def run(self, network, in_data, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from genomicode import parallel from Betsy import module_utils bam_filenames = module_utils.find_bam_files(in_data.identifier) assert bam_filenames, "No .bam files." filelib.safe_mkdir(out_path) jobs = [] # list of (in_filename, out_filename) for in_filename in bam_filenames: p, f = os.path.split(in_filename) s, ext = os.path.splitext(f) out_filename = os.path.join(out_path, "%s.matches.txt" % s) x = in_filename, out_filename jobs.append(x) jobs2 = [] # list of (function, args, keywds) for x in jobs: in_filename, out_filename = x x = summarize_bam_file, (in_filename, out_filename), None jobs2.append(x) parallel.pyfun(jobs2, num_procs=num_cores, DELAY=0.1) # Make sure the analysis completed successfully. out_filenames = [x[-1] for x in jobs] filelib.assert_exists_nz_many(out_filenames)
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import parallel from genomicode import hashlib from genomicode import filelib from Betsy import module_utils import run_MACS14 bam_node, group_node = antecedents bam_path = module_utils.check_inpath(bam_node.identifier) sample_groups = module_utils.read_sample_group_file( group_node.identifier) # Get options. treat_sample = module_utils.get_user_option(user_options, "treatment_sample", not_empty=True) control_sample = module_utils.get_user_option(user_options, "control_sample", not_empty=True) # Set the experiment name. name1 = hashlib.hash_var(treat_sample) name2 = hashlib.hash_var(control_sample) experiment_name = "%s_vs_%s" % (name1, name2) # Make sure the samples exist. samples = [x[1] for x in sample_groups] assert treat_sample in samples, "Unknown sample: %s" % treat_sample assert control_sample in samples, "Unknown sample: %s" % control_sample # Find the BAM files. treat_filename = run_MACS14.find_bam_file(bam_path, treat_sample, sample_groups) control_filename = run_MACS14.find_bam_file(bam_path, control_sample, sample_groups) assert treat_filename, "Missing bam file for %s" % treat_sample assert control_filename, "Missing bam file for %s" % control_sample cmd = make_pyspp_command(treat_filename, control_filename, out_path, num_procs=num_cores) log_file = "%s.log" % experiment_name cmd = "%s >& %s" % (cmd, log_file) parallel.sshell(cmd, path=out_path) files = [ "binding.positions.txt", #"broadPeak", "crosscorrelation.pdf", "density.wig", "enrichment.estimates.wig", "enrichment.wig", #"narrowPeak", # might be empty if no peaks found log_file, ] filenames = [os.path.join(out_path, x) for x in files] filelib.assert_exists_nz_many(filenames)
def run( self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from genomicode import parallel from genomicode import alignlib from Betsy import module_utils as mlib MAX_RAM = 64 # maximum amount of ram to use in Gb. bam_node, ref_node = antecedents bam_filenames = mlib.find_bam_files(bam_node.identifier) assert bam_filenames, "No .bam files." ref = alignlib.create_reference_genome(ref_node.identifier) filelib.safe_mkdir(out_path) metadata = {} jobs = [] # list of (in_filename, log_filename, out_filename) for in_filename in bam_filenames: p, f = os.path.split(in_filename) s, ext = os.path.splitext(f) log_filename = os.path.join(out_path, "%s.log" % s) out_filename = os.path.join(out_path, f) x = in_filename, log_filename, out_filename jobs.append(x) # java -Xmx5g -jar /usr/local/bin/GATK/GenomeAnalysisTK.jar # -T SplitNCigarReads -R ../hg19.fa -I $i -o $j # -rf ReassignOneMappingQuality -RMQF 255 -RMQT 60 # -U ALLOW_N_CIGAR_READS # Start with 5 Gb RAM. commands = make_commands(jobs, ref.fasta_file_full, 5) nc = mlib.calc_max_procs_from_ram(5, upper_max=num_cores) parallel.pshell(commands, max_procs=nc) metadata["commands"] = commands metadata["num_procs"] = nc # If any of the analyses didn't finish, try again with more # RAM. jobs2 = [] for x in jobs: in_filename, log_filename, out_filename = x if filelib.exists_nz(out_filename): continue jobs2.append(x) if jobs2: commands = make_commands(jobs2, ref.fasta_file_full, MAX_RAM) nc = mlib.calc_max_procs_from_ram(MAX_RAM, upper_max=num_cores) parallel.pshell(commands, max_procs=nc) metadata["commands"] += commands # Make sure the analysis completed successfully. out_filenames = [x[-1] for x in jobs] filelib.assert_exists_nz_many(out_filenames) return metadata
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import parallel from genomicode import alignlib from genomicode import filelib from Betsy import module_utils as mlib bam_node, ref_node = antecedents bam_filenames = mlib.find_bam_files(bam_node.identifier) assert bam_filenames, "No .bam files." ref = alignlib.create_reference_genome(ref_node.identifier) filelib.safe_mkdir(out_path) metadata = {} metadata["tool"] = "samtools %s" % alignlib.get_samtools_version() # list of (in_filename, err_filename, out_filename) jobs = [] for in_filename in bam_filenames: p, f = os.path.split(in_filename) sample, ext = os.path.splitext(f) err_filename = os.path.join(out_path, "%s.log" % sample) out_filename = os.path.join(out_path, "%s.pileup" % sample) x = in_filename, err_filename, out_filename jobs.append(x) # samtools mpileup -f [reference sequence] [BAM file(s)] # > myData.mpileup samtools = mlib.findbin("samtools") sq = mlib.sq commands = [] for x in jobs: in_filename, err_filename, out_filename = x x = [ sq(samtools), "mpileup", "-f", sq(ref.fasta_file_full), ] x.append(sq(in_filename)) x = " ".join(map(str, x)) x = "%s 2> %s 1> %s" % (x, err_filename, out_filename) commands.append(x) parallel.pshell(commands, max_procs=num_cores) metadata["num_cores"] = num_cores metadata["commands"] = commands x = [x[-1] for x in jobs] filelib.assert_exists_nz_many(x) return metadata
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import parallel from genomicode import filelib from genomicode import alignlib from Betsy import module_utils as mlib fastq_node, sample_node, reference_node = antecedents fastq_files = mlib.find_merged_fastq_files(sample_node.identifier, fastq_node.identifier) ref = alignlib.create_reference_genome(reference_node.identifier) assert os.path.exists(ref.fasta_file_full) filelib.safe_mkdir(out_path) metadata = {} metadata["tool"] = "bowtie2 %s" % alignlib.get_bowtie2_version() # Make a list of the jobs to run. jobs = [] for x in fastq_files: sample, pair1, pair2 = x sam_filename = os.path.join(out_path, "%s.sam" % sample) log_filename = os.path.join(out_path, "%s.log" % sample) x = sample, pair1, pair2, sam_filename, log_filename jobs.append(x) sq = mlib.sq commands = [] for x in jobs: sample, pair1, pair2, sam_filename, log_filename = x nc = max(1, num_cores / len(jobs)) x = alignlib.make_bowtie2_command(ref.fasta_file_full, pair1, fastq_file2=pair2, sam_file=sam_filename, num_threads=nc) x = "%s >& %s" % (x, sq(log_filename)) commands.append(x) metadata["commands"] = commands metadata["num_cores"] = num_cores parallel.pshell(commands, max_procs=num_cores) # Make sure the analysis completed successfully. x = [x[-2] for x in jobs] filelib.assert_exists_nz_many(x) return metadata
def run( self, network, in_data, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib # If align_with_star is run with two_pass=yes, this will leave # two BAM files for every sample. # p1.<sample>.Aligned.out.bam pass 1 # <sample>.Aligned.out.bam pass 2 # Make sure to ignore the pass1 files. x = filelib.list_files_in_path( in_data.identifier, endswith=".Aligned.out.bam", file_not_startswith="p1.") bam_filenames = x if not bam_filenames: x = filelib.list_files_in_path( in_data.identifier, endswith=".Aligned.out.sam") sam_filenames = x if sam_filenames: assert bam_filenames, \ "No .Aligned.out.bam files. Looks like .sam generated." assert bam_filenames, "No .Aligned.out.bam files." filelib.safe_mkdir(out_path) jobs = [] # list of (in_filename, out_filename) for in_filename in bam_filenames: # in_filename has format: # <path>/<sample>.Aligned.out.sam path, f = os.path.split(in_filename) sample, x = f.split(".", 1) assert x == "Aligned.out.bam", f out_filename = os.path.join(out_path, "%s.bam" % sample) assert in_filename != out_filename jobs.append((in_filename, out_filename)) # Make sure outfiles are unique. x = [x[-1] for x in jobs] x = {}.fromkeys(x) assert len(jobs) == len(x), "Duplicate sample names." for x in jobs: in_filename, out_filename = x os.symlink(in_filename, out_filename) # Make sure the analysis completed successfully. out_filenames = [x[-1] for x in jobs] filelib.assert_exists_nz_many(out_filenames)
def run(self, network, in_data, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from genomicode import parallel from Betsy import module_utils as mlib sam_filenames = mlib.find_sam_files(in_data.identifier) assert sam_filenames, "No .sam files." filelib.safe_mkdir(out_path) metadata = {} samtools = mlib.findbin("samtools") jobs = [] # list of (sam_filename, bam_filename) for sam_filename in sam_filenames: p, f = os.path.split(sam_filename) assert f.endswith(".sam") f = f.replace(".sam", ".bam") bam_filename = os.path.join(out_path, f) x = sam_filename, bam_filename jobs.append(x) # Make a list of samtools commands. sq = parallel.quote commands = [] for x in jobs: sam_filename, bam_filename = x # samtools view -bS -o <bam_filename> <sam_filename> x = [ sq(samtools), "view", "-bS", "-o", sq(bam_filename), sq(sam_filename), ] x = " ".join(x) commands.append(x) metadata["commands"] = commands metadata["num_cores"] = num_cores parallel.pshell(commands, max_procs=num_cores) # Make sure the analysis completed successfully. x = [x[-1] for x in jobs] filelib.assert_exists_nz_many(x) return metadata
def run(self, network, in_data, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from genomicode import parallel from genomicode import alignlib from Betsy import module_utils vcf_node = in_data vcf_filenames = filelib.list_files_in_path(vcf_node.identifier, endswith=".vcf") assert vcf_filenames, "No .vcf files." filelib.safe_mkdir(out_path) buildver = module_utils.get_user_option(user_options, "buildver", allowed_values=["hg19"], not_empty=True) jobs = [] # list of (in_filename, log_filename, out_filestem) for in_filename in vcf_filenames: # Annovar takes a filestem, without the ".vcf". p, f = os.path.split(in_filename) f, exp = os.path.splitext(f) log_filename = os.path.join(out_path, "%s.log" % f) out_filestem = os.path.join(out_path, f) x = in_filename, log_filename, out_filestem jobs.append(x) # Make a list of commands. commands = [] for x in jobs: in_filename, log_filename, out_filestem = x x = alignlib.make_annovar_command(in_filename, log_filename, out_filestem, buildver) commands.append(x) #for x in commands: # print x #import sys; sys.exit(0) parallel.pshell(commands, max_procs=num_cores) # Make sure the analysis completed successfully. x = [x[-1] for x in jobs] # out_filestems x = ["%s.%s_multianno.vcf" % (x, buildver) for x in x] filelib.assert_exists_nz_many(x)
def get_radia_files(radia_path, assembly): import os from genomicode import filelib opj = os.path.join radia_py = opj(radia_path, "scripts", "radia.py") filterRadia_py = opj(radia_path, "scripts", "filterRadia.py") mergeChroms_py = opj(radia_path, "scripts", "mergeChroms.py") # For hg19 only. scripts_dir = opj(radia_path, "scripts") blacklist_dir = opj(radia_path, "data/%s/blacklists/1000Genomes/phase1" % assembly) snp_dir = opj(radia_path, "data/%s/snp135" % assembly) retro_dir = opj(radia_path, "data/%s/retroGenes" % assembly) pseudo_dir = opj(radia_path, "data/%s/pseudoGenes" % assembly) cosmic_dir = opj(radia_path, "data/%s/cosmic" % assembly) target_dir = opj(radia_path, "data/%s/gaf/2_1" % assembly) rnageneblck_file = opj(radia_path, "data/rnaGeneBlacklist.tab") rnagenefamilyblck_file = opj(radia_path, "data/rnaGeneFamilyBlacklist.tab") files = [ radia_py, filterRadia_py, mergeChroms_py, rnageneblck_file, rnagenefamilyblck_file, ] paths = [ scripts_dir, blacklist_dir, snp_dir, retro_dir, pseudo_dir, cosmic_dir, target_dir, ] filelib.assert_exists_nz_many(files) filelib.assert_exists_many(paths) x = RadiaFiles(radia_py, filterRadia_py, mergeChroms_py, scripts_dir, blacklist_dir, snp_dir, retro_dir, pseudo_dir, cosmic_dir, target_dir, rnageneblck_file, rnagenefamilyblck_file) return x
def run(self, network, in_data, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from Betsy import module_utils align_node = in_data x = module_utils.find_bam_files(align_node.identifier) x = [x for x in x if x.endswith("accepted_hits.bam")] bam_filenames = x assert bam_filenames, "No accepted_hits.bam files." filelib.safe_mkdir(out_path) jobs = [] # list of (in_filename, out_filename) for in_filename in bam_filenames: # Names must in the format: # <path>/<sample>.tophat/accepted_hits.bam # full_path <path>/<sample>.tophat # path <path> # tophat_dir <sample>.tophat # file_ accepted_hits.bam # sample <sample> full_path, file_ = os.path.split(in_filename) path, tophat_dir = os.path.split(full_path) assert file_ == "accepted_hits.bam" assert tophat_dir.endswith(".tophat") sample = tophat_dir[:-7] out_filename = os.path.join(out_path, "%s.bam" % sample) assert in_filename != out_filename jobs.append((in_filename, out_filename)) # Make sure outfiles are unique. x = [x[-1] for x in jobs] x = {}.fromkeys(x) assert len(jobs) == len(x), "Duplicate sample names." for x in jobs: in_filename, out_filename = x os.symlink(in_filename, out_filename) # Make sure the analysis completed successfully. out_filenames = [x[-1] for x in jobs] filelib.assert_exists_nz_many(out_filenames)
def _run_genomecov(jobs, reference_file, num_cores): from genomicode import parallel from genomicode import filelib from genomicode import ngslib # Set up the commands to run. commands = [] for x in jobs: x = ngslib.make_bedtools_genomecov_command(x.bam_filename, reference_file, x.genomecov_filename) commands.append(x) parallel.pshell(commands, max_procs=num_cores) # Make sure the analysis completed successfully. x = [x.genomecov_filename for x in jobs] filelib.assert_exists_nz_many(x) return commands
def assert_sample_group_file(filename, fastq_path): import os from genomicode import filelib x = read_sample_group_file(filename) x = fix_sample_group_filenames(x, fastq_path) sample_groups = x # Make sure each file can be found in the fastq folder. # If not all found, see if it's possible the files are already merged. all_found = True filenames = [x[0] for x in sample_groups] for x in filenames: if not os.path.exists(x): all_found = False break if not all_found: possible_merged_filenames = [] for x in sample_groups: filename, sample, pair = x ms = os.path.join(fastq_path, "%s.fastq" % sample) mp1 = os.path.join(fastq_path, "%s_1.fastq" % sample) mp2 = os.path.join(fastq_path, "%s_2.fastq" % sample) possible_merged_filenames += [ms, mp1, mp2] x = [x for x in possible_merged_filenames if os.path.exists(x)] if x: print "Fastq files may already be merged." filelib.assert_exists_nz_many(filenames) # Make sure there are no duplicate files. x = [x[0] for x in sample_groups] x = [os.path.split(x)[1] for x in x] files = x file2count = {} for x in files: file2count[x] = file2count.get(x, 0) + 1 dups = sorted([x for x in file2count if file2count[x] > 1]) assert not dups, "Duplicate files"
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from genomicode import parallel from genomicode import alignlib from Betsy import module_utils as mlib bam_node, ref_node = antecedents in_filenames = mlib.find_bam_files(bam_node.identifier) assert in_filenames, "No .bam files." ref = alignlib.create_reference_genome(ref_node.identifier) filelib.safe_mkdir(out_path) metadata = {} jobs = [] # list of (in_filename, log_filename, out_filename) for in_filename in in_filenames: p, f = os.path.split(in_filename) f, ext = os.path.splitext(f) log_filename = os.path.join(out_path, "%s.log" % f) out_filename = os.path.join(out_path, "%s.intervals" % f) x = in_filename, log_filename, out_filename jobs.append(x) filter_reads_with_N_cigar = mlib.get_user_option( user_options, "filter_reads_with_N_cigar", allowed_values=["no", "yes"]) known_sites = [] x1 = mlib.get_user_option(user_options, "realign_known_sites1", check_file=True) x2 = mlib.get_user_option(user_options, "realign_known_sites2", check_file=True) x3 = mlib.get_user_option(user_options, "realign_known_sites3", check_file=True) x = [x1, x2, x3] x = [x for x in x if x] known_sites = x assert known_sites # I/O bound, so not likely to get a big speedup with nt. # java -Xmx5g -jar /usr/local/bin/GATK/GenomeAnalysisTK.jar -nt 4 # -T RealignerTargetCreator -R ../genome.idx/erdman.fa -I $i -o $j # --known <known_vcf_file> # RealignerTargetCreator takes ~10Gb per process. Each thread # takes the full amount of memory. nc = mlib.calc_max_procs_from_ram(12, upper_max=num_cores) # Make a list of commands. commands = [] for x in jobs: in_filename, log_filename, out_filename = x n = max(1, nc / len(jobs)) x = [("-known", x) for x in known_sites] if filter_reads_with_N_cigar == "yes": x.append(("-filter_reads_with_N_cigar", None)) x = alignlib.make_GATK_command(nt=n, T="RealignerTargetCreator", R=ref.fasta_file_full, I=in_filename, o=out_filename, _UNHASHABLE=x) x = "%s >& %s" % (x, log_filename) commands.append(x) parallel.pshell(commands, max_procs=nc) metadata["num_procs"] = nc metadata["commands"] = commands # Make sure the analysis completed successfully. out_filenames = [x[-1] for x in jobs] filelib.assert_exists_nz_many(out_filenames) return metadata
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import parallel from genomicode import hashlib from genomicode import filelib from Betsy import module_utils import run_MACS14 bam_node, group_node = antecedents bam_path = module_utils.check_inpath(bam_node.identifier) sample_groups = module_utils.read_sample_group_file( group_node.identifier) # Get options. treat_sample = module_utils.get_user_option(user_options, "treatment_sample", not_empty=True) control_sample = module_utils.get_user_option(user_options, "control_sample") fragment_length = module_utils.get_user_option( user_options, "peakseq_fragment_length", not_empty=True, type=int) mappability_file = module_utils.get_user_option(user_options, "mappability_file", not_empty=True, check_file=True) assert fragment_length > 0 and fragment_length < 1000 # Set the experiment name. name1 = hashlib.hash_var(treat_sample) name2 = hashlib.hash_var(control_sample) experiment_name = "%s_vs_%s" % (name1, name2) # Make sure the samples exist. samples = [x[1] for x in sample_groups] assert treat_sample in samples, "Unknown sample: %s" % treat_sample if control_sample: assert control_sample in samples, \ "Unknown sample: %s" % control_sample # Find the BAM files. treat_filename = run_MACS14.find_bam_file(bam_path, treat_sample, sample_groups) control_filename = run_MACS14.find_bam_file(bam_path, control_sample, sample_groups) assert treat_filename, "Missing bam file for %s" % treat_sample assert control_filename, "Missing bam file for %s" % control_sample cmd = make_peakseq_command(treat_filename, control_filename, out_path, experiment_name, fragment_length, mappability_file) log_file = "%s.log" % experiment_name cmd = "%s >& %s" % (cmd, log_file) parallel.sshell(cmd, path=out_path) files = [ "config.dat", log_file, "%s.txt" % experiment_name, # Can be length 0, if no peaks found. #"%s_narrowPeak.txt" % experiment_name, ] filenames = [os.path.join(out_path, x) for x in files] filelib.assert_exists_nz_many(filenames)
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from genomicode import parallel from genomicode import alignlib from Betsy import module_utils as mlib # For debugging. RUN_VARIANT_CALLING = True FILTER_CALLS = True MERGE_CALLS = True FIX_VCF_FILES = True dna_bam_node, rna_bam_node, nc_node, ref_node = antecedents dna_bam_filenames = mlib.find_bam_files(dna_bam_node.identifier) assert dna_bam_filenames, "No DNA .bam files." rna_bam_filenames = mlib.find_bam_files(rna_bam_node.identifier) assert rna_bam_filenames, "No RNA .bam files." nc_match = mlib.read_normal_cancer_file(nc_node.identifier) ref = alignlib.create_reference_genome(ref_node.identifier) filelib.safe_mkdir(out_path) metadata = {} metadata["tool"] = "Radia %s" % alignlib.get_radia_version() ## Make sure the BAM files do not contain spaces in the ## filenames. Radia doesn't work well with spaces. #filenames = dna_bam_filenames + rna_bam_filenames #has_spaces = [] #for filename in filenames: # if filename.find(" ") >= 0: # has_spaces.append(filename) #x = has_spaces #if len(x) > 5: # x = x[:5] + ["..."] #x = ", ".join(x) #msg = "Radia breaks if there are spaces in filenames: %s" % x #assert not has_spaces, msg # sample -> bam filename dnasample2bamfile = mlib.root2filename(dna_bam_filenames) rnasample2bamfile = mlib.root2filename(rna_bam_filenames) # Make sure files exist for all the samples. The DNA-Seq # should have both normal and cancer. RNA is not needed for # normal sample. mlib.assert_normal_cancer_samples(nc_match, dnasample2bamfile) mlib.assert_normal_cancer_samples(nc_match, rnasample2bamfile, ignore_normal_sample=True) # Make sure Radia and snpEff are configured. radia_genome_assembly = mlib.get_user_option(user_options, "radia_genome_assembly", not_empty=True) assert radia_genome_assembly == "hg19", "Only hg19 handled." snp_eff_genome = mlib.get_user_option(user_options, "snp_eff_genome", not_empty=True) radia_path = mlib.get_config("radia_path", assert_exists=True) snp_eff_path = mlib.get_config("snp_eff_path", assert_exists=True) radia_files = get_radia_files(radia_path, radia_genome_assembly) # Make a list of the chromosomes to use. Pick an arbitrarily # BAM file. Look at only the chromosomes that are present in # all files. all_bamfiles = dnasample2bamfile.values() + rnasample2bamfile.values() chroms = list_common_chromosomes(all_bamfiles) assert chroms, "No chromosomes found in all files." # Only use the chromosomes that can be filtered by Radia. chroms = filter_radia_chromosomes(chroms, radia_files) # Make output directories. radia_outpath = "radia1.tmp" filter_outpath = "radia2.tmp" merge_outpath = "radia3.tmp" if not os.path.exists(radia_outpath): os.mkdir(radia_outpath) if not os.path.exists(filter_outpath): os.mkdir(filter_outpath) if not os.path.exists(merge_outpath): os.mkdir(merge_outpath) # Steps: # 1. Call variants (radia.py) # -o <file.vcf> # 2. Filter variants (filterRadia.py) # <outpath> # Creates a file: <filter_outpath>/<patient_id>_chr<chrom>.vcf # 3. Merge (mergeChroms.py) # Takes as input: <filter_outpath> # Produces: <merge_outpath>/<patient_id>.vcf # list of (normal_sample, cancer_sample, chrom, # normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile, # radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, # final_vcf_outfile, # radia_logfile, filter_logfile, merge_logfile) opj = os.path.join jobs = [] for i, (normal_sample, cancer_sample) in enumerate(nc_match): normal_bamfile = dnasample2bamfile[normal_sample] dna_tumor_bamfile = dnasample2bamfile[cancer_sample] rna_tumor_bamfile = rnasample2bamfile[cancer_sample] merge_vcf_outfile = opj(merge_outpath, "%s.vcf" % cancer_sample) merge_logfile = opj(merge_outpath, "%s.log" % cancer_sample) final_vcf_outfile = opj(out_path, "%s.vcf" % cancer_sample) for chrom in chroms: radia_vcf_outfile = opj( radia_outpath, "%s_chr%s.vcf" % (cancer_sample, chrom)) filter_vcf_outfile = opj( filter_outpath, "%s_chr%s.vcf" % (cancer_sample, chrom)) radia_logfile = opj(radia_outpath, "%s_chr%s.log" % (cancer_sample, chrom)) filter_logfile = opj(filter_outpath, "%s_chr%s.log" % (cancer_sample, chrom)) x = normal_sample, cancer_sample, chrom, \ normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile, \ radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \ final_vcf_outfile, \ radia_logfile, filter_logfile, merge_logfile jobs.append(x) # Since Radia doesn't work well if there are spaces in the # filenames, symlink these files here to guarantee that there # are no spaces. normal_path = "normal.bam" dna_path = "dna.bam" rna_path = "rna.bam" if not os.path.exists(normal_path): os.mkdir(normal_path) if not os.path.exists(dna_path): os.mkdir(dna_path) if not os.path.exists(rna_path): os.mkdir(rna_path) for i, x in enumerate(jobs): normal_sample, cancer_sample, chrom, \ normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile, \ radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \ final_vcf_outfile, \ radia_logfile, filter_logfile, merge_logfile = x x1 = hash_and_symlink_bamfile(normal_bamfile, normal_path) x2 = hash_and_symlink_bamfile(dna_tumor_bamfile, dna_path) x3 = hash_and_symlink_bamfile(rna_tumor_bamfile, rna_path) clean_normal, clean_dna, clean_rna = x1, x2, x3 x = normal_sample, cancer_sample, chrom, \ clean_normal, clean_dna, clean_rna, \ radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \ final_vcf_outfile, \ radia_logfile, filter_logfile, merge_logfile jobs[i] = x # Generate the commands for doing variant calling. python = mlib.get_config("python", which_assert_file=True) # filterRadia.py calls the "blat" command, and there's no way # to set the path. Make sure "blat" is executable. if not filelib.which("blat"): # Find "blat" in the configuration and add it to the path. x = mlib.get_config("blat", which_assert_file=True) path, x = os.path.split(x) if os.environ["PATH"]: path = "%s:%s" % (os.environ["PATH"], path) os.environ["PATH"] = path # Make sure it's findable now. filelib.which_assert("blat") # STEP 1. Call variants with radia.py. # python radia.py test31 5 \ # -n bam04/PIM001_G.bam \ # -t bam04/196B-MG.bam \ # -r bam34/196B-MG.bam \ # -f genomes/Broad.hg19/Homo_sapiens_assembly19.fa \ # -o test32.vcf # --dnaTumorMitochon MT \ # --rnaTumorMitochon MT \ sq = mlib.sq commands = [] for x in jobs: normal_sample, cancer_sample, chrom, \ normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile, \ radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \ final_vcf_outfile, \ radia_logfile, filter_logfile, merge_logfile = x x = [ sq(python), sq(radia_files.radia_py), cancer_sample, chrom, "-n", sq(normal_bamfile), "-t", sq(dna_tumor_bamfile), "-r", sq(rna_tumor_bamfile), "-f", sq(ref.fasta_file_full), "-o", radia_vcf_outfile, ] if "MT" in chroms: x += [ "--dnaNormalMitochon MT", "--dnaTumorMitochon MT", "--rnaTumorMitochon MT", ] x = " ".join(x) x = "%s >& %s" % (x, radia_logfile) commands.append(x) assert len(commands) == len(jobs) # Only uses ~200 Mb of ram. if RUN_VARIANT_CALLING: parallel.pshell(commands, max_procs=num_cores) metadata["num_cores"] = num_cores metadata["commands"] = commands # Make sure log files are empty. logfiles = [x[10] for x in jobs] filelib.assert_exists_z_many(logfiles) # STEP 2. Filter variants with filterRadia.py. commands = [] for x in jobs: normal_sample, cancer_sample, chrom, \ normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile, \ radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \ final_vcf_outfile, \ radia_logfile, filter_logfile, merge_logfile = x x = [ sq(python), sq(radia_files.filterRadia_py), cancer_sample, chrom, sq(radia_vcf_outfile), sq(filter_outpath), sq(radia_files.scripts_dir), "-b", sq(radia_files.blacklist_dir), "-d", sq(radia_files.snp_dir), "-r", sq(radia_files.retro_dir), "-p", sq(radia_files.pseudo_dir), "-c", sq(radia_files.cosmic_dir), "-t", sq(radia_files.target_dir), "-s", sq(snp_eff_path), "-e", snp_eff_genome, "--rnaGeneBlckFile", sq(radia_files.rnageneblck_file), "--rnaGeneFamilyBlckFile", sq(radia_files.rnagenefamilyblck_file), ] x = " ".join(x) x = "%s >& %s" % (x, filter_logfile) commands.append(x) assert len(commands) == len(jobs) # Sometimes samtools crashes in the middle of a run. Detect # this case, and re-run the analysis if needed. assert len(commands) == len(jobs) py_commands = [] for x, cmd in zip(jobs, commands): normal_sample, cancer_sample, chrom, \ normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile, \ radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \ final_vcf_outfile, \ radia_logfile, filter_logfile, merge_logfile = x args = cmd, cancer_sample, chrom, filter_logfile x = _run_filterRadia_with_restart, args, {} py_commands.append(x) # Takes ~10 Gb each. nc = mlib.calc_max_procs_from_ram(25, upper_max=num_cores) if FILTER_CALLS: parallel.pyfun(py_commands, num_procs=nc) metadata["commands"] += commands # Make sure log files are empty. logfiles = [x[11] for x in jobs] filelib.assert_exists_z_many(logfiles) # Make sure filter_vcf_outfile exists. outfiles = [x[7] for x in jobs] filelib.assert_exists_nz_many(outfiles) # STEP 3. Merge the results. commands = [] for x in jobs: normal_sample, cancer_sample, chrom, \ normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile, \ radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \ final_vcf_outfile, \ radia_logfile, filter_logfile, merge_logfile = x # python /usr/local/radia/scripts/mergeChroms.py 196B-MG \ # radia2.tmp/ radia3.tmp # The "/" after radia2.tmp is important. If not given, # will generate some files with only newlines. fo = filter_outpath if not fo.endswith("/"): fo = "%s/" % fo x = [ sq(python), sq(radia_files.mergeChroms_py), cancer_sample, fo, merge_outpath, ] x = " ".join(x) x = "%s >& %s" % (x, merge_logfile) commands.append(x) assert len(commands) == len(jobs) # Since the chromosomes were separated for the previous steps, # this will generate one merge for each chromosome. This is # unnecessary, since we only need to merge once per sample. # Get rid of duplicates. commands = sorted({}.fromkeys(commands)) if MERGE_CALLS: parallel.pshell(commands, max_procs=num_cores) metadata["commands"] += commands # Make sure log files are empty. logfiles = [x[12] for x in jobs] logfiles = sorted({}.fromkeys(logfiles)) filelib.assert_exists_z_many(logfiles) # Fix the VCF files. commands = [] for x in jobs: normal_sample, cancer_sample, chrom, \ normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile, \ radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \ final_vcf_outfile, \ radia_logfile, filter_logfile, merge_logfile = x args = normal_sample, cancer_sample, \ merge_vcf_outfile, final_vcf_outfile x = alignlib.clean_radia_vcf, args, {} commands.append(x) if FIX_VCF_FILES: parallel.pyfun(commands, num_procs=num_cores) # Make sure output VCF files exist. x = [x[9] for x in jobs] filelib.assert_exists_nz_many(x) return metadata
def run( self, network, in_data, out_attributes, user_options, num_cores, out_path): import os import shutil from genomicode import parallel from genomicode import filelib from genomicode import alignlib from Betsy import module_utils as mlib bam_filenames = mlib.find_bam_files(in_data.identifier) filelib.safe_mkdir(out_path) metadata = {} metadata["tool"] = "bam2fastx (unknown version)" # Somehow bam2fastx doesn't work if there are spaces in the # filename. Make a temporary filename with no spaces, and # then rename it later. # Actually, may not be bam2fastx's fault. jobs = [] for i, bam_filename in enumerate(bam_filenames): p, f, e = mlib.splitpath(bam_filename) #bai_filename = alignlib.find_bai_file(bam_filename) #assert bai_filename, "Missing index for: %s" % bam_filename #temp_bam_filename = "%d.bam" % i #temp_bai_filename = "%d.bam.bai" % i #temp_fa_filename = "%d.fa" % i fa_filename = os.path.join(out_path, "%s.fa" % f) x = filelib.GenericObject( bam_filename=bam_filename, #bai_filename=bai_filename, #temp_bam_filename=temp_bam_filename, #temp_bai_filename=temp_bai_filename, #temp_fa_filename=temp_fa_filename, fa_filename=fa_filename) jobs.append(x) bam2fastx = mlib.findbin("bam2fastx") # Link all the bam files. #for j in jobs: # assert not os.path.exists(j.temp_bam_filename) # #assert not os.path.exists(j.temp_bai_filename) # os.symlink(j.bam_filename, j.temp_bam_filename) # #os.symlink(j.bai_filename, j.temp_bai_filename) commands = [] for j in jobs: # bam2fastx -A --fasta -o rqc14.fa rqc11.bam x = [ mlib.sq(bam2fastx), "-A", "--fasta", #"-o", mlib.sq(j.temp_fa_filename), #mlib.sq(j.temp_bam_filename), "-o", mlib.sq(j.fa_filename), mlib.sq(j.bam_filename), ] x = " ".join(x) commands.append(x) metadata["commands"] = commands metadata["num_cores"] = num_cores parallel.pshell(commands, max_procs=num_cores) #for j in jobs: # # Move the temporary files to the final location. # shutil.move(j.temp_fa_filename, j.fa_filename) # # Remove the link to the BAM file. # os.unlink(j.temp_bam_filename) x = [j.fa_filename for x in jobs] filelib.assert_exists_nz_many(x) return metadata
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from genomicode import parallel from genomicode import alignlib from Betsy import module_utils bam_node, ref_node = antecedents bam_filenames = module_utils.find_bam_files(bam_node.identifier) assert bam_filenames, "No .bam files." ref = alignlib.create_reference_genome(ref_node.identifier) filelib.safe_mkdir(out_path) metadata = {} # TODO: Figure out GATK version. ## Figure out whether the user wants SNPs or INDELs. #assert "vartype" in out_attributes #vartype = out_attributes["vartype"] #assert vartype in ["all", "snp", "indel"] jobs = [] for bam_filename in bam_filenames: p, f = os.path.split(bam_filename) sample, ext = os.path.splitext(f) #raw_outfile = os.path.join(out_path, "%s.raw" % sample) vcf_outfile = os.path.join(out_path, "%s.vcf" % sample) log_filename = os.path.join(out_path, "%s.log" % sample) x = filelib.GenericObject(bam_filename=bam_filename, vcf_outfile=vcf_outfile, log_filename=log_filename) jobs.append(x) # java -Xmx5g -jar /usr/local/bin/GATK/GenomeAnalysisTK.jar # -T HaplotypeCaller -R ucsc.hg19.fasta # -dontUseSoftClippedBases -stand_call_conf 20.0 # -stand_emit_conf 20.0 -I $i -o $j # Make a list of commands. commands = [] for j in jobs: # For debugging. If exists, don't do it again. #if filelib.exists_nz(j.raw_outfile): if filelib.exists_nz(j.vcf_outfile): continue x = alignlib.make_GATK_command(T="HaplotypeCaller", R=ref.fasta_file_full, dontUseSoftClippedBases=None, stand_call_conf=20.0, stand_emit_conf=20.0, I=j.bam_filename, o=j.vcf_outfile) x = "%s >& %s" % (x, j.log_filename) commands.append(x) parallel.pshell(commands, max_procs=num_cores) # Filter each of the VCF files. #for j in jobs: # filter_by_vartype(vartype, j.raw_outfile, j.vcf_outfile) #metadata["filter"] = vartype # Make sure the analysis completed successfully. x = [j.vcf_outfile for j in jobs] filelib.assert_exists_nz_many(x) return metadata
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import config from genomicode import filelib from genomicode import parallel from genomicode import alignlib from Betsy import module_utils ## Importing pysam is hard! #import sys #sys_path_old = sys.path[:] #sys.path = [x for x in sys.path if x.find("RSeQC") < 0] #import pysam #sys.path = sys_path_old bam_node, ref_node = antecedents bam_filenames = module_utils.find_bam_files(bam_node.identifier) assert bam_filenames, "No .bam files." ref = alignlib.create_reference_genome(ref_node.identifier) filelib.safe_mkdir(out_path) # list of (in_filename, err_filename, out_filename) jobs = [] for in_filename in bam_filenames: p, f = os.path.split(in_filename) s, ext = os.path.splitext(f) log_filename = os.path.join(out_path, "%s.log" % s) out_filename = os.path.join(out_path, f) assert in_filename != out_filename x = in_filename, log_filename, out_filename jobs.append(x) # Don't do this. Need MD, NM, NH in # summarize_alignment_cigar. To be sure, just redo it. ## If the files already have MD tags, then just symlink the ## files. Don't add again. #i = 0 #while i < len(jobs): # in_filename, out_filename = jobs[i] # # handle = pysam.AlignmentFile(in_filename, "rb") # align = handle.next() # tag_dict = dict(align.tags) # if "MD" not in tag_dict: # i += 1 # continue # # Has MD tags. Just symlink and continue. # os.symlink(in_filename, out_filename) # del jobs[i] # Make a list of samtools commands. # Takes ~200 Mb per process, so should not be a big issue. samtools = filelib.which_assert(config.samtools) sq = parallel.quote commands = [] for x in jobs: in_filename, log_filename, out_filename = x # samtools calmd -b <in.bam> <ref.fasta> > <out.bam> # May generate error: # [bam_fillmd1] different NM for read # 'ST-J00106:118:H75L3BBXX:3:2128:21846:47014': 0 -> 19 # Pipe stderr to different file. x = [ samtools, "calmd", "-b", sq(in_filename), sq(ref.fasta_file_full), ] x = " ".join(x) x = "%s 2> %s 1> %s" % (x, sq(log_filename), sq(out_filename)) commands.append(x) parallel.pshell(commands, max_procs=num_cores) # Make sure the analysis completed successfully. x = [x[-1] for x in jobs] filelib.assert_exists_nz_many(x)
def run( self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from genomicode import parallel from genomicode import alignlib from Betsy import module_utils as mlib bam_node, ref_node = antecedents bam_filenames = mlib.find_bam_files(bam_node.identifier) assert bam_filenames, "No .bam files." ref = alignlib.create_reference_genome(ref_node.identifier) filelib.safe_mkdir(out_path) metadata = {} # java -jar picard.jar CollectAlignmentSummaryMetrics \ # R=reference_sequence.fasta \ # I=input.bam \ # O=output.txt opj = os.path.join jobs = [] # list of filelib.GenericObject for bam_filename in bam_filenames: # <in_path>/<sample>.bam in_path, sample, ext = mlib.splitpath(bam_filename) assert ext == ".bam" out_filename = opj(out_path, "%s.alignment_metrics.txt" % sample) log_filename = opj(out_path, "%s.log" % sample) x = filelib.GenericObject( sample=sample, bam_filename=bam_filename, out_filename=out_filename, log_filename=log_filename) jobs.append(x) # Make the commands to run picard. picard_jar = alignlib.find_picard_jar("picard") sq = parallel.quote commands = [] for j in jobs: # Should have better way of getting java path. cmd = [ "java", "-Xmx10g", "-jar", sq(picard_jar), "CollectAlignmentSummaryMetrics", "I=%s" % sq(j.bam_filename), "R=%s" % sq(ref.fasta_file_full), "O=%s" % sq(j.out_filename), ] cmd = " ".join(cmd) cmd = "%s >& %s" % (cmd, sq(j.log_filename)) commands.append(cmd) metadata["commands"] = commands parallel.pshell(commands, max_procs=num_cores) x = [x.out_filename for x in jobs] filelib.assert_exists_nz_many(x) # Summarize the insert size files. outfile = opj(out_path, "summary.txt") _summarize_alignment_summary_metrics(jobs, outfile) filelib.assert_exists_nz(outfile) return metadata
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from genomicode import parallel from genomicode import alignlib from Betsy import module_utils as mlib #import call_variants_GATK bam_node, ref_node = antecedents bam_filenames = mlib.find_bam_files(bam_node.identifier) assert bam_filenames, "No .bam files." ref = alignlib.create_reference_genome(ref_node.identifier) filelib.safe_mkdir(out_path) metadata = {} # Figure out whether the user wants SNPs or INDELs. #assert "vartype" in out_attributes #vartype = out_attributes["vartype"] #assert vartype in ["all", "snp", "indel"] # Platypus generates an error if there are spaces in the BAM # filename. Symlink the file to a local directory to make # sure there are no spaces. bam_path = "bam" jobs = [] # list of filelib.GenericObject for bam_filename in bam_filenames: p, f = os.path.split(bam_filename) sample, ext = os.path.splitext(f) bai_filename = "%s.bai" % bam_filename filelib.assert_exists_nz(bai_filename) x = sample.replace(" ", "_") local_bam = os.path.join(bam_path, "%s.bam" % x) local_bai = os.path.join(bam_path, "%s.bam.bai" % x) log_filename = os.path.join(out_path, "%s.log" % sample) err_filename = os.path.join(out_path, "%s.err" % sample) # Unfiltered file. #raw_filename = os.path.join(out_path, "%s.raw" % sample) # Final VCF file. out_filename = os.path.join(out_path, "%s.vcf" % sample) x = filelib.GenericObject(bam_filename=bam_filename, bai_filename=bai_filename, local_bam=local_bam, local_bai=local_bai, log_filename=log_filename, err_filename=err_filename, out_filename=out_filename) jobs.append(x) filelib.safe_mkdir(bam_path) for j in jobs: assert " " not in j.local_bam filelib.assert_exists_nz(j.bam_filename) filelib.assert_exists_nz(j.bai_filename) if not os.path.exists(j.local_bam): os.symlink(j.bam_filename, j.local_bam) if not os.path.exists(j.local_bai): os.symlink(j.bai_filename, j.local_bai) # TODO: Keep better track of the metadata. buffer_size = 100000 max_reads = 5E6 # Running into errors sometimes, so increase these numbers. # WARNING - Too many reads (5000000) in region # 1:500000-600000. Quitting now. Either reduce --bufferSize or # increase --maxReads. buffer_size = buffer_size * 10 max_reads = max_reads * 10 # Make a list of commands. commands = [] for j in jobs: #nc = max(1, num_cores/len(jobs)) x = alignlib.make_platypus_command(bam_file=j.local_bam, ref_file=ref.fasta_file_full, log_file=j.log_filename, out_file=j.out_filename, buffer_size=buffer_size, max_reads=max_reads) x = "%s >& %s" % (x, j.err_filename) commands.append(x) #for x in commands: # print x #import sys; sys.exit(0) parallel.pshell(commands, max_procs=num_cores) # Make sure the analysis completed successfully. If not, try # to diagnose. for j in jobs: if filelib.exists_nz(j.out_filename): continue for line in open(j.err_filename): if line.find("WARNING - Too many reads") >= 0: print line, x = [j.out_filename for j in jobs] filelib.assert_exists_nz_many(x) # Filter each of the VCF files. #for j in jobs: # call_variants_GATK.filter_by_vartype( # vartype, j.raw_filename, j.out_filename) #metadata["filter"] = vartype return metadata
def run(self, network, in_data, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from genomicode import parallel from genomicode import alignlib from genomicode import config from Betsy import module_utils as mlib mpileup_node = in_data mpileup_filenames = filelib.list_files_in_path(mpileup_node.identifier, endswith=".pileup") assert mpileup_filenames, "No .pileup files." #nc_match = mlib.read_normal_cancer_file(nc_node.identifier) #ref = alignlib.create_reference_genome(ref_node.identifier) filelib.safe_mkdir(out_path) # Figure out whether the purpose is to get coverage. Change # the parameters if it is. assert "vartype" in out_attributes vartype = out_attributes["vartype"] assert vartype in ["snp", "indel"] tool = "mpileup2snp" if vartype == "indel": tool = "mpileup2indel" # list of (sample, in_filename, tmp1_filename, tmp2_filename, # out_filename) jobs = [] for in_filename in mpileup_filenames: p, sample, ext = mlib.splitpath(in_filename) tmp1_filename = os.path.join(out_path, "%s.tmp1" % sample) tmp2_filename = os.path.join(out_path, "%s.tmp2" % sample) out_filename = os.path.join(out_path, "%s.vcf" % sample) x = sample, in_filename, tmp1_filename, tmp2_filename, out_filename jobs.append(x) # VarScan will generate a "Parsing Exception" if there are 0 # reads in a location. Filter those out. sq = parallel.quote commands = [] for x in jobs: sample, in_filename, tmp1_filename, tmp2_filename, out_filename = x x = "awk -F'\t' '$4 != 0 {print}' %s > %s" % (in_filename, tmp1_filename) commands.append(x) parallel.pshell(commands, max_procs=num_cores) x = [x[2] for x in jobs] filelib.assert_exists_nz_many(x) # java -jar /usr/local/bin/VarScan.jar <tool> $i --output_vcf 1 > $j varscan = filelib.which_assert(config.varscan_jar) # Make a list of commands. commands = [] for x in jobs: sample, in_filename, tmp1_filename, tmp2_filename, out_filename = x x = [ "java", "-jar", sq(varscan), tool, tmp1_filename, "--p-value", 0.05, "--output-vcf", 1, ] x = " ".join(map(str, x)) x = "%s >& %s" % (x, tmp2_filename) commands.append(x) #for x in commands: # print x #import sys; sys.exit(0) parallel.pshell(commands, max_procs=num_cores) x = [x[3] for x in jobs] filelib.assert_exists_nz_many(x) # Clean up the VCF files. VarScan leaves extraneous lines # there. for x in jobs: sample, in_filename, tmp1_filename, tmp2_filename, out_filename = x alignlib.clean_varscan_vcf(sample, tmp2_filename, out_filename) x = [x[-1] for x in jobs] filelib.assert_exists_nz_many(x) # The tmp files are really big. Don't save those. for x in jobs: sample, in_filename, tmp1_filename, tmp2_filename, out_filename = x filelib.safe_unlink(tmp1_filename) filelib.safe_unlink(tmp2_filename)
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import parallel from genomicode import filelib from genomicode import alignlib from Betsy import module_utils as mlib bam_folder, sample_node, gene_node, strand_node = antecedents bam_path = bam_folder.identifier assert filelib.dir_exists(bam_path) gtf_file = gene_node.identifier filelib.assert_exists_nz(gtf_file) stranded = mlib.read_stranded(strand_node.identifier) filelib.safe_mkdir(out_path) metadata = {} attr2order = { "name": "name", "coordinate": "pos", } x = bam_folder.data.attributes["sorted"] sort_order = attr2order.get(x) assert sort_order, "Cannot handle sorted: %s" % x #attr2stranded = { # "single" : "no", # "paired" : "no", # "paired_ff" : None, # "paired_fr" : "yes", # "paired_rf" : "reverse", # } #x = sample_node.data.attributes["orientation"] #stranded = attr2stranded.get(x) #assert stranded, "Cannot handle orientation: %s" % x ht_stranded = None if stranded.stranded == "unstranded": ht_stranded = "no" elif stranded.stranded == "firststrand": ht_stranded = "reverse" elif stranded.stranded == "secondstrand": ht_stranded = "yes" assert ht_stranded is not None #gtf_file = mlib.get_user_option( # user_options, "gtf_file", not_empty=True) #assert os.path.exists(gtf_file), "File not found: %s" % gtf_file mode = mlib.get_user_option(user_options, "htseq_count_mode", allowed_values=[ "union", "intersection-strict", "intersection-nonempty" ]) # Make a list of the jobs to run. jobs = [] for bam_filename in filelib.list_files_in_path(bam_path, endswith=".bam", case_insensitive=True): x = os.path.split(bam_filename)[1] x = os.path.splitext(x)[0] x = "%s.count" % x out_file = x x = bam_filename, out_file jobs.append(x) # Generate commands for each of the files. sq = parallel.quote commands = [] for x in jobs: bam_filename, out_file = x x = alignlib.make_htseq_count_command(bam_filename, gtf_file, sort_order, ht_stranded, mode=mode) x = "%s >& %s" % (x, sq(out_file)) commands.append(x) metadata["commands"] = commands metadata["num_cores"] = num_cores parallel.pshell(commands, max_procs=num_cores, path=out_path) # Make sure the analysis completed successfully. x = [x[1] for x in jobs] x = [os.path.join(out_path, x) for x in x] output_filenames = x filelib.assert_exists_nz_many(output_filenames) return metadata
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from genomicode import parallel from genomicode import alignlib from Betsy import module_utils as mlib bam_node, nc_node, ref_node = antecedents bam_filenames = mlib.find_bam_files(bam_node.identifier) assert bam_filenames, "No .bam files." nc_match = mlib.read_normal_cancer_file(nc_node.identifier) ref = alignlib.create_reference_genome(ref_node.identifier) filelib.safe_mkdir(out_path) metadata = {} metadata["tool"] = "MuSE %s" % alignlib.get_muse_version() wgs_or_wes = mlib.get_user_option(user_options, "wgs_or_wes", not_empty=True, allowed_values=["wgs", "wes"]) dbsnp_file = mlib.get_user_option(user_options, "muse_dbsnp_vcf", not_empty=True, check_file=True) # Make sure dbsnp_file is compressed and indexed. assert dbsnp_file.endswith(".vcf.gz"), \ "muse_dbsnp_vcf must be bgzip compressed." x = "%s.tbi" % dbsnp_file assert filelib.exists_nz(x), "muse_dbsnp_vcf must be tabix indexed." # sample -> bam filename sample2bamfile = mlib.root2filename(bam_filenames) # Make sure files exist for all the samples. mlib.assert_normal_cancer_samples(nc_match, sample2bamfile) # list of (normal_sample, cancer_sample, normal_bamfile, tumor_bamfile, # muse_call_stem, muse_call_file, raw_vcf_outfile, vcf_outfile, # logfile1, logfile2) opj = os.path.join jobs = [] for (normal_sample, cancer_sample) in nc_match: normal_bamfile = sample2bamfile[normal_sample] cancer_bamfile = sample2bamfile[cancer_sample] path, sample, ext = mlib.splitpath(cancer_bamfile) muse_call_stem = opj(out_path, "%s.call" % cancer_sample) muse_call_file = "%s.MuSE.txt" % muse_call_stem raw_vcf_outfile = opj(out_path, "%s.vcf.raw" % cancer_sample) vcf_outfile = opj(out_path, "%s.vcf" % cancer_sample) log_outfile1 = opj(out_path, "%s.call.log" % cancer_sample) log_outfile2 = opj(out_path, "%s.sump.log" % cancer_sample) x = normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \ muse_call_stem, muse_call_file, raw_vcf_outfile, vcf_outfile, \ log_outfile1, log_outfile2 jobs.append(x) # Generate the commands. # MuSE call -O test11 -f genomes/Broad.hg19/Homo_sapiens_assembly19.fa\ # bam04/196B-MG.bam bam04/PIM001_G.bam # MuSE sump -I test11.MuSE.txt -E -O test12.vcf \ # -D MuSE/dbsnp_132_b37.leftAligned.vcf.gz MuSE = mlib.findbin("muse") sq = mlib.sq commands = [] for x in jobs: normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \ muse_call_stem, muse_call_file, raw_vcf_outfile, vcf_outfile, \ log_outfile1, log_outfile2 = x x = [ sq(MuSE), "call", "-O", muse_call_stem, "-f", sq(ref.fasta_file_full), cancer_bamfile, normal_bamfile, ] x = " ".join(x) x = "%s >& %s" % (x, log_outfile1) commands.append(x) assert len(commands) == len(jobs) # Not sure about RAM. nc = mlib.calc_max_procs_from_ram(10, upper_max=num_cores) parallel.pshell(commands, max_procs=nc) metadata["num_cores"] = nc metadata["commands"] = commands # Make sure the log files have no errors. The files should be # empty. log_files = [x[8] for x in jobs] filelib.assert_exists_z_many(log_files) # Make sure the call files are created and not empty. call_files = [x[5] for x in jobs] filelib.assert_exists_nz_many(call_files) # Run the "sump" step. commands = [] for x in jobs: normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \ muse_call_stem, muse_call_file, raw_vcf_outfile, vcf_outfile, \ log_outfile1, log_outfile2 = x x = [ sq(MuSE), "sump", "-I", sq(muse_call_file), ] assert wgs_or_wes in ["wgs", "wes"] if wgs_or_wes == "wgs": x += ["-G"] else: x += ["-E"] x += [ "-O", sq(raw_vcf_outfile), "-D", sq(dbsnp_file), ] x = " ".join(x) x = "%s >& %s" % (x, log_outfile2) commands.append(x) assert len(commands) == len(jobs) # Not sure about RAM. nc = mlib.calc_max_procs_from_ram(10, upper_max=num_cores) parallel.pshell(commands, max_procs=nc) metadata["commands"] = metadata["commands"] + commands # Make sure the log files have no errors. The files should be # empty. log_files = [x[9] for x in jobs] filelib.assert_exists_z_many(log_files) # Make sure the raw files are created and not empty. vcf_files = [x[6] for x in jobs] filelib.assert_exists_nz_many(vcf_files) # Fix the files. commands = [] # Should be python commands. for x in jobs: normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \ muse_call_stem, muse_call_file, raw_vcf_outfile, vcf_outfile, \ log_outfile1, log_outfile2 = x args = normal_sample, cancer_sample, raw_vcf_outfile, vcf_outfile x = alignlib.clean_muse_vcf, args, {} commands.append(x) parallel.pyfun(commands, num_procs=num_cores) # Delete the log_outfiles if empty. for x in jobs: normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \ muse_call_stem, muse_call_file, raw_vcf_outfile, vcf_outfile, \ log_outfile1, log_outfile2 = x if os.path.exists(log_outfile1): os.unlink(log_outfile1) if os.path.exists(log_outfile2): os.unlink(log_outfile2) # Make sure output VCF files exist. x = [x[7] for x in jobs] filelib.assert_exists_many(x) return metadata
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from genomicode import parallel from genomicode import alignlib from Betsy import module_utils bam_node, ref_node, target_node = antecedents bam_filenames = module_utils.find_bam_files(bam_node.identifier) assert bam_filenames, "No .bam files." target_filenames = filelib.list_files_in_path(target_node.identifier, endswith=".intervals") assert target_filenames, "No .intervals files." ref = alignlib.create_reference_genome(ref_node.identifier) filelib.safe_mkdir(out_path) assert len(bam_filenames) == len(target_filenames), \ "Should have an .intervals file for each bam file." sample2bamfilename = {} for filename in bam_filenames: p, f = os.path.split(filename) sample, ext = os.path.splitext(f) assert sample not in sample2bamfilename sample2bamfilename[sample] = filename sample2targetfilename = {} for filename in target_filenames: p, f = os.path.split(filename) sample, ext = os.path.splitext(f) assert sample not in sample2targetfilename sample2targetfilename[sample] = filename assert len(sample2bamfilename) == len(sample2targetfilename) missing = [ x for x in sample2bamfilename if x not in sample2targetfilename ] assert not missing, "Missing interval files for %d bam files." % \ len(missing) # list of (bam_filename, target_filename, log_filename, out_filename) jobs = [] for sample in sample2bamfilename: bam_filename = sample2bamfilename[sample] target_filename = sample2targetfilename[sample] p, f = os.path.split(bam_filename) sample, ext = os.path.splitext(f) out_filename = os.path.join(out_path, "%s.bam" % sample) log_filename = os.path.join(out_path, "%s.log" % sample) x = bam_filename, target_filename, log_filename, out_filename jobs.append(x) known_sites = [] x1 = module_utils.get_user_option(user_options, "realign_known_sites1", check_file=True) x2 = module_utils.get_user_option(user_options, "realign_known_sites2", check_file=True) x3 = module_utils.get_user_option(user_options, "realign_known_sites3", check_file=True) x = [x1, x2, x3] x = [x for x in x if x] known_sites = x assert known_sites # java -Xmx5g -jar /usr/local/bin/GATK/GenomeAnalysisTK.jar \ # -T IndelRealigner -R <ref.fa> \ # -I <bam_file> -targetIntervals <target_file> -o <bam_file> # Make a list of commands. commands = [] for x in jobs: bam_filename, target_filename, log_filename, out_filename = x x = [("known", x) for x in known_sites] x = alignlib.make_GATK_command(T="IndelRealigner", R=ref.fasta_file_full, I=bam_filename, targetIntervals=target_filename, o=out_filename, _UNHASHABLE=x) x = "%s >& %s" % (x, log_filename) commands.append(x) #for x in commands: # print x #import sys; sys.exit(0) parallel.pshell(commands, max_procs=num_cores) # Make sure the analysis completed successfully. out_filenames = [x[-1] for x in jobs] filelib.assert_exists_nz_many(out_filenames)
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from genomicode import parallel from genomicode import alignlib from Betsy import module_utils vcf_node, ref_node = antecedents vcf_filenames = filelib.list_files_in_path(vcf_node.identifier, endswith=".vcf") assert vcf_filenames, "No .vcf files." ref = alignlib.create_reference_genome(ref_node.identifier) filelib.safe_mkdir(out_path) jobs = [] for in_filename in vcf_filenames: p, f = os.path.split(in_filename) f, exp = os.path.splitext(f) out_filename = os.path.join(out_path, "%s.grp" % f) log_filename = os.path.join(out_path, "%s.log" % f) recal_filename = os.path.join(out_path, "%s.recalibrate_SNP.recal" % f) tranches_filename = os.path.join(out_path, "%s.recalibrate_SNP.tranches" % f) rscript_filename = os.path.join(out_path, "%s.recalibrate_SNP_plots.R" % f) assert in_filename != out_filename x = (in_filename, log_filename, recal_filename, tranches_filename, rscript_filename) jobs.append(x) # -resource:dbsnp,known=true,training=false,truth=false,prior=6.0 # dbsnp_135.b37.vcf # -resource:hapmap,known=false,training=true,truth=true,prior=15.0 # hapmap_3.3.b37.sites.vcf # -resource:1000G,known=false,training=true,truth=false,prior=10.0 # 1000G_phase1.snps.high_confidence.vcf # -resource:omni,known=false,training=true,truth=false,prior=12.0 # 1000G_omni2.5.b37.sites.vcf known_sites = [] x1 = module_utils.get_user_option(user_options, "vcf_recal_dbsnp", not_empty=True, check_file=True) x2 = module_utils.get_user_option(user_options, "vcf_recal_mills_indels", not_empty=True, check_file=True) x3 = module_utils.get_user_option(user_options, "vcf_recal_1kg_indels", not_empty=True, check_file=True) x4 = module_utils.get_user_option(user_options, "vcf_recal_omni", not_empty=True, check_file=True) y1 = "resource:dbsnp,known=true,training=false,truth=false,prior=6.0" y2 = "resource:hapmap,known=false,training=true,truth=true,prior=15.0" y3 = "resource:1000G,known=false,training=true,truth=false,prior=10.0" y4 = "resource:omni,known=false,training=true,truth=false,prior=12.0" known_sites = [(y1, x1), (y2, x2), (y3, x3), (y4, x4)] # Names of annotations to be used for annotations. AN = [ "DP", "QD", "FS", "SOR", "MQ", "MQRankSum", "ReadPosRankSum", "InbreedingCoeff" ] TRANCHE = ["100.0", "99.9", "99.0", "90.0"] # Make a list of commands. commands = [] for x in jobs: (in_filename, log_filename, recal_filename, tranches_filename, rscript_filename) = x x1 = known_sites x2 = [("an", x) for x in AN] x3 = [("tranche", x) for x in TRANCHE] unhash = x1 + x2 + x3 x = alignlib.make_GATK_command(T="VariantRecalibrator", R=ref.fasta_file_full, input=in_filename, mode="SNP", recalFile=recal_filename, tranchesFile=tranches_filename, rscriptFile=rscript_filename, _UNHASHABLE=unhash) x = "%s >& %s" % (x, log_filename) commands.append(x) #for x in commands: # print x #import sys; sys.exit(0) parallel.pshell(commands, max_procs=num_cores) # Make sure the analysis completed successfully. out_filenames = [x[-1] for x in jobs] filelib.assert_exists_nz_many(out_filenames)
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from genomicode import parallel from genomicode import alignlib from Betsy import module_utils as mlib bam_node, ref_node, insert_size_node, alignment_node = antecedents bam_filenames = mlib.find_bam_files(bam_node.identifier) assert bam_filenames, "No .bam files." ref = alignlib.create_reference_genome(ref_node.identifier) filelib.safe_mkdir(out_path) metadata = {} # ./pindel -f <reference.fa> -i <bam_configuration_file> # -c <chromosome_name> -o <out_prefix> # -T <num threads> # # Creates files: # <out_prefix>_D Deletion # <out_prefix>_SI Short insertion # <out_prefix>_LI Long insertion # <out_prefix>_INV Inversion # <out_prefix>_TD Tandem deletion # <out_prefix>_BP Breakpoint # <out_prefix>_RP ??? read pair??? # <out_prefix>_CloseEndMapped Only on end could be mapped. # Pindel cannot handle spaces in the BAM filenames (because of # the config file). Symlink the file to a local directory to make # sure there are no spaces. bam_path = "bam" opj = os.path.join jobs = [] # list of filelib.GenericObject for bam_filename in bam_filenames: p, f = os.path.split(bam_filename) sample, ext = os.path.splitext(f) bai_filename = "%s.bai" % bam_filename filelib.assert_exists_nz(bai_filename) x = sample.replace(" ", "_") local_bam = opj(bam_path, "%s.bam" % x) local_bai = opj(bam_path, "%s.bam.bai" % x) config_filename = opj(out_path, "%s.config.txt" % sample) out_prefix = opj(out_path, sample) log_filename = opj(out_path, "%s.log" % sample) x = filelib.GenericObject(sample=sample, bam_filename=bam_filename, bai_filename=bai_filename, local_bam=local_bam, local_bai=local_bai, config_filename=config_filename, out_prefix=out_prefix, log_filename=log_filename) jobs.append(x) filelib.safe_mkdir(bam_path) for j in jobs: assert " " not in j.local_bam filelib.assert_exists_nz(j.bam_filename) filelib.assert_exists_nz(j.bai_filename) if not os.path.exists(j.local_bam): os.symlink(j.bam_filename, j.local_bam) if not os.path.exists(j.local_bai): os.symlink(j.bai_filename, j.local_bai) # Read the insert sizes. summary_file = opj(insert_size_node.identifier, "summary.txt") filelib.assert_exists_nz(summary_file) sample2size = _read_insert_sizes(summary_file) # Make sure all the samples have inserts. for j in jobs: assert j.sample in sample2size, \ "Missing in insert size file: %s" % j.sample # Read the fragment sizes. summary_file = opj(alignment_node.identifier, "summary.txt") filelib.assert_exists_nz(summary_file) sample2readlen = _read_fragment_sizes(summary_file) # Make sure all the samples have read lengths. for j in jobs: assert j.sample in sample2readlen, \ "Missing in alignment summary file: %s" % j.sample # Make the config file. for j in jobs: # <insert size> is the whole length to be sequenced, including # the length of the pair of reads. Picard only counts the # sequence between the reads. size = sample2size[j.sample] read_length = sample2readlen[j.sample] insert_size = size + read_length * 2 handle = open(j.config_filename, 'w') print >> handle, "%s %s %s" % (j.local_bam, insert_size, j.sample) handle.close() # Make a list of commands. pindel = mlib.get_config("pindel", which_assert_file=True) sq = parallel.quote commands = [] for j in jobs: cmd = [ sq(pindel), "-f", sq(ref.fasta_file_full), "-i", sq(j.config_filename), "-c", "ALL", "-T", 1, "-o", sq(j.out_prefix), ] cmd = " ".join(map(str, cmd)) cmd = "%s >& %s" % (cmd, j.log_filename) commands.append(cmd) parallel.pshell(commands, max_procs=num_cores) metadata["num_cores"] = num_cores metadata["commands"] = commands # Make sure the analysis completed successfully. If not, try # to diagnose. x = [x.log_filename for x in jobs] filelib.assert_exists_nz_many(x) x1 = ["%s_D" % x.out_prefix for x in jobs] x2 = ["%s_SI" % x.out_prefix for x in jobs] x3 = ["%s_LI" % x.out_prefix for x in jobs] x4 = ["%s_INV" % x.out_prefix for x in jobs] x5 = ["%s_TD" % x.out_prefix for x in jobs] x6 = ["%s_BP" % x.out_prefix for x in jobs] x = x1 + x2 + x3 + x4 + x5 + x6 filelib.assert_exists_many(x) return metadata
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import parallel from genomicode import filelib from genomicode import alignlib from Betsy import module_utils as mlib fastq_node, sample_node, strand_node, ref_node = antecedents fastq_files = mlib.find_merged_fastq_files(sample_node.identifier, fastq_node.identifier) ref = alignlib.create_reference_genome(ref_node.identifier) stranded = mlib.read_stranded(strand_node.identifier) filelib.safe_mkdir(out_path) # Do a quick check to make sure the reference is correct. # Otherwise, error may be hard to disgnose. alignlib.assert_is_STAR_reference(ref.path) metadata = {} metadata["tool"] = "STAR %s" % alignlib.get_STAR_version() x = mlib.get_user_option(user_options, "two_pass", allowed_values=["no", "yes"]) two_pass = (x == "yes") # Figure out the strandedness. is_stranded = stranded.stranded != "unstranded" # STAR --runThreadN 40 --genomeDir test05 \ # --readFilesIn test.fastq/test03_R1_001.fastq \ # test.fastq/test03_R2_001.fastq --outFileNamePrefix test06. # If unstranded, add --outSAMstrandField intronMotif # Make a list of the jobs to run. jobs = [] # list of filelib.GenericObject objects for x in fastq_files: sample, pair1, pair2 = x pass1_out_prefix = "p1.%s." % sample pass2_out_prefix = "%s." % sample pass1_bam_filename = os.path.join( out_path, "%sAligned.out.bam" % pass1_out_prefix) pass2_bam_filename = os.path.join( out_path, "%sAligned.out.bam" % pass2_out_prefix) sjdb_filename = os.path.join(out_path, "p1.%s.SJ.out.tab" % sample) log1_filename = os.path.join(out_path, "p1.%s.log" % sample) log2_filename = os.path.join(out_path, "%s.log" % sample) x = filelib.GenericObject( sample=sample, pair1=pair1, pair2=pair2, pass1_out_prefix=pass1_out_prefix, pass2_out_prefix=pass2_out_prefix, pass1_bam_filename=pass1_bam_filename, pass2_bam_filename=pass2_bam_filename, sjdb_filename=sjdb_filename, log1_filename=log1_filename, log2_filename=log2_filename, ) jobs.append(x) # Run pass 1. commands = [] for j in jobs: x = os.path.join(out_path, j.pass1_out_prefix) cmd = alignlib.make_STAR_command(ref.path, x, num_cores, is_stranded, j.pair1, j.pair2, j.log1_filename) # For debugging. If this file already exists, skip it. if not filelib.exists_nz(j.pass1_bam_filename): parallel.sshell(cmd, path=out_path) filelib.assert_exists_nz(j.pass1_bam_filename) commands.append(cmd) if two_pass: # Make a new index with the splice junction information. sj_index = os.path.join(out_path, "genome.2pass") x = [x.sjdb_filename for x in jobs] filelib.assert_exists_nz_many(x) x = alignlib.make_STAR_index_command(ref.fasta_file_full, sj_index, sjdb_files=x, num_cores=num_cores) x = "%s >& genome.2pass.log" % x commands.append(x) # For debugging. If this file already exists, skip it. if not filelib.exists_nz("genome.2pass.log"): parallel.sshell(x, path=out_path) alignlib.assert_is_STAR_reference(sj_index) # Run pass 2. for j in jobs: # For debugging. If this file already exists, skip it. if os.path.exists(j.pass2_bam_filename): continue if two_pass: x = os.path.join(out_path, j.pass2_out_prefix) cmd = alignlib.make_STAR_command(sj_index, x, num_cores, is_stranded, j.pair1, j.pair2, j.log2_filename) parallel.sshell(cmd, path=out_path) commands.append(cmd) else: # link pass1_bam_filename to pass2_bam_filename os.symlink(j.pass1_bam_filename, j.pass2_bam_filename) continue filelib.assert_exists_nz(j.pass2_bam_filename) metadata["commands"] = commands metadata["num_cores"] = num_cores # STAR takes 28 Gb per process. Make sure we don't use up # more memory than is available on the machine. # Defaults: # --limitGenomeGenerateRAM 31000000000 # --outFilterMismatchNmax 10 Num mismatches. #nc = mlib.calc_max_procs_from_ram(50, buffer=100, upper_max=num_cores) #metadata["num_cores"] = nc #parallel.pshell(commands, max_procs=nc, path=out_path) # Make sure the analysis completed successfully. #x = [x[-2] for x in jobs] # sam_filename #filelib.assert_exists_nz_many(x) return metadata
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import filelib from genomicode import alignlib from genomicode import parallel from genomicode import hashlib from Betsy import module_utils as mlib fastq_node, sample_node, strand_node, reference_node = antecedents fastq_files = mlib.find_merged_fastq_files(sample_node.identifier, fastq_node.identifier) assert fastq_files, "I could not find any FASTQ files." ref = alignlib.create_reference_genome(reference_node.identifier) stranded = mlib.read_stranded(strand_node.identifier) filelib.safe_mkdir(out_path) metadata = {} metadata["tool"] = "RSEM %s" % alignlib.get_rsem_version() # Figure out whether to align to genome or transcriptome. x = out_attributes["align_to"] assert x in ["genome", "transcriptome"] align_to_genome = (x == "genome") # RSEM makes files: # <sample_name>.genome.bam # <sample_name>.transcript.bam # <sample_name>.genes.results # <sample_name>.isoforms.results # <sample_name>.stat # # Does not work right if there is a space in the sample name. # Therefore, give a hashed sample name, and then re-name # later. # Make a list of the jobs to run. jobs = [] for x in fastq_files: sample, pair1, pair2 = x sample_h = hashlib.hash_var(sample) x1, x2, x3 = mlib.splitpath(pair1) x = "%s%s" % (hashlib.hash_var(x2), x3) pair1_h = os.path.join(out_path, x) if pair2: x1, x2, x3 = mlib.splitpath(pair2) x = "%s%s" % (hashlib.hash_var(x2), x3) pair2_h = os.path.join(out_path, x) results_filename = os.path.join(out_path, "%s.genes.results" % sample) log_filename = os.path.join(out_path, "%s.log" % sample) x = filelib.GenericObject(sample=sample, sample_h=sample_h, pair1=pair1, pair2=pair2, pair1_h=pair1_h, pair2_h=pair2_h, results_filename=results_filename, log_filename=log_filename) jobs.append(x) # Make sure hashed samples are unique. seen = {} for j in jobs: assert j.sample_h not in seen, \ "Dup (%d): %s" % (len(jobs), j.sample_h) assert j.pair1_h not in seen assert j.pair2_h not in seen seen[j.sample_h] = 1 seen[j.pair1_h] = 1 seen[j.pair2_h] = 1 # Symlink the fastq files. for j in jobs: os.symlink(j.pair1, j.pair1_h) if j.pair2: os.symlink(j.pair2, j.pair2_h) s2fprob = { "unstranded": None, "firststrand": 0.0, "secondstrand": 1.0, } assert stranded.stranded in s2fprob, "Unknown stranded: %s" % \ stranded.stranded forward_prob = s2fprob[stranded.stranded] # How much memory for bowtie. May need to increase this if # there are lots of memory warnings in the log files: # Warning: Exhausted best-first chunk memory for read # ST-J00106:110:H5NY5BBXX:6:1101:18203:44675 1:N:0:1/1 # (patid 2076693); skipping read # Default is 64. # Seems like too high a value can cause problems. #chunkmbs = 4*1024 # Generates warnings. chunkmbs = 512 # Get lots of warnings with bowtie: # Warning: Detected a read pair whose two mates have different names # Use STAR aligner instead. use_STAR = True sq = parallel.quote commands = [] for j in jobs: # Debug: If the results file exists, don't run it again. if filelib.exists_nz(j.results_filename) and \ filelib.exists(j.log_filename): continue # If using the STAR aligner, then most memory efficient # way is to let STAR take care of the multiprocessing. nc = max(1, num_cores / len(jobs)) if use_STAR: nc = num_cores keywds = {} if use_STAR: keywds["align_with_star"] = True else: keywds["align_with_bowtie2"] = True x = alignlib.make_rsem_command(ref.fasta_file_full, j.sample_h, j.pair1_h, fastq_file2=j.pair2_h, forward_prob=forward_prob, output_genome_bam=align_to_genome, bowtie_chunkmbs=chunkmbs, num_threads=nc, **keywds) x = "%s >& %s" % (x, sq(j.log_filename)) commands.append(x) metadata["commands"] = commands metadata["num cores"] = num_cores # Need to run in out_path. Otherwise, files will be everywhere. nc = num_cores if use_STAR: nc = 1 parallel.pshell(commands, max_procs=nc, path=out_path) # Rename the hashed sample names back to the original unhashed # ones. files = os.listdir(out_path) rename_files = [] # list of (src, dst) for j in jobs: if j.sample == j.sample_h: continue for f in files: if not f.startswith(j.sample_h): continue src = os.path.join(out_path, f) x = j.sample + f[len(j.sample_h):] dst = os.path.join(out_path, x) rename_files.append((src, dst)) for src, dst in rename_files: filelib.assert_exists(src) os.rename(src, dst) # Delete the symlinked fastq files. for j in jobs: filelib.safe_unlink(j.pair1_h) filelib.safe_unlink(j.pair2_h) # Make sure the analysis completed successfully. x1 = [x.results_filename for x in jobs] x2 = [x.log_filename for x in jobs] filelib.assert_exists_nz_many(x1 + x2) return metadata
def run(self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import parallel from genomicode import hashlib from genomicode import filelib from genomicode import config from Betsy import module_utils bam_node, group_node = antecedents bam_path = module_utils.check_inpath(bam_node.identifier) sample_groups = module_utils.read_sample_group_file( group_node.identifier) # Get options. treat_sample = module_utils.get_user_option(user_options, "treatment_sample", not_empty=True) control_sample = module_utils.get_user_option(user_options, "control_sample") genome_size = module_utils.get_user_option(user_options, "macs_genome", not_empty=True) shiftsize = module_utils.get_user_option(user_options, "macs_shiftsize") if shiftsize: shiftsize = int(shiftsize) # Set the name. name = hashlib.hash_var(treat_sample) if control_sample: x = hashlib.hash_var(control_sample) name = "%s_vs_%s" % (treat_sample, x) # Make sure the samples exist. samples = [x[1] for x in sample_groups] assert treat_sample in samples, "Unknown sample: %s" % treat_sample if control_sample: assert control_sample in samples, \ "Unknown sample: %s" % control_sample # Find the BAM files. treat_filename = find_bam_file(bam_path, treat_sample, sample_groups) assert treat_filename, "Missing bam file for %s" % treat_sample control_filename = None if control_sample: control_filename = find_bam_file(bam_path, control_sample, sample_groups) assert control_filename, "Missing bam file for %s" % control_sample cmd = make_macs14_command(treat_filename, control_filename, name=name, genome_size=genome_size, shiftsize=shiftsize, save_bedgraph_file=True) parallel.sshell(cmd, path=out_path) # Run Rscript on the model, if one was generated. model_file = os.path.join(out_path, "%s_model.r" % name) if os.path.exists(model_file): Rscript = filelib.which_assert(config.Rscript) cmd = [parallel.quote(Rscript), model_file] parallel.sshell(cmd, path=out_path) files = [ "%s_peaks.xls" % name, "%s_summits.bed" % name, ] filenames = [os.path.join(out_path, x) for x in files] filelib.assert_exists_nz_many(filenames)
def run( self, network, antecedents, out_attributes, user_options, num_cores, out_path): import os from genomicode import parallel from genomicode import filelib from genomicode import alignlib from Betsy import module_utils fastq_node, group_node, reference_node = antecedents fastq_path = fastq_node.identifier assert os.path.exists(fastq_path) assert os.path.isdir(fastq_path) ref = alignlib.create_reference_genome(reference_node.identifier) filelib.safe_mkdir(out_path) #reference_fa = module_utils.find_bwa_reference(index_path) metadata = {} metadata["tool"] = "bwa %s" % alignlib.get_bwa_version() # Find the merged fastq files. x = module_utils.find_merged_fastq_files( group_node.identifier, fastq_path) grouped_fastq_files = x # Make sure no duplicate samples. x1 = [x[0] for x in grouped_fastq_files] x2 = {}.fromkeys(x1).keys() assert len(x1) == len(x2), "dup sample" # Make a list of all the jobs to do. jobs = [] # list of (sample, pair1, pair2, bam_filename) for x in grouped_fastq_files: sample, pair1, pair2 = x bam_filename = os.path.join(out_path, "%s.bam" % sample) log_filename = os.path.join(out_path, "%s.log" % sample) x = sample, pair1, pair2, bam_filename, log_filename jobs.append(x) # Uses ~6 Gb per process. # Calculate the number of cores per job. nc = max(1, num_cores/len(jobs)) metadata["num cores"] = nc # Make the bwa commands. commands = [] for x in jobs: sample, pair1, pair2, bam_filename, log_filename = x x = alignlib.make_bwa_mem_command( ref.fasta_file_full, log_filename, pair1, fastq_file2=pair2, bam_filename=bam_filename, num_threads=nc) commands.append(x) metadata["commands"] = commands parallel.pshell(commands, max_procs=num_cores) # Make sure the analysis completed successfully. x1 = [x[-2] for x in jobs] x2 = [x[-1] for x in jobs] filelib.assert_exists_nz_many(x1 + x2) return metadata