def snp_workflow(ex, job, assembly, minsnp=40., mincov=5, path_to_ref=None, via='local',
logfile=sys.stdout, debugfile=sys.stderr):
"""Main function of the workflow"""
ref_genome = assembly.fasta_by_chrom
sample_names = [job.groups[gid]['name'] for gid in sorted(job.files.keys())]
logfile.write("\n* Generate vcfs for each chrom/group\n"); logfile.flush()
vcfs = dict((chrom,{}) for chrom in ref_genome.keys()) # {chr: {}}
bams = {}
# Launch the jobs
for gid in sorted(job.files.keys()):
# Merge all bams belonging to the same group
runs = [r['bam'] for r in job.files[gid].itervalues()]
bam = Samfile(runs[0])
header = bam.header
headerfile = unique_filename_in()
for h in header["SQ"]:
if h["SN"] in assembly.chrmeta:
h["SN"] = assembly.chrmeta[h["SN"]]["ac"]
head = Samfile( headerfile, "wh", header=header )
head.close()
if len(runs) > 1:
_b = merge_bam(ex,runs)
index_bam(ex,_b)
bams[gid] = _b
else:
bams[gid] = runs[0]
# Samtools mpileup + bcftools + vcfutils.pl
for chrom,ref in ref_genome.iteritems():
vcf = unique_filename_in()
vcfs[chrom][gid] = (vcf,
pileup.nonblocking(ex, bams[gid], ref, header=headerfile,
via=via, stdout=vcf))
logfile.write(" ...Group %s running.\n" %job.groups[gid]['name']); logfile.flush()
# Wait for vcfs to finish and store them in *vcfs[chrom][gid]*
for gid in sorted(job.files.keys()):
for chrom,ref in ref_genome.iteritems():
vcfs[chrom][gid][1].wait()
vcfs[chrom][gid] = vcfs[chrom][gid][0]
logfile.write(" ...Group %s done.\n" %job.groups[gid]['name']); logfile.flush()
# Targz the pileup files (vcf)
tarname = unique_filename_in()
tarfh = tarfile.open(tarname, "w:gz")
for chrom,v in vcfs.iteritems():
for gid,vcf in v.iteritems():
tarfh.add(vcf, arcname="%s_%s.vcf" % (job.groups[gid]['name'],chrom))
tarfh.close()
ex.add( tarname, description=set_file_descr("vcfs_files.tar.gz",step="pileup",type="tar",view='admin') )
logfile.write("\n* Merge info from vcf files\n"); logfile.flush()
outall = unique_filename_in()
outexons = unique_filename_in()
with open(outall,"w") as fout:
fout.write('#'+'\t'.join(['chromosome','position','reference']+sample_names+ \
['gene','location_type','distance'])+'\n')
with open(outexons,"w") as fout:
fout.write('#'+'\t'.join(['chromosome','position','reference']+sample_names+['exon','strand','ref_aa'] \
+ ['new_aa_'+s for s in sample_names])+'\n')
msa_table = dict((s,'') for s in [assembly.name]+sample_names)
for chrom,v in vcfs.iteritems():
logfile.write(" > Chromosome '%s'\n" % chrom); logfile.flush()
# Put together info from all vcf files
logfile.write(" - All SNPs\n"); logfile.flush()
allsnps = all_snps(ex,chrom,vcfs[chrom],bams,outall,assembly,
sample_names,mincov,float(minsnp),logfile,debugfile)
# Annotate SNPs and check synonymy
logfile.write(" - Exonic SNPs\n"); logfile.flush()
exon_snps(chrom,outexons,allsnps,assembly,sample_names,ref_genome,logfile,debugfile)
for snprow in allsnps:
for n,k in enumerate([assembly.name]+sample_names):
msa_table[k] += snprow[3+n][0]
description = set_file_descr("allSNP.txt",step="SNPs",type="txt")
ex.add(outall,description=description)
description = set_file_descr("exonsSNP.txt",step="SNPs",type="txt")
ex.add(outexons,description=description)
msafile = unique_filename_in()
with open(msafile,"w") as msa:
msa.write(" %i %i\n"%(len(msa_table),len(msa_table.values()[0])))
for name,seq in msa_table.iteritems():
msa.write("%s\t%s\n" %(name,seq))
msa_table = {}
description = set_file_descr("SNPalignment.txt",step="SNPs",type="txt")
ex.add(msafile,description=description)
# Create UCSC bed tracks
logfile.write("\n* Create tracks\n"); logfile.flush()
create_tracks(ex,outall,sample_names,assembly)
# Create quantitative tracks
logfile.write("\n* Create heteroz. and quality tracks\n"); logfile.flush()
def _process_pileup(pileups, seq, startpos, endpos):
atoi = {'A': 0, 'C': 1, 'G': 2, 'T': 3}
vectors = ([],[],[])
for pileupcolumn in pileups:
position = pileupcolumn.pos
if position < startpos: continue
if position >= endpos: break
coverage = pileupcolumn.n
ref_symbol = seq[position-startpos]
ref = atoi.get(ref_symbol, 4)
symbols = [0,0,0,0,0]
quality = 0
for pileupread in pileupcolumn.pileups:
symbols[atoi.get(pileupread.alignment.seq[pileupread.qpos], 4)] += 1
quality += ord(pileupread.alignment.qual[pileupread.qpos])-33
quality = float(quality)/coverage
info = heterozygosity(ref, symbols[0:4])
if coverage > 0: vectors[0].append((position, position+1, coverage))
if info > 0: vectors[1].append((position, position+1, info))
if quality > 0: vectors[2].append((position, position+1, quality))
# yield (position, position+1, coverage, info, quality)
return vectors
if job.options.get('make_bigwigs',False):
_descr = {'groupId':0,'step':"tracks",'type':"bigWig",'ucsc':'1'}
for gid,bamfile in bams.iteritems():
_descr['groupId'] = gid
bamtr = track(bamfile,format="bam")
covname = unique_filename_in()+".bw"
out_cov = track(covname, chrmeta=assembly.chrmeta)
hetname = unique_filename_in()+".bw"
out_het = track(hetname, chrmeta=assembly.chrmeta)
qualname = unique_filename_in()+".bw"
out_qual = track(qualname, chrmeta=assembly.chrmeta)
for chrom, cinfo in assembly.chrmeta.iteritems():
fasta = Fastafile(ref_genome[chrom])
#process fasta and bam by 10Mb chunks
for chunk in range(0,cinfo["length"],10**7):
fastaseq = fasta.fetch(cinfo['ac'], chunk, chunk+10**7)
vecs = _process_pileup(bamtr.pileup(chrom, chunk, chunk+10**7), fastaseq, chunk, chunk+10**7)
out_cov.write(vecs[0], fields=['start','end','score'], chrom=chrom)
out_het.write(vecs[1], fields=['start','end','score'], chrom=chrom)
out_qual.write(vecs[2], fields=['start','end','score'], chrom=chrom)
out_cov.close()
out_het.close()
out_qual.close()
description = set_file_descr(job.groups[gid]['name']+"_coverage.bw",**_descr)
ex.add(covname,description=description)
description = set_file_descr(job.groups[gid]['name']+"_heterozygosity.bw",**_descr)
ex.add(hetname,description=description)
description = set_file_descr(job.groups[gid]['name']+"_quality.bw",**_descr)
ex.add(qualname,description=description)
return 0
def dnaseseq_workflow( ex, job, assembly, logfile=sys.stdout, via='lsf' ):
"""
This workflow performs the following steps:
* BAM files from replicates within the same group are merged
* MACS is called to identify enriched regions (only peak summit +- 300 will be used), this can be by-passed by provinding a bed file to any group
* Wellington is called to identify footprints within these enriched regions
* If a list of motifs is provided (by group), footprints are scanned and motif occurences (log-likelihood ratio > 0) are recorded in a bed file
* Average DNAse profiles around motifs are plotted
"""
tests = []
controls = []
names = {'tests': [], 'controls': []}
supdir = os.path.split(ex.remote_working_directory)[0]
for gid,mapped in job.files.iteritems():
group_name = job.groups[gid]['name']
if not isinstance(mapped,dict):
raise TypeError("Files values must be dictionaries with keys *run_ids* or 'bam'.")
if 'bam' in mapped: mapped = {'_': mapped}
if len(mapped)>1:
bamfile = merge_bam(ex, [m['bam'] for m in mapped.values()])
index = index_bam(ex, bamfile)
else:
bamfile = mapped.values()[0]['bam']
if job.groups[gid]['control']:
controls.append(bamfile)
names['controls'].append((gid,group_name))
else:
if os.path.exists(job.groups[gid].get('bedfile','null')):
bedfile = job.groups[gid]['bedfile']
elif os.path.exists(os.path.join(supdir,job.groups[gid].get('bedfile','null'))):
bedfile = os.path.join(supdir,job.groups[gid]['bedfile'])
else:
bedfile = None
tests.append((bedfile,bamfile))
names['tests'].append((gid,group_name))
if len(controls)<1:
controls = [None]
names['controls'] = [(0,None)]
tests = macs_bedfiles( ex, assembly.chrmeta, tests, controls, names,
job.options.get('macs_args',["--keep-dup","10"]), via, logfile )
bedlist = run_wellington(ex, tests, names, assembly, via, logfile)
######################### Motif scanning / plotting
if any([gr.get('motif') != 'null' and gr.get('motif')
for gr in job.groups.values()]):
motifbeds = motif_scan( ex, bedlist, assembly, job.groups, via, logfile )
siglist = dict((gid[0],[]) for gid in names['tests'])
for gid,mapped in job.files.iteritems():
wig = []
suffixes = ["fwd","rev"]
merge_strands = int(job.options.get('merge_strands',-1))
read_extension = int(job.options.get('read_extension') or -1)
make_wigs = merge_strands >= 0 or read_extension != 1
for m in mapped.values():
if make_wigs or not('wig' in m) or len(m['wig'])<2:
output = mapseq.parallel_density_sql( ex, m["bam"], assembly.chrmeta,
nreads=m["stats"]["total"],
merge=-1, read_extension=1,
convert=False,
b2w_args=[], via=via )
wig.append(dict((s,output+s+'.sql') for s in suffixes))
else:
wig.append(m['wig'])
if len(wig) > 1:
wig[0] = dict((s,merge_sql(ex, [x[s] for x in wig], via=via))
for s in suffixes)
_trn = job.groups[gid]['name']+"_%s"
if job.groups[gid]['control']:
for s,w in wig[0].iteritems():
for _g in siglist.keys():
siglist[_g].append(track(w,info={'name': _trn%s}))
else:
siglist[gid].extend([track(w,info={'name': _trn%s})
for s,w in wig[0].iteritems()])
plot_files = plot_footprint_profile( ex, motifbeds, siglist,
assembly.chrnames,
job.groups, logfile )
for gid, flist in plot_files.iteritems():
gname = job.groups[gid]['name']
plotall = unique_filename_in()
touch( ex, plotall )
ex.add(plotall, description=set_file_descr(gname+'_footprints_plots',
type='none', view='admin',
step='motifs', groupId=gid))
ex.add(flist['pdf'], description=set_file_descr(gname+'_footprints_plots.pdf',
type='pdf', step='motifs',
groupId=gid),
associate_to_filename=plotall, template='%s.pdf')
tarname = unique_filename_in()
tarfh = tarfile.open(tarname, "w:gz")
for mname,matf in flist['mat']:
tarfh.add(matf, arcname="%s_%s.txt" % (gname,mname))
tarfh.close()
ex.add( tarname, description=set_file_descr(gname+'_footprints_plots.tar.gz',
type='tar', step='motifs', groupId=gid),
associate_to_filename=plotall, template='%s.tar.gz')
logfile.write("\nDone.\n ");logfile.flush()
return 0
def dnaseseq_workflow(ex, job, assembly, logfile=sys.stdout, via='lsf'):
"""
This workflow performs the following steps:
* BAM files from replicates within the same group are merged
* MACS is called to identify enriched regions (only peak summit +- 300 will be used), this can be by-passed by provinding a bed file to any group
* Wellington is called to identify footprints within these enriched regions
* If a list of motifs is provided (by group), footprints are scanned and motif occurences (log-likelihood ratio > 0) are recorded in a bed file
* Average DNAse profiles around motifs are plotted
"""
tests = []
controls = []
names = {'tests': [], 'controls': []}
supdir = os.path.split(ex.remote_working_directory)[0]
for gid, mapped in job.files.iteritems():
group_name = job.groups[gid]['name']
if not isinstance(mapped, dict):
raise TypeError(
"Files values must be dictionaries with keys *run_ids* or 'bam'."
)
if 'bam' in mapped: mapped = {'_': mapped}
if len(mapped) > 1:
bamfile = merge_bam(ex, [m['bam'] for m in mapped.values()])
index = index_bam(ex, bamfile)
else:
bamfile = mapped.values()[0]['bam']
if job.groups[gid]['control']:
controls.append(bamfile)
names['controls'].append((gid, group_name))
else:
if os.path.exists(job.groups[gid].get('bedfile', 'null')):
bedfile = job.groups[gid]['bedfile']
elif os.path.exists(
os.path.join(supdir,
job.groups[gid].get('bedfile', 'null'))):
bedfile = os.path.join(supdir, job.groups[gid]['bedfile'])
else:
bedfile = None
tests.append((bedfile, bamfile))
names['tests'].append((gid, group_name))
if len(controls) < 1:
controls = [None]
names['controls'] = [(0, None)]
tests = macs_bedfiles(ex, assembly.chrmeta, tests, controls, names,
job.options.get('macs_args', ["--keep-dup", "10"]),
via, logfile)
bedlist = run_wellington(ex, tests, names, assembly, via, logfile)
######################### Motif scanning / plotting
if any([
gr.get('motif') != 'null' and gr.get('motif')
for gr in job.groups.values()
]):
motifbeds = motif_scan(ex, bedlist, assembly, job.groups, via, logfile)
siglist = dict((gid[0], []) for gid in names['tests'])
for gid, mapped in job.files.iteritems():
wig = []
suffixes = ["fwd", "rev"]
merge_strands = int(job.options.get('merge_strands', -1))
read_extension = int(job.options.get('read_extension') or -1)
make_wigs = merge_strands >= 0 or read_extension != 1
for m in mapped.values():
if make_wigs or not ('wig' in m) or len(m['wig']) < 2:
output = mapseq.parallel_density_sql(
ex,
m["bam"],
assembly.chrmeta,
nreads=m["stats"]["total"],
merge=-1,
read_extension=1,
convert=False,
b2w_args=[],
via=via)
wig.append(dict(
(s, output + s + '.sql') for s in suffixes))
else:
wig.append(m['wig'])
if len(wig) > 1:
wig[0] = dict((s, merge_sql(ex, [x[s] for x in wig], via=via))
for s in suffixes)
_trn = job.groups[gid]['name'] + "_%s"
if job.groups[gid]['control']:
for s, w in wig[0].iteritems():
for _g in siglist.keys():
siglist[_g].append(track(w, info={'name': _trn % s}))
else:
siglist[gid].extend([
track(w, info={'name': _trn % s})
for s, w in wig[0].iteritems()
])
plot_files = plot_footprint_profile(ex, motifbeds, siglist,
assembly.chrnames, job.groups,
logfile)
for gid, flist in plot_files.iteritems():
gname = job.groups[gid]['name']
plotall = unique_filename_in()
touch(ex, plotall)
ex.add(plotall,
description=set_file_descr(gname + '_footprints_plots',
type='none',
view='admin',
step='motifs',
groupId=gid))
ex.add(flist['pdf'],
description=set_file_descr(gname + '_footprints_plots.pdf',
type='pdf',
step='motifs',
groupId=gid),
associate_to_filename=plotall,
template='%s.pdf')
tarname = unique_filename_in()
tarfh = tarfile.open(tarname, "w:gz")
for mname, matf in flist['mat']:
tarfh.add(matf, arcname="%s_%s.txt" % (gname, mname))
tarfh.close()
ex.add(tarname,
description=set_file_descr(gname +
'_footprints_plots.tar.gz',
type='tar',
step='motifs',
groupId=gid),
associate_to_filename=plotall,
template='%s.tar.gz')
logfile.write("\nDone.\n ")
logfile.flush()
return 0
def snp_workflow(ex,
job,
assembly,
minsnp=40.,
mincov=5,
path_to_ref=None,
via='local',
logfile=sys.stdout,
debugfile=sys.stderr):
"""Main function of the workflow"""
ref_genome = assembly.fasta_by_chrom
sample_names = [
job.groups[gid]['name'] for gid in sorted(job.files.keys())
]
logfile.write("\n* Generate vcfs for each chrom/group\n")
logfile.flush()
vcfs = dict((chrom, {}) for chrom in ref_genome.keys()) # {chr: {}}
bams = {}
# Launch the jobs
bam = Samfile(job.files.values()[0].values()[0]['bam'])
header = bam.header
headerfile = unique_filename_in()
for h in header["SQ"]:
if h["SN"] in assembly.chrmeta:
h["SN"] = assembly.chrmeta[h["SN"]]["ac"]
head = Samfile(headerfile, "wh", header=header)
head.close()
for gid in job.files.keys():
# Merge all bams belonging to the same group
runs = [r['bam'] for r in job.files[gid].itervalues()]
if len(runs) > 1:
_b = merge_bam(ex, runs)
index_bam(ex, _b)
bams[gid] = _b
else:
index_bam(ex, runs[0])
bams[gid] = runs[0]
# Samtools mpileup + bcftools + vcfutils.pl
for chrom, ref in ref_genome.iteritems():
vcf = unique_filename_in()
vcfs[chrom][gid] = (vcf,
pileup.nonblocking(ex,
bams[gid],
ref,
header=headerfile,
via=via,
stdout=vcf))
logfile.write(" ...Group %s running.\n" % job.groups[gid]['name'])
logfile.flush()
# Wait for vcfs to finish and store them in *vcfs[chrom][gid]*
for gid in job.files.keys():
for chrom, ref in ref_genome.iteritems():
vcfs[chrom][gid][1].wait()
vcfs[chrom][gid] = vcfs[chrom][gid][0]
logfile.write(" ...Group %s done.\n" % job.groups[gid]['name'])
logfile.flush()
# Targz the pileup files (vcf)
tarname = unique_filename_in()
tarfh = tarfile.open(tarname, "w:gz")
for chrom, v in vcfs.iteritems():
for gid, vcf in v.iteritems():
tarfh.add(vcf,
arcname="%s_%s.vcf" % (job.groups[gid]['name'], chrom))
tarfh.close()
ex.add(tarname,
description=set_file_descr("vcf_files.tar.gz",
step="pileup",
type="tar",
view='admin'))
logfile.write("\n* Merge info from vcf files\n")
logfile.flush()
outall = unique_filename_in()
outexons = unique_filename_in()
with open(outall, "w") as fout:
fout.write('#'+'\t'.join(['chromosome','position','reference']+sample_names+ \
['gene','location_type','distance'])+'\n')
with open(outexons, "w") as fout:
fout.write('#'+'\t'.join(['chromosome','position','reference']+sample_names+['exon','strand','ref_aa'] \
+ ['new_aa_'+s for s in sample_names])+'\n')
msa_table = dict((s, '') for s in [assembly.name] + sample_names)
for chrom, v in vcfs.iteritems():
logfile.write(" > Chromosome '%s'\n" % chrom)
logfile.flush()
# Put together info from all vcf files
logfile.write(" - All SNPs\n")
logfile.flush()
allsnps = all_snps(ex, chrom, vcfs[chrom], bams, outall,
assembly, headerfile, sample_names, mincov,
float(minsnp), logfile, debugfile, via)
# Annotate SNPs and check synonymy
logfile.write(" - Exonic SNPs\n")
logfile.flush()
exon_snps(chrom, outexons, allsnps, assembly, sample_names, ref_genome,
logfile, debugfile)
for snprow in allsnps:
for n, k in enumerate([assembly.name] + sample_names):
base = snprow[3 + n][0]
if base == "-": base = snprow[3][0]
if base not in 'ACGTacgt': base = "N"
msa_table[k] += base
description = set_file_descr("allSNP.txt", step="SNPs", type="txt")
ex.add(outall, description=description)
description = set_file_descr("exonsSNP.txt", step="SNPs", type="txt")
ex.add(outexons, description=description)
msafile = unique_filename_in()
with open(msafile, "w") as msa:
msa.write(" %i %i\n" % (len(msa_table), len(msa_table.values()[0])))
for name, seq in msa_table.iteritems():
msa.write("%s\t%s\n" % (name, seq))
msa_table = {}
description = set_file_descr("SNPalignment.txt", step="SNPs", type="txt")
ex.add(msafile, description=description)
# Create UCSC bed tracks
logfile.write("\n* Create tracks\n")
logfile.flush()
create_tracks(ex, outall, sample_names, assembly)
# Create quantitative tracks
logfile.write("\n* Create heteroz. and quality tracks\n")
logfile.flush()
def _process_pileup(pileups, seq, startpos, endpos):
atoi = {'A': 0, 'C': 1, 'G': 2, 'T': 3}
vectors = ([], [], [])
for pileupcolumn in pileups:
position = pileupcolumn.pos
if position < startpos: continue
if position >= endpos: break
coverage = pileupcolumn.n
ref_symbol = seq[position - startpos]
ref = atoi.get(ref_symbol, 4)
symbols = [0, 0, 0, 0, 0]
quality = 0
for pileupread in pileupcolumn.pileups:
if pileupread.qpos >= len(pileupread.alignment.seq):
coverage -= 1
else:
symbols[atoi.get(pileupread.alignment.seq[pileupread.qpos],
4)] += 1
quality += ord(
pileupread.alignment.qual[pileupread.qpos]) - 33
quality = float(quality) / coverage
info = heterozygosity(ref, symbols[0:4])
if coverage > 0:
vectors[0].append((position, position + 1, coverage))
if info > 0: vectors[1].append((position, position + 1, info))
if quality > 0:
vectors[2].append((position, position + 1, quality))
# yield (position, position+1, coverage, info, quality)
return vectors
if job.options.get('make_bigwigs', False):
_descr = {
'groupId': 0,
'step': "tracks",
'type': "bigWig",
'ucsc': '1'
}
for gid, bamfile in bams.iteritems():
_descr['groupId'] = gid
bamtr = track(bamfile, format="bam")
covname = unique_filename_in() + ".bw"
out_cov = track(covname, chrmeta=assembly.chrmeta)
hetname = unique_filename_in() + ".bw"
out_het = track(hetname, chrmeta=assembly.chrmeta)
qualname = unique_filename_in() + ".bw"
out_qual = track(qualname, chrmeta=assembly.chrmeta)
for chrom, cinfo in assembly.chrmeta.iteritems():
fasta = Fastafile(ref_genome[chrom])
#process fasta and bam by 10Mb chunks
for chunk in range(0, cinfo["length"], 10**7):
fastaseq = fasta.fetch(cinfo['ac'], chunk, chunk + 10**7)
vecs = _process_pileup(
bamtr.pileup(chrom, chunk, chunk + 10**7), fastaseq,
chunk, chunk + 10**7)
out_cov.write(vecs[0],
fields=['start', 'end', 'score'],
chrom=chrom)
out_het.write(vecs[1],
fields=['start', 'end', 'score'],
chrom=chrom)
out_qual.write(vecs[2],
fields=['start', 'end', 'score'],
chrom=chrom)
out_cov.close()
out_het.close()
out_qual.close()
description = set_file_descr(
job.groups[gid]['name'] + "_coverage.bw", **_descr)
ex.add(covname, description=description)
description = set_file_descr(
job.groups[gid]['name'] + "_heterozygosity.bw", **_descr)
ex.add(hetname, description=description)
description = set_file_descr(
job.groups[gid]['name'] + "_quality.bw", **_descr)
ex.add(qualname, description=description)
return 0