key regions coverage and average coverage depth.' ) if not bam_fpath: critical('BAM file is required.') index_bam(bam_fpath) chry_mean_coverage = _calc_mean_coverage(work_dir, male_bed, bam_fpath, 1) debug('Y key regions average depth: ' + str(chry_mean_coverage)) avg_depth = float(avg_depth) debug('Sample average depth: ' + str(avg_depth)) if avg_depth < AVG_DEPTH_THRESHOLD_TO_DETERMINE_SEX: debug('Sample average depth is too low (less than ' + str(AVG_DEPTH_THRESHOLD_TO_DETERMINE_SEX) + ') - cannot determine sex') return None if chry_mean_coverage == 0: debug('Y depth is 0 - it\s female') sex = 'F' else: factor = avg_depth / chry_mean_coverage debug('Sample depth / Y depth = ' + str(factor)) if factor > FEMALE_Y_COVERAGE_FACTOR: # if mean target coverage much higher than chrY coverage debug('Sample depth is more than ' + str(FEMALE_Y_COVERAGE_FACTOR) + ' times higher than Y depth - it\s female') sex = 'F' else: debug('Sample depth is not more than ' + str(FEMALE_Y_COVERAGE_FACTOR) + ' times higher than Y depth - it\s male') sex = 'M' debug('Sex is ' + sex) debug() return sex
def get_total_bed_size(bed_fpath, work_dir=None): if work_dir: pybedtools.set_tempdir(safe_mkdir(join(work_dir, 'pybedtools_tmp'))) return sum(len(x) for x in BedTool(bed_fpath).merge())