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def index ( in_cram , config ) : out_file = in_cram + ".crai" if not utils . file_uptodate ( out_file , in_cram ) : with file_transaction ( config , in_cram + ".crai" ) as tx_out_file : tx_in_file = os . path . splitext ( tx_out_file ) [ 0 ] utils . symlink_plus ( in_cram , tx_in_file ) cmd = "samtools index {tx_in_fil...
Ensure CRAM file has a . crai index file .
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def to_bam ( in_file , out_file , data ) : if not utils . file_uptodate ( out_file , in_file ) : with file_transaction ( data , out_file ) as tx_out_file : cmd = [ "samtools" , "view" , "-O" , "BAM" , "-o" , tx_out_file , in_file ] do . run ( cmd , "Convert CRAM to BAM" ) bam . index ( out_file , data [ "config" ] ) re...
Convert CRAM file into BAM .
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def start ( parallel , items , config , dirs = None , name = None , multiplier = 1 , max_multicore = None ) : if name : checkpoint_dir = utils . safe_makedir ( os . path . join ( dirs [ "work" ] , "checkpoints_parallel" ) ) checkpoint_file = os . path . join ( checkpoint_dir , "%s.done" % name ) else : checkpoint_file ...
Start a parallel cluster or machines to be used for running remote functions .
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def recalibrate_quality ( bam_file , sam_ref , dbsnp_file , picard_dir ) : cl = [ "picard_gatk_recalibrate.py" , picard_dir , sam_ref , bam_file ] if dbsnp_file : cl . append ( dbsnp_file ) subprocess . check_call ( cl ) out_file = glob . glob ( "%s*gatkrecal.bam" % os . path . splitext ( bam_file ) [ 0 ] ) [ 0 ] retur...
Recalibrate alignments with GATK and provide pdf summary .
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def run_genotyper ( bam_file , ref_file , dbsnp_file , config_file ) : cl = [ "gatk_genotyper.py" , config_file , ref_file , bam_file ] if dbsnp_file : cl . append ( dbsnp_file ) subprocess . check_call ( cl )
Perform SNP genotyping and analysis using GATK .
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def _do_run ( paired ) : work_dir = _sv_workdir ( paired . tumor_data ) out = _get_battenberg_out ( paired , work_dir ) ignore_file = os . path . join ( work_dir , "ignore_chromosomes.txt" ) if len ( _missing_files ( out ) ) > 0 : ref_file = dd . get_ref_file ( paired . tumor_data ) bat_datadir = os . path . normpath (...
Perform Battenberg caling with the paired dataset .
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def _make_ignore_file ( work_dir , ref_file , ignore_file , impute_file ) : gl_file = os . path . join ( work_dir , "gender_loci.txt" ) chroms = set ( [ ] ) with open ( impute_file ) as in_handle : for line in in_handle : chrom = line . split ( ) [ 0 ] chroms . add ( chrom ) if not chrom . startswith ( "chr" ) : chroms...
Create input files with chromosomes to ignore and gender loci .
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def copy_flowcell ( dname , fastq_dir , sample_cfile , config ) : with utils . chdir ( dname ) : reports = reduce ( operator . add , [ glob . glob ( "*.xml" ) , glob . glob ( "Data/Intensities/BaseCalls/*.xml" ) , glob . glob ( "Data/Intensities/BaseCalls/*.xsl" ) , glob . glob ( "Data/Intensities/BaseCalls/*.htm" ) , ...
Copy required files for processing using rsync potentially to a remote server .
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def _umis_cmd ( data ) : return "%s %s %s" % ( utils . locale_export ( ) , utils . get_program_python ( "umis" ) , config_utils . get_program ( "umis" , data [ "config" ] , default = "umis" ) )
Return umis command line argument with correct python and locale .
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def demultiplex_samples ( data ) : work_dir = os . path . join ( dd . get_work_dir ( data ) , "umis" ) sample_dir = os . path . join ( work_dir , dd . get_sample_name ( data ) ) demulti_dir = os . path . join ( sample_dir , "demultiplexed" ) files = data [ "files" ] if len ( files ) == 2 : logger . error ( "Sample demu...
demultiplex a fastqtransformed FASTQ file into separate sample barcode files
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def split_demultiplexed_sampledata ( data , demultiplexed ) : datadicts = [ ] samplename = dd . get_sample_name ( data ) for fastq in demultiplexed : barcode = os . path . basename ( fastq ) . split ( "." ) [ 0 ] datadict = copy . deepcopy ( data ) datadict = dd . set_sample_name ( datadict , samplename + "-" + barcode...
splits demultiplexed samples into separate entries in the global sample datadict
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def is_transformed ( fastq ) : with open_fastq ( fastq ) as in_handle : for line in islice ( in_handle , 400 ) : if "UMI_" in line : return True return False
check the first 100 reads to see if a FASTQ file has already been transformed by umis
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def read ( self , filename , rowprefix = None , colprefix = None , delim = ":" ) : self . matrix = scipy . io . mmread ( filename ) with open ( filename + ".rownames" ) as in_handle : self . rownames = [ x . strip ( ) for x in in_handle ] if rowprefix : self . rownames = [ rowprefix + delim + x for x in self . rownames...
read a sparse matrix loading row and column name files . if specified will add a prefix to the row or column names
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def write ( self , filename ) : if file_exists ( filename ) : return filename out_files = [ filename , filename + ".rownames" , filename + ".colnames" ] with file_transaction ( out_files ) as tx_out_files : with open ( tx_out_files [ 0 ] , "wb" ) as out_handle : scipy . io . mmwrite ( out_handle , scipy . sparse . csr_...
read a sparse matrix loading row and column name files
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def sample_annotation ( data ) : names = data [ "rgnames" ] [ 'sample' ] tools = dd . get_expression_caller ( data ) work_dir = os . path . join ( dd . get_work_dir ( data ) , "mirbase" ) out_dir = os . path . join ( work_dir , names ) utils . safe_makedir ( out_dir ) out_file = op . join ( out_dir , names ) if dd . ge...
Annotate miRNAs using miRBase database with seqbuster tool
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def _prepare_file ( fn , out_dir ) : atropos = _get_atropos ( ) cmd = "{atropos} trim --max-reads 500000 -u 22 -se {fn} -o {tx_file}" out_file = os . path . join ( out_dir , append_stem ( os . path . basename ( fn ) , "end" ) ) if file_exists ( out_file ) : return out_file with file_transaction ( out_file ) as tx_file ...
Cut the beginning of the reads to avoid detection of miRNAs
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def _collapse ( in_file ) : seqcluster = op . join ( utils . get_bcbio_bin ( ) , "seqcluster" ) out_file = "%s.fastq" % utils . splitext_plus ( append_stem ( in_file , "_trimmed" ) ) [ 0 ] out_dir = os . path . dirname ( in_file ) if file_exists ( out_file ) : return out_file cmd = ( "{seqcluster} collapse -o {out_dir}...
Collpase reads into unique sequences with seqcluster
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def _summary ( in_file ) : data = Counter ( ) out_file = in_file + "_size_stats" if file_exists ( out_file ) : return out_file with open ( in_file ) as in_handle : for line in in_handle : counts = int ( line . strip ( ) . split ( "_x" ) [ 1 ] ) line = next ( in_handle ) l = len ( line . strip ( ) ) next ( in_handle ) n...
Calculate size distribution after adapter removal
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def _mirtop ( input_fn , sps , db , out_dir , config ) : hairpin = os . path . join ( db , "hairpin.fa" ) gtf = os . path . join ( db , "mirbase.gff3" ) if not file_exists ( hairpin ) or not file_exists ( gtf ) : logger . warning ( "%s or %s are not installed. Skipping." % ( hairpin , gtf ) ) return None out_gtf_fn = "...
Convert to GFF3 standard format
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def _trna_annotation ( data ) : trna_ref = op . join ( dd . get_srna_trna_file ( data ) ) name = dd . get_sample_name ( data ) work_dir = utils . safe_makedir ( os . path . join ( dd . get_work_dir ( data ) , "trna" , name ) ) in_file = op . basename ( data [ "clean_fastq" ] ) tdrmapper = os . path . join ( os . path ....
use tDRmapper to quantify tRNAs
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def _mint_trna_annotation ( data ) : trna_lookup = op . join ( dd . get_srna_mint_lookup ( data ) ) trna_space = op . join ( dd . get_srna_mint_space ( data ) ) trna_other = op . join ( dd . get_srna_mint_other ( data ) ) name = dd . get_sample_name ( data ) work_dir = utils . safe_makedir ( os . path . join ( dd . get...
use MINTmap to quantify tRNAs
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def sequence_length ( fasta ) : sequences = SeqIO . parse ( fasta , "fasta" ) records = { record . id : len ( record ) for record in sequences } return records
return a dict of the lengths of sequences in a fasta file
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def sequence_names ( fasta ) : sequences = SeqIO . parse ( fasta , "fasta" ) records = [ record . id for record in sequences ] return records
return a list of the sequence IDs in a FASTA file
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def _prepare_inputs ( ma_fn , bam_file , out_dir ) : fixed_fa = os . path . join ( out_dir , "file_reads.fa" ) count_name = dict ( ) with file_transaction ( fixed_fa ) as out_tx : with open ( out_tx , 'w' ) as out_handle : with open ( ma_fn ) as in_handle : h = next ( in_handle ) for line in in_handle : cols = line . s...
Convert to fastq with counts
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def _parse_novel ( csv_file , sps = "new" ) : read = 0 seen = set ( ) safe_makedir ( "novel" ) with open ( "novel/hairpin.fa" , "w" ) as fa_handle , open ( "novel/miRNA.str" , "w" ) as str_handle : with open ( csv_file ) as in_handle : for line in in_handle : if line . startswith ( "mature miRBase miRNAs detected by mi...
Create input of novel miRNAs from miRDeep2
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def _run_smoove ( full_bams , sr_bams , disc_bams , work_dir , items ) : batch = sshared . get_cur_batch ( items ) ext = "-%s-svs" % batch if batch else "-svs" name = "%s%s" % ( dd . get_sample_name ( items [ 0 ] ) , ext ) out_file = os . path . join ( work_dir , "%s-smoove.genotyped.vcf.gz" % name ) sv_exclude_bed = s...
Run lumpy - sv using smoove .
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def _filter_by_support ( in_file , data ) : rc_filter = ( "FORMAT/SU < 4 || " "(FORMAT/SR == 0 && FORMAT/SU < 15 && ABS(SVLEN)>50000) || " "(FORMAT/SR == 0 && FORMAT/SU < 5 && ABS(SVLEN)<2000) || " "(FORMAT/SR == 0 && FORMAT/SU < 15 && ABS(SVLEN)<300)" ) return vfilter . cutoff_w_expression ( in_file , rc_filter , data...
Filter call file based on supporting evidence adding FILTER annotations to VCF .
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def _filter_by_background ( base_name , back_samples , gt_vcfs , data ) : filtname = "InBackground" filtdoc = "Variant also present in background samples with same genotype" orig_vcf = gt_vcfs [ base_name ] out_file = "%s-backfilter.vcf" % ( utils . splitext_plus ( orig_vcf ) [ 0 ] ) if not utils . file_exists ( out_fi...
Filter base samples marking any also present in the background .
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def _genotype_in_background ( rec , base_name , back_samples ) : def passes ( rec ) : return not rec . FILTER or len ( rec . FILTER ) == 0 return ( passes ( rec ) and any ( rec . genotype ( base_name ) . gt_alleles == rec . genotype ( back_name ) . gt_alleles for back_name in back_samples ) )
Check if the genotype in the record of interest is present in the background records .
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def run ( items ) : paired = vcfutils . get_paired ( items ) work_dir = _sv_workdir ( paired . tumor_data if paired and paired . tumor_data else items [ 0 ] ) previous_evidence = { } full_bams , sr_bams , disc_bams = [ ] , [ ] , [ ] for data in items : full_bams . append ( dd . get_align_bam ( data ) ) sr_bam , disc_ba...
Perform detection of structural variations with lumpy .
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def _bedpes_from_cnv_caller ( data , work_dir ) : supported = set ( [ "cnvkit" ] ) cns_file = None for sv in data . get ( "sv" , [ ] ) : if sv [ "variantcaller" ] in supported and "cns" in sv and "lumpy_usecnv" in dd . get_tools_on ( data ) : cns_file = sv [ "cns" ] break if not cns_file : return None , None else : out...
Retrieve BEDPEs deletion and duplications from CNV callers .
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def _prepend ( original , to_prepend ) : if to_prepend is None : return original sep = os . pathsep def split_path_value ( path_value ) : return [ ] if path_value == '' else path_value . split ( sep ) seen = set ( ) components = [ ] for path in split_path_value ( to_prepend ) + split_path_value ( original ) : if path n...
Prepend paths in a string representing a list of paths to another .
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def remove_bcbiopath ( ) : to_remove = os . environ . get ( "BCBIOPATH" , utils . get_bcbio_bin ( ) ) + ":" if os . environ [ "PATH" ] . startswith ( to_remove ) : os . environ [ "PATH" ] = os . environ [ "PATH" ] [ len ( to_remove ) : ]
Remove bcbio internal path from first element in PATH .
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def calculate_sv_bins ( * items ) : calcfns = { "cnvkit" : _calculate_sv_bins_cnvkit , "gatk-cnv" : _calculate_sv_bins_gatk } from bcbio . structural import cnvkit items = [ utils . to_single_data ( x ) for x in cwlutils . handle_combined_input ( items ) ] if all ( not cnvkit . use_general_sv_bins ( x ) for x in items ...
Determine bin sizes and regions to use for samples .
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def _calculate_sv_bins_gatk ( data , cnv_group , size_calc_fn ) : if dd . get_background_cnv_reference ( data , "gatk-cnv" ) : target_bed = gatkcnv . pon_to_bed ( dd . get_background_cnv_reference ( data , "gatk-cnv" ) , cnv_group . work_dir , data ) else : target_bed = gatkcnv . prepare_intervals ( data , cnv_group . ...
Calculate structural variant bins using GATK4 CNV callers region or even intervals approach .
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def calculate_sv_coverage ( data ) : calcfns = { "cnvkit" : _calculate_sv_coverage_cnvkit , "gatk-cnv" : _calculate_sv_coverage_gatk } from bcbio . structural import cnvkit data = utils . to_single_data ( data ) if not cnvkit . use_general_sv_bins ( data ) : out_target_file , out_anti_file = ( None , None ) else : work...
Calculate coverage within bins for downstream CNV calling .
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def _calculate_sv_coverage_gatk ( data , work_dir ) : from bcbio . variation import coverage from bcbio . structural import annotate target_file = gatkcnv . collect_read_counts ( data , work_dir ) target_in = bedutils . clean_file ( tz . get_in ( [ "regions" , "bins" , "target" ] , data ) , data , bedprep_dir = work_di...
Calculate coverage in defined regions using GATK tools
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def _calculate_sv_coverage_cnvkit ( data , work_dir ) : from bcbio . variation import coverage from bcbio . structural import annotate out_target_file = os . path . join ( work_dir , "%s-target-coverage.cnn" % dd . get_sample_name ( data ) ) out_anti_file = os . path . join ( work_dir , "%s-antitarget-coverage.cnn" % d...
Calculate coverage in an CNVkit ready format using mosdepth .
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def normalize_sv_coverage ( * items ) : calcfns = { "cnvkit" : _normalize_sv_coverage_cnvkit , "gatk-cnv" : _normalize_sv_coverage_gatk } from bcbio . structural import cnvkit from bcbio . structural import shared as sshared items = [ utils . to_single_data ( x ) for x in cwlutils . handle_combined_input ( items ) ] if...
Normalize CNV coverage providing flexible point for multiple methods .
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def _normalize_sv_coverage_gatk ( group_id , inputs , backgrounds , work_dir , back_files , out_files ) : input_backs = set ( filter ( lambda x : x is not None , [ dd . get_background_cnv_reference ( d , "gatk-cnv" ) for d in inputs ] ) ) if input_backs : assert len ( input_backs ) == 1 , "Multiple backgrounds in group...
Normalize CNV coverage using panel of normals with GATK s de - noise approaches .
29,540
def _normalize_sv_coverage_cnvkit ( group_id , inputs , backgrounds , work_dir , back_files , out_files ) : from bcbio . structural import cnvkit cnns = reduce ( operator . add , [ [ tz . get_in ( [ "depth" , "bins" , "target" ] , x ) , tz . get_in ( [ "depth" , "bins" , "antitarget" ] , x ) ] for x in backgrounds ] , ...
Normalize CNV coverage depths by GC repeats and background using CNVkit
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def get_base_cnv_regions ( data , work_dir , genome_default = "transcripts1e4" , include_gene_names = True ) : cov_interval = dd . get_coverage_interval ( data ) base_regions = get_sv_bed ( data , include_gene_names = include_gene_names ) if not base_regions : if cov_interval == "genome" : base_regions = get_sv_bed ( d...
Retrieve set of target regions for CNV analysis .
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def remove_exclude_regions ( orig_bed , base_file , items , remove_entire_feature = False ) : from bcbio . structural import shared as sshared out_bed = os . path . join ( "%s-noexclude.bed" % ( utils . splitext_plus ( base_file ) [ 0 ] ) ) if not utils . file_uptodate ( out_bed , orig_bed ) : exclude_bed = sshared . p...
Remove centromere and short end regions from an existing BED file of regions to target .
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def get_sv_bed ( data , method = None , out_dir = None , include_gene_names = True ) : if method is None : method = ( tz . get_in ( [ "config" , "algorithm" , "sv_regions" ] , data ) or dd . get_variant_regions ( data ) or dd . get_sample_callable ( data ) ) gene_file = dd . get_gene_bed ( data ) if method and os . pat...
Retrieve a BED file of regions for SV and heterogeneity calling using the provided method .
29,544
def _group_coords ( rs ) : max_intron_size = 1e5 coords = [ ] for r in rs : coords . append ( r . start ) coords . append ( r . end ) coord_groups = [ ] cur_group = [ ] for coord in sorted ( coords ) : if not cur_group or coord - cur_group [ - 1 ] < max_intron_size : cur_group . append ( coord ) else : coord_groups . a...
Organize coordinate regions into groups for each transcript .
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def _calc_sizes ( self , cnv_file , items ) : bp_per_bin = 100000 range_map = { "target" : ( 100 , 250 ) , "antitarget" : ( 10000 , 1000000 ) } target_bps = [ ] anti_bps = [ ] checked_beds = set ( [ ] ) for data in items : region_bed = tz . get_in ( [ "depth" , "variant_regions" , "regions" ] , data ) if region_bed and...
Retrieve target and antitarget bin sizes based on depth .
29,546
def phmmer ( ** kwargs ) : logging . debug ( kwargs ) args = { 'seq' : kwargs . get ( 'seq' ) , 'seqdb' : kwargs . get ( 'seqdb' ) } args2 = { 'output' : kwargs . get ( 'output' , 'json' ) , 'range' : kwargs . get ( 'range' ) } return _hmmer ( "http://hmmer.janelia.org/search/phmmer" , args , args2 )
Search a protein sequence against a HMMER sequence database .
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def scrnaseq_concatenate_metadata ( samples ) : barcodes = { } counts = "" metadata = { } has_sample_barcodes = False for sample in dd . sample_data_iterator ( samples ) : if dd . get_sample_barcodes ( sample ) : has_sample_barcodes = True with open ( dd . get_sample_barcodes ( sample ) ) as inh : for line in inh : col...
Create file same dimension than mtx . colnames with metadata and sample name to help in the creation of the SC object .
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def rnaseq_variant_calling ( samples , run_parallel ) : samples = run_parallel ( "run_rnaseq_variant_calling" , samples ) variantcaller = dd . get_variantcaller ( to_single_data ( samples [ 0 ] ) ) if variantcaller and ( "gatk-haplotype" in variantcaller ) : out = [ ] for d in joint . square_off ( samples , run_paralle...
run RNA - seq variant calling using GATK
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def run_rnaseq_variant_calling ( data ) : variantcaller = dd . get_variantcaller ( data ) if isinstance ( variantcaller , list ) and len ( variantcaller ) > 1 : logger . error ( "Only one variantcaller can be run for RNA-seq at " "this time. Post an issue here " "(https://github.com/bcbio/bcbio-nextgen/issues) " "if th...
run RNA - seq variant calling variation file is stored in vrn_file in the datadict
29,550
def run_rnaseq_ann_filter ( data ) : data = to_single_data ( data ) if dd . get_vrn_file ( data ) : eff_file = effects . add_to_vcf ( dd . get_vrn_file ( data ) , data ) [ 0 ] if eff_file : data = dd . set_vrn_file ( data , eff_file ) ann_file = population . run_vcfanno ( dd . get_vrn_file ( data ) , data ) if ann_file...
Run RNA - seq annotation and filtering .
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def quantitate ( data ) : data = to_single_data ( to_single_data ( data ) ) data = generate_transcript_counts ( data ) [ 0 ] [ 0 ] data [ "quant" ] = { } if "sailfish" in dd . get_expression_caller ( data ) : data = to_single_data ( sailfish . run_sailfish ( data ) [ 0 ] ) data [ "quant" ] [ "tsv" ] = data [ "sailfish"...
CWL target for quantitation .
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def quantitate_expression_parallel ( samples , run_parallel ) : data = samples [ 0 ] [ 0 ] samples = run_parallel ( "generate_transcript_counts" , samples ) if "cufflinks" in dd . get_expression_caller ( data ) : samples = run_parallel ( "run_cufflinks" , samples ) if "stringtie" in dd . get_expression_caller ( data ) ...
quantitate expression all programs run here should be multithreaded to take advantage of the threaded run_parallel environment
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def quantitate_expression_noparallel ( samples , run_parallel ) : data = samples [ 0 ] [ 0 ] if "express" in dd . get_expression_caller ( data ) : samples = run_parallel ( "run_express" , samples ) if "dexseq" in dd . get_expression_caller ( data ) : samples = run_parallel ( "run_dexseq" , samples ) return samples
run transcript quantitation for algorithms that don t run in parallel
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def generate_transcript_counts ( data ) : data [ "count_file" ] = featureCounts . count ( data ) if dd . get_fusion_mode ( data , False ) and not dd . get_fusion_caller ( data ) : oncofuse_file = oncofuse . run ( data ) if oncofuse_file : data = dd . set_oncofuse_file ( data , oncofuse_file ) if dd . get_transcriptome_...
Generate counts per transcript and per exon from an alignment
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def run_dexseq ( data ) : if dd . get_dexseq_gff ( data , None ) : data = dexseq . bcbio_run ( data ) return [ [ data ] ]
Quantitate exon - level counts with DEXSeq
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def combine_express ( samples , combined ) : if not combined : return None to_combine = [ dd . get_express_counts ( x ) for x in dd . sample_data_iterator ( samples ) if dd . get_express_counts ( x ) ] gtf_file = dd . get_gtf_file ( samples [ 0 ] [ 0 ] ) isoform_to_gene_file = os . path . join ( os . path . dirname ( c...
Combine tpm effective counts and fpkm from express results
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def run_cufflinks ( data ) : if "cufflinks" in dd . get_tools_off ( data ) : return [ [ data ] ] work_bam = dd . get_work_bam ( data ) ref_file = dd . get_sam_ref ( data ) out_dir , fpkm_file , fpkm_isoform_file = cufflinks . run ( work_bam , ref_file , data ) data = dd . set_cufflinks_dir ( data , out_dir ) data = dd ...
Quantitate transcript expression with Cufflinks
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def run ( data ) : name = dd . get_sample_name ( data ) in_bam = dd . get_transcriptome_bam ( data ) config = data [ 'config' ] if not in_bam : logger . info ( "Transcriptome-mapped BAM file not found, skipping eXpress." ) return data out_dir = os . path . join ( dd . get_work_dir ( data ) , "express" , name ) out_file...
Quantitaive isoforms expression by eXpress
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def _get_column ( in_file , out_file , column , data = None ) : with file_transaction ( data , out_file ) as tx_out_file : with open ( in_file ) as in_handle : with open ( tx_out_file , 'w' ) as out_handle : for line in in_handle : cols = line . strip ( ) . split ( "\t" ) if line . find ( "eff_count" ) > 0 : continue n...
Subset one column from a file
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def _prepare_bam_file ( bam_file , tmp_dir , config ) : sort_mode = _get_sort_order ( bam_file , config ) if sort_mode != "queryname" : bam_file = sort ( bam_file , config , "queryname" ) return bam_file
Pipe sort by name cmd in case sort by coordinates
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def isoform_to_gene_name ( gtf_file , out_file , data ) : if not out_file : out_file = tempfile . NamedTemporaryFile ( delete = False ) . name if file_exists ( out_file ) : return out_file db = gtf . get_gtf_db ( gtf_file ) line_format = "{transcript}\t{gene}\n" with file_transaction ( data , out_file ) as tx_out_file ...
produce a table of isoform - > gene mappings for loading into EBSeq
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def shared_variantcall ( call_fn , name , align_bams , ref_file , items , assoc_files , region = None , out_file = None ) : config = items [ 0 ] [ "config" ] if out_file is None : if vcfutils . is_paired_analysis ( align_bams , items ) : out_file = "%s-paired-variants.vcf.gz" % config [ "metdata" ] [ "batch" ] else : o...
Provide base functionality for prepping and indexing for variant calling .
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def run_samtools ( align_bams , items , ref_file , assoc_files , region = None , out_file = None ) : return shared_variantcall ( _call_variants_samtools , "samtools" , align_bams , ref_file , items , assoc_files , region , out_file )
Detect SNPs and indels with samtools mpileup and bcftools .
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def _call_variants_samtools ( align_bams , ref_file , items , target_regions , tx_out_file ) : config = items [ 0 ] [ "config" ] mpileup = prep_mpileup ( align_bams , ref_file , config , target_regions = target_regions , want_bcf = True ) bcftools = config_utils . get_program ( "bcftools" , config ) samtools_version = ...
Call variants with samtools in target_regions .
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def _convert_fastq ( srafn , outdir , single = False ) : "convert sra to fastq" cmd = "fastq-dump --split-files --gzip {srafn}" cmd = "%s %s" % ( utils . local_path_export ( ) , cmd ) sraid = os . path . basename ( utils . splitext_plus ( srafn ) [ 0 ] ) if not srafn : return None if not single : out_file = [ os . path...
convert sra to fastq
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def check_and_postprocess ( args ) : with open ( args . process_config ) as in_handle : config = yaml . safe_load ( in_handle ) setup_local_logging ( config ) for dname in _find_unprocessed ( config ) : lane_details = nglims . get_runinfo ( config [ "galaxy_url" ] , config [ "galaxy_apikey" ] , dname , utils . get_in (...
Check for newly dumped sequencer output post - processing and transferring .
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def _tweak_lane ( lane_details , dname ) : tweak_config_file = os . path . join ( dname , "lane_config.yaml" ) if os . path . exists ( tweak_config_file ) : with open ( tweak_config_file ) as in_handle : tweak_config = yaml . safe_load ( in_handle ) if tweak_config . get ( "uniquify_lanes" ) : out = [ ] for ld in lane_...
Potentially tweak lane information to handle custom processing reading a lane_config . yaml file .
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def _remap_dirname ( local , remote ) : def do ( x ) : return x . replace ( local , remote , 1 ) return do
Remap directory names from local to remote .
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def _find_unprocessed ( config ) : reported = _read_reported ( config [ "msg_db" ] ) for dname in _get_directories ( config ) : if os . path . isdir ( dname ) and dname not in reported : if _is_finished_dumping ( dname ) : yield dname
Find any finished directories that have not been processed .
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def _is_finished_dumping ( directory ) : run_info = os . path . join ( directory , "RunInfo.xml" ) hi_seq_checkpoint = "Basecalling_Netcopy_complete_Read%s.txt" % _expected_reads ( run_info ) to_check = [ "Basecalling_Netcopy_complete_SINGLEREAD.txt" , "Basecalling_Netcopy_complete_READ2.txt" , hi_seq_checkpoint ] retu...
Determine if the sequencing directory has all files .
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def _expected_reads ( run_info_file ) : reads = [ ] if os . path . exists ( run_info_file ) : tree = ElementTree ( ) tree . parse ( run_info_file ) read_elem = tree . find ( "Run/Reads" ) reads = read_elem . findall ( "Read" ) return len ( reads )
Parse the number of expected reads from the RunInfo . xml file .
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def _read_reported ( msg_db ) : reported = [ ] if os . path . exists ( msg_db ) : with open ( msg_db ) as in_handle : for line in in_handle : reported . append ( line . strip ( ) ) return reported
Retrieve a list of directories previous reported .
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def get_files ( data ) : all_files = glob . glob ( os . path . normpath ( os . path . join ( os . path . dirname ( dd . get_ref_file ( data ) ) , os . pardir , "viral" , "*" ) ) ) return sorted ( all_files )
Retrieve pre - installed viral reference files .
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def gatk_splitreads ( data ) : broad_runner = broad . runner_from_config ( dd . get_config ( data ) ) ref_file = dd . get_ref_file ( data ) deduped_bam = dd . get_deduped_bam ( data ) base , ext = os . path . splitext ( deduped_bam ) split_bam = base + ".splitN" + ext if file_exists ( split_bam ) : data = dd . set_spli...
use GATK to split reads with Ns in the CIGAR string hard clipping regions that end up in introns
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def _setup_variant_regions ( data , out_dir ) : vr_file = dd . get_variant_regions ( data ) if not vr_file : vr_file = regions . get_sv_bed ( data , "transcripts" , out_dir = out_dir ) contigs = set ( [ c . name for c in ref . file_contigs ( dd . get_ref_file ( data ) ) ] ) out_file = os . path . join ( utils . safe_ma...
Ensure we have variant regions for calling using transcript if not present .
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def gatk_rnaseq_calling ( data ) : from bcbio . bam import callable data = utils . deepish_copy ( data ) tools_on = dd . get_tools_on ( data ) if not tools_on : tools_on = [ ] tools_on . append ( "gvcf" ) data = dd . set_tools_on ( data , tools_on ) data = dd . set_jointcaller ( data , [ "%s-joint" % v for v in dd . ge...
Use GATK to perform gVCF variant calling on RNA - seq data
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def filter_junction_variants ( vrn_file , data ) : SJ_BP_MASK = 10 vrn_dir = os . path . dirname ( vrn_file ) splicebed = dd . get_junction_bed ( data ) if not file_exists ( splicebed ) : logger . info ( "Splice junction BED file not found, skipping filtering of " "variants closed to splice junctions." ) return vrn_fil...
filter out variants within 10 basepairs of a splice junction these are very prone to being false positives with RNA - seq data
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def _clean_args ( sys_argv , args ) : base = [ x for x in sys_argv if x . startswith ( "-" ) or not args . datadir == os . path . abspath ( os . path . expanduser ( x ) ) ] base = [ x for x in base if x not in set ( [ "--minimize-disk" ] ) ] if "--nodata" in base : base . remove ( "--nodata" ) else : base . append ( "-...
Remove data directory from arguments to pass to upgrade function .
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def setup_manifest ( datadir ) : manifest_dir = os . path . join ( datadir , "manifest" ) if not os . path . exists ( manifest_dir ) : os . makedirs ( manifest_dir )
Create barebones manifest to be filled in during update
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def write_system_config ( base_url , datadir , tooldir ) : out_file = os . path . join ( datadir , "galaxy" , os . path . basename ( base_url ) ) if not os . path . exists ( os . path . dirname ( out_file ) ) : os . makedirs ( os . path . dirname ( out_file ) ) if os . path . exists ( out_file ) : if tooldir is None : ...
Write a bcbio_system . yaml configuration file with tool information .
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def check_dependencies ( ) : print ( "Checking required dependencies" ) for dep , msg in [ ( [ "git" , "--version" ] , "Git (http://git-scm.com/)" ) , ( [ "wget" , "--version" ] , "wget" ) , ( [ "bzip2" , "-h" ] , "bzip2" ) ] : try : p = subprocess . Popen ( dep , stderr = subprocess . STDOUT , stdout = subprocess . PI...
Ensure required tools for installation are present .
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def process ( args ) : os . environ [ "LC_ALL" ] = "C" os . environ [ "LC" ] = "C" os . environ [ "LANG" ] = "C" setpath . prepend_bcbiopath ( ) try : fn = getattr ( multitasks , args . name ) except AttributeError : raise AttributeError ( "Did not find exposed function in bcbio.distributed.multitasks named '%s'" % arg...
Run the function in args . name given arguments in args . argfile .
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def _cwlvar_to_wdl ( var ) : if isinstance ( var , ( list , tuple ) ) : return [ _cwlvar_to_wdl ( x ) for x in var ] elif isinstance ( var , dict ) : assert var . get ( "class" ) == "File" , var return var . get ( "path" ) or var [ "value" ] else : return var
Convert a CWL output object into a WDL output .
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def _write_out_argfile ( argfile , out , fnargs , parallel , out_keys , input_files , work_dir ) : with open ( argfile , "w" ) as out_handle : if argfile . endswith ( ".json" ) : record_name , record_attrs = _get_record_attrs ( out_keys ) if record_name : if parallel in [ "multi-batch" ] : recs = _nested_cwl_record ( o...
Write output argfile preparing a CWL ready JSON or YAML representation of the world .
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def _get_record_attrs ( out_keys ) : if len ( out_keys ) == 1 : attr = list ( out_keys . keys ( ) ) [ 0 ] if out_keys [ attr ] : return attr , out_keys [ attr ] return None , None
Check for records a single key plus output attributes .
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def _add_resources ( data , runtime ) : if "config" not in data : data [ "config" ] = { } resources = data . get ( "resources" , { } ) or { } if isinstance ( resources , six . string_types ) and resources . startswith ( ( "{" , "[" ) ) : resources = json . loads ( resources ) data [ "resources" ] = resources assert isi...
Merge input resources with current CWL runtime parameters .
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def _world_from_cwl ( fn_name , fnargs , work_dir ) : parallel = None output_cwl_keys = None runtime = { } out = [ ] data = { } passed_keys = [ ] for fnarg in fnargs : key , val = fnarg . split ( "=" ) if key == "ignore" : continue if key == "sentinel_parallel" : parallel = val continue if key == "sentinel_runtime" : r...
Reconstitute a bcbio world data object from flattened CWL - compatible inputs .
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def _parse_output_keys ( val ) : out = { } for k in val . split ( "," ) : if ":" in k : name , attrs = k . split ( ":" ) out [ name ] = attrs . split ( ";" ) else : out [ k ] = None return out
Parse expected output keys from string handling records .
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def _find_input_files ( var , out ) : if isinstance ( var , ( list , tuple ) ) : for x in var : out = _find_input_files ( x , out ) elif isinstance ( var , dict ) : if var . get ( "class" ) == "File" : out . append ( var [ "path" ] ) out = _find_input_files ( var . get ( "secondaryFiles" , [ ] ) , out ) for key , val i...
Find input files within the given CWL object .
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def _read_from_cwlinput ( in_file , work_dir , runtime , parallel , input_order , output_cwl_keys ) : with open ( in_file ) as in_handle : inputs = json . load ( in_handle ) items_by_key = { } input_files = [ ] passed_keys = set ( [ ] ) for key , input_val in ( ( k , v ) for ( k , v ) in inputs . items ( ) if not k . s...
Read data records from a JSON dump of inputs . Avoids command line flattening of records .
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def _maybe_nest_bare_single ( items_by_key , parallel ) : if ( parallel == "multi-parallel" and ( sum ( [ 1 for x in items_by_key . values ( ) if not _is_nested_item ( x ) ] ) >= sum ( [ 1 for x in items_by_key . values ( ) if _is_nested_item ( x ) ] ) ) ) : out = { } for k , v in items_by_key . items ( ) : out [ k ] =...
Nest single inputs to avoid confusing single items and lists like files .
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def _check_for_single_nested ( target , items_by_key , input_order ) : out = utils . deepish_copy ( items_by_key ) for ( k , t ) in input_order . items ( ) : if t == "var" : v = items_by_key [ tuple ( k . split ( "__" ) ) ] if _is_nested_single ( v , target ) : out [ tuple ( k . split ( "__" ) ) ] = v [ 0 ] return out
Check for single nested inputs that match our target count and unnest .
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def _concat_records ( items_by_key , input_order ) : all_records = [ ] for ( k , t ) in input_order . items ( ) : if t == "record" : all_records . append ( k ) out_items_by_key = utils . deepish_copy ( items_by_key ) out_input_order = utils . deepish_copy ( input_order ) if len ( all_records ) > 1 : final_k = all_recor...
Concatenate records into a single key to avoid merging .
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def _nest_vars_in_rec ( var_items , rec_items , input_order , items_by_key , parallel ) : num_items = var_items var_items = list ( var_items ) [ 0 ] if rec_items : rec_items = list ( rec_items ) [ 0 ] if ( ( rec_items == 1 and var_items > 1 ) or parallel . startswith ( "batch" ) ) : num_items = set ( [ rec_items ] ) fo...
Nest multiple variable inputs into a single record or list of batch records .
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def _expand_rec_to_vars ( var_items , rec_items , input_order , items_by_key , parallel ) : num_items = var_items var_items = list ( var_items ) [ 0 ] if rec_items : for rec_key in ( k for ( k , t ) in input_order . items ( ) if t == "record" ) : rec_vals = items_by_key [ rec_key ] if len ( rec_vals ) == 1 and var_item...
Expand record to apply to number of variants .
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def _read_cwl_record ( rec ) : keys = set ( [ ] ) out = [ ] if isinstance ( rec , dict ) : is_batched = all ( [ isinstance ( v , ( list , tuple ) ) for v in rec . values ( ) ] ) cur = [ { } for _ in range ( len ( rec . values ( ) [ 0 ] ) if is_batched else 1 ) ] for k in rec . keys ( ) : keys . add ( k ) val = rec [ k ...
Read CWL records handling multiple nesting and batching cases .
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def _finalize_cwl_in ( data , work_dir , passed_keys , output_cwl_keys , runtime ) : data [ "dirs" ] = { "work" : work_dir } if not tz . get_in ( [ "config" , "algorithm" ] , data ) : if "config" not in data : data [ "config" ] = { } data [ "config" ] [ "algorithm" ] = { } if "rgnames" not in data and "description" in ...
Finalize data object with inputs from CWL .
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def _convert_value ( val ) : def _is_number ( x , op ) : try : op ( x ) return True except ValueError : return False if isinstance ( val , ( list , tuple ) ) : return [ _convert_value ( x ) for x in val ] elif val is None : return val elif _is_number ( val , int ) : return int ( val ) elif _is_number ( val , float ) : ...
Handle multiple input type values .
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def _get_output_cwl_keys ( fnargs ) : for d in utils . flatten ( fnargs ) : if isinstance ( d , dict ) and d . get ( "output_cwl_keys" ) : return d [ "output_cwl_keys" ] raise ValueError ( "Did not find output_cwl_keys in %s" % ( pprint . pformat ( fnargs ) ) )
Retrieve output_cwl_keys from potentially nested input arguments .