def main(): try: description = [ '~~~CRISPRessoPooledWGSCompare~~~', '-Comparison of two CRISPRessoPooled or CRISPRessoWGS analyses-' ] compare_header = r''' ____________________________________ | __ __ __ __ __ __ __ | ||__)/ \/ \| |_ | \ /| |/ _ (_ | || \__/\__/|__|__|__// |/\|\__)__) | | __ __ __ __ __ | | / / \|\/||__) /\ |__)|_ | | \__\__/| || /--\| \ |__ | |____________________________________| ''' compare_header = CRISPRessoShared.get_crispresso_header( description, compare_header) print(compare_header) parser = argparse.ArgumentParser( description='CRISPRessoPooledWGSCompare Parameters', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument( 'crispresso_pooled_wgs_output_folder_1', type=str, help= 'First output folder with CRISPRessoPooled or CRISPRessoWGS analysis' ) parser.add_argument( 'crispresso_pooled_wgs_output_folder_2', type=str, help= 'Second output folder with CRISPRessoPooled or CRISPRessoWGS analysis' ) #OPTIONALS parser.add_argument('-n', '--name', help='Output name', default='') parser.add_argument('-n1', '--sample_1_name', help='Sample 1 name', default='Sample_1') parser.add_argument('-n2', '--sample_2_name', help='Sample 2 name', default='Sample_2') parser.add_argument('-o', '--output_folder', help='', default='') parser.add_argument( '-p', '--n_processes', type=int, help='Number of processes to use for CRISPResso comparison', default=1) parser.add_argument( '--save_also_png', help='Save also .png images additionally to .pdf files', action='store_true') parser.add_argument('--debug', help='Show debug messages', action='store_true') args = parser.parse_args() debug_flag = args.debug crispresso_compare_options = [ 'save_also_png', ] #check that the CRISPRessoPooled output is present quantification_summary_file_1 = check_PooledWGS_output_folder( args.crispresso_pooled_wgs_output_folder_1) quantification_summary_file_2 = check_PooledWGS_output_folder( args.crispresso_pooled_wgs_output_folder_2) #create outputfolder and initialize the log get_name_from_folder = lambda x: os.path.basename(os.path.abspath( x)).replace('CRISPRessoPooled_on_', '').replace( 'CRISPRessoWGS_on_', '') if not args.name: database_id = '%s_VS_%s' % ( get_name_from_folder( args.crispresso_pooled_wgs_output_folder_1), get_name_from_folder( args.crispresso_pooled_wgs_output_folder_2)) else: database_id = args.name OUTPUT_DIRECTORY = 'CRISPRessoPooledWGSCompare_on_%s' % database_id if args.output_folder: OUTPUT_DIRECTORY = os.path.join( os.path.abspath(args.output_folder), OUTPUT_DIRECTORY) _jp = lambda filename: os.path.join( OUTPUT_DIRECTORY, filename ) #handy function to put a file in the output directory log_filename = _jp('CRISPRessoPooledWGSCompare_RUNNING_LOG.txt') try: info('Creating Folder %s' % OUTPUT_DIRECTORY) os.makedirs(OUTPUT_DIRECTORY) info('Done!') except: warn('Folder %s already exists.' % OUTPUT_DIRECTORY) log_filename = _jp('CRISPRessoPooledWGSCompare_RUNNING_LOG.txt') logging.getLogger().addHandler(logging.FileHandler(log_filename)) with open(log_filename, 'w+') as outfile: outfile.write( '[Command used]:\nCRISPRessoPooledWGSCompare %s\n\n[Execution log]:\n' % ' '.join(sys.argv)) #load data and calculate the difference df_quant_1 = pd.read_table(quantification_summary_file_1) df_quant_2 = pd.read_table(quantification_summary_file_2) df_comp = df_quant_1.set_index(['Name', 'Amplicon']).join( df_quant_2.set_index(['Name', 'Amplicon']), lsuffix='_%s' % args.sample_1_name, rsuffix='_%s' % args.sample_2_name) #df_comp=df_quant_1.set_index('Name').join(df_quant_2.set_index('Name'),lsuffix='_%s' % args.sample_1_name,rsuffix='_%s' % args.sample_2_name) df_comp['(%s-%s)_Unmodified%%' % (args.sample_1_name, args.sample_2_name )] = df_comp['Unmodified%%_%s' % args.sample_1_name] - df_comp['Unmodified%%_%s' % args.sample_2_name] df_comp.fillna('NA').to_csv( _jp('COMPARISON_SAMPLES_QUANTIFICATION_SUMMARIES.txt'), sep='\t') #now run CRISPRessoCompare for the pairs for wich we have data in both folders crispresso_cmds = [] processed_regions = set([]) for idx, row in df_comp.iterrows(): if idx[0] in processed_regions: continue if row.isnull().any(): warn( 'Skipping sample %s since it was not processed in one or both conditions' % idx[0]) else: processed_regions.add(idx[0]) #crispresso_output_folder_1=os.path.join(args.crispresso_pooled_wgs_output_folder_1,'CRISPResso_on_%s' % idx) #crispresso_output_folder_2=os.path.join(args.crispresso_pooled_wgs_output_folder_2,'CRISPResso_on_%s' % idx) crispresso_output_folder_1 = os.path.join( args.crispresso_pooled_wgs_output_folder_1, 'CRISPResso_on_%s' % idx[0]) crispresso_output_folder_2 = os.path.join( args.crispresso_pooled_wgs_output_folder_2, 'CRISPResso_on_%s' % idx[0]) crispresso_compare_cmd = CRISPResso_compare_to_call + ' "%s" "%s" -o "%s" -n1 "%s" -n2 "%s" ' % ( crispresso_output_folder_1, crispresso_output_folder_2, OUTPUT_DIRECTORY, args.sample_1_name + '_%s' % idx[0], args.sample_2_name + '_%s' % idx[0], ) crispresso_compare_cmd = CRISPRessoShared.propagate_crispresso_options( crispresso_compare_cmd, crispresso_compare_options, args) info('Running CRISPRessoCompare:%s' % crispresso_compare_cmd) crispresso_cmds.append(crispresso_compare_cmd) CRISPRessoMultiProcessing.run_crispresso_cmds(crispresso_cmds, args.n_processes, 'Comparison') info('All Done!') print(CRISPRessoShared.get_crispresso_footer()) sys.exit(0) except Exception as e: debug_flag = False if 'args' in vars() and 'debug' in args: debug_flag = args.debug if debug_flag: traceback.print_exc(file=sys.stdout) error('\n\nERROR: %s' % e) sys.exit(-1)
def main(): def print_stacktrace_if_debug(): debug_flag = False if 'args' in vars() and 'debug' in args: debug_flag = args.debug if debug_flag: traceback.print_exc(file=sys.stdout) error(traceback.format_exc()) try: description = [ '~~~CRISPRessoWGS~~~', '-Analysis of CRISPR/Cas9 outcomes from WGS data-' ] wgs_string = r''' ____________ | __ __ | || |/ _ (_ | ||/\|\__)__) | |____________| ''' print(CRISPRessoShared.get_crispresso_header(description, wgs_string)) parser = CRISPRessoShared.getCRISPRessoArgParser( parserTitle='CRISPRessoWGS Parameters', requiredParams={}) #tool specific optional parser.add_argument('-b', '--bam_file', type=str, help='WGS aligned bam file', required=True, default='bam filename') parser.add_argument( '-f', '--region_file', type=str, help= 'Regions description file. A BED format file containing the regions to analyze, one per line. The REQUIRED\ columns are: chr_id(chromosome name), bpstart(start position), bpend(end position), the optional columns are:name (an unique indentifier for the region), guide_seq, expected_hdr_amplicon_seq,coding_seq, see CRISPResso help for more details on these last 3 parameters)', required=True) parser.add_argument( '-r', '--reference_file', type=str, help= 'A FASTA format reference file (for example hg19.fa for the human genome)', default='', required=True) parser.add_argument( '--min_reads_to_use_region', type=float, help= 'Minimum number of reads that align to a region to perform the CRISPResso analysis', default=10) parser.add_argument( '--skip_failed', help='Continue with pooled analysis even if one sample fails', action='store_true') parser.add_argument( '--gene_annotations', type=str, help= 'Gene Annotation Table from UCSC Genome Browser Tables (http://genome.ucsc.edu/cgi-bin/hgTables?command=start), \ please select as table "knowGene", as output format "all fields from selected table" and as file returned "gzip compressed"', default='') parser.add_argument( '-p', '--n_processes', type=int, help='Specify the number of processes to use for the quantification.\ Please use with caution since increasing this parameter will increase the memory required to run CRISPResso.', default=1) parser.add_argument('--crispresso_command', help='CRISPResso command to call', default='CRISPResso') args = parser.parse_args() crispresso_options = CRISPRessoShared.get_crispresso_options() options_to_ignore = set([ 'fastq_r1', 'fastq_r2', 'amplicon_seq', 'amplicon_name', 'output_folder', 'name' ]) crispresso_options_for_wgs = list(crispresso_options - options_to_ignore) info('Checking dependencies...') if check_samtools() and check_bowtie2(): info('\n All the required dependencies are present!') else: sys.exit(1) #check files check_file(args.bam_file) check_file(args.reference_file) check_file(args.region_file) if args.gene_annotations: check_file(args.gene_annotations) #INIT get_name_from_bam = lambda x: os.path.basename(x).replace('.bam', '') if not args.name: database_id = '%s' % get_name_from_bam(args.bam_file) else: database_id = args.name OUTPUT_DIRECTORY = 'CRISPRessoWGS_on_%s' % database_id if args.output_folder: OUTPUT_DIRECTORY = os.path.join( os.path.abspath(args.output_folder), OUTPUT_DIRECTORY) _jp = lambda filename: os.path.join( OUTPUT_DIRECTORY, filename ) #handy function to put a file in the output directory try: info('Creating Folder %s' % OUTPUT_DIRECTORY) os.makedirs(OUTPUT_DIRECTORY) info('Done!') except: warn('Folder %s already exists.' % OUTPUT_DIRECTORY) log_filename = _jp('CRISPRessoWGS_RUNNING_LOG.txt') logging.getLogger().addHandler(logging.FileHandler(log_filename)) with open(log_filename, 'w+') as outfile: outfile.write('[Command used]:\n%s\n\n[Execution log]:\n' % ' '.join(sys.argv)) crispresso2WGS_info_file = os.path.join(OUTPUT_DIRECTORY, 'CRISPResso2WGS_info.pickle') crispresso2_info = { } #keep track of all information for this run to be pickled and saved at the end of the run crispresso2_info['version'] = CRISPRessoShared.__version__ crispresso2_info['args'] = deepcopy(args) crispresso2_info['log_filename'] = os.path.basename(log_filename) def rreplace(s, old, new): li = s.rsplit(old) return new.join(li) bam_index = '' #check if bam has the index already if os.path.exists(rreplace(args.bam_file, ".bam", ".bai")): info('Index file for input .bam file exists, skipping generation.') bam_index = args.bam_file.rreplace(".bam", ".bai") elif os.path.exists(args.bam_file + '.bai'): info('Index file for input .bam file exists, skipping generation.') bam_index = args.bam_file + '.bai' else: info('Creating index file for input .bam file...') sb.call('samtools index %s ' % (args.bam_file), shell=True) bam_index = args.bam_file + '.bai' #load gene annotation if args.gene_annotations: info('Loading gene coordinates from annotation file: %s...' % args.gene_annotations) try: df_genes = pd.read_table(args.gene_annotations, compression='gzip') df_genes.txEnd = df_genes.txEnd.astype(int) df_genes.txStart = df_genes.txStart.astype(int) df_genes.head() except: info('Failed to load the gene annotations file.') #Load and validate the REGION FILE df_regions = pd.read_csv(args.region_file, names=[ 'chr_id', 'bpstart', 'bpend', 'Name', 'sgRNA', 'Expected_HDR', 'Coding_sequence' ], comment='#', sep='\t', dtype={'Name': str}) #remove empty amplicons/lines df_regions.dropna(subset=['chr_id', 'bpstart', 'bpend'], inplace=True) df_regions.Expected_HDR = df_regions.Expected_HDR.apply( capitalize_sequence) df_regions.sgRNA = df_regions.sgRNA.apply(capitalize_sequence) df_regions.Coding_sequence = df_regions.Coding_sequence.apply( capitalize_sequence) #check or create names for idx, row in df_regions.iterrows(): if pd.isnull(row.Name): df_regions.ix[idx, 'Name'] = '_'.join( map(str, [row['chr_id'], row['bpstart'], row['bpend']])) if not len(df_regions.Name.unique()) == df_regions.shape[0]: raise Exception('The amplicon names should be all distinct!') df_regions = df_regions.set_index('Name') #df_regions.index=df_regions.index.str.replace(' ','_') df_regions.index = df_regions.index.to_series().str.replace(' ', '_') #extract sequence for each region uncompressed_reference = args.reference_file if os.path.exists(uncompressed_reference + '.fai'): info( 'The index for the reference fasta file is already present! Skipping generation.' ) else: info('Indexing reference file... Please be patient!') sb.call('samtools faidx %s >>%s 2>&1' % (uncompressed_reference, log_filename), shell=True) df_regions['sequence'] = df_regions.apply( lambda row: get_region_from_fa(row.chr_id, row.bpstart, row.bpend, uncompressed_reference), axis=1) for idx, row in df_regions.iterrows(): if not pd.isnull(row.sgRNA): cut_points = [] for current_guide_seq in row.sgRNA.strip().upper().split(','): wrong_nt = find_wrong_nt(current_guide_seq) if wrong_nt: raise NTException( 'The sgRNA sequence %s contains wrong characters:%s' % (current_guide_seq, ' '.join(wrong_nt))) offset_fw = args.quantification_window_center + len( current_guide_seq) - 1 offset_rc = (-args.quantification_window_center) - 1 cut_points+=[m.start() + offset_fw for \ m in re.finditer(current_guide_seq, row.sequence)]+[m.start() + offset_rc for m in re.finditer(CRISPRessoShared.reverse_complement(current_guide_seq), row.sequence)] if not cut_points: df_regions.ix[idx, 'sgRNA'] = '' df_regions['bpstart'] = pd.to_numeric(df_regions['bpstart']) df_regions['bpend'] = pd.to_numeric(df_regions['bpend']) df_regions.bpstart = df_regions.bpstart.astype(int) df_regions.bpend = df_regions.bpend.astype(int) if args.gene_annotations: df_regions = df_regions.apply( lambda row: find_overlapping_genes(row, df_genes), axis=1) #extract reads with samtools in that region and create a bam #create a fasta file with all the trimmed reads info('\nProcessing each region...') ANALYZED_REGIONS = _jp('ANALYZED_REGIONS/') if not os.path.exists(ANALYZED_REGIONS): os.mkdir(ANALYZED_REGIONS) df_regions['n_reads'] = 0 df_regions['bam_file_with_reads_in_region'] = '' df_regions['fastq.gz_file_trimmed_reads_in_region'] = '' for idx, row in df_regions.iterrows(): if row['sequence']: fastq_gz_filename = os.path.join( ANALYZED_REGIONS, '%s.fastq.gz' % clean_filename('REGION_' + str(idx))) bam_region_filename = os.path.join( ANALYZED_REGIONS, '%s.bam' % clean_filename('REGION_' + str(idx))) #create place-holder fastq files open(fastq_gz_filename, 'w+').close() region = '%s:%d-%d' % (row.chr_id, row.bpstart, row.bpend - 1) info('\nExtracting reads in:%s and create the .bam file: %s' % (region, bam_region_filename)) #extract reads in region cmd = r'''samtools view -b -F 4 %s %s > %s ''' % ( args.bam_file, region, bam_region_filename) #print cmd sb.call(cmd, shell=True) #index bam file cmd = r'''samtools index %s ''' % (bam_region_filename) #print cmd sb.call(cmd, shell=True) info('Trim reads and create a fastq.gz file in: %s' % fastq_gz_filename) #trim reads in bam and convert in fastq n_reads = write_trimmed_fastq(bam_region_filename, row['bpstart'], row['bpend'], fastq_gz_filename) df_regions.ix[idx, 'n_reads'] = n_reads df_regions.ix[ idx, 'bam_file_with_reads_in_region'] = bam_region_filename df_regions.ix[ idx, 'fastq.gz_file_trimmed_reads_in_region'] = fastq_gz_filename df_regions.fillna('NA').to_csv( _jp('REPORT_READS_ALIGNED_TO_SELECTED_REGIONS_WGS.txt'), sep='\t') #Run Crispresso info('\nRunning CRISPResso on each region...') crispresso_cmds = [] for idx, row in df_regions.iterrows(): if row['n_reads'] >= args.min_reads_to_use_region: info('\nThe region [%s] has enough reads (%d) mapped to it!' % (idx, row['n_reads'])) crispresso_cmd= args.crispresso_command + ' -r1 %s -a %s -o %s --name %s' %\ (row['fastq.gz_file_trimmed_reads_in_region'],row['sequence'],OUTPUT_DIRECTORY,idx) if row['sgRNA'] and not pd.isnull(row['sgRNA']): crispresso_cmd += ' -g %s' % row['sgRNA'] if row['Expected_HDR'] and not pd.isnull(row['Expected_HDR']): crispresso_cmd += ' -e %s' % row['Expected_HDR'] if row['Coding_sequence'] and not pd.isnull( row['Coding_sequence']): crispresso_cmd += ' -c %s' % row['Coding_sequence'] crispresso_cmd = CRISPRessoShared.propagate_crispresso_options( crispresso_cmd, crispresso_options_for_wgs, args) crispresso_cmds.append(crispresso_cmd) # info('Running CRISPResso:%s' % crispresso_cmd) # sb.call(crispresso_cmd,shell=True) else: info( '\nThe region [%s] has too few reads mapped to it (%d)! Not running CRISPResso!' % (idx, row['n_reads'])) CRISPRessoMultiProcessing.run_crispresso_cmds(crispresso_cmds, args.n_processes, 'region', args.skip_failed) quantification_summary = [] all_region_names = [] all_region_read_counts = {} good_region_names = [] good_region_folders = {} header = 'Name\tUnmodified%\tModified%\tReads_aligned\tReads_total\tUnmodified\tModified\tDiscarded\tInsertions\tDeletions\tSubstitutions\tOnly Insertions\tOnly Deletions\tOnly Substitutions\tInsertions and Deletions\tInsertions and Substitutions\tDeletions and Substitutions\tInsertions Deletions and Substitutions' header_els = header.split("\t") header_el_count = len(header_els) empty_line_els = [np.nan] * (header_el_count - 1) n_reads_index = header_els.index('Reads_total') - 1 for idx, row in df_regions.iterrows(): folder_name = 'CRISPResso_on_%s' % idx run_name = idx all_region_names.append(run_name) all_region_read_counts[run_name] = row.n_reads run_file = os.path.join(_jp(folder_name), 'CRISPResso2_info.pickle') if not os.path.exists(run_file): warn( 'Skipping the folder %s: not enough reads, incomplete, or empty folder.' % folder_name) this_els = empty_line_els[:] this_els[n_reads_index] = row.n_reads to_add = [run_name] to_add.extend(this_els) quantification_summary.append(to_add) else: run_data = cp.load(open(run_file, 'rb')) ref_name = run_data['ref_names'][ 0] #only expect one amplicon sequence n_tot = row.n_reads n_aligned = run_data['counts_total'][ref_name] n_unmod = run_data['counts_unmodified'][ref_name] n_mod = run_data['counts_modified'][ref_name] n_discarded = run_data['counts_discarded'][ref_name] n_insertion = run_data['counts_insertion'][ref_name] n_deletion = run_data['counts_deletion'][ref_name] n_substitution = run_data['counts_substitution'][ref_name] n_only_insertion = run_data['counts_only_insertion'][ref_name] n_only_deletion = run_data['counts_only_deletion'][ref_name] n_only_substitution = run_data['counts_only_substitution'][ ref_name] n_insertion_and_deletion = run_data[ 'counts_insertion_and_deletion'][ref_name] n_insertion_and_substitution = run_data[ 'counts_insertion_and_substitution'][ref_name] n_deletion_and_substitution = run_data[ 'counts_deletion_and_substitution'][ref_name] n_insertion_and_deletion_and_substitution = run_data[ 'counts_insertion_and_deletion_and_substitution'][ref_name] unmod_pct = "NA" mod_pct = "NA" if n_aligned > 0: unmod_pct = 100 * n_unmod / float(n_aligned) mod_pct = 100 * n_mod / float(n_aligned) vals = [run_name] vals.extend([ round(unmod_pct, 8), round(mod_pct, 8), n_aligned, n_tot, n_unmod, n_mod, n_discarded, n_insertion, n_deletion, n_substitution, n_only_insertion, n_only_deletion, n_only_substitution, n_insertion_and_deletion, n_insertion_and_substitution, n_deletion_and_substitution, n_insertion_and_deletion_and_substitution ]) quantification_summary.append(vals) good_region_names.append(idx) good_region_folders[idx] = folder_name samples_quantification_summary_filename = _jp( 'SAMPLES_QUANTIFICATION_SUMMARY.txt') df_summary_quantification = pd.DataFrame(quantification_summary, columns=header_els) if args.crispresso1_mode: crispresso1_columns = [ 'Name', 'Unmodified%', 'Modified%', 'Reads_aligned', 'Reads_total' ] df_summary_quantification.fillna('NA').to_csv( samples_quantification_summary_filename, sep='\t', index=None, columns=crispresso1_columns) else: df_summary_quantification.fillna('NA').to_csv( samples_quantification_summary_filename, sep='\t', index=None) crispresso2_info[ 'samples_quantification_summary_filename'] = os.path.basename( samples_quantification_summary_filename) crispresso2_info['regions'] = df_regions crispresso2_info['all_region_names'] = all_region_names crispresso2_info['all_region_read_counts'] = all_region_read_counts crispresso2_info['good_region_names'] = good_region_names crispresso2_info['good_region_folders'] = good_region_folders crispresso2_info['summary_plot_names'] = [] crispresso2_info['summary_plot_titles'] = {} crispresso2_info['summary_plot_labels'] = {} crispresso2_info['summary_plot_datas'] = {} df_summary_quantification.set_index('Name') save_png = True if args.suppress_report: save_png = False plot_root = _jp("CRISPRessoWGS_modification_summary") CRISPRessoPlot.plot_unmod_mod_pcts(plot_root, df_summary_quantification, save_png, args.min_reads_to_use_region) plot_name = os.path.basename(plot_root) crispresso2_info['summary_plot_root'] = plot_name crispresso2_info['summary_plot_names'].append(plot_name) crispresso2_info['summary_plot_titles'][ plot_name] = 'CRISPRessoWGS Modification Summary' crispresso2_info['summary_plot_labels'][ plot_name] = 'Each bar shows the total number of reads aligned to each amplicon, divided into the reads that are modified and unmodified. The vertical line shows the cutoff for analysis, set using the --min_reads_to_use_region parameter.' crispresso2_info['summary_plot_datas'][plot_name] = [ ('CRISPRessoWGS summary', os.path.basename(samples_quantification_summary_filename)) ] plot_root = _jp("CRISPRessoWGS_reads_summary") CRISPRessoPlot.plot_reads_total(plot_root, df_summary_quantification, save_png, args.min_reads_to_use_region) plot_name = os.path.basename(plot_root) crispresso2_info['summary_plot_root'] = plot_name crispresso2_info['summary_plot_names'].append(plot_name) crispresso2_info['summary_plot_titles'][ plot_name] = 'CRISPRessoWGS Read Allocation Summary' crispresso2_info['summary_plot_labels'][ plot_name] = 'Each bar shows the total number of reads allocated to each amplicon. The vertical line shows the cutoff for analysis, set using the --min_reads_to_use_region parameter.' crispresso2_info['summary_plot_datas'][plot_name] = [ ('CRISPRessoWGS summary', os.path.basename(samples_quantification_summary_filename)) ] if not args.suppress_report: report_name = _jp('CRISPResso2WGS_report.html') CRISPRessoReport.make_wgs_report_from_folder( report_name, crispresso2_info, OUTPUT_DIRECTORY, _ROOT) cp.dump(crispresso2_info, open(crispresso2WGS_info_file, 'wb')) info('Analysis Complete!') print(CRISPRessoShared.get_crispresso_footer()) sys.exit(0) except Exception as e: print_stacktrace_if_debug() error('\n\nERROR: %s' % e) sys.exit(-1)
def main(): def print_stacktrace_if_debug(): debug_flag = False if 'args' in vars() and 'debug' in args: debug_flag = args.debug if debug_flag: traceback.print_exc(file=sys.stdout) error(traceback.format_exc()) try: start_time = datetime.now() start_time_string = start_time.strftime('%Y-%m-%d %H:%M:%S') description = [ '~~~CRISPRessoWGS~~~', '-Analysis of CRISPR/Cas9 outcomes from WGS data-' ] wgs_string = r''' ____________ | __ __ | || |/ _ (_ | ||/\|\__)__) | |____________| ''' print(CRISPRessoShared.get_crispresso_header(description, wgs_string)) parser = CRISPRessoShared.getCRISPRessoArgParser( parserTitle='CRISPRessoWGS Parameters', requiredParams={}) #tool specific optional parser.add_argument('-b', '--bam_file', type=str, help='WGS aligned bam file', required=True, default='bam filename') parser.add_argument( '-f', '--region_file', type=str, help= 'Regions description file. A BED format file containing the regions to analyze, one per line. The REQUIRED\ columns are: chr_id(chromosome name), bpstart(start position), bpend(end position), the optional columns are:name (an unique indentifier for the region), guide_seq, expected_hdr_amplicon_seq,coding_seq, see CRISPResso help for more details on these last 3 parameters)', required=True) parser.add_argument( '-r', '--reference_file', type=str, help= 'A FASTA format reference file (for example hg19.fa for the human genome)', default='', required=True) parser.add_argument( '--min_reads_to_use_region', type=float, help= 'Minimum number of reads that align to a region to perform the CRISPResso analysis', default=10) parser.add_argument( '--skip_failed', help='Continue with pooled analysis even if one sample fails', action='store_true') parser.add_argument( '--gene_annotations', type=str, help= 'Gene Annotation Table from UCSC Genome Browser Tables (http://genome.ucsc.edu/cgi-bin/hgTables?command=start), \ please select as table "knownGene", as output format "all fields from selected table" and as file returned "gzip compressed"', default='') parser.add_argument('--crispresso_command', help='CRISPResso command to call', default='CRISPResso') args = parser.parse_args() crispresso_options = CRISPRessoShared.get_crispresso_options() options_to_ignore = { 'fastq_r1', 'fastq_r2', 'amplicon_seq', 'amplicon_name', 'output_folder', 'name' } crispresso_options_for_wgs = list(crispresso_options - options_to_ignore) info('Checking dependencies...') if check_samtools() and check_bowtie2(): info('\n All the required dependencies are present!') else: sys.exit(1) #check files check_file(args.bam_file) check_file(args.reference_file) check_file(args.region_file) if args.gene_annotations: check_file(args.gene_annotations) # for computation performed in CRISPRessoWGS (e.g. bowtie alignment, etc) use n_processes_for_wgs n_processes_for_wgs = 1 if args.n_processes == "max": n_processes_for_wgs = CRISPRessoMultiProcessing.get_max_processes() else: n_processes_for_wgs = int(args.n_processes) # here, we set args.n_processes as 1 because this value is propagated to sub-CRISPResso runs (not for usage in CRISPRessoWGS) args.n_processes = 1 #INIT get_name_from_bam = lambda x: os.path.basename(x).replace('.bam', '') if not args.name: database_id = '%s' % get_name_from_bam(args.bam_file) else: clean_name = CRISPRessoShared.slugify(args.name) if args.name != clean_name: warn( 'The specified name {0} contained invalid characters and was changed to: {1}' .format( args.name, clean_name, ), ) database_id = clean_name OUTPUT_DIRECTORY = 'CRISPRessoWGS_on_%s' % database_id if args.output_folder: OUTPUT_DIRECTORY = os.path.join( os.path.abspath(args.output_folder), OUTPUT_DIRECTORY) _jp = lambda filename: os.path.join( OUTPUT_DIRECTORY, filename ) #handy function to put a file in the output directory try: info('Creating Folder %s' % OUTPUT_DIRECTORY) os.makedirs(OUTPUT_DIRECTORY) info('Done!') except: warn('Folder %s already exists.' % OUTPUT_DIRECTORY) log_filename = _jp('CRISPRessoWGS_RUNNING_LOG.txt') logger.addHandler(logging.FileHandler(log_filename)) crispresso2_info_file = os.path.join(OUTPUT_DIRECTORY, 'CRISPResso2WGS_info.json') crispresso2_info = { 'running_info': {}, 'results': { 'alignment_stats': {}, 'general_plots': {} } } #keep track of all information for this run to be pickled and saved at the end of the run crispresso2_info['running_info'][ 'version'] = CRISPRessoShared.__version__ crispresso2_info['running_info']['args'] = deepcopy(args) crispresso2_info['running_info']['log_filename'] = os.path.basename( log_filename) crispresso2_info['running_info']['finished_steps'] = {} crispresso_cmd_to_write = ' '.join(sys.argv) if args.write_cleaned_report: cmd_copy = sys.argv[:] cmd_copy[0] = 'CRISPRessoWGS' for i in range(len(cmd_copy)): if os.sep in cmd_copy[i]: cmd_copy[i] = os.path.basename(cmd_copy[i]) crispresso_cmd_to_write = ' '.join( cmd_copy ) #clean command doesn't show the absolute path to the executable or other files crispresso2_info['running_info'][ 'command_used'] = crispresso_cmd_to_write with open(log_filename, 'w+') as outfile: outfile.write( 'CRISPResso version %s\n[Command used]:\n%s\n\n[Execution log]:\n' % (CRISPRessoShared.__version__, crispresso_cmd_to_write)) #keep track of args to see if it is possible to skip computation steps on rerun can_finish_incomplete_run = False if args.no_rerun: if os.path.exists(crispresso2_info_file): previous_run_data = CRISPRessoShared.load_crispresso_info( OUTPUT_DIRECTORY) if previous_run_data['running_info'][ 'version'] == CRISPRessoShared.__version__: args_are_same = True for arg in vars(args): if arg == "no_rerun" or arg == "debug" or arg == "n_processes": continue if arg not in vars( previous_run_data['running_info']['args']): info( 'Comparing current run to previous run: old run had argument ' + str(arg) + ' \nRerunning.') args_are_same = False elif str( getattr( previous_run_data['running_info']['args'], arg)) != str(getattr(args, arg)): info( 'Comparing current run to previous run:\n\told argument ' + str(arg) + ' = ' + str( getattr( previous_run_data['running_info'] ['args'], arg)) + '\n\tnew argument: ' + str(arg) + ' = ' + str(getattr(args, arg)) + '\nRerunning.') args_are_same = False if args_are_same: if 'end_time_string' in previous_run_data: info('Analysis already completed on %s!' % previous_run_data['running_info'] ['end_time_string']) sys.exit(0) else: can_finish_incomplete_run = True if 'finished_steps' in previous_run_data[ 'running_info']: for key in previous_run_data['running_info'][ 'finished_steps'].keys(): crispresso2_info['running_info'][ 'finished_steps'][ key] = previous_run_data[ 'running_info'][ 'finished_steps'][key] if args.debug: info('finished: ' + key) else: info( 'The no_rerun flag is set, but this analysis will be rerun because the existing run was performed using an old version of CRISPResso (' + str(previous_run_data['running_info']['version']) + ').') #write this file early on so we can check the params if we have to rerun CRISPRessoShared.write_crispresso_info( crispresso2_info_file, crispresso2_info, ) def rreplace(s, old, new): li = s.rsplit(old) return new.join(li) #check if bam has the index already if os.path.exists(rreplace(args.bam_file, ".bam", ".bai")): info('Index file for input .bam file exists, skipping generation.') elif os.path.exists(args.bam_file + '.bai'): info('Index file for input .bam file exists, skipping generation.') else: info('Creating index file for input .bam file...') sb.call('samtools index %s ' % (args.bam_file), shell=True) #load gene annotation if args.gene_annotations: info('Loading gene coordinates from annotation file: %s...' % args.gene_annotations) try: df_genes = pd.read_csv(args.gene_annotations, compression='gzip', sep="\t") df_genes.txEnd = df_genes.txEnd.astype(int) df_genes.txStart = df_genes.txStart.astype(int) df_genes.head() except: raise Exception('Failed to load the gene annotations file.') #Load and validate the REGION FILE df_regions = pd.read_csv(args.region_file, names=[ 'chr_id', 'bpstart', 'bpend', 'Name', 'sgRNA', 'Expected_HDR', 'Coding_sequence' ], comment='#', sep='\t', dtype={ 'Name': str, 'chr_id': str }) #remove empty amplicons/lines df_regions.dropna(subset=['chr_id', 'bpstart', 'bpend'], inplace=True) df_regions.Expected_HDR = df_regions.Expected_HDR.apply( capitalize_sequence) df_regions.sgRNA = df_regions.sgRNA.apply(capitalize_sequence) df_regions.Coding_sequence = df_regions.Coding_sequence.apply( capitalize_sequence) #check or create names for idx, row in df_regions.iterrows(): if pd.isnull(row.Name): df_regions.iloc[idx, ]['Name'] = '_'.join( map(str, [row['chr_id'], row['bpstart'], row['bpend']])) if not len(df_regions.Name.unique()) == df_regions.shape[0]: raise Exception('The amplicon names should be all distinct!') df_regions.set_index('Name', inplace=True) #df_regions.index=df_regions.index.str.replace(' ','_') df_regions.index = df_regions.index.to_series().str.replace(' ', '_') #extract sequence for each region uncompressed_reference = args.reference_file if os.path.exists(uncompressed_reference + '.fai'): info( 'The index for the reference fasta file is already present! Skipping generation.' ) else: info('Indexing reference file... Please be patient!') sb.call('samtools faidx %s >>%s 2>&1' % (uncompressed_reference, log_filename), shell=True) info( 'Retrieving reference sequences for amplicons and checking for sgRNAs' ) df_regions['sequence'] = df_regions.apply( lambda row: get_region_from_fa(row.chr_id, row.bpstart, row.bpend, uncompressed_reference), axis=1) for idx, row in df_regions.iterrows(): if not pd.isnull(row.sgRNA): cut_points = [] guides = row.sgRNA.strip().upper().split(',') guide_qw_centers = CRISPRessoShared.set_guide_array( args.quantification_window_center, guides, 'guide quantification center') for idx, current_guide_seq in enumerate(guides): wrong_nt = find_wrong_nt(current_guide_seq) if wrong_nt: raise NTException( 'The sgRNA sequence %s contains wrong characters:%s' % (current_guide_seq, ' '.join(wrong_nt))) offset_fw = guide_qw_centers[idx] + len( current_guide_seq) - 1 offset_rc = (-guide_qw_centers[idx]) - 1 cut_points+=[m.start() + offset_fw for \ m in re.finditer(current_guide_seq, row.sequence)]+[m.start() + offset_rc for m in re.finditer(CRISPRessoShared.reverse_complement(current_guide_seq), row.sequence)] if not cut_points: df_regions.iloc[idx, :]['sgRNA'] = '' info('Cannot find guide ' + str(row.sgRNA) + ' in amplicon ' + str(idx) + ' (' + str(row) + ')') df_regions['bpstart'] = pd.to_numeric(df_regions['bpstart']) df_regions['bpend'] = pd.to_numeric(df_regions['bpend']) df_regions.bpstart = df_regions.bpstart.astype(int) df_regions.bpend = df_regions.bpend.astype(int) if args.gene_annotations: df_regions = df_regions.apply( lambda row: find_overlapping_genes(row, df_genes), axis=1) #extract reads with samtools in that region and create a bam #create a fasta file with all the trimmed reads info('\nProcessing each region...') ANALYZED_REGIONS = _jp('ANALYZED_REGIONS/') if not os.path.exists(ANALYZED_REGIONS): os.mkdir(ANALYZED_REGIONS) df_regions['region_number'] = np.arange(len(df_regions)) def set_filenames(row): row_fastq_exists = False fastq_gz_filename = os.path.join( ANALYZED_REGIONS, '%s.fastq.gz' % clean_filename('REGION_' + str(row.region_number))) bam_region_filename = os.path.join( ANALYZED_REGIONS, '%s.bam' % clean_filename('REGION_' + str(row.region_number))) #if bam file already exists, don't regenerate it if os.path.isfile(fastq_gz_filename): row_fastq_exists = True return bam_region_filename, fastq_gz_filename, row_fastq_exists df_regions['bam_file_with_reads_in_region'], df_regions[ 'fastq_file_trimmed_reads_in_region'], df_regions[ 'row_fastq_exists'] = zip( *df_regions.apply(set_filenames, axis=1)) df_regions['n_reads'] = 0 df_regions[ 'original_bam'] = args.bam_file #stick this in the df so we can parallelize the analysis and not pass params report_reads_aligned_filename = _jp( 'REPORT_READS_ALIGNED_TO_SELECTED_REGIONS_WGS.txt') num_rows_without_fastq = len( df_regions[df_regions.row_fastq_exists == False]) if can_finish_incomplete_run and num_rows_without_fastq == 0 and os.path.isfile( report_reads_aligned_filename ) and 'generation_of_fastq_files_for_each_amplicon' in crispresso2_info[ 'running_info']['finished_steps']: info('Skipping generation of fastq files for each amplicon.') df_regions = pd.read_csv(report_reads_aligned_filename, comment='#', sep='\t', dtype={ 'Name': str, 'chr_id': str }) df_regions.set_index('Name', inplace=True) else: #run region extraction here df_regions = CRISPRessoMultiProcessing.run_pandas_apply_parallel( df_regions, extract_reads_chunk, n_processes_for_wgs) df_regions.sort_values('region_number', inplace=True) cols_to_print = [ "chr_id", "bpstart", "bpend", "sgRNA", "Expected_HDR", "Coding_sequence", "sequence", "n_reads", "bam_file_with_reads_in_region", "fastq_file_trimmed_reads_in_region" ] if args.gene_annotations: cols_to_print.append('gene_overlapping') df_regions.fillna('NA').to_csv(report_reads_aligned_filename, sep='\t', columns=cols_to_print, index_label="Name") #save progress crispresso2_info['running_info']['finished_steps'][ 'generation_of_fastq_files_for_each_amplicon'] = True CRISPRessoShared.write_crispresso_info( crispresso2_info_file, crispresso2_info, ) #Run Crispresso info('Running CRISPResso on each region...') crispresso_cmds = [] for idx, row in df_regions.iterrows(): if row['n_reads'] >= args.min_reads_to_use_region: info('\nThe region [%s] has enough reads (%d) mapped to it!' % (idx, row['n_reads'])) crispresso_cmd= args.crispresso_command + ' -r1 %s -a %s -o %s --name %s' %\ (row['fastq_file_trimmed_reads_in_region'], row['sequence'], OUTPUT_DIRECTORY, idx) if row['sgRNA'] and not pd.isnull(row['sgRNA']): crispresso_cmd += ' -g %s' % row['sgRNA'] if row['Expected_HDR'] and not pd.isnull(row['Expected_HDR']): crispresso_cmd += ' -e %s' % row['Expected_HDR'] if row['Coding_sequence'] and not pd.isnull( row['Coding_sequence']): crispresso_cmd += ' -c %s' % row['Coding_sequence'] crispresso_cmd = CRISPRessoShared.propagate_crispresso_options( crispresso_cmd, crispresso_options_for_wgs, args) #logging like this causes the multiprocessing step to not block for some reason #mysteriesOfThPythonUniverse #log_name = _jp("CRISPResso_on_"+idx) +".log" #crispresso_cmd += " &> %s"%log_name crispresso_cmds.append(crispresso_cmd) # info('Running CRISPResso:%s' % crispresso_cmd) # sb.call(crispresso_cmd,shell=True) else: info( '\nThe region [%s] has too few reads mapped to it (%d)! Not running CRISPResso!' % (idx, row['n_reads'])) CRISPRessoMultiProcessing.run_crispresso_cmds(crispresso_cmds, n_processes_for_wgs, 'region', args.skip_failed) quantification_summary = [] all_region_names = [] all_region_read_counts = {} good_region_names = [] good_region_folders = {} header = 'Name\tUnmodified%\tModified%\tReads_total\tReads_aligned\tUnmodified\tModified\tDiscarded\tInsertions\tDeletions\tSubstitutions\tOnly Insertions\tOnly Deletions\tOnly Substitutions\tInsertions and Deletions\tInsertions and Substitutions\tDeletions and Substitutions\tInsertions Deletions and Substitutions' header_els = header.split("\t") header_el_count = len(header_els) empty_line_els = [np.nan] * (header_el_count - 1) n_reads_index = header_els.index('Reads_total') - 1 for idx, row in df_regions.iterrows(): folder_name = 'CRISPResso_on_%s' % idx run_name = idx all_region_names.append(run_name) all_region_read_counts[run_name] = row.n_reads run_file = os.path.join(_jp(folder_name), 'CRISPResso2_info.json') if not os.path.exists(run_file): warn( 'Skipping the folder %s: not enough reads, incomplete, or empty folder.' % folder_name) this_els = empty_line_els[:] this_els[n_reads_index] = row.n_reads to_add = [run_name] to_add.extend(this_els) quantification_summary.append(to_add) else: run_data = CRISPRessoShared.load_crispresso_info( _jp(folder_name), ) ref_name = run_data['results']['ref_names'][ 0] #only expect one amplicon sequence n_tot = row.n_reads n_aligned = run_data['results']['alignment_stats'][ 'counts_total'][ref_name] n_unmod = run_data['results']['alignment_stats'][ 'counts_unmodified'][ref_name] n_mod = run_data['results']['alignment_stats'][ 'counts_modified'][ref_name] n_discarded = run_data['results']['alignment_stats'][ 'counts_discarded'][ref_name] n_insertion = run_data['results']['alignment_stats'][ 'counts_insertion'][ref_name] n_deletion = run_data['results']['alignment_stats'][ 'counts_deletion'][ref_name] n_substitution = run_data['results']['alignment_stats'][ 'counts_substitution'][ref_name] n_only_insertion = run_data['results']['alignment_stats'][ 'counts_only_insertion'][ref_name] n_only_deletion = run_data['results']['alignment_stats'][ 'counts_only_deletion'][ref_name] n_only_substitution = run_data['results']['alignment_stats'][ 'counts_only_substitution'][ref_name] n_insertion_and_deletion = run_data['results'][ 'alignment_stats']['counts_insertion_and_deletion'][ ref_name] n_insertion_and_substitution = run_data['results'][ 'alignment_stats']['counts_insertion_and_substitution'][ ref_name] n_deletion_and_substitution = run_data['results'][ 'alignment_stats']['counts_deletion_and_substitution'][ ref_name] n_insertion_and_deletion_and_substitution = run_data[ 'results']['alignment_stats'][ 'counts_insertion_and_deletion_and_substitution'][ ref_name] unmod_pct = "NA" mod_pct = "NA" if n_aligned > 0: unmod_pct = 100 * n_unmod / float(n_aligned) mod_pct = 100 * n_mod / float(n_aligned) vals = [run_name] vals.extend([ round(unmod_pct, 8), round(mod_pct, 8), n_aligned, n_tot, n_unmod, n_mod, n_discarded, n_insertion, n_deletion, n_substitution, n_only_insertion, n_only_deletion, n_only_substitution, n_insertion_and_deletion, n_insertion_and_substitution, n_deletion_and_substitution, n_insertion_and_deletion_and_substitution ]) quantification_summary.append(vals) good_region_names.append(idx) good_region_folders[idx] = folder_name samples_quantification_summary_filename = _jp( 'SAMPLES_QUANTIFICATION_SUMMARY.txt') df_summary_quantification = pd.DataFrame(quantification_summary, columns=header_els) if args.crispresso1_mode: crispresso1_columns = [ 'Name', 'Unmodified%', 'Modified%', 'Reads_aligned', 'Reads_total' ] df_summary_quantification.fillna('NA').to_csv( samples_quantification_summary_filename, sep='\t', index=None, columns=crispresso1_columns) else: df_summary_quantification.fillna('NA').to_csv( samples_quantification_summary_filename, sep='\t', index=None) crispresso2_info['results']['alignment_stats'][ 'samples_quantification_summary_filename'] = os.path.basename( samples_quantification_summary_filename) crispresso2_info['results']['regions'] = df_regions crispresso2_info['results']['all_region_names'] = all_region_names crispresso2_info['results'][ 'all_region_read_counts'] = all_region_read_counts crispresso2_info['results']['good_region_names'] = good_region_names crispresso2_info['results'][ 'good_region_folders'] = good_region_folders crispresso2_info['results']['general_plots']['summary_plot_names'] = [] crispresso2_info['results']['general_plots'][ 'summary_plot_titles'] = {} crispresso2_info['results']['general_plots'][ 'summary_plot_labels'] = {} crispresso2_info['results']['general_plots']['summary_plot_datas'] = {} df_summary_quantification.set_index('Name') save_png = True if args.suppress_report: save_png = False if not args.suppress_plots: plot_root = _jp("CRISPRessoWGS_reads_summary") CRISPRessoPlot.plot_reads_total(plot_root, df_summary_quantification, save_png, args.min_reads_to_use_region) plot_name = os.path.basename(plot_root) crispresso2_info['results']['general_plots'][ 'reads_summary_plot'] = plot_name crispresso2_info['results']['general_plots'][ 'summary_plot_names'].append(plot_name) crispresso2_info['results']['general_plots'][ 'summary_plot_titles'][ plot_name] = 'CRISPRessoWGS Read Allocation Summary' crispresso2_info['results']['general_plots']['summary_plot_labels'][ plot_name] = 'Each bar shows the total number of reads allocated to each amplicon. The vertical line shows the cutoff for analysis, set using the --min_reads_to_use_region parameter.' crispresso2_info['results']['general_plots']['summary_plot_datas'][ plot_name] = [ ('CRISPRessoWGS summary', os.path.basename(samples_quantification_summary_filename)) ] plot_root = _jp("CRISPRessoWGS_modification_summary") CRISPRessoPlot.plot_unmod_mod_pcts(plot_root, df_summary_quantification, save_png, args.min_reads_to_use_region) plot_name = os.path.basename(plot_root) crispresso2_info['results']['general_plots'][ 'modification_summary_plot'] = plot_name crispresso2_info['results']['general_plots'][ 'summary_plot_names'].append(plot_name) crispresso2_info['results']['general_plots'][ 'summary_plot_titles'][ plot_name] = 'CRISPRessoWGS Modification Summary' crispresso2_info['results']['general_plots']['summary_plot_labels'][ plot_name] = 'Each bar shows the total number of reads aligned to each amplicon, divided into the reads that are modified and unmodified. The vertical line shows the cutoff for analysis, set using the --min_reads_to_use_region parameter.' crispresso2_info['results']['general_plots']['summary_plot_datas'][ plot_name] = [ ('CRISPRessoWGS summary', os.path.basename(samples_quantification_summary_filename)) ] if not args.suppress_report and not args.suppress_plots: if (args.place_report_in_output_folder): report_name = _jp("CRISPResso2WGS_report.html") else: report_name = OUTPUT_DIRECTORY + '.html' CRISPRessoReport.make_wgs_report_from_folder( report_name, crispresso2_info, OUTPUT_DIRECTORY, _ROOT) crispresso2_info['running_info']['report_location'] = report_name crispresso2_info['running_info'][ 'report_filename'] = os.path.basename(report_name) end_time = datetime.now() end_time_string = end_time.strftime('%Y-%m-%d %H:%M:%S') running_time = end_time - start_time running_time_string = str(running_time) crispresso2_info['running_info']['end_time'] = end_time crispresso2_info['running_info']['end_time_string'] = end_time_string crispresso2_info['running_info']['running_time'] = running_time crispresso2_info['running_info'][ 'running_time_string'] = running_time_string CRISPRessoShared.write_crispresso_info( crispresso2_info_file, crispresso2_info, ) info('Analysis Complete!') print(CRISPRessoShared.get_crispresso_footer()) sys.exit(0) except Exception as e: print_stacktrace_if_debug() error('\n\nERROR: %s' % e) sys.exit(-1)
def main(): try: description = ['~~~CRISPRessoPooled~~~','-Analysis of CRISPR/Cas9 outcomes from POOLED deep sequencing data-'] pooled_string = r''' _______________________ | __ __ __ __ __ | ||__)/ \/ \| |_ | \ | || \__/\__/|__|__|__/ | |_______________________| ''' print(CRISPRessoShared.get_crispresso_header(description,pooled_string)) parser = CRISPRessoShared.getCRISPRessoArgParser(parserTitle = 'CRISPRessoPooled Parameters',requiredParams={'fastq_r1':True}) parser.add_argument('-f','--amplicons_file', type=str, help='Amplicons description file. This file is a tab-delimited text file with up to 5 columns (2 required):\ \nAMPLICON_NAME: an identifier for the amplicon (must be unique)\nAMPLICON_SEQUENCE: amplicon sequence used in the experiment\n\ \nsgRNA_SEQUENCE (OPTIONAL): sgRNA sequence used for this amplicon without the PAM sequence. Multiple guides can be given separated by commas and not spaces. If not available enter NA.\ \nEXPECTED_AMPLICON_AFTER_HDR (OPTIONAL): expected amplicon sequence in case of HDR. If not available enter NA.\ \nCODING_SEQUENCE (OPTIONAL): Subsequence(s) of the amplicon corresponding to coding sequences. If more than one separate them by commas and not spaces. If not available enter NA.', default='') #tool specific optional parser.add_argument('--gene_annotations', type=str, help='Gene Annotation Table from UCSC Genome Browser Tables (http://genome.ucsc.edu/cgi-bin/hgTables?command=start), \ please select as table "knownGene", as output format "all fields from selected table" and as file returned "gzip compressed"', default='') parser.add_argument('-p','--n_processes',type=int, help='Specify the number of processes to use for Bowtie2.\ Please use with caution since increasing this parameter will increase significantly the memory required to run CRISPResso.',default=1) parser.add_argument('-x','--bowtie2_index', type=str, help='Basename of Bowtie2 index for the reference genome', default='') parser.add_argument('--bowtie2_options_string', type=str, help='Override options for the Bowtie2 alignment command',default=' -k 1 --end-to-end -N 0 --np 0 ') parser.add_argument('--min_reads_to_use_region', type=float, help='Minimum number of reads that align to a region to perform the CRISPResso analysis', default=1000) parser.add_argument('--skip_failed', help='Continue with pooled analysis even if one sample fails',action='store_true') parser.add_argument('--crispresso_command', help='CRISPResso command to call',default='CRISPResso') args = parser.parse_args() crispresso_options = CRISPRessoShared.get_crispresso_options() options_to_ignore = set(['fastq_r1','fastq_r2','amplicon_seq','amplicon_name','output_folder','name']) crispresso_options_for_pooled = list(crispresso_options-options_to_ignore) info('Checking dependencies...') if check_samtools() and check_bowtie2(): info('All the required dependencies are present!') else: sys.exit(1) #check files check_file(args.fastq_r1) if args.fastq_r2: check_file(args.fastq_r2) if args.bowtie2_index: check_file(args.bowtie2_index+'.1.bt2') if args.amplicons_file: check_file(args.amplicons_file) if args.gene_annotations: check_file(args.gene_annotations) if args.amplicons_file and not args.bowtie2_index: RUNNING_MODE='ONLY_AMPLICONS' info('Only the Amplicon description file was provided. The analysis will be perfomed using only the provided amplicons sequences.') elif args.bowtie2_index and not args.amplicons_file: RUNNING_MODE='ONLY_GENOME' info('Only the bowtie2 reference genome index file was provided. The analysis will be perfomed using only genomic regions where enough reads align.') elif args.bowtie2_index and args.amplicons_file: RUNNING_MODE='AMPLICONS_AND_GENOME' info('Amplicon description file and bowtie2 reference genome index files provided. The analysis will be perfomed using the reads that are aligned ony to the amplicons provided and not to other genomic regions.') else: error('Please provide the amplicons description file (-f or --amplicons_file option) or the bowtie2 reference genome index file (-x or --bowtie2_index option) or both.') sys.exit(1) ####TRIMMING AND MERGING get_name_from_fasta=lambda x: os.path.basename(x).replace('.fastq','').replace('.gz','') if not args.name: if args.fastq_r2!='': database_id='%s_%s' % (get_name_from_fasta(args.fastq_r1),get_name_from_fasta(args.fastq_r2)) else: database_id='%s' % get_name_from_fasta(args.fastq_r1) else: database_id=args.name OUTPUT_DIRECTORY='CRISPRessoPooled_on_%s' % database_id if args.output_folder: OUTPUT_DIRECTORY=os.path.join(os.path.abspath(args.output_folder),OUTPUT_DIRECTORY) _jp=lambda filename: os.path.join(OUTPUT_DIRECTORY,filename) #handy function to put a file in the output directory try: info('Creating Folder %s' % OUTPUT_DIRECTORY) os.makedirs(OUTPUT_DIRECTORY) info('Done!') except: warn('Folder %s already exists.' % OUTPUT_DIRECTORY) log_filename=_jp('CRISPRessoPooled_RUNNING_LOG.txt') logging.getLogger().addHandler(logging.FileHandler(log_filename)) crispresso2WGS_info_file = os.path.join(OUTPUT_DIRECTORY,'CRISPResso2Pooled_info.pickle') crispresso2_info = {} #keep track of all information for this run to be pickled and saved at the end of the run crispresso2_info['version'] = CRISPRessoShared.__version__ crispresso2_info['args'] = deepcopy(args) crispresso2_info['log_filename'] = os.path.basename(log_filename) with open(log_filename,'w+') as outfile: outfile.write('[Command used]:\n%s\n\n[Execution log]:\n' % ' '.join(sys.argv)) if args.fastq_r2=='': #single end reads #check if we need to trim if not args.trim_sequences: #create a symbolic link symlink_filename=_jp(os.path.basename(args.fastq_r1)) force_symlink(os.path.abspath(args.fastq_r1),symlink_filename) output_forward_filename=symlink_filename else: output_forward_filename=_jp('reads.trimmed.fq.gz') #Trimming with trimmomatic cmd='%s SE -phred33 %s %s %s >>%s 2>&1'\ % (args.trimmomatic_command,args.fastq_r1, output_forward_filename, args.trimmomatic_options_string, log_filename) #print cmd TRIMMOMATIC_STATUS=sb.call(cmd,shell=True) if TRIMMOMATIC_STATUS: raise TrimmomaticException('TRIMMOMATIC failed to run, please check the log file.') processed_output_filename=output_forward_filename else:#paired end reads case if not args.trim_sequences: output_forward_paired_filename=args.fastq_r1 output_reverse_paired_filename=args.fastq_r2 else: info('Trimming sequences with Trimmomatic...') output_forward_paired_filename=_jp('output_forward_paired.fq.gz') output_forward_unpaired_filename=_jp('output_forward_unpaired.fq.gz') output_reverse_paired_filename=_jp('output_reverse_paired.fq.gz') output_reverse_unpaired_filename=_jp('output_reverse_unpaired.fq.gz') #Trimming with trimmomatic cmd='%s PE -phred33 %s %s %s %s %s %s %s >>%s 2>&1'\ % (args.trimmomatic_command, args.fastq_r1,args.fastq_r2,output_forward_paired_filename, output_forward_unpaired_filename,output_reverse_paired_filename, output_reverse_unpaired_filename,args.trimmomatic_options_string,log_filename) #print cmd TRIMMOMATIC_STATUS=sb.call(cmd,shell=True) if TRIMMOMATIC_STATUS: raise TrimmomaticException('TRIMMOMATIC failed to run, please check the log file.') info('Done!') max_overlap_string = "" min_overlap_string = "" if args.max_paired_end_reads_overlap: max_overlap_string = "--max-overlap " + str(args.max_paired_end_reads_overlap) if args.min_paired_end_reads_overlap: min_overlap_string = args.min_paired_end_reads_overlap #Merging with Flash info('Merging paired sequences with Flash...') cmd=args.flash_command+' --allow-outies %s %s %s %s -z -d %s >>%s 2>&1' %\ (output_forward_paired_filename, output_reverse_paired_filename, max_overlap_string, max_overlap_string, OUTPUT_DIRECTORY,log_filename) FLASH_STATUS=sb.call(cmd,shell=True) if FLASH_STATUS: raise FlashException('Flash failed to run, please check the log file.') info('Done!') flash_hist_filename=_jp('out.hist') flash_histogram_filename=_jp('out.histogram') flash_not_combined_1_filename=_jp('out.notCombined_1.fastq.gz') flash_not_combined_2_filename=_jp('out.notCombined_2.fastq.gz') processed_output_filename=_jp('out.extendedFrags.fastq.gz') #count reads N_READS_INPUT=get_n_reads_fastq(args.fastq_r1) N_READS_AFTER_PREPROCESSING=get_n_reads_fastq(processed_output_filename) #load gene annotation if args.gene_annotations: info('Loading gene coordinates from annotation file: %s...' % args.gene_annotations) try: df_genes=pd.read_table(args.gene_annotations,compression='gzip') df_genes.txEnd=df_genes.txEnd.astype(int) df_genes.txStart=df_genes.txStart.astype(int) df_genes.head() except: info('Failed to load the gene annotations file.') if RUNNING_MODE=='ONLY_AMPLICONS' or RUNNING_MODE=='AMPLICONS_AND_GENOME': #load and validate template file df_template=pd.read_csv(args.amplicons_file,names=[ 'Name','Amplicon_Sequence','sgRNA', 'Expected_HDR','Coding_sequence'],comment='#',sep='\t',dtype={'Name':str}) if str(df_template.iloc[0,1]).lower() == "amplicon_sequence": df_template.drop(0,axis=0,inplace=True) info('Detected header in amplicon file.') #remove empty amplicons/lines df_template.dropna(subset=['Amplicon_Sequence'],inplace=True) df_template.dropna(subset=['Name'],inplace=True) df_template.Amplicon_Sequence=df_template.Amplicon_Sequence.apply(capitalize_sequence) df_template.Expected_HDR=df_template.Expected_HDR.apply(capitalize_sequence) df_template.sgRNA=df_template.sgRNA.apply(capitalize_sequence) df_template.Coding_sequence=df_template.Coding_sequence.apply(capitalize_sequence) if not len(df_template.Amplicon_Sequence.unique())==df_template.shape[0]: duplicated_entries = df_template.Amplicon_Sequence[df_template.Amplicon_Sequence.duplicated()] raise Exception('The amplicon sequences must be distinct! (Duplicated entries: ' + str(duplicated_entries.values) + ')') if not len(df_template.Name.unique())==df_template.shape[0]: duplicated_entries = df_template.Name[df_template.Name.duplicated()] raise Exception('The amplicon names must be distinct! (Duplicated names: ' + str(duplicated_entries.values) + ')') df_template=df_template.set_index('Name') df_template.index=df_template.index.to_series().str.replace(' ','_') for idx,row in df_template.iterrows(): wrong_nt=find_wrong_nt(row.Amplicon_Sequence) if wrong_nt: raise NTException('The amplicon sequence %s contains wrong characters:%s' % (idx,' '.join(wrong_nt))) if not pd.isnull(row.sgRNA): cut_points=[] for current_guide_seq in row.sgRNA.strip().upper().split(','): wrong_nt=find_wrong_nt(current_guide_seq) if wrong_nt: raise NTException('The sgRNA sequence %s contains wrong characters:%s' % (current_guide_seq, ' '.join(wrong_nt))) offset_fw=args.quantification_window_center+len(current_guide_seq)-1 offset_rc=(-args.quantification_window_center)-1 cut_points+=[m.start() + offset_fw for \ m in re.finditer(current_guide_seq, row.Amplicon_Sequence)]+[m.start() + offset_rc for m in re.finditer(reverse_complement(current_guide_seq), row.Amplicon_Sequence)] if not cut_points: warn('\nThe guide sequence/s provided: %s is(are) not present in the amplicon sequence:%s! \nNOTE: The guide will be ignored for the analysis. Please check your input!' % (row.sgRNA,row.Amplicon_Sequence)) df_template.ix[idx,'sgRNA']='' if RUNNING_MODE=='ONLY_AMPLICONS': #create a fasta file with all the amplicons amplicon_fa_filename=_jp('AMPLICONS.fa') fastq_gz_amplicon_filenames=[] with open(amplicon_fa_filename,'w+') as outfile: for idx,row in df_template.iterrows(): if row['Amplicon_Sequence']: outfile.write('>%s\n%s\n' %(clean_filename('AMPL_'+idx),row['Amplicon_Sequence'])) #create place-holder fastq files fastq_gz_amplicon_filenames.append(_jp('%s.fastq.gz' % clean_filename('AMPL_'+idx))) open(fastq_gz_amplicon_filenames[-1], 'w+').close() df_template['Demultiplexed_fastq.gz_filename']=fastq_gz_amplicon_filenames info('Creating a custom index file with all the amplicons...') custom_index_filename=_jp('CUSTOM_BOWTIE2_INDEX') sb.call('bowtie2-build %s %s >>%s 2>&1' %(amplicon_fa_filename,custom_index_filename,log_filename), shell=True) #align the file to the amplicons (MODE 1) info('Align reads to the amplicons...') bam_filename_amplicons= _jp('CRISPResso_AMPLICONS_ALIGNED.bam') aligner_command= 'bowtie2 -x %s -p %s %s -U %s 2>>%s | samtools view -bS - > %s' %(custom_index_filename,args.n_processes,args.bowtie2_options_string,processed_output_filename,log_filename,bam_filename_amplicons) info('Alignment command: ' + aligner_command) sb.call(aligner_command,shell=True) N_READS_ALIGNED=get_n_aligned_bam(bam_filename_amplicons) s1=r"samtools view -F 4 %s 2>>%s | grep -v ^'@'" % (bam_filename_amplicons,log_filename) s2=r'''|awk '{ gzip_filename=sprintf("gzip >> OUTPUTPATH%s.fastq.gz",$3);\ print "@"$1"\n"$10"\n+\n"$11 | gzip_filename;}' ''' cmd=s1+s2.replace('OUTPUTPATH',_jp('')) sb.call(cmd,shell=True) info('Demultiplex reads and run CRISPResso on each amplicon...') n_reads_aligned_amplicons=[] crispresso_cmds = [] for idx,row in df_template.iterrows(): info('\n Processing:%s' %idx) n_reads_aligned_amplicons.append(get_n_reads_fastq(row['Demultiplexed_fastq.gz_filename'])) crispresso_cmd= args.crispresso_command + ' -r1 %s -a %s -o %s --name %s' % (row['Demultiplexed_fastq.gz_filename'],row['Amplicon_Sequence'],OUTPUT_DIRECTORY,idx) if n_reads_aligned_amplicons[-1]>args.min_reads_to_use_region: if row['sgRNA'] and not pd.isnull(row['sgRNA']): crispresso_cmd+=' -g %s' % row['sgRNA'] if row['Expected_HDR'] and not pd.isnull(row['Expected_HDR']): crispresso_cmd+=' -e %s' % row['Expected_HDR'] if row['Coding_sequence'] and not pd.isnull(row['Coding_sequence']): crispresso_cmd+=' -c %s' % row['Coding_sequence'] crispresso_cmd=CRISPRessoShared.propagate_crispresso_options(crispresso_cmd,crispresso_options_for_pooled,args) crispresso_cmds.append(crispresso_cmd) else: warn('Skipping amplicon [%s] because no reads align to it\n'% idx) CRISPRessoMultiProcessing.run_crispresso_cmds(crispresso_cmds,args.n_processes,'amplicon',args.skip_failed) df_template['n_reads']=n_reads_aligned_amplicons df_template['n_reads_aligned_%']=df_template['n_reads']/float(N_READS_ALIGNED)*100 df_template.fillna('NA').to_csv(_jp('REPORT_READS_ALIGNED_TO_AMPLICONS.txt'),sep='\t') if RUNNING_MODE=='AMPLICONS_AND_GENOME': print 'Mapping amplicons to the reference genome...' #find the locations of the amplicons on the genome and their strand and check if there are mutations in the reference genome additional_columns=[] for idx,row in df_template.iterrows(): fields_to_append=list(np.take(get_align_sequence(row.Amplicon_Sequence, args.bowtie2_index).split('\t'),[0,1,2,3,5])) if fields_to_append[0]=='*': info('The amplicon [%s] is not mappable to the reference genome provided!' % idx ) additional_columns.append([idx,'NOT_ALIGNED',0,-1,'+','']) else: additional_columns.append([idx]+fields_to_append) info('The amplicon [%s] was mapped to: %s ' % (idx,' '.join(fields_to_append[:3]) )) df_template=df_template.join(pd.DataFrame(additional_columns,columns=['Name','chr_id','bpstart','bpend','strand','Reference_Sequence']).set_index('Name')) df_template.bpstart=df_template.bpstart.astype(int) df_template.bpend=df_template.bpend.astype(int) #Check reference is the same otherwise throw a warning for idx,row in df_template.iterrows(): if row.Amplicon_Sequence != row.Reference_Sequence and row.Amplicon_Sequence != reverse_complement(row.Reference_Sequence): warn('The amplicon sequence %s provided:\n%s\n\nis different from the reference sequence(both strand):\n\n%s\n\n%s\n' %(row.name,row.Amplicon_Sequence,row.Amplicon_Sequence,reverse_complement(row.Amplicon_Sequence))) if RUNNING_MODE=='ONLY_GENOME' or RUNNING_MODE=='AMPLICONS_AND_GENOME': ###HERE we recreate the uncompressed genome file if not available### #check you have all the files for the genome and create a fa idx for samtools uncompressed_reference=args.bowtie2_index+'.fa' #if not os.path.exists(GENOME_LOCAL_FOLDER): # os.mkdir(GENOME_LOCAL_FOLDER) if os.path.exists(uncompressed_reference): info('The uncompressed reference fasta file for %s is already present! Skipping generation.' % args.bowtie2_index) else: #uncompressed_reference=os.path.join(GENOME_LOCAL_FOLDER,'UNCOMPRESSED_REFERENCE_FROM_'+args.bowtie2_index.replace('/','_')+'.fa') info('Extracting uncompressed reference from the provided bowtie2 index since it is not available... Please be patient!') cmd_to_uncompress='bowtie2-inspect %s > %s 2>>%s' % (args.bowtie2_index,uncompressed_reference,log_filename) sb.call(cmd_to_uncompress,shell=True) info('Indexing fasta file with samtools...') #!samtools faidx {uncompressed_reference} sb.call('samtools faidx %s 2>>%s ' % (uncompressed_reference,log_filename),shell=True) #####CORRECT ONE#### #align in unbiased way the reads to the genome if RUNNING_MODE=='ONLY_GENOME' or RUNNING_MODE=='AMPLICONS_AND_GENOME': info('Aligning reads to the provided genome index...') bam_filename_genome = _jp('%s_GENOME_ALIGNED.bam' % database_id) aligner_command= 'bowtie2 -x %s -p %s %s -U %s 2>>%s| samtools view -bS - > %s' %(args.bowtie2_index,args.n_processes,args.bowtie2_options_string,processed_output_filename,log_filename,bam_filename_genome) info('aligning with command: ' + aligner_command) sb.call(aligner_command,shell=True) N_READS_ALIGNED=get_n_aligned_bam(bam_filename_genome) #REDISCOVER LOCATIONS and DEMULTIPLEX READS MAPPED_REGIONS=_jp('MAPPED_REGIONS/') if not os.path.exists(MAPPED_REGIONS): os.mkdir(MAPPED_REGIONS) s1=r'''samtools view -F 0x0004 %s 2>>%s |''' % (bam_filename_genome,log_filename)+\ r'''awk '{OFS="\t"; bpstart=$4; bpend=bpstart; split ($6,a,"[MIDNSHP]"); n=0;\ for (i=1; i in a; i++){\ n+=1+length(a[i]);\ if (substr($6,n,1)=="S"){\ if (bpend==$4)\ bpstart-=a[i];\ else bpend+=a[i]; }\ else if( (substr($6,n,1)!="I") && (substr($6,n,1)!="H") )\ bpend+=a[i];\ }\ if ( ($2 % 32)>=16)\ print $3,bpstart,bpend,"-",$1,$10,$11;\ else\ print $3,bpstart,bpend,"+",$1,$10,$11;}' | ''' s2=r''' sort -k1,1 -k2,2n | awk \ 'BEGIN{chr_id="NA";bpstart=-1;bpend=-1; fastq_filename="NA"}\ { if ( (chr_id!=$1) || (bpstart!=$2) || (bpend!=$3) )\ {\ if (fastq_filename!="NA") {close(fastq_filename); system("gzip -f "fastq_filename)}\ chr_id=$1; bpstart=$2; bpend=$3;\ fastq_filename=sprintf("__OUTPUTPATH__REGION_%s_%s_%s.fastq",$1,$2,$3);\ }\ print "@"$5"\n"$6"\n+\n"$7 >> fastq_filename;\ }' ''' cmd=s1+s2.replace('__OUTPUTPATH__',MAPPED_REGIONS) info('Demultiplexing reads by location...') sb.call(cmd,shell=True) #gzip the missing ones sb.call('gzip -f %s/*.fastq' % MAPPED_REGIONS,shell=True) ''' The most common use case, where many different target sites are pooled into a single high-throughput sequencing library for quantification, is not directly addressed by this implementation. Potential users of CRISPResso would need to write their own code to generate separate input files for processing. Importantly, this preprocessing code would need to remove any PCR amplification artifacts (such as amplification of sequences from a gene and a highly similar pseudogene ) which may confound the interpretation of results. This can be done by mapping of input sequences to a reference genome and removing those that do not map to the expected genomic location, but is non-trivial for an end-user to implement. ''' if RUNNING_MODE=='AMPLICONS_AND_GENOME': files_to_match=glob.glob(os.path.join(MAPPED_REGIONS,'REGION*')) n_reads_aligned_genome=[] fastq_region_filenames=[] crispresso_cmds = [] for idx,row in df_template.iterrows(): info('Processing amplicon: %s' % idx ) #check if we have reads fastq_filename_region=os.path.join(MAPPED_REGIONS,'REGION_%s_%s_%s.fastq.gz' % (row['chr_id'],row['bpstart'],row['bpend'])) if os.path.exists(fastq_filename_region): N_READS=get_n_reads_fastq(fastq_filename_region) n_reads_aligned_genome.append(N_READS) fastq_region_filenames.append(fastq_filename_region) files_to_match.remove(fastq_filename_region) if N_READS>=args.min_reads_to_use_region: info('\nThe amplicon [%s] has enough reads (%d) mapped to it! Running CRISPResso!\n' % (idx,N_READS)) crispresso_cmd= args.crispresso_command + ' -r1 %s -a %s -o %s --name %s' % (fastq_filename_region,row['Amplicon_Sequence'],OUTPUT_DIRECTORY,idx) if row['sgRNA'] and not pd.isnull(row['sgRNA']): crispresso_cmd+=' -g %s' % row['sgRNA'] if row['Expected_HDR'] and not pd.isnull(row['Expected_HDR']): crispresso_cmd+=' -e %s' % row['Expected_HDR'] if row['Coding_sequence'] and not pd.isnull(row['Coding_sequence']): crispresso_cmd+=' -c %s' % row['Coding_sequence'] crispresso_cmd=CRISPRessoShared.propagate_crispresso_options(crispresso_cmd,crispresso_options_for_pooled,args) info('Running CRISPResso:%s' % crispresso_cmd) crispresso_cmds.append(crispresso_cmd) else: warn('The amplicon [%s] has not enough reads (%d) mapped to it! Skipping the execution of CRISPResso!' % (idx,N_READS)) else: fastq_region_filenames.append('') n_reads_aligned_genome.append(0) warn("The amplicon %s doesn't have any read mapped to it!\n Please check your amplicon sequence." % idx) CRISPRessoMultiProcessing.run_crispresso_cmds(crispresso_cmds,args.n_processes,'amplicon',args.skip_failed) df_template['Amplicon_Specific_fastq.gz_filename']=fastq_region_filenames df_template['n_reads']=n_reads_aligned_genome df_template['n_reads_aligned_%']=df_template['n_reads']/float(N_READS_ALIGNED)*100 if args.gene_annotations: df_template=df_template.apply(lambda row: find_overlapping_genes(row, df_genes),axis=1) df_template.fillna('NA').to_csv(_jp('REPORT_READS_ALIGNED_TO_GENOME_AND_AMPLICONS.txt'),sep='\t') #write another file with the not amplicon regions info('Reporting problematic regions...') coordinates=[] for region in files_to_match: coordinates.append(os.path.basename(region).replace('.fastq.gz','').replace('.fastq','').split('_')[1:4]+[region,get_n_reads_fastq(region)]) df_regions=pd.DataFrame(coordinates,columns=['chr_id','bpstart','bpend','fastq_file','n_reads']) df_regions.dropna(inplace=True) #remove regions in chrUn df_regions['bpstart'] = pd.to_numeric(df_regions['bpstart']) df_regions['bpend'] = pd.to_numeric(df_regions['bpend']) df_regions['n_reads'] = pd.to_numeric(df_regions['n_reads']) df_regions.bpstart=df_regions.bpstart.astype(int) df_regions.bpend=df_regions.bpend.astype(int) df_regions['n_reads_aligned_%']=df_regions['n_reads']/float(N_READS_ALIGNED)*100 df_regions['Reference_sequence']=df_regions.apply(lambda row: get_region_from_fa(row.chr_id,row.bpstart,row.bpend,uncompressed_reference),axis=1) if args.gene_annotations: info('Checking overlapping genes...') df_regions=df_regions.apply(lambda row: find_overlapping_genes(row, df_genes),axis=1) if np.sum(np.array(map(int,pd.__version__.split('.')))*(100,10,1))< 170: df_regions.sort('n_reads',ascending=False,inplace=True) else: df_regions.sort_values(by='n_reads',ascending=False,inplace=True) df_regions.fillna('NA').to_csv(_jp('REPORTS_READS_ALIGNED_TO_GENOME_NOT_MATCHING_AMPLICONS.txt'),sep='\t',index=None) if RUNNING_MODE=='ONLY_GENOME' : #Load regions and build REFERENCE TABLES info('Parsing the demultiplexed files and extracting locations and reference sequences...') coordinates=[] for region in glob.glob(os.path.join(MAPPED_REGIONS,'REGION*.fastq.gz')): coord_from_filename = os.path.basename(region).replace('.fastq.gz','').split('_')[1:4] # print('ccord from filename: ' + str(coord_from_filename)) if not (coord_from_filename[1].isdigit() and coord_from_filename[2].isdigit()): warn('Skipping region [%s] because the region name cannot be parsed\n'% region) continue coordinates.append(coord_from_filename+[region,get_n_reads_fastq(region)]) df_regions=pd.DataFrame(coordinates,columns=['chr_id','bpstart','bpend','fastq_file','n_reads']) df_regions.dropna(inplace=True) #remove regions in chrUn df_regions['bpstart'] = pd.to_numeric(df_regions['bpstart']) df_regions['bpend'] = pd.to_numeric(df_regions['bpend']) df_regions['n_reads'] = pd.to_numeric(df_regions['n_reads']) df_regions.bpstart=df_regions.bpstart.astype(int) df_regions.bpend=df_regions.bpend.astype(int) df_regions['sequence']=df_regions.apply(lambda row: get_region_from_fa(row.chr_id,row.bpstart,row.bpend,uncompressed_reference),axis=1) df_regions['n_reads_aligned_%']=df_regions['n_reads']/float(N_READS_ALIGNED)*100 if args.gene_annotations: info('Checking overlapping genes...') df_regions=df_regions.apply(lambda row: find_overlapping_genes(row, df_genes),axis=1) if np.sum(np.array(map(int,pd.__version__.split('.')))*(100,10,1))< 170: df_regions.sort('n_reads',ascending=False,inplace=True) else: df_regions.sort_values(by='n_reads',ascending=False,inplace=True) df_regions.fillna('NA').to_csv(_jp('REPORT_READS_ALIGNED_TO_GENOME_ONLY.txt'),sep='\t',index=None) #run CRISPResso #demultiplex reads in the amplicons and call crispresso! info('Running CRISPResso on the regions discovered...') crispresso_cmds = [] for idx,row in df_regions.iterrows(): if row.n_reads > args.min_reads_to_use_region: info('\nRunning CRISPResso on: %s-%d-%d...'%(row.chr_id,row.bpstart,row.bpend )) crispresso_cmd= args.crispresso_command + ' -r1 %s -a %s -o %s' %(row.fastq_file,row.sequence,OUTPUT_DIRECTORY) crispresso_cmd=CRISPRessoShared.propagate_crispresso_options(crispresso_cmd,crispresso_options_for_pooled,args) crispresso_cmds.append(crispresso_cmd) else: info('Skipping region: %s-%d-%d , not enough reads (%d)' %(row.chr_id,row.bpstart,row.bpend, row.n_reads)) CRISPRessoMultiProcessing.run_crispresso_cmds(crispresso_cmds,args.n_processes,'region',args.skip_failed) #write alignment statistics with open(_jp('MAPPING_STATISTICS.txt'),'w+') as outfile: outfile.write('READS IN INPUTS:%d\nREADS AFTER PREPROCESSING:%d\nREADS ALIGNED:%d' % (N_READS_INPUT,N_READS_AFTER_PREPROCESSING,N_READS_ALIGNED)) quantification_summary=[] if RUNNING_MODE=='ONLY_AMPLICONS' or RUNNING_MODE=='AMPLICONS_AND_GENOME': df_final_data=df_template else: df_final_data=df_regions all_region_names = [] all_region_read_counts = {} good_region_names = [] good_region_folders = {} header = 'Name\tUnmodified%\tModified%\tReads_aligned\tReads_total\tUnmodified\tModified\tDiscarded\tInsertions\tDeletions\tSubstitutions\tOnly Insertions\tOnly Deletions\tOnly Substitutions\tInsertions and Deletions\tInsertions and Substitutions\tDeletions and Substitutions\tInsertions Deletions and Substitutions' header_els = header.split("\t") header_el_count = len(header_els) empty_line_els = [np.nan]*(header_el_count-1) n_reads_index = header_els.index('Reads_total') - 1 for idx,row in df_final_data.iterrows(): run_name = idx if RUNNING_MODE=='ONLY_AMPLICONS' or RUNNING_MODE=='AMPLICONS_AND_GENOME': run_name=idx else: run_name='REGION_%s_%d_%d' %(row.chr_id,row.bpstart,row.bpend ) folder_name = 'CRISPResso_on_%s'%run_name all_region_names.append(run_name) all_region_read_counts[run_name] = row.n_reads run_file = os.path.join(_jp(folder_name),'CRISPResso2_info.pickle') if not os.path.exists(run_file): warn('Skipping the folder %s: not enough reads, incomplete, or empty folder.'% folder_name) this_els = empty_line_els[:] this_els[n_reads_index] = row.n_reads to_add = [run_name] to_add.extend(this_els) quantification_summary.append(to_add) else: run_data = cp.load(open(run_file,'rb')) ref_name = run_data['ref_names'][0] #only expect one amplicon sequence n_tot = row.n_reads n_aligned = run_data['counts_total'][ref_name] n_unmod = run_data['counts_unmodified'][ref_name] n_mod = run_data['counts_modified'][ref_name] n_discarded = run_data['counts_discarded'][ref_name] n_insertion = run_data['counts_insertion'][ref_name] n_deletion = run_data['counts_deletion'][ref_name] n_substitution = run_data['counts_substitution'][ref_name] n_only_insertion = run_data['counts_only_insertion'][ref_name] n_only_deletion = run_data['counts_only_deletion'][ref_name] n_only_substitution = run_data['counts_only_substitution'][ref_name] n_insertion_and_deletion = run_data['counts_insertion_and_deletion'][ref_name] n_insertion_and_substitution = run_data['counts_insertion_and_substitution'][ref_name] n_deletion_and_substitution = run_data['counts_deletion_and_substitution'][ref_name] n_insertion_and_deletion_and_substitution = run_data['counts_insertion_and_deletion_and_substitution'][ref_name] unmod_pct = np.nan mod_pct = np.nan if n_aligned > 0: unmod_pct = 100*n_unmod/float(n_aligned) mod_pct = 100*n_mod/float(n_aligned) vals = [run_name] vals.extend([round(unmod_pct,8),round(mod_pct,8),n_aligned,n_tot,n_unmod,n_mod,n_discarded,n_insertion,n_deletion,n_substitution,n_only_insertion,n_only_deletion,n_only_substitution,n_insertion_and_deletion,n_insertion_and_substitution,n_deletion_and_substitution,n_insertion_and_deletion_and_substitution]) quantification_summary.append(vals) good_region_names.append(run_name) good_region_folders[idx] = folder_name samples_quantification_summary_filename = _jp('SAMPLES_QUANTIFICATION_SUMMARY.txt') df_summary_quantification=pd.DataFrame(quantification_summary,columns=header_els) if args.crispresso1_mode: crispresso1_columns=['Name','Unmodified%','Modified%','Reads_aligned','Reads_total'] df_summary_quantification.fillna('NA').to_csv(samples_quantification_summary_filename,sep='\t',index=None,columns=crispresso1_columns) else: df_summary_quantification.fillna('NA').to_csv(samples_quantification_summary_filename,sep='\t',index=None) crispresso2_info['samples_quantification_summary_filename'] = os.path.basename(samples_quantification_summary_filename) crispresso2_info['final_data'] = df_final_data crispresso2_info['all_region_names'] = all_region_names crispresso2_info['all_region_read_counts'] = all_region_read_counts crispresso2_info['good_region_names'] = good_region_names crispresso2_info['good_region_folders'] = good_region_folders crispresso2_info['running_mode'] = RUNNING_MODE crispresso2_info['summary_plot_names'] = [] crispresso2_info['summary_plot_titles'] = {} crispresso2_info['summary_plot_labels'] = {} crispresso2_info['summary_plot_datas'] = {} df_summary_quantification.set_index('Name') save_png = True if args.suppress_report: save_png = False plot_root = _jp("CRISPRessoPooled_reads_summary") CRISPRessoPlot.plot_reads_total(plot_root,df_summary_quantification,save_png,args.min_reads_to_use_region) plot_name = os.path.basename(plot_root) crispresso2_info['summary_plot_root'] = plot_name crispresso2_info['summary_plot_names'].append(plot_name) crispresso2_info['summary_plot_titles'][plot_name] = 'CRISPRessoPooled Read Allocation Summary' crispresso2_info['summary_plot_labels'][plot_name] = 'Each bar shows the total number of reads allocated to each amplicon. The vertical line shows the cutoff for analysis, set using the --min_reads_to_use_region parameter.' crispresso2_info['summary_plot_datas'][plot_name] = [('CRISPRessoPooled summary',os.path.basename(samples_quantification_summary_filename))] plot_root = _jp("CRISPRessoPooled_modification_summary") CRISPRessoPlot.plot_unmod_mod_pcts(plot_root,df_summary_quantification,save_png,args.min_reads_to_use_region) plot_name = os.path.basename(plot_root) crispresso2_info['summary_plot_root'] = plot_name crispresso2_info['summary_plot_names'].append(plot_name) crispresso2_info['summary_plot_titles'][plot_name] = 'CRISPRessoPooled Modification Summary' crispresso2_info['summary_plot_labels'][plot_name] = 'Each bar shows the total number of reads aligned to each amplicon, divided into the reads that are modified and unmodified. The vertical line shows the cutoff for analysis, set using the --min_reads_to_use_region parameter.' crispresso2_info['summary_plot_datas'][plot_name] = [('CRISPRessoPooled summary',os.path.basename(samples_quantification_summary_filename))] #if many reads weren't aligned, print those out for the user if RUNNING_MODE != 'ONLY_GENOME': #N_READS_INPUT=get_n_reads_fastq(args.fastq_r1) #N_READS_AFTER_PREPROCESSING=get_n_reads_fastq(processed_output_filename) tot_reads_aligned = df_summary_quantification['Reads_aligned'].fillna(0).sum() tot_reads = df_summary_quantification['Reads_total'].sum() if RUNNING_MODE=='AMPLICONS_AND_GENOME': this_bam_filename = bam_filename_genome if RUNNING_MODE=='ONLY_AMPLICONS': this_bam_filename = bam_filename_amplicons #if less than 1/2 of reads aligned, find most common unaligned reads and advise the user if N_READS_INPUT > 0 and tot_reads/float(N_READS_INPUT) < 0.5: warn('Less than half (%d/%d) of reads aligned. Finding most frequent unaligned reads.'%(tot_reads,N_READS_INPUT)) ### ###this results in the unpretty messages being printed: ### sort: write failed: standard output: Broken pipe ### sort: write error ### #cmd = "samtools view -f 4 %s | awk '{print $10}' | sort | uniq -c | sort -nr | head -n 10"%this_bam_filename import signal def default_sigpipe(): signal.signal(signal.SIGPIPE, signal.SIG_DFL) cmd = "samtools view -f 4 %s | head -n 10000 | awk '{print $10}' | sort | uniq -c | sort -nr | head -n 10 | awk '{print $2}'"%this_bam_filename # print("command is: "+cmd) # p = sb.Popen(cmd, shell=True,stdout=sb.PIPE) p = sb.Popen(cmd, shell=True,stdout=sb.PIPE,preexec_fn=default_sigpipe) top_unaligned = p.communicate()[0] top_unaligned_filename=_jp('CRISPRessoPooled_TOP_UNALIGNED.txt') with open(top_unaligned_filename,'w') as outfile: outfile.write(top_unaligned) warn('Perhaps one or more of the given amplicon sequences were incomplete or incorrect. Below is a list of the most frequent unaligned reads (in the first 10000 unaligned reads). Check this list to see if an amplicon is among these reads.\n%s'%top_unaligned) #cleaning up if not args.keep_intermediate: info('Removing Intermediate files...') if args.fastq_r2!='': files_to_remove=[processed_output_filename,flash_hist_filename,flash_histogram_filename,\ flash_not_combined_1_filename,flash_not_combined_2_filename] else: files_to_remove=[processed_output_filename] if args.trim_sequences and args.fastq_r2!='': files_to_remove+=[output_forward_paired_filename,output_reverse_paired_filename,\ output_forward_unpaired_filename,output_reverse_unpaired_filename] if RUNNING_MODE=='ONLY_GENOME' or RUNNING_MODE=='AMPLICONS_AND_GENOME': files_to_remove+=[bam_filename_genome] if RUNNING_MODE=='ONLY_AMPLICONS': files_to_remove+=[bam_filename_amplicons,amplicon_fa_filename] for bowtie2_file in glob.glob(_jp('CUSTOM_BOWTIE2_INDEX.*')): files_to_remove.append(bowtie2_file) for file_to_remove in files_to_remove: try: if os.path.islink(file_to_remove): #print 'LINK',file_to_remove os.unlink(file_to_remove) else: os.remove(file_to_remove) except: warn('Skipping:%s' %file_to_remove) if not args.suppress_report: if (args.place_report_in_output_folder): report_name = _jp("CRISPResso2Pooled_report.html") else: report_name = OUTPUT_DIRECTORY+'.html' CRISPRessoReport.make_pooled_report_from_folder(report_name,crispresso2_info,OUTPUT_DIRECTORY,_ROOT) crispresso2_info['report_location'] = report_name crispresso2_info['report_filename'] = os.path.basename(report_name) cp.dump(crispresso2_info, open(crispresso2WGS_info_file, 'wb' ) ) info('All Done!') print CRISPRessoShared.get_crispresso_footer() sys.exit(0) except Exception as e: debug_flag = False if 'args' in vars() and 'debug' in args: debug_flag = args.debug if debug_flag: traceback.print_exc(file=sys.stdout) error('\n\nERROR: %s' % e) sys.exit(-1)
def main(): def print_stacktrace_if_debug(): debug_flag = False if 'args' in vars() and 'debug' in args: debug_flag = args.debug if debug_flag: traceback.print_exc(file=sys.stdout) error(traceback.format_exc()) try: description = [ '~~~CRISPRessoWGS~~~', '-Analysis of CRISPR/Cas9 outcomes from WGS data-' ] wgs_string = r''' ____________ | __ __ | || |/ _ (_ | ||/\|\__)__) | |____________| ''' print(CRISPRessoShared.get_crispresso_header(description, wgs_string)) parser = CRISPRessoShared.getCRISPRessoArgParser( parserTitle='CRISPRessoWGS Parameters', requiredParams={}) #tool specific optional parser.add_argument('-b', '--bam_file', type=str, help='WGS aligned bam file', required=True, default='bam filename') parser.add_argument( '-f', '--region_file', type=str, help= 'Regions description file. A BED format file containing the regions to analyze, one per line. The REQUIRED\ columns are: chr_id(chromosome name), bpstart(start position), bpend(end position), the optional columns are:name (an unique indentifier for the region), guide_seq, expected_hdr_amplicon_seq,coding_seq, see CRISPResso help for more details on these last 3 parameters)', required=True) parser.add_argument( '-r', '--reference_file', type=str, help= 'A FASTA format reference file (for example hg19.fa for the human genome)', default='', required=True) parser.add_argument( '--min_reads_to_use_region', type=float, help= 'Minimum number of reads that align to a region to perform the CRISPResso analysis', default=10) parser.add_argument( '--skip_failed', help='Continue with pooled analysis even if one sample fails', action='store_true') parser.add_argument( '--gene_annotations', type=str, help= 'Gene Annotation Table from UCSC Genome Browser Tables (http://genome.ucsc.edu/cgi-bin/hgTables?command=start), \ please select as table "knowGene", as output format "all fields from selected table" and as file returned "gzip compressed"', default='') parser.add_argument( '-p', '--n_processes', type=int, help='Specify the number of processes to use for the quantification.\ Please use with caution since increasing this parameter will increase the memory required to run CRISPResso.', default=1) parser.add_argument('--crispresso_command', help='CRISPResso command to call', default='CRISPResso') args = parser.parse_args() crispresso_options = CRISPRessoShared.get_crispresso_options() options_to_ignore = set([ 'fastq_r1', 'fastq_r2', 'amplicon_seq', 'amplicon_name', 'output_folder', 'name' ]) crispresso_options_for_wgs = list(crispresso_options - options_to_ignore) info('Checking dependencies...') if check_samtools() and check_bowtie2(): info('\n All the required dependencies are present!') else: sys.exit(1) #check files check_file(args.bam_file) check_file(args.reference_file) check_file(args.region_file) if args.gene_annotations: check_file(args.gene_annotations) #INIT get_name_from_bam = lambda x: os.path.basename(x).replace('.bam', '') if not args.name: database_id = '%s' % get_name_from_bam(args.bam_file) else: database_id = args.name OUTPUT_DIRECTORY = 'CRISPRessoWGS_on_%s' % database_id if args.output_folder: OUTPUT_DIRECTORY = os.path.join( os.path.abspath(args.output_folder), OUTPUT_DIRECTORY) _jp = lambda filename: os.path.join( OUTPUT_DIRECTORY, filename ) #handy function to put a file in the output directory try: info('Creating Folder %s' % OUTPUT_DIRECTORY) os.makedirs(OUTPUT_DIRECTORY) info('Done!') except: warn('Folder %s already exists.' % OUTPUT_DIRECTORY) log_filename = _jp('CRISPRessoWGS_RUNNING_LOG.txt') logging.getLogger().addHandler(logging.FileHandler(log_filename)) with open(log_filename, 'w+') as outfile: outfile.write( '[Command used]:\nCRISPRessoWGS %s\n\n[Execution log]:\n' % ' '.join(sys.argv)) #check if bam has the index already if os.path.exists(args.bam_file + '.bai'): info('Index file for input .bam file exists, skipping generation.') else: info('Creating index file for input .bam file...') sb.call('samtools index %s ' % (args.bam_file), shell=True) #load gene annotation if args.gene_annotations: info('Loading gene coordinates from annotation file: %s...' % args.gene_annotations) try: df_genes = pd.read_table(args.gene_annotations, compression='gzip') df_genes.txEnd = df_genes.txEnd.astype(int) df_genes.txStart = df_genes.txStart.astype(int) df_genes.head() except: info('Failed to load the gene annotations file.') #Load and validate the REGION FILE df_regions = pd.read_csv(args.region_file, names=[ 'chr_id', 'bpstart', 'bpend', 'Name', 'sgRNA', 'Expected_HDR', 'Coding_sequence' ], comment='#', sep='\t', dtype={'Name': str}) #remove empty amplicons/lines df_regions.dropna(subset=['chr_id', 'bpstart', 'bpend'], inplace=True) df_regions.Expected_HDR = df_regions.Expected_HDR.apply( capitalize_sequence) df_regions.sgRNA = df_regions.sgRNA.apply(capitalize_sequence) df_regions.Coding_sequence = df_regions.Coding_sequence.apply( capitalize_sequence) #check or create names for idx, row in df_regions.iterrows(): if pd.isnull(row.Name): df_regions.ix[idx, 'Name'] = '_'.join( map(str, [row['chr_id'], row['bpstart'], row['bpend']])) if not len(df_regions.Name.unique()) == df_regions.shape[0]: raise Exception('The amplicon names should be all distinct!') df_regions = df_regions.set_index('Name') #df_regions.index=df_regions.index.str.replace(' ','_') df_regions.index = df_regions.index.to_series().str.replace(' ', '_') #extract sequence for each region uncompressed_reference = args.reference_file if os.path.exists(uncompressed_reference + '.fai'): info( 'The index for the reference fasta file is already present! Skipping generation.' ) else: info('Indexing reference file... Please be patient!') sb.call('samtools faidx %s >>%s 2>&1' % (uncompressed_reference, log_filename), shell=True) df_regions['sequence'] = df_regions.apply( lambda row: get_region_from_fa(row.chr_id, row.bpstart, row.bpend, uncompressed_reference), axis=1) for idx, row in df_regions.iterrows(): if not pd.isnull(row.sgRNA): cut_points = [] for current_guide_seq in row.sgRNA.strip().upper().split(','): wrong_nt = find_wrong_nt(current_guide_seq) if wrong_nt: raise NTException( 'The sgRNA sequence %s contains wrong characters:%s' % (current_guide_seq, ' '.join(wrong_nt))) offset_fw = args.quantification_window_center + len( current_guide_seq) - 1 offset_rc = (-args.quantification_window_center) - 1 cut_points+=[m.start() + offset_fw for \ m in re.finditer(current_guide_seq, row.sequence)]+[m.start() + offset_rc for m in re.finditer(CRISPRessoShared.reverse_complement(current_guide_seq), row.sequence)] if not cut_points: df_regions.ix[idx, 'sgRNA'] = '' df_regions = df_regions.convert_objects(convert_numeric=True) df_regions.bpstart = df_regions.bpstart.astype(int) df_regions.bpend = df_regions.bpend.astype(int) if args.gene_annotations: df_regions = df_regions.apply( lambda row: find_overlapping_genes(row, df_genes), axis=1) #extract reads with samtools in that region and create a bam #create a fasta file with all the trimmed reads info('\nProcessing each regions...') ANALYZED_REGIONS = _jp('ANALYZED_REGIONS/') if not os.path.exists(ANALYZED_REGIONS): os.mkdir(ANALYZED_REGIONS) df_regions['n_reads'] = 0 df_regions['bam_file_with_reads_in_region'] = '' df_regions['fastq.gz_file_trimmed_reads_in_region'] = '' for idx, row in df_regions.iterrows(): if row['sequence']: fastq_gz_filename = os.path.join( ANALYZED_REGIONS, '%s.fastq.gz' % clean_filename('REGION_' + str(idx))) bam_region_filename = os.path.join( ANALYZED_REGIONS, '%s.bam' % clean_filename('REGION_' + str(idx))) #create place-holder fastq files open(fastq_gz_filename, 'w+').close() region = '%s:%d-%d' % (row.chr_id, row.bpstart, row.bpend - 1) info('\nExtracting reads in:%s and create the .bam file: %s' % (region, bam_region_filename)) #extract reads in region cmd = r'''samtools view -b -F 4 %s %s > %s ''' % ( args.bam_file, region, bam_region_filename) #print cmd sb.call(cmd, shell=True) #index bam file cmd = r'''samtools index %s ''' % (bam_region_filename) #print cmd sb.call(cmd, shell=True) info('Trim reads and create a fastq.gz file in: %s' % fastq_gz_filename) #trim reads in bam and convert in fastq n_reads = write_trimmed_fastq(bam_region_filename, row['bpstart'], row['bpend'], fastq_gz_filename) df_regions.ix[idx, 'n_reads'] = n_reads df_regions.ix[ idx, 'bam_file_with_reads_in_region'] = bam_region_filename df_regions.ix[ idx, 'fastq.gz_file_trimmed_reads_in_region'] = fastq_gz_filename df_regions.fillna('NA').to_csv( _jp('REPORT_READS_ALIGNED_TO_SELECTED_REGIONS_WGS.txt'), sep='\t') #Run Crispresso info('\nRunning CRISPResso on each regions...') crispresso_cmds = [] for idx, row in df_regions.iterrows(): if row['n_reads'] >= args.min_reads_to_use_region: info('\nThe region [%s] has enough reads (%d) mapped to it!' % (idx, row['n_reads'])) crispresso_cmd= args.crispresso_command + ' -r1 %s -a %s -o %s --name %s' %\ (row['fastq.gz_file_trimmed_reads_in_region'],row['sequence'],OUTPUT_DIRECTORY,idx) if row['sgRNA'] and not pd.isnull(row['sgRNA']): crispresso_cmd += ' -g %s' % row['sgRNA'] if row['Expected_HDR'] and not pd.isnull(row['Expected_HDR']): crispresso_cmd += ' -e %s' % row['Expected_HDR'] if row['Coding_sequence'] and not pd.isnull( row['Coding_sequence']): crispresso_cmd += ' -c %s' % row['Coding_sequence'] crispresso_cmd = CRISPRessoShared.propagate_crispresso_options( crispresso_cmd, crispresso_options_for_wgs, args) crispresso_cmds.append(crispresso_cmd) # info('Running CRISPResso:%s' % crispresso_cmd) # sb.call(crispresso_cmd,shell=True) else: info( '\nThe region [%s] has too few reads mapped to it (%d)! Skipping the running of CRISPResso!' % (idx, row['n_reads'])) CRISPRessoMultiProcessing.run_crispresso_cmds(crispresso_cmds, args.n_processes, 'region', args.skip_failed) quantification_summary = [] for idx, row in df_regions.iterrows(): folder_name = 'CRISPResso_on_%s' % idx try: quantification_file, amplicon_names, amplicon_info = CRISPRessoShared.check_output_folder( _jp(folder_name)) for amplicon_name in amplicon_names: N_TOTAL = float(amplicon_info[amplicon_name]['Total']) N_UNMODIFIED = float( amplicon_info[amplicon_name]['Unmodified']) N_MODIFIED = float( amplicon_info[amplicon_name]['Modified']) quantification_summary.append([ idx, amplicon_name, N_UNMODIFIED / N_TOTAL * 100, N_MODIFIED / N_TOTAL * 100, N_TOTAL, row.n_reads ]) except CRISPRessoShared.OutputFolderIncompleteException as e: quantification_summary.append( [idx, "", np.nan, np.nan, np.nan, row.n_reads]) warn( 'Skipping the folder %s: not enough reads, incomplete, or empty folder.' % folder_name) df_summary_quantification = pd.DataFrame(quantification_summary, columns=[ 'Name', 'Amplicon', 'Unmodified%', 'Modified%', 'Reads_aligned', 'Reads_total' ]) df_summary_quantification.fillna('NA').to_csv( _jp('SAMPLES_QUANTIFICATION_SUMMARY.txt'), sep='\t', index=None) info('All Done!') print(CRISPRessoShared.get_crispresso_footer()) sys.exit(0) except Exception as e: print_stacktrace_if_debug() error('\n\nERROR: %s' % e) sys.exit(-1)
def main(): try: description = [ '~~~CRISPRessoPooledWGSCompare~~~', '-Comparison of two CRISPRessoPooled or CRISPRessoWGS analyses-', ] compare_header = r''' ____________________________________ | __ __ __ __ __ __ __ | ||__)/ \/ \| |_ | \ /| |/ _ (_ | || \__/\__/|__|__|__// |/\|\__)__) | | __ __ __ __ __ | | / / \|\/||__) /\ |__)|_ | | \__\__/| || /--\| \ |__ | |____________________________________| ''' compare_header = CRISPRessoShared.get_crispresso_header( description, compare_header, ) print(compare_header) parser = argparse.ArgumentParser( description='CRISPRessoPooledWGSCompare Parameters', formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) parser.add_argument( 'crispresso_pooled_wgs_output_folder_1', type=str, help= 'First output folder with CRISPRessoPooled or CRISPRessoWGS analysis', ) parser.add_argument( 'crispresso_pooled_wgs_output_folder_2', type=str, help= 'Second output folder with CRISPRessoPooled or CRISPRessoWGS analysis', ) #OPTIONALS parser.add_argument('-n', '--name', help='Output name', default='') parser.add_argument( '-n1', '--sample_1_name', help='Sample 1 name', default='Sample_1', ) parser.add_argument( '-n2', '--sample_2_name', help='Sample 2 name', default='Sample_2', ) parser.add_argument('-o', '--output_folder', help='', default='') parser.add_argument( '-p', '--n_processes', type=str, help=""" Specify the number of processes to use for analysis. Please use with caution since increasing this parameter will significantly increase the memory required to run CRISPResso. Can be set to 'max'. """, default='1', ) parser.add_argument( '--min_frequency_alleles_around_cut_to_plot', type=float, help= 'Minimum %% reads required to report an allele in the alleles table plot.', default=0.2, ) parser.add_argument( '--max_rows_alleles_around_cut_to_plot', type=int, help='Maximum number of rows to report in the alleles table plot. ', default=50, ) parser.add_argument( '--place_report_in_output_folder', help= 'If true, report will be written inside the CRISPResso output folder. By default, the report will be written one directory up from the report output.', action='store_true', ) parser.add_argument( '--suppress_report', help='Suppress output report', action='store_true', ) parser.add_argument( '--debug', help='Show debug messages', action='store_true', ) args = parser.parse_args() debug_flag = args.debug crispresso_compare_options = [ 'min_frequency_alleles_around_cut_to_plot', 'max_rows_alleles_around_cut_to_plot', 'place_report_in_output_folder', 'suppress_report', 'debug', ] sample_1_name = CRISPRessoShared.slugify(args.sample_1_name) sample_2_name = CRISPRessoShared.slugify(args.sample_2_name) n_processes = 1 if args.n_processes == 'max': n_processes = CRISPRessoMultiProcessing.get_max_processes() else: n_processes = int(args.n_processes) # check that the CRISPRessoPooled output is present quantification_summary_file_1 = check_PooledWGS_output_folder( args.crispresso_pooled_wgs_output_folder_1, ) quantification_summary_file_2 = check_PooledWGS_output_folder( args.crispresso_pooled_wgs_output_folder_2, ) # create outputfolder and initialize the log get_name_from_folder = lambda x: os.path.basename(os.path.abspath( x)).replace('CRISPRessoPooled_on_', '').replace( 'CRISPRessoWGS_on_', '') if not args.name: database_id = '{0}_VS_{1}'.format( get_name_from_folder( args.crispresso_pooled_wgs_output_folder_1, ), get_name_from_folder( args.crispresso_pooled_wgs_output_folder_2, ), ) else: database_id = CRISPRessoShared.slugify(args.name) OUTPUT_DIRECTORY = 'CRISPRessoPooledWGSCompare_on_{0}'.format( database_id) if args.output_folder: OUTPUT_DIRECTORY = os.path.join( os.path.abspath(args.output_folder), OUTPUT_DIRECTORY, ) _jp = lambda filename: os.path.join( OUTPUT_DIRECTORY, filename ) #handy function to put a file in the output directory log_filename = _jp('CRISPRessoPooledWGSCompare_RUNNING_LOG.txt') try: info('Creating Folder %s' % OUTPUT_DIRECTORY) os.makedirs(OUTPUT_DIRECTORY) info('Done!') except: warn('Folder %s already exists.' % OUTPUT_DIRECTORY) log_filename = _jp('CRISPRessoPooledWGSCompare_RUNNING_LOG.txt') logger.addHandler(logging.FileHandler(log_filename)) with open(log_filename, 'w+') as outfile: outfile.write( '[Command used]:\nCRISPRessoPooledWGSCompare {0}\n\n[Execution log]:\n' .format(' '.join(sys.argv), ), ) crispresso2Compare_info_file = os.path.join( OUTPUT_DIRECTORY, 'CRISPResso2PooledWGSCompare_info.pickle') crispresso2_info = { 'running_info': {}, 'results': { 'alignment_stats': {}, 'general_plots': {} } } #keep track of all information for this run to be saved at the end of the run crispresso2_info['running_info'][ 'version'] = CRISPRessoShared.__version__ crispresso2_info['running_info']['args'] = deepcopy(args) crispresso2_info['running_info']['log_filename'] = os.path.basename( log_filename) crispresso2_info['results']['general_plots']['summary_plot_names'] = [] crispresso2_info['results']['general_plots'][ 'summary_plot_titles'] = {} crispresso2_info['results']['general_plots'][ 'summary_plot_labels'] = {} crispresso2_info['results']['general_plots']['summary_plot_datas'] = {} save_png = True if args.suppress_report: save_png = False # load data and calculate the difference df_quant_1 = pd.read_csv(quantification_summary_file_1, sep='\t') df_quant_2 = pd.read_csv(quantification_summary_file_2, sep='\t') # df_comp=df_quant_1.set_index(['Name','Amplicon']).join(df_quant_2.set_index(['Name','Amplicon']),lsuffix='_%s' % args.sample_1_name,rsuffix='_%s' % args.sample_2_name) df_comp = df_quant_1.set_index('Name').join( df_quant_2.set_index('Name'), lsuffix='_{0}'.format(sample_1_name), rsuffix='_{0}'.format(sample_2_name), ) df_comp['({0}-{1})_Unmodified%%'.format( sample_1_name, args.sample_2_name)] = df_comp['Unmodified%%_{0}'.format( sample_1_name)] - df_comp['Unmodified%%_{0}'.format( args.sample_2_name)] df_comp.fillna('NA').to_csv( _jp('COMPARISON_SAMPLES_QUANTIFICATION_SUMMARIES.txt'), sep='\t') # now run CRISPRessoCompare for the pairs for wich we have data in both folders crispresso_cmds = [] processed_regions = [] processed_region_folder_names = {} processed_region_html_files = {} for idx, row in df_comp.iterrows(): if idx in processed_regions: continue if row.isnull().any(): warn( 'Skipping sample {0} since it was not processed in one or both conditions' .format(idx)) else: processed_regions.add(idx) crispresso_output_folder_1 = os.path.join( args.crispresso_pooled_wgs_output_folder_1, 'CRISPResso_on_{0}'.format(idx), ) crispresso_output_folder_2 = os.path.join( args.crispresso_pooled_wgs_output_folder_2, 'CRISPResso_on_{0}'.format(idx), ) compare_output_name = '{0}_{1}_VS_{2}'.format( idx, sample_1_name, sample_2_name, ) crispresso_compare_cmd = CRISPResso_compare_to_call + \ ' "{0}" "{1}" -o "{2}" -n {3} -n1 "{4}" -n2 "{5}" '.format( crispresso_output_folder_1, crispresso_output_folder_2, OUTPUT_DIRECTORY, compare_output_name, args.sample_1_name + '_' + idx, args.sample_2_name + '_' + idx, ) crispresso_compare_cmd = CRISPRessoShared.propagate_crispresso_options( crispresso_compare_cmd, crispresso_compare_options, args, ) info('Running CRISPRessoCompare:%s' % crispresso_compare_cmd) crispresso_cmds.append(crispresso_compare_cmd) sub_folder = os.path.join( OUTPUT_DIRECTORY, 'CRISPRessoCompare_on_' + compare_output_name, ) this_sub_html_file = os.path.basename(sub_folder) + ".html" if args.place_report_in_output_folder: this_sub_html_file = os.path.join( os.path.basename(sub_folder), "CRISPResso2Compare_report.html", ) processed_region_html_files[idx] = this_sub_html_file processed_region_folder_names[idx] = compare_output_name CRISPRessoMultiProcessing.run_crispresso_cmds( crispresso_cmds, n_processes, 'Comparison', ) crispresso2_info['results']['processed_regions'] = processed_regions crispresso2_info['results'][ 'processed_region_folder_names'] = processed_region_folder_names if not args.suppress_report: if args.place_report_in_output_folder: report_name = _jp("CRISPResso2PooledWGSCompare_report.html") else: report_name = OUTPUT_DIRECTORY + '.html' CRISPRessoReport.make_multi_report( processed_regions, processed_region_html_files, report_name, OUTPUT_DIRECTORY, _ROOT, 'CRISPREssoPooledWGSCompare Report<br>{0} vs {1}'.format( sample_1_name, sample_2_name, ), ) crispresso2_info['running_info']['report_location'] = report_name crispresso2_info['running_info'][ 'report_filename'] = os.path.basename(report_name) CRISPRessoShared.write_crispresso_info( crispresso2Compare_info_file, crispresso2_info, ) info('All Done!') print(CRISPRessoShared.get_crispresso_footer()) sys.exit(0) except Exception as e: debug_flag = False if 'args' in vars() and 'debug' in args: debug_flag = args.debug if debug_flag: traceback.print_exc(file=sys.stdout) error('\n\nERROR: %s' % e) sys.exit(-1)