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#!/usr/bin/python3
__doc__="""
pliu 20150304
python function for pRSEM
"""
import os
import sys
import Util
def genChIPSeqSignalFilesFromBed(param):
import ChIPSeqReplicate
fbeds = param.chipseq_bed_files_multi_targets.split(',')
for fbed in fbeds:
csr = ChIPSeqReplicate.initFromBedFile(fbed)
ta = csr.tagalign
param.targetid2fchipseq_alignment[ta.basename] = ta.fullname
def genChIPSeqSignalFilesFromReads(param):
import ChIPSeqExperiment
cse_target = ChIPSeqExperiment.initFromParam(param, 'multi-targets')
cse_target.getFastqEncoding()
cse_target.alignReadByBowtie()
param.chipseqexperiment_target = cse_target
for rep in cse_target.reps:
ta = rep.tagalign
param.targetid2fchipseq_alignment[ta.basename] = ta.fullname
def genChIPSeqPeakFileBySPPIDR(param):
import ChIPSeqExperiment
cse_target = ChIPSeqExperiment.initFromParam(param, 'target')
cse_target.getFastqEncoding()
cse_target.alignReadByBowtie()
cse_target.poolTagAlign()
param.chipseqexperiment_target = cse_target
if param.chipseq_control_read_files is not None:
cse_control = ChIPSeqExperiment.initFromParam(param, 'control')
cse_control.getFastqEncoding()
cse_control.alignReadByBowtie()
cse_control.poolTagAlign()
cse_target.callPeaksBySPP(cse_control.pooled_tagalign)
cse_target.getPeaksByIDR(cse_control.pooled_tagalign)
param.chipseq_peak_file = cse_target.final_peaks.fullname
param.chipseqexperiment_control = cse_control
else:
pass ## to-be-implemented, call peaks by MOSAiCS without ChIP-seq control
def buildTrainingSet(prm):
"""
write training set in file Param.ftraining_tr_crd
transcript as listed in the same order as RSEM's .ti file
The order is required by rsem-run-gibbs so that prior can be assigned to
transcript correctly
"""
ogot_genes = [g for g in prm.genes if len(g.transcripts) == 1 and
(g.end - g.start + 1) >=
prm.TRAINING_GENE_MIN_LEN]
trs = [tr for g in ogot_genes for tr in g.transcripts]
trid2mpps = Util.runMPOverAList(prm.num_threads, calTSSBodyTESMappability,
[trs, prm])
with open(prm.fall_tr_crd, 'w') as f_fout:
f_fout.write("geneid\ttrid\tchrom\tstrand\tstart\tend\t")
f_fout.write("tss_mpp\tbody_mpp\ttes_mpp\n")
for tr in prm.transcripts: ## in the same order as RSEM's .ti file
f_fout.write("%s\t%s\t%s\t%s\t%d\t%d\t" % ( tr.gene_id,
tr.transcript_id, tr.chrom, tr.strand, tr.start, tr.end))
if tr.transcript_id in trid2mpps:
mpps = trid2mpps[tr.transcript_id]
f_fout.write("%5.3f\t%5.3f\t%5.3f\n" % mpps)
else:
f_fout.write("NA\tNA\tNA\n")
with open(prm.fall_exon_crd, 'w') as f_fexon:
f_fexon.write("trid\texon_index\tchrom\tstrand\tstart\tend\n")
for tr in prm.transcripts:
for (i, (exon_start, exon_end)) in enumerate(tr.exon_ranges):
f_fexon.write("%s\t%d\t%s\t%s\t%d\t%d\n" % (tr.transcript_id, i+1,
tr.chrom, tr.strand, exon_start, exon_end))
Util.runCommand('/bin/env', 'Rscript', prm.rnaseq_rscript, 'selTrainingTr',
prm.prsem_rlib_dir, prm.fall_tr_crd, prm.fall_exon_crd,
prm.TRAINING_MIN_MAPPABILITY, prm.FLANKING_WIDTH,
prm.ftraining_tr_crd, quiet=prm.quiet)
if not os.path.exists(prm.ftraining_tr_crd):
sys.exit("Failed to generate file: %s\n" % prm.ftraining_tr_crd)
def calTSSBodyTESMappability(trs, prm, out_q):
"""
calculate average mappability around TSS, body, and TES for all transcripts of
given list of genes
save results in transcript's attribute
"""
outdict = {}
for tr in trs:
tr.calculateMappability(prm.bigwigsummary_bin, prm.mappability_bigwig_file,
prm.FLANKING_WIDTH, prm.quiet)
outdict[tr.transcript_id] = (tr.ave_mpp_around_TSS, tr.ave_mpp_around_body,
tr.ave_mpp_around_TES)
out_q.put(outdict)
def genPriorByCombinedTSSSignals(prm):
"""
calculate TSS signals for all external data sets
compute informative p-value, LL for individual data set and combined one
learn prior from training set partitioned by combined TSS signals
derive priors for all isoforms
"""
f_fout = open(prm.finfo_multi_targets, 'w')
f_fout.write("targetid\tfaln\tfftrs\n")
for (tgtid, faln) in list(prm.targetid2fchipseq_alignment.items()):
fftrs = prm.imd_name + '_prsem.' + tgtid + '.all_tr_features'
f_fout.write("%s\t%s\t%s\n" % (tgtid, faln, fftrs))
f_fout.close()
Util.runCommand('/bin/env', 'Rscript', prm.rnaseq_rscript,
'prepMultiTargetsFeatures', prm.prsem_rlib_dir,
prm.fall_tr_crd, prm.ftraining_tr_crd,
prm.fisoforms_results, prm.FLANKING_WIDTH,
prm.cap_stacked_chipseq_reads,
prm.n_max_stacked_chipseq_reads,
prm.finfo_multi_targets, prm.num_threads, quiet=prm.quiet)
## learn prior from partitioning by combined external data set
Util.runCommand('/bin/env', 'Rscript', prm.rnaseq_rscript,
'genPriorByCombinedTSSSignals', prm.prsem_rlib_dir,
prm.finfo_multi_targets, prm.flgt_model_multi_targets,
prm.fall_tr_features, prm.fpvalLL, prm.fall_tr_prior,
quiet=prm.quiet)
pval = float(Util.readFile(prm.fpvalLL)[1].split("\t")[0])
if pval > prm.INFORMATIVE_DATA_MAX_P_VALUE:
err_msg = "\nError: current external data is NOT informative for RNA-seq quantification\n" + \
"\tp-value %.10e > %.3f\n" % (pval, prm.INFORMATIVE_DATA_MAX_P_VALUE) + \
"pRSEM STOPs here. Please use other external data set(s)\n\n"
sys.stderr.write(err_msg)
sys.exit(0)
if not os.path.exists(prm.fall_tr_prior):
sys.exit("Failed to generate file: %s\n" % prm.fall_tr_prior)
def genPriorByPeakSignalGCLen(prm):
"""
calculate peaks/signals for the TSS, body, and TES regions
calculate GC contenct and effective length
learn prior from training set and derived priors for all isoforms
"""
## calculate GC contect for isoforms
trid2seq = Util.getFastaID2Seq(prm.ffasta)
with open(prm.fall_tr_gc, 'w') as f_fall_tr_gc:
f_fall_tr_gc.write("trid\tGC_fraction\n")
for tr in prm.transcripts:
gc_frac = Util.getGCFraction(trid2seq[tr.transcript_id])
f_fall_tr_gc.write("%s\t%.2f\n" % (tr.transcript_id, gc_frac) )
with open(prm.fsppout_target, 'r') as f_fsppout_target:
words = f_fsppout_target.read().split("\t")
prm.chipseq_target_fraglen = int(words[2])
## prepare a feature file of peaks and signals for all isoforms,
## isoforms in training set will be labeled
if not os.path.exists(prm.fchipseq_peaks):
sys.exit("File not exists: %s\n" % prm.fchipseq_peaks)
Util.runCommand('/bin/env', 'Rscript', prm.rnaseq_rscript,
'prepPeakSignalGCLenFeatures', prm.prsem_rlib_dir,
prm.fall_tr_crd, prm.ftraining_tr_crd, prm.fall_tr_features,
prm.fisoforms_results, prm.FLANKING_WIDTH,
prm.partition_model, prm.fchipseq_peaks,
prm.fchipseq_target_signals, prm.fall_tr_gc, prm.num_threads,
prm.chipseq_target_fraglen, quiet=prm.quiet)
if not os.path.exists(prm.fall_tr_gc):
sys.exit("Failed to generate file: %s\n" % prm.fall_tr_gc)
## learn and generate prior for all transcripts
Util.runCommand('/bin/env', 'Rscript', prm.rnaseq_rscript,
'genPriorByPeakSignalGCLen', prm.prsem_rlib_dir,
prm.fall_tr_features, prm.partition_model, prm.fall_tr_prior,
quiet=prm.quiet)
if not os.path.exists(prm.fall_tr_prior):
sys.exit("Failed to generate file: %s\n" % prm.fall_tr_prior)
def genPriorByTSSPeak(prm):
"""
determine if isoform have TSS peak or not
learn priors from training set and derived priors for all isoforms
"""
## prepare a feature file of TSS peaks for all isoforms,
## isoforms in training set will be labeled
if not os.path.exists(prm.fchipseq_peaks):
sys.exit("File not exists: %s\n" % prm.fchipseq_peaks)
Util.runCommand('/bin/env', 'Rscript', prm.rnaseq_rscript,
'prepTSSPeakFeatures', prm.prsem_rlib_dir,
prm.fall_tr_crd, prm.ftraining_tr_crd, prm.fall_tr_features,
prm.fisoforms_results, prm.FLANKING_WIDTH,
prm.fchipseq_peaks, quiet=prm.quiet)
if not os.path.exists(prm.fall_tr_features):
sys.exit("Failed to generate file: %s\n" % prm.fall_tr_features)
Util.runCommand('/bin/env', 'Rscript', prm.rnaseq_rscript,
'genPriorByTSSPeak', prm.prsem_rlib_dir,
prm.fall_tr_features, prm.fpvalLL, prm.fall_tr_prior,
quiet=prm.quiet)
pval = float(Util.readFile(prm.fpvalLL)[1].split("\t")[0])
if pval > prm.INFORMATIVE_DATA_MAX_P_VALUE:
err_msg = "\nError: current external data is NOT informative for RNA-seq quantification\n" + \
"\tp-value %.10e > %.3f\n" % (pval, prm.INFORMATIVE_DATA_MAX_P_VALUE) + \
"pRSEM STOPs here. Please use other external data set(s)\n\n"
sys.stderr.write(err_msg)
sys.exit(0)
if not os.path.exists(prm.fall_tr_prior):
sys.exit("Failed to generate file: %s\n" % prm.fall_tr_prior)
def runGibbsSampling(prm):
if prm.quiet:
run_gibbs_quiet = '-q'
else:
run_gibbs_quiet = ''
Util.runCommand("%s/../rsem-run-gibbs" % prm.prsem_scr_dir,
prm.ref_name, prm.imd_name, prm.stat_name, prm.gibbs_burnin,
prm.gibbs_number_of_samples, prm.gibbs_sampling_gap,
'-p', prm.num_threads, run_gibbs_quiet,
'--prior', prm.fall_tr_prior,
quiet=prm.quiet)
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