def createLibrary(ex, assembly_or_fasta, params, url=GlobalHtsUrl, via='local'):
"""
Main call to create the library
"""
if len(params['primary'])<2:
print('Some parameters are missing, cannot create the library')
print('primary='+params['primary']+" ; "+'secondary='+params['secondary'])
return [None,None,None,None]
if not isinstance(assembly_or_fasta,genrep.Assembly):
assembly_or_fasta = genrep.Assembly( ex=ex, fasta=assembly_or_fasta )
chrnames = assembly_or_fasta.chrnames
chrom_map = dict((v['ac'],k) for k,v in assembly_or_fasta.chrmeta.iteritems())
allfiles = assembly_or_fasta.fasta_by_chrom #assembly_or_fasta.untar_genome_fasta()
libfiles = dict((c, getRestEnzymeOccAndSeq.nonblocking( ex, f,
params['primary'], params['secondary'],
params['length'], params['type'],
via=via ))
for c, f in allfiles.iteritems())
resfile = unique_filename_in()
os.mkdir(resfile)
bedfiles = {}
for chrom, future in libfiles.iteritems():
libfiles[chrom] = future.wait()
if not os.path.getsize(libfiles[chrom][1])>0:
time.sleep(60)
touch(ex,libfiles[chrom][1])
bedfiles[chrom] = parse_fragFile(libfiles[chrom][1],chrom_map)
rescov = coverageInRepeats(ex, bedfiles, params['species'], outdir=resfile, via=via)
bedchrom = [os.path.join(resfile,chrom+".bed") for chrom in chrnames]
cat(bedchrom,out=resfile+".bed")
gzipfile(ex,[resfile+".bed"]+bedchrom)
# resfile_sql = resfile+".sql"
# track.convert((resfile,'bed'),(resfile_sql,'sql'),assembly=params['species'])
enz_list = []
infos_lib = { 'assembly_name': params['species'],
'enzyme1_id': getEnzymeSeqId(params['primary'], True, enz_list, url),
'enzyme2_id': getEnzymeSeqId(params['secondary'], True, enz_list, url),
'segment_length': params['length'],
'type': params['type'],
'filename': resfile }
return [ libfiles, bedfiles, resfile, infos_lib ]
def count_reads(self, bamfiles, gtf):
self.write_log("* Counting reads")
# Count reads on genes, transcripts with "rnacounter"
ncond = len(self.conditions)
tablenames = [None]*ncond
futures = [None]*ncond
max_rlen = 0
counter_options = ["--nh"]
for bam in bamfiles:
sam = pysam.Samfile(bam,'rb')
max_rlen = max(max_rlen, sam.next().rlen)
counter_options += ["--exon_cutoff", str(max_rlen)]
bwt_args = self.job.options.get('map_args',{}).get('bwt_args',[])
# if not "--local" in bwt_args:
# counter_options += ["--nh"]
if hasattr(self.assembly,"fasta_origin") or self.assembly.intype==2:
counter_options += ["--type","transcripts", "--method","raw"]
else:
counter_options += ["--type","genes,transcripts", "--method","raw,nnls"]
if self.stranded:
counter_options += ["--stranded"]
for i,c in enumerate(self.conditions):
tablenames[i] = unique_filename_in()
futures[i] = rnacounter.nonblocking(self.ex, bamfiles[i], gtf, stdout=tablenames[i], via=self.via,
options=counter_options)
# Put samples together
for i,c in enumerate(self.conditions):
try:
futures[i].wait()
except Exception as err:
self.write_debug("Counting failed: %s." % str(err))
raise err
if futures[i] is None:
self.write_debug("Counting failed.")
raise ValueError("Counting failed.")
if len(tablenames) > 1:
joined = unique_filename_in()
rnacounter_join.nonblocking(self.ex, tablenames, stdout=joined, via=self.via).wait()
else:
joined = tablenames[0]
# Split genes and transcripts into separate files
genes_filename = unique_filename_in()
trans_filename = unique_filename_in()
genes_file = open(genes_filename,"wb")
trans_file = open(trans_filename,"wb")
if self.stranded:
genes_anti_filename = unique_filename_in()
trans_anti_filename = unique_filename_in()
genes_anti_file = open(genes_anti_filename,"wb")
trans_anti_file = open(trans_anti_filename,"wb")
with open(joined) as jfile:
header = jfile.readline()
hconds = ["counts."+c for c in self.conditions] + ["rpkm."+c for c in self.conditions]
hinfo = header.strip().split('\t')[2*ncond+1:]
header = '\t'.join(["ID"] + hconds + hinfo)+'\n'
genes_file.write(header)
trans_file.write(header)
type_idx = header.split('\t').index("Type")
if self.stranded:
genes_anti_file.write(header)
trans_anti_file.write(header)
sense_idx = header.split('\t').index("Sense")
for line in jfile:
L = line.split('\t')
ftype = L[type_idx].lower()
sense = L[sense_idx].lower()
if ftype == 'gene':
if sense == 'antisense':
genes_anti_file.write(line)
else:
genes_file.write(line)
elif ftype == 'transcript':
if sense == 'antisense':
trans_anti_file.write(line)
else:
trans_file.write(line)
else:
for line in jfile:
L = line.split('\t')
ftype = L[type_idx].lower()
if ftype == 'gene':
genes_file.write(line)
elif ftype == 'transcript':
trans_file.write(line)
genes_file.close()
trans_file.close()
# Keep intermediate tables
for i,c in enumerate(self.conditions):
#shutil.copy(tablenames[i], "../counts%d.txt"%i)
descr = set_file_descr(self.conditions[i]+'_'+tablenames[i]+'.gz', type='txt', step='pileup', view='admin')
gzipfile(self.ex, tablenames[i])
self.ex.add(tablenames[i]+'.gz', description=descr)
if self.stranded:
count_files = {'genes':genes_filename, 'transcripts':trans_filename,
'genes_anti':genes_anti_filename, 'transcripts_anti':trans_anti_filename}
else:
count_files = {'genes':genes_filename, 'transcripts':trans_filename}
return count_files
def count_reads(self, bamfiles, gtf):
self.write_log("* Counting reads")
# Count reads on genes, transcripts with "rnacounter"
ncond = len(self.conditions)
tablenames = [None] * ncond
futures = [None] * ncond
max_rlen = 0
counter_options = ["--nh"]
for bam in bamfiles:
sam = pysam.Samfile(bam, 'rb')
max_rlen = max(max_rlen, sam.next().rlen)
counter_options += ["--exon_cutoff", str(max_rlen)]
bwt_args = self.job.options.get('map_args', {}).get('bwt_args', [])
# if not "--local" in bwt_args:
# counter_options += ["--nh"]
if hasattr(self.assembly, "fasta_origin") or self.assembly.intype == 2:
counter_options += ["--type", "transcripts", "--method", "raw"]
else:
counter_options += [
"--type", "genes,transcripts", "--method", "raw,nnls"
]
if self.stranded:
counter_options += ["--stranded"]
for i, c in enumerate(self.conditions):
tablenames[i] = unique_filename_in()
futures[i] = rnacounter.nonblocking(self.ex,
bamfiles[i],
gtf,
stdout=tablenames[i],
via=self.via,
options=counter_options)
# Put samples together
for i, c in enumerate(self.conditions):
try:
futures[i].wait()
except Exception as err:
self.write_debug("Counting failed: %s." % str(err))
raise err
if futures[i] is None:
self.write_debug("Counting failed.")
raise ValueError("Counting failed.")
if len(tablenames) > 1:
joined = unique_filename_in()
rnacounter_join.nonblocking(self.ex,
tablenames,
stdout=joined,
via=self.via).wait()
else:
joined = tablenames[0]
# Split genes and transcripts into separate files
genes_filename = unique_filename_in()
trans_filename = unique_filename_in()
genes_file = open(genes_filename, "wb")
trans_file = open(trans_filename, "wb")
if self.stranded:
genes_anti_filename = unique_filename_in()
trans_anti_filename = unique_filename_in()
genes_anti_file = open(genes_anti_filename, "wb")
trans_anti_file = open(trans_anti_filename, "wb")
with open(joined) as jfile:
header = jfile.readline()
hconds = ["counts." + c for c in self.conditions
] + ["rpkm." + c for c in self.conditions]
hinfo = header.strip().split('\t')[2 * ncond + 1:]
header = '\t'.join(["ID"] + hconds + hinfo) + '\n'
genes_file.write(header)
trans_file.write(header)
type_idx = header.split('\t').index("Type")
if self.stranded:
genes_anti_file.write(header)
trans_anti_file.write(header)
sense_idx = header.split('\t').index("Sense")
for line in jfile:
L = line.split('\t')
ftype = L[type_idx].lower()
sense = L[sense_idx].lower()
if ftype == 'gene':
if sense == 'antisense':
genes_anti_file.write(line)
else:
genes_file.write(line)
elif ftype == 'transcript':
if sense == 'antisense':
trans_anti_file.write(line)
else:
trans_file.write(line)
else:
for line in jfile:
L = line.split('\t')
ftype = L[type_idx].lower()
if ftype == 'gene':
genes_file.write(line)
elif ftype == 'transcript':
trans_file.write(line)
genes_file.close()
trans_file.close()
# Keep intermediate tables
for i, c in enumerate(self.conditions):
#shutil.copy(tablenames[i], "../counts%d.txt"%i)
descr = set_file_descr(self.conditions[i] + '_' + tablenames[i] +
'.gz',
type='txt',
step='pileup',
view='admin')
gzipfile(self.ex, tablenames[i])
self.ex.add(tablenames[i] + '.gz', description=descr)
if self.stranded:
count_files = {
'genes': genes_filename,
'transcripts': trans_filename,
'genes_anti': genes_anti_filename,
'transcripts_anti': trans_anti_filename
}
else:
count_files = {
'genes': genes_filename,
'transcripts': trans_filename
}
return count_files
def chipseq_workflow( ex, job_or_dict, assembly, script_path='', logfile=sys.stdout, via='lsf' ):
"""Runs a chipseq workflow over bam files obtained by mapseq. Will optionally run ``macs`` and 'run_deconv'.
:param ex: a 'bein' execution environment to run jobs in,
:param job_or_dict: a 'Frontend' 'job' object, or a dictionary with key 'groups', 'files' and 'options' if applicable,
:param assembly: a genrep.Assembly object,
:param script_path: only needed if 'run_deconv' is in the job options, must point to the location of the R scripts.
Defaults ``macs`` parameters (overriden by ``job_or_dict['options']['macs_args']``) are set as follows:
* ``'-bw'``: 200 ('bandwith')
* ``'-m'``: 10,100 ('minimum and maximum enrichments relative to background or control')
The enrichment bounds will be computed from a Poisson threshold *T*, if available, as *(min(30,5*(T+1)),50*(T+1))*.
Returns a tuple of a dictionary with keys *group_id* from the job groups, *macs* and *deconv* if applicable and values file description dictionaries and a dictionary of *group_ids* to *names* used in file descriptions.
"""
options = {}
if logfile is None: logfile = sys.stdout
if isinstance(job_or_dict,frontend.Job):
options = job_or_dict.options
groups = job_or_dict.groups
mapseq_files = job_or_dict.files
elif isinstance(job_or_dict,dict) and 'groups' in job_or_dict:
if 'options' in job_or_dict:
options = job_or_dict['options']
groups = job_or_dict['groups']
for gid in groups.keys():
if not('name' in groups[gid]):
groups[gid]['name'] = gid
mapseq_files = job_or_dict.get('files',{})
else:
raise TypeError("job_or_dict must be a frontend. Job object or a dictionary with key 'groups'.")
merge_strands = int(options.get('merge_strands',-1))
suffixes = ["fwd","rev"]
peak_deconvolution = options.get('peak_deconvolution',False)
if isinstance(peak_deconvolution,basestring):
peak_deconvolution = peak_deconvolution.lower() in ['1','true','t']
run_meme = options.get('run_meme',False)
if isinstance(run_meme,basestring):
run_meme = run_meme.lower() in ['1','true','t']
macs_args = options.get('macs_args',["--bw","200"])
b2w_args = options.get('b2w_args',[])
if not(isinstance(mapseq_files,dict)):
raise TypeError("Mapseq_files must be a dictionary.")
tests = []
controls = []
names = {'tests': [], 'controls': []}
read_length = []
p_thresh = {}
for gid,mapped in mapseq_files.iteritems():
group_name = groups[gid]['name']
if not(isinstance(mapped,dict)):
raise TypeError("Mapseq_files values must be dictionaries with keys *run_ids* or 'bam'.")
if 'bam' in mapped:
mapped = {'_': mapped}
futures = {}
ptruns = []
for k in mapped.keys():
if not 'libname' in mapped[k]:
mapped[k]['libname'] = group_name+"_"+str(k)
if not 'stats' in mapped[k]:
futures[k] = mapseq.bamstats.nonblocking( ex, mapped[k]["bam"], via=via )
if mapped[k].get('poisson_threshold',-1)>0:
ptruns.append(mapped[k]['poisson_threshold'])
if len(ptruns)>0:
p_thresh['group_name'] = sum(ptruns)/len(ptruns)
for k in futures.keys():
mapped[k]['stats'] = f.wait()
if len(mapped)>1:
bamfile = mapseq.merge_bam(ex, [m['bam'] for m in mapped.values()])
else:
bamfile = mapped.values()[0]['bam']
if groups[gid]['control']:
controls.append(bamfile)
names['controls'].append((gid,group_name))
else:
tests.append(bamfile)
names['tests'].append((gid,group_name))
read_length.append(mapped.values()[0]['stats']['read_length'])
genome_size = mapped.values()[0]['stats']['genome_size']
if len(controls)<1:
controls = [None]
names['controls'] = [(0,None)]
logfile.write("Starting MACS.\n");logfile.flush()
processed = {'macs': add_macs_results( ex, read_length, genome_size,
tests, ctrlbam=controls, name=names,
poisson_threshold=p_thresh,
macs_args=macs_args, via=via ) }
logfile.write("Done MACS.\n");logfile.flush()
peak_list = {}
chrlist = assembly.chrmeta
## select only peaks with p-val <= 1e-0.6 = .25 => score = -10log10(p) >= 6
_select = {'score':(6,sys.maxint)}
_fields = ['chr','start','end','name','score']
for i,name in enumerate(names['tests']):
if len(names['controls']) < 2:
ctrl = (name,names['controls'][0])
macsbed = track(processed['macs'][ctrl]+"_summits.bed",
chrmeta=chrlist, fields=_fields).read(selection=_select)
else:
macsbed = concatenate([apply(track(processed['macs'][(name,x)]+"_summits.bed",
chrmeta=chrlist, fields=_fields).read(selection=_select),
'name', lambda __n,_n=xn: "%s:%i" %(__n,_n))
for xn,x in enumerate(names['controls'])])
##############################
macs_neighb = neighborhood( macsbed, before_start=150, after_end=150 )
peak_list[name] = unique_filename_in()+".sql"
macs_final = track( peak_list[name], chrmeta=chrlist,
info={'datatype':'qualitative'},
fields=['start','end','name','score'] )
macs_final.write(fusion(macs_neighb),clip=True)
macs_final.close()
##############################
merged_wig = {}
options['read_extension'] = int(options.get('read_extension') or read_length[0])
if options['read_extension'] < 1: options['read_extension'] = read_length[0]
make_wigs = merge_strands >= 0 or options['read_extension']>100
if options['read_extension'] > 100: options['read_extension'] = 50
for gid,mapped in mapseq_files.iteritems():
# if groups[gid]['control']: continue
group_name = groups[gid]['name']
wig = []
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=options['read_extension'],
convert=False,
b2w_args=b2w_args, via=via )
wig.append(dict((s,output+s+'.sql') for s in suffixes))
else:
wig.append(m['wig'])
if len(wig) > 1:
merged_wig[group_name] = dict((s,merge_sql(ex, [x[s] for x in wig], via=via))
for s in suffixes)
else:
merged_wig[group_name] = wig[0]
if peak_deconvolution:
##############################
def _filter_deconv( stream, pval ):
ferr = re.compile(r';FERR=([\d\.]+)$')
return FeatureStream( ((x[0],)+((x[2]+x[1])/2-150,(x[2]+x[1])/2+150)+x[3:]
for x in stream
if "FERR=" in x[3] and float(ferr.search(x[3]).groups()[0]) <= pval),
fields=stream.fields )
##############################
processed['deconv'] = {}
for name in names['tests']:
logfile.write(name[1]+" deconvolution.\n");logfile.flush()
if len(names['controls']) < 2:
ctrl = (name,names['controls'][0])
macsbed = processed['macs'][ctrl]+"_peaks.bed"
else:
macsbed = intersect_many_bed( ex, [processed['macs'][(name,x)]+"_peaks.bed"
for x in names['controls']], via=via )
deconv = run_deconv( ex, merged_wig[name[1]], macsbed, assembly.chrmeta,
options['read_extension'], script_path, via=via )
peak_list[name] = unique_filename_in()+".bed"
trbed = track(deconv['peaks']).read()
with track(peak_list[name], chrmeta=chrlist, fields=trbed.fields) as bedfile:
bedfile.write(fusion(_filter_deconv(trbed,0.65)))
ex.add(deconv['peaks'],
description=set_file_descr(name[1]+'_peaks.sql', type='sql',
step='deconvolution', groupId=name[0]))
ex.add(deconv['profile'],
description=set_file_descr(name[1]+'_deconv.sql', type='sql',
step='deconvolution', groupId=name[0]))
bigwig = unique_filename_in()
try:
convert(deconv['profile'],(bigwig,"bigWig"))
ex.add(bigwig,
description=set_file_descr(name[1]+'_deconv.bw', type='bigWig',
ucsc='1', step='deconvolution',
groupId=name[0]))
except OSError as e:
logfile.write(str(e));logfile.flush()
ex.add(deconv['pdf'],
description=set_file_descr(name[1]+'_deconv.pdf', type='pdf',
step='deconvolution', groupId=name[0]))
processed['deconv'][name] = deconv
##############################
def _join_macs( stream, xlsl, _f ):
def _macs_row(_s):
for _p in _s:
for _n in _p[3].split("|"):
if len(xlsl) == 1:
nb = int(_n.split(";")[0][13:]) if _n[:3] == "ID=" else int(_n[10:])
yield _p+xlsl[0][nb-1][1:]
else:
nb = _n.split(";")[0][13:] if _n[:3] == "ID=" else _n[10:]
nb = nb.split(":")
yield _p+xlsl[int(nb[1])][int(nb[0])-1][1:]
return FeatureStream( _macs_row(stream), fields=_f )
##############################
peakfile_list = []
for name, plist in peak_list.iteritems():
ptrack = track(plist,chrmeta=chrlist,fields=["chr","start","end","name","score"])
peakfile = unique_filename_in()
xlsh, xlsl = parse_MACS_xls([processed['macs'][(name,_c)]+"_peaks.xls" for _c in names['controls']])
try:
###### if assembly doesn't have annotations, we skip the "getNearestFeature" but still go through "_join_macs"
assembly.gene_track()
_fields = ['chr','start','end','name','score','gene','location_type','distance']\
+["MACS_%s"%h for h in xlsh[1:5]]+xlsh[5:]
peakout = track(peakfile, format='txt', chrmeta=chrlist, fields=_fields)
peakout.make_header("#"+"\t".join(['chromosome','start','end','info','peak_height','gene(s)','location_type','distance']+_fields[8:]))
for chrom in assembly.chrnames:
_feat = assembly.gene_track(chrom)
peakout.write(_join_macs(getNearestFeature(ptrack.read(selection=chrom),_feat),
xlsl, _fields), mode='append')
except ValueError:
_fields = ['chr','start','end','name','score']+["MACS_%s"%h for h in xlsh[1:5]]+xlsh[5:]
peakout = track(peakfile, format='txt', chrmeta=chrlist, fields=_fields)
peakout.make_header("#"+"\t".join(['chromosome','start','end','info','peak_height']+_fields[8:]))
for chrom in assembly.chrnames:
peakout.write(_join_macs(ptrack.read(selection=chrom), xlsl, _fields), mode='append')
peakout.close()
gzipfile(ex,peakfile)
peakfile_list.append(track(peakfile+".gz", format='txt', fields=_fields))
ex.add(peakfile+".gz",
description=set_file_descr(name[1]+'_annotated_peaks.txt.gz',type='text',
step='annotation',groupId=name[0]))
stracks = [track(wig,info={'name':name+"_"+st})
for name,wigdict in merged_wig.iteritems() for st,wig in wigdict.iteritems()]
tablefile = unique_filename_in()
with open(tablefile,"w") as _tf:
_pnames = ["MACS_%s_vs_%s" %(_s[1],_c[1]) if _c[1] else "MACS_%s" %_s[1]
for _s in names['tests'] for _c in names['controls']]
_tf.write("\t".join(['#chromosome','start','end',]+_pnames+[s.name for s in stracks])+"\n")
#### need to do something about peak origin (split names, write to separate columns?)
for chrom in assembly.chrnames:
pk_lst = [apply(pt.read(chrom,fields=['chr','start','end','name']),
'name', lambda __n,_n=npt: "%s:%i" %(__n,_n))
for npt,pt in enumerate(peakfile_list)]
features = fusion(concatenate(pk_lst, fields=['chr','start','end','name'],
remove_duplicates=True, group_by=['chr','start','end']))
sread = [sig.read(chrom) for sig in stracks]
quantifs = score_by_feature(sread, features, method='sum')
nidx = quantifs.fields.index('name')
_ns = len(tests)
_nc = len(controls)
with open(tablefile,"a") as _tf:
for row in quantifs:
pcols = ['']*_ns*_nc
_rnsplit = row[nidx].split(":")
_n1 = _rnsplit[0]
_k = 0
while ( _k < len(_rnsplit)-1-int(_nc>1) ):
if _nc > 1:
_k += 2
_n2 = _rnsplit[_k-1]
_n = _rnsplit[_k].split("|")
pcols[int(_n[0])*_nc+int(_n2)] = _n1
else:
_k += 1
_n = _rnsplit[_k].split("|")
pcols[int(_n[0])] = _n1
_n1 = "|".join(_n[1:])
_tf.write("\t".join(str(tt) for tt in row[:nidx]+tuple(pcols)+row[nidx+1:])+"\n")
gzipfile(ex,tablefile)
ex.add(tablefile+".gz",
description=set_file_descr('Combined_peak_quantifications.txt.gz',type='text',
step='summary'))
if run_meme:
from bbcflib.motif import parallel_meme
logfile.write("Starting MEME.\n");logfile.flush()
processed['meme'] = parallel_meme( ex, assembly,
peak_list.values(), name=peak_list.keys(),
chip=True, meme_args=['-meme-nmotifs','4','-meme-mod','zoops'],
via=via )
return processed
def c4seq_workflow( ex, job, primers_dict, assembly,
c4_url=None, script_path='', logfile=sys.stdout, via='lsf' ):
'''
Main
* open the 4C-seq minilims and create execution
* 0. get/create the library
* 1. if necessary, calculate the density file from the bam file (mapseq.parallel_density_sql)
* 2. calculate the count per fragment for each denstiy file with gfminer:score_by_feature to calculate)
'''
mapseq_files = job.files
### outputs
processed = {'lib': {}, 'density': {}, '4cseq': {}}
processed['4cseq'] = {'density_files' : {},
'countsPerFrag' : {},
'countsPerFrag_grp' : {},
'norm' : {},
'norm_grp' : {},
'profileCorrection': {},
'profileCorrection_grp' : {},
'smooth_grp' : {},
'domainogram_grp' : {},
'bricks2frags' : {}}
# was 'smoothFrag': {}, 'domainogram': {}}
regToExclude = {}
new_libs=[]
### options
run_domainogram = {}
before_profile_correction = {}
if not job.options.get('viewpoints_chrs',False):
out_chromosomes = ','.join([ch for ch in assembly.chrnames])
else:
out_chromosomes = ','.join([primers_dict.get(group['name'],{}).get('baitcoord').split(':')[0] for gid,group in job.groups.iteritems()])
print "out_chromosomes=" + out_chromosomes + "\n"
sizeExt = job.options.get('norm_reg',1000000)
print "region considered for normalisation: mid viewpoint +/-" + str(sizeExt) + 'bps'
### do it
for gid, group in job.groups.iteritems():
run_domainogram[gid] = group.get('run_domainogram',False)
if isinstance(run_domainogram[gid],basestring):
run_domainogram[gid] = (run_domainogram[gid].lower() in ['1','true','on','t'])
before_profile_correction[gid] = group.get('before_profile_correction',False)
if isinstance(before_profile_correction[gid],basestring):
before_profile_correction[gid] = (before_profile_correction[gid].lower() in ['1','true','on','t'])
processed['lib'][gid] = get_libForGrp(ex, group, assembly,
new_libs, gid, c4_url, via=via)
#reffile='/archive/epfl/bbcf/data/DubouleDaan/library_Nla_30bps/library_Nla_30bps_segmentInfos.bed'
processed['4cseq']['density_files'][gid] = {}
regToExclude[gid] = primers_dict.get(group['name'],{}).get('regToExclude',"").replace('\r','')
# if no regToExclude defined, set it as mid_baitCoord +/-5kb
if len(regToExclude[gid])==0 :
baitcoord_mid = int(0.5 * (int(primers_dict.get(group['name'],{}).get('baitcoord').split(':')[1].split('-')[0]) + int(primers_dict.get(group['name'],{}).get('baitcoord').split(':')[1].split('-')[1]) ))
regToExclude[gid] = primers_dict.get(group['name'],{}).get('baitcoord').split(':')[0] + ':' + str(baitcoord_mid-5000) + '-' + str(baitcoord_mid+5000)
#print(';'.join([k+"="+v for k,v in primers_dict.get(group['name'],{}).iteritems()]))
print(primers_dict.get(group['name'],{}))
print "regToExclude["+str(gid)+"]="+regToExclude[gid]
for rid,run in group['runs'].iteritems():
libname = mapseq_files[gid][rid]['libname']
if job.options.get('merge_strands') != 0 or not('wig' in mapseq_files[gid][rid]):
density_file=parallel_density_sql( ex, mapseq_files[gid][rid]['bam'],
assembly.chrmeta,
nreads=mapseq_files[gid][rid]['stats']["total"],
merge=0,
read_extension=mapseq_files[gid][rid]['stats']['read_length'],
convert=False,
via=via )
density_file += "merged.sql"
ex.add( density_file,
description=set_file_descr("density_file_"+libname+".sql",
groupId=gid,step="density",type="sql",view='admin',gdv="1") )
else:
density_file = mapseq_files[gid][rid]['wig']['merged']
#density_files.append(density_file)
processed['4cseq']['density_files'][gid][rid]=density_file
# back to grp level!
# not anymore:
# processed['density'][gid] = merge_sql(ex, density_files, via=via)
processed['4cseq']['countsPerFrag'] = density_to_countsPerFrag( ex, processed, job.groups, assembly, regToExclude, script_path, via )
## access per gid+rid
futures_norm = {}
countsPerFrags_bedGraph = {}
futures_merged_raw = {}
for gid, group in job.groups.iteritems():
futures_norm[gid] = {}
countsPerFrags_bedGraph[gid] = {}
processed['4cseq']['norm'][gid] = {}
for rid,run in group['runs'].iteritems():
normfile = unique_filename_in()
touch(ex, normfile)
resfile = unique_filename_in()+".bedGraph"
resfiles = processed['4cseq']['countsPerFrag'][gid][rid] # _all.sql
convert(resfiles[3],resfile)
countsPerFrags_bedGraph[gid][rid] = resfile
print "call normFrags: infiles="+resfile+", normfile="+normfile+"baitCoord="+primers_dict[group['name']]['baitcoord']+", sizeExt=sizeExt, name="+ group['name']+"rep_"+str(rid) + "regToExclude="+regToExclude[gid]+"\n"
futures_norm[gid][rid] = normFrags.nonblocking( ex, resfile, normfile, baitCoord=primers_dict[group['name']]['baitcoord'], sizeExt=sizeExt, name=group['name']+"rep_"+str(rid) ,regToExclude=regToExclude[gid], script_path=script_path, via=via )
processed['4cseq']['norm'][gid][rid] = normfile
if len(group) > 1:
## merge replicates before normalisation.
mergefile = unique_filename_in()
touch(ex, mergefile)
titleName=group['name']+"_raw_mergedRep"
print "gid="+group['name']
print "call mergeRep for replicates before normalisation: infiles="+",".join([res_rid for rid,res_rid in countsPerFrags_bedGraph[gid].iteritems()])+", mergedfile="+mergefile+", regToExclude="+regToExclude[gid]+"\n"
futures_merged_raw[gid] = mergeRep.nonblocking( ex, ",".join([res_rid for rid,res_rid in countsPerFrags_bedGraph[gid].iteritems()]), mergefile, regToExclude[gid], name=titleName, script_path=script_path, via=via , memory= 8)
processed['4cseq']['countsPerFrag_grp'][gid] = mergefile
else:
futures_merged_raw[gid] = None
processed['4cseq']['countsPerFrag_grp'][gid] = countsPerFrags_bedGraph[gid][0] #if no replicates, then the file we want is the 1st one
print "***** profile correction / sample + merge normalised data"
futures_merged = {} # per gid
futures_profcor = {} # per gid, per rid
for gid, group in job.groups.iteritems():
## run profile correction per run then merge them
futures_profcor[gid] = {}
processed['4cseq']['profileCorrection'][gid] = {}
for rid, run in group['runs'].iteritems():
# wait for normalisation of all replicates to be finished
futures_norm[gid][rid].wait() ## normalised files, per grp, per rep
normfile = processed['4cseq']['norm'][gid][rid]
file1 = unique_filename_in() #track file
touch(ex,file1)
file2 = unique_filename_in() #report file
touch(ex,file2)
file3 = unique_filename_in() #table file
touch(ex, file3)
print "call profileCorrection: normfile="+normfile+", baitCoord="+primers_dict[group['name']]['baitcoord']+", name="+group['name']+", file1="+file1+", file2="+file2+", file3= "+file3+"\n"
futures_profcor[gid][rid] = profileCorrection.nonblocking( ex, normfile,
primers_dict[group['name']]['baitcoord'],
group['name'], file1, file2, file3, script_path,
via=via )
processed['4cseq']['profileCorrection'][gid][rid] = [file1, file2, file3]
## merge replicates before profile correction. Needs all normalisation for the given grp to be finished, this is why it comes after the rid loop.
if len(group)>1:
mergefile = unique_filename_in()
touch(ex, mergefile)
titleName=group['name']+"_norm_mergedRep"
print "gid="+group['name']
print "call mergeRep: infiles="+",".join([res_rid for rid,res_rid in processed['4cseq']['norm'][gid].iteritems()])+", mergedfile="+mergefile+", regToExclude="+regToExclude[gid]+"\n"
futures_merged[gid] = mergeRep.nonblocking( ex, ",".join([res_rid for rid,res_rid in processed['4cseq']['norm'][gid].iteritems()]), mergefile, regToExclude[gid], name=titleName, script_path=script_path, via=via , memory= 8)
processed['4cseq']['norm_grp'][gid] = mergefile
else:
futures_merged[gid] = None
processed['4cseq']['norm_grp'][gid] = processed['4cseq']['norm'][gid][0] ##if no replicates, then the file we want is the 1st one
print "***** merge profile corrected data"
futures_profcor_merged = {} # per gid
for gid, group in job.groups.iteritems():
processed['4cseq']['profileCorrection_grp'][gid] = {}
for rid, run in group['runs'].iteritems():
futures_profcor[gid][rid].wait() ## wait for ProfileCorrection to be finished
## merge replicates after profile correction
if len(group)>1:
mergefile = unique_filename_in()
touch(ex, mergefile)
titleName=group['name']+"_ProfCor_mergedRep"
pcfiles = [ processed['4cseq']['profileCorrection'][gid][rid][0] for rid,res_rid in processed['4cseq']['profileCorrection'][gid].iteritems()]
print "call mergeRep (for PC tables): infiles="+",".join(pcfiles)+", mergedfile="+mergefile+", regToExclude="+regToExclude[gid]+"\n"
futures_profcor_merged[gid] = mergeRep.nonblocking( ex, ",".join(pcfiles), mergefile, regToExclude[gid], name=titleName, script_path=script_path, via=via , memory= 8)
processed['4cseq']['profileCorrection_grp'][gid] = mergefile
else:
futures_profcor_merged[gid] = None
processed['4cseq']['profileCorrection_grp'][gid] = processed['4cseq']['profileCorrection'][gid][0] ##if no replicates, then the file we want is the 1st one
print "***** smooth data"
futures_smoothed = {}
for gid, group in job.groups.iteritems():
file1 = unique_filename_in()
touch(ex,file1)
file2 = unique_filename_in()
touch(ex, file2)
file3 = unique_filename_in()
touch(ex, file3)
nFragsPerWin = group['window_size']
futures_merged_raw[gid].wait() ## wait for merging of raw_grp to be completed
futures_smoothed[gid] = ( smoothFragFile.nonblocking( ex, processed['4cseq']['countsPerFrag_grp'][gid], nFragsPerWin, group['name'],
file1, regToExclude[gid], script_path=script_path, via=via, memory=6 ), )
futures_merged[gid].wait() ## wait for merging of norm_grp to be completed
futures_smoothed[gid] += ( smoothFragFile.nonblocking( ex, processed['4cseq']['norm_grp'][gid], nFragsPerWin, group['name']+"_norm",
file2, regToExclude[gid], script_path=script_path, via=via, memory=6 ), )
futures_profcor_merged[gid].wait() # wait for the merging of profile corrected data to be done
futures_smoothed[gid] += ( smoothFragFile.nonblocking( ex, processed['4cseq']['profileCorrection_grp'][gid], nFragsPerWin, group['name']+"_fromProfileCorrected",
file3, regToExclude[gid], script_path=script_path, via=via, memory=6 ), )
processed['4cseq']['smooth_grp'][gid] = [file1,file2,file3] #[smoothed_file_before_Norm, smoothed file before PC, smoothed file after PC]
print "***** Domainograms"
futures_domainograms = {}
for gid, group in job.groups.iteritems():
grName = job.groups[gid]['name']
if run_domainogram[gid]:
regCoord = regToExclude[gid] or primers_dict[grName]['baitcoord']
if before_profile_correction[gid]:
futures_domainograms[gid] = runDomainogram.nonblocking( ex, processed['4cseq']['norm_grp'][gid],
grName, regCoord=regCoord, skip=1,
script_path=script_path, via=via, memory=15 )
else:
futures_domainograms[gid] = runDomainogram.nonblocking( ex, processed['4cseq']['profileCorrection_grp'][gid],
grName, regCoord=regCoord.split(':')[0], skip=1,
script_path=script_path, via=via, memory=15 )
## prepare tar files for domainogram results (if any)
## and create "BRICKS to frags" files
print "***** BRICKS to Frags"
futures_BRICKS2Frags = {}
for gid, f in futures_domainograms.iteritems():
if run_domainogram[gid]: # if domainogram has been run
resFiles = []
logFile = f.wait()
start = False
tarname = job.groups[gid]['name']+"_domainogram.tar.gz"
res_tar = tarfile.open(tarname, "w:gz")
futures_BRICKS2Frags[gid] = []
processed['4cseq']['bricks2frags'][gid] = []
if logFile is None: continue
with open(logFile) as f:
for s in f:
s = s.strip()
if '####resfiles####' in s:
start = True
elif start and "RData" not in s:
resFiles.append(s)
res_tar.add(s)
if start and "foundBRICKS" in s:
bricks2fragsfile = unique_filename_in()+".bedGraph"
touch(ex, bricks2fragsfile)
futures_BRICKS2Frags[gid] += [ BRICKSToFrag.nonblocking(ex, s, processed['4cseq']['norm_grp'][gid], bricks2fragsfile, script_path=script_path, via=via, memory=4 ) ]
processed['4cseq']['bricks2frags'][gid] += [ bricks2fragsfile ]
res_tar.close()
processed['4cseq']['domainogram_grp'][gid] = resFiles + [tarname]
############### prepare tables for global results
print "***** combine results into tables "
allNames=[]
allFiles=[]
allRegToExclude=[]
for gid, group in job.groups.iteritems():
for rid,run in group['runs'].iteritems():
allNames += [ group['name']+"_rep"+str(rid)+"_norm", group['name']+"_rep"+str(rid)+"_fit" ]
allFiles += [ processed['4cseq']['profileCorrection'][gid][rid][2] ]
allRegToExclude += [ regToExclude[gid] ]
tablePC=unique_filename_in()+".txt"
print("***will call makeTable with:")
print(",".join(allFiles))
print("resfile="+tablePC)
print(",".join(allNames))
touch(ex,tablePC)
#regToExclude[gid]
futures_tables = (makeTable.nonblocking(ex, ",".join(allFiles), tablePC, ",".join(allNames), idCols="4,5", all_regToExclude=','.join(allRegToExclude), script_path=script_path, via=via, memory=8 ), )
# wait for all smoothing to be done
for gid, fg in futures_smoothed.iteritems():
for f in fg: f.wait()
## make Table raw/smoothed_raw
print("** make Table raw/smoothed_raw")
allNames=[]
allFiles=[]
allRegToExclude=[]
for gid, group in job.groups.iteritems():
futures_merged_raw[gid].wait()
allNames += [ group['name']+"_raw", group['name']+"_rawSmoothed" ]
allFiles += [ processed['4cseq']['countsPerFrag_grp'][gid], processed['4cseq']['smooth_grp'][gid][0] ]
allRegToExclude += [ 'NA', regToExclude[gid] ]
tableSmoothedRaw_grp=unique_filename_in()+".txt"
touch(ex,tableSmoothedRaw_grp)
futures_tables += (makeTable.nonblocking(ex, ",".join(allFiles), tableSmoothedRaw_grp, ",".join(allNames), idCols="4", out_chromosomes = out_chromosomes, all_regToExclude=','.join(allRegToExclude), script_path=script_path, via=via, memory=8 ), )
## make Table norm/smoothed_norm before PC
print("** make Table norm/smoothed_norm befor PC")
allNames=[]
allFiles=[]
allRegToExclude=[]
for gid, group in job.groups.iteritems():
allNames += [ group['name']+"_norm", group['name']+"_smoothed" ]
allFiles += [ processed['4cseq']['norm_grp'][gid], processed['4cseq']['smooth_grp'][gid][1] ]
allRegToExclude += [ regToExclude[gid], regToExclude[gid] ]
tableSmoothed_grp=unique_filename_in()+".txt"
touch(ex,tableSmoothed_grp)
futures_tables += (makeTable.nonblocking(ex, ",".join(allFiles), tableSmoothed_grp, ",".join(allNames), idCols="4", out_chromosomes = out_chromosomes, all_regToExclude=','.join(allRegToExclude), script_path=script_path, via=via, memory=8 ), )
## make Table norm/smoothed_norm after PC
print("** make Table norm/smoothed_norm after PC")
allNames=[]
allFiles=[]
allRegToExclude=[]
for gid, group in job.groups.iteritems():
allNames += [ group['name']+"_normPC", group['name']+"_smoothedPC" ]
allFiles += [ processed['4cseq']['profileCorrection_grp'][gid], processed['4cseq']['smooth_grp'][gid][2] ]
allRegToExclude += [ regToExclude[gid], regToExclude[gid] ]
tableSmoothedPC_grp=unique_filename_in()+".txt"
touch(ex,tableSmoothedPC_grp)
futures_tables += (makeTable.nonblocking(ex, ",".join(allFiles), tableSmoothedPC_grp, ",".join(allNames), idCols="4", out_chromosomes = out_chromosomes, all_regToExclude=','.join(allRegToExclude), script_path=script_path, via=via, memory=8 ), )
## combine BRICKS2Frags files
allNames=[]
allFiles=[]
for gid, fg in futures_BRICKS2Frags.iteritems():
for f in fg: f.wait()
allNames += [ job.groups[gid]['name']+"_BRICKSpval" ]
cat_bricks2frags = unique_filename_in()+".txt"
print ','.join(processed['4cseq']['bricks2frags'][gid])
cat_bricks2frags = cat(processed['4cseq']['bricks2frags'][gid],out=cat_bricks2frags)
allFiles += [ cat_bricks2frags ]
for gid, fg in futures_smoothed.iteritems():
for f in fg: f.wait()
tableBRICKS2Frags = unique_filename_in()+".txt"
touch(ex,tableBRICKS2Frags)
futures_tables += (makeTable.nonblocking(ex, ",".join(allFiles), tableBRICKS2Frags, ",".join(allNames), idCols="4", out_chromosomes = out_chromosomes, defVal="NA", script_path=script_path, via=via, memory=8 ), )
for f in futures_tables: f.wait()
################ Add everything to minilims below!
step = "density"
for gid in processed['4cseq']['density_files'].keys():
for rid, sql in processed['4cseq']['density_files'][gid].iteritems():
fname = "density_file_"+job.groups[gid]['name']+"_merged_rep"+str(rid)
ex.add( sql, description=set_file_descr( fname+".sql",
groupId=gid,step=step,type="sql",gdv="1" ) )
wig = unique_filename_in()+".bw"
convert( sql, wig )
ex.add( wig, description=set_file_descr( fname+".bw",
groupId=gid,step=step,type="bigWig",ucsc="1") )
step = "counts_per_frag" #was _norm_counts_per_frags # before normalisation process, per replicate
for gid in processed['4cseq']['countsPerFrag'].keys():
for rid, resfiles in processed['4cseq']['countsPerFrag'][gid].iteritems():
fname = "meanScorePerFeature_"+job.groups[gid]['name']+"_rep"+str(rid)
ex.add( resfiles[1], description=set_file_descr( fname+".sql",
groupId=gid,step=step,type="sql",view="admin",gdv='1'))
#gzipfile(ex,resfiles[0])
#ex.add( resfiles[0]+".gz", description=set_file_descr( fname+".bed.gz",
# groupId=gid,step=step,type="bed",view="admin" ))
fname = "segToFrag_"+job.groups[gid]['name']+"_rep"+str(rid)
ex.add( resfiles[3], description=set_file_descr( fname+"_all.sql",
groupId=gid,step=step,type="sql",
comment="all informative frags - null included" ))
trsql = track(resfiles[3])
bwig = unique_filename_in()+".bw"
trwig = track(bwig,chrmeta=trsql.chrmeta)
trwig.write(trsql.read(fields=['chr','start','end','score'],
selection={'score':(0.01,sys.maxint)}))
trwig.close()
ex.add( bwig, set_file_descr(fname+".bw",groupId=gid,step=step,type="bigWig",ucsc='1'))
## add segToFrags before normalisation
futures_merged_raw[gid].wait()
trbedgraph = track(removeNA(processed['4cseq']['countsPerFrag_grp'][gid]),format='bedgraph')
bwig = unique_filename_in()+".bw"
trwig = track(bwig,chrmeta=assembly.chrmeta)
trwig.write(trbedgraph.read(fields=['chr','start','end','score'],
selection={'score':(0.01,sys.maxint)}))
trwig.close()
fname = "segToFrag_"+job.groups[gid]['name']
ex.add( bwig, description=set_file_descr( fname+".bw",
groupId=gid,step=step,type="bigWig",
comment="segToFrag file before normalisation" ))
step = "norm_counts_per_frags" # after new normalisation process, combined replicates
for gid, resfile in processed['4cseq']['norm_grp'].iteritems():
fname = "normalised_scorePerFeature_"+job.groups[gid]['name']
gzipfile(ex,resfile)
ex.add( resfile+".gz", description=set_file_descr( fname+".bedGraph.gz", groupId=gid,step=step, type="bedGraph",ucsc='1'))
# norm files, per replicates (might be removed)
for gid, dict_gid in processed['4cseq']['norm'].iteritems():
for rid, resfile in dict_gid.iteritems():
fname = "normalised_scorePerFeature_"+job.groups[gid]['name']+"_rep"+str(rid)
gzipfile(ex,resfile)
ex.add(resfile+".gz",
description=set_file_descr(fname+".bedGraph.gz",groupId=gid,step=step,type="bedGraph",ucsc='1',gdv='1'))
step = "profile_correction" # Profile corrected data, combined replicates
for gid, profileCorrectedFile in processed['4cseq']['profileCorrection_grp'].iteritems():
fname = "segToFrag_"+job.groups[gid]['name']+"_profileCorrected"
gzipfile(ex,profileCorrectedFile)
ex.add( profileCorrectedFile+".gz",
description=set_file_descr(fname+".bedGraph.gz",groupId=gid,step=step,type="bedGraph",ucsc='1',gdv='1'))
# Profile corrected, per replicate (might be removed)
for gid, dict_gid in processed['4cseq']['profileCorrection'].iteritems():
for rid, resfiles in dict_gid.iteritems():
# profileCorrectedFile = resfiles[0]
reportProfileCorrection = resfiles[1]
fname = "segToFrag_"+job.groups[gid]['name']+"_profileCorrected_rep"+str(rid)
# gzipfile(ex,profileCorrectedFile)
# ex.add( profileCorrectedFile+".gz",
# description=set_file_descr(fname+".bedGraph.gz",groupId=gid,step=step,type="bedGraph",ucsc='1',gdv='1'))
ex.add( reportProfileCorrection, description=set_file_descr(fname+".pdf",
groupId=gid,step=step,type="pdf"))
step = "smoothing"
for gid, resfiles in processed['4cseq']['smooth_grp'].iteritems():
rawSmoothFile = resfiles[0]
smoothFile = resfiles[1]
afterProfileCorrection = resfiles[2]
nFrags = str(job.groups[gid]['window_size'])
## smoothed file before normalisation
fname = "segToFrag_"+job.groups[gid]['name']+"_smoothed_"+nFrags+"FragsPerWin.bedGraph.gz"
gzipfile(ex,rawSmoothFile)
ex.add(rawSmoothFile+".gz",
description=set_file_descr(fname,groupId=gid,step=step,type="bedGraph",ucsc='1',gdv='1'))
## smoothed file after normalisation, before Profile correction
fname = "segToFrag_"+job.groups[gid]['name']+"_norm_smoothed_"+nFrags+"FragsPerWin.bedGraph.gz"
gzipfile(ex,smoothFile)
ex.add(smoothFile+".gz",
description=set_file_descr(fname,groupId=gid,step=step,type="bedGraph",ucsc='1',gdv='1'))
## smoothed file after normalisation, after Profile correction
fname = "segToFrag_"+job.groups[gid]['name']+"_profileCorrected_smoothed_"+nFrags+"FragsPerWin.bedGraph.gz"
gzipfile(ex,afterProfileCorrection)
ex.add(afterProfileCorrection+".gz",
description=set_file_descr(fname,groupId=gid,step=step,type="bedGraph",ucsc='1',gdv='1'))
step = "domainograms"
for gid, resfiles in processed['4cseq']['domainogram_grp'].iteritems():
tarFile = resfiles.pop()
fname = job.groups[gid]['name']+"_domainogram.tar.gz"
ex.add(tarFile, description=set_file_descr(fname,
groupId=gid,step=step,type="tgz"))
for s in resfiles:
if s[-8:] == "bedGraph":
gzipfile(ex,s)
s += ".gz"
ex.add( s, description=set_file_descr( s, groupId=gid,step=step,type="bedGraph",ucsc="1",gdv="1"))
step = "combined_results"
gzipfile(ex,tableSmoothedRaw_grp)
ex.add(tableSmoothedRaw_grp+".gz", description=set_file_descr("table_segToFrags_smoothed_combined_replicates.txt.gz",step=step,type="txt"))
gzipfile(ex,tableSmoothed_grp)
ex.add(tableSmoothed_grp+".gz", description=set_file_descr("table_normalised_smoothed_combined_replicates.txt.gz",step=step,type="txt"))
gzipfile(ex,tableSmoothedPC_grp)
ex.add(tableSmoothedPC_grp+".gz", description=set_file_descr("table_profileCorrected_smoothed_combined_replicates.txt.gz",step=step,type="txt"))
gzipfile(ex,tablePC)
ex.add(tablePC+".gz", description=set_file_descr("table_normalised_fit_per_replicates.txt.gz",step=step,type="txt"))
gzipfile(ex,tableBRICKS2Frags)
ex.add(tableBRICKS2Frags+".gz", description=set_file_descr("table_frags_in_BRICKS_combined_replicates.txt.gz",step=step,type="txt"))
return processed
def chipseq_workflow(ex,
job_or_dict,
assembly,
script_path='',
logfile=sys.stdout,
via='lsf'):
"""Runs a chipseq workflow over bam files obtained by mapseq. Will optionally run ``macs`` and 'run_deconv'.
:param ex: a 'bein' execution environment to run jobs in,
:param job_or_dict: a 'Frontend' 'job' object, or a dictionary with key 'groups', 'files' and 'options' if applicable,
:param assembly: a genrep.Assembly object,
:param script_path: only needed if 'run_deconv' is in the job options, must point to the location of the R scripts.
Defaults ``macs`` parameters (overriden by ``job_or_dict['options']['macs_args']``) are set as follows:
* ``'-bw'``: 200 ('bandwith')
* ``'-m'``: 10,100 ('minimum and maximum enrichments relative to background or control')
The enrichment bounds will be computed from a Poisson threshold *T*, if available, as *(min(30,5*(T+1)),50*(T+1))*.
Returns a tuple of a dictionary with keys *group_id* from the job groups, *macs* and *deconv* if applicable and values file description dictionaries and a dictionary of *group_ids* to *names* used in file descriptions.
"""
options = {}
if logfile is None: logfile = sys.stdout
if isinstance(job_or_dict, frontend.Job):
options = job_or_dict.options
groups = job_or_dict.groups
mapseq_files = job_or_dict.files
elif isinstance(job_or_dict, dict) and 'groups' in job_or_dict:
if 'options' in job_or_dict:
options = job_or_dict['options']
groups = job_or_dict['groups']
for gid in groups.keys():
if not ('name' in groups[gid]):
groups[gid]['name'] = gid
mapseq_files = job_or_dict.get('files', {})
else:
raise TypeError(
"job_or_dict must be a frontend. Job object or a dictionary with key 'groups'."
)
merge_strands = int(options.get('merge_strands', -1))
suffixes = ["fwd", "rev"]
peak_deconvolution = options.get('peak_deconvolution', False)
if isinstance(peak_deconvolution, basestring):
peak_deconvolution = peak_deconvolution.lower() in ['1', 'true', 't']
run_meme = options.get('run_meme', False)
if isinstance(run_meme, basestring):
run_meme = run_meme.lower() in ['1', 'true', 't']
macs_args = options.get('macs_args', ["--bw", "200"])
b2w_args = options.get('b2w_args', [])
if not (isinstance(mapseq_files, dict)):
raise TypeError("Mapseq_files must be a dictionary.")
tests = []
controls = []
names = {'tests': [], 'controls': []}
read_length = []
p_thresh = {}
for gid, mapped in mapseq_files.iteritems():
group_name = groups[gid]['name']
if not (isinstance(mapped, dict)):
raise TypeError(
"Mapseq_files values must be dictionaries with keys *run_ids* or 'bam'."
)
if 'bam' in mapped:
mapped = {'_': mapped}
futures = {}
ptruns = []
for k in mapped.keys():
if not 'libname' in mapped[k]:
mapped[k]['libname'] = group_name + "_" + str(k)
if not 'stats' in mapped[k]:
futures[k] = mapseq.bamstats.nonblocking(ex,
mapped[k]["bam"],
via=via)
if mapped[k].get('poisson_threshold', -1) > 0:
ptruns.append(mapped[k]['poisson_threshold'])
if len(ptruns) > 0:
p_thresh['group_name'] = sum(ptruns) / len(ptruns)
for k in futures.keys():
mapped[k]['stats'] = f.wait()
if len(mapped) > 1:
bamfile = mapseq.merge_bam(ex, [m['bam'] for m in mapped.values()])
else:
bamfile = mapped.values()[0]['bam']
if groups[gid]['control']:
controls.append(bamfile)
names['controls'].append((gid, group_name))
else:
tests.append(bamfile)
names['tests'].append((gid, group_name))
read_length.append(mapped.values()[0]['stats']['read_length'])
genome_size = mapped.values()[0]['stats']['genome_size']
if len(controls) < 1:
controls = [None]
names['controls'] = [(0, None)]
logfile.write("Starting MACS.\n")
logfile.flush()
processed = {
'macs':
add_macs_results(ex,
read_length,
genome_size,
tests,
ctrlbam=controls,
name=names,
poisson_threshold=p_thresh,
macs_args=macs_args,
via=via)
}
logfile.write("Done MACS.\n")
logfile.flush()
peak_list = {}
chrlist = assembly.chrmeta
## select only peaks with p-val <= 1e-0.6 = .25 => score = -10log10(p) >= 6
_select = {'score': (6, sys.maxint)}
_fields = ['chr', 'start', 'end', 'name', 'score']
for i, name in enumerate(names['tests']):
if len(names['controls']) < 2:
ctrl = (name, names['controls'][0])
macsbed = track(processed['macs'][ctrl] + "_summits.bed",
chrmeta=chrlist,
fields=_fields).read(selection=_select)
else:
macsbed = concatenate([
apply(track(processed['macs'][(name, x)] + "_summits.bed",
chrmeta=chrlist,
fields=_fields).read(selection=_select),
'name',
lambda __n, _n=xn: "%s:%i" % (__n, _n))
for xn, x in enumerate(names['controls'])
])
##############################
macs_neighb = neighborhood(macsbed, before_start=150, after_end=150)
peak_list[name] = unique_filename_in() + ".sql"
macs_final = track(peak_list[name],
chrmeta=chrlist,
info={'datatype': 'qualitative'},
fields=['start', 'end', 'name', 'score'])
macs_final.write(fusion(macs_neighb), clip=True)
macs_final.close()
##############################
merged_wig = {}
options['read_extension'] = int(
options.get('read_extension') or read_length[0])
if options['read_extension'] < 1:
options['read_extension'] = read_length[0]
make_wigs = merge_strands >= 0 or options['read_extension'] > 100
if options['read_extension'] > 100: options['read_extension'] = 50
for gid, mapped in mapseq_files.iteritems():
# if groups[gid]['control']: continue
group_name = groups[gid]['name']
wig = []
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=options['read_extension'],
convert=False,
b2w_args=b2w_args,
via=via)
wig.append(dict((s, output + s + '.sql') for s in suffixes))
else:
wig.append(m['wig'])
if len(wig) > 1:
merged_wig[group_name] = dict(
(s, merge_sql(ex, [x[s] for x in wig], via=via))
for s in suffixes)
else:
merged_wig[group_name] = wig[0]
if peak_deconvolution:
##############################
def _filter_deconv(stream, pval):
ferr = re.compile(r';FERR=([\d\.]+)$')
return FeatureStream(
((x[0], ) + ((x[2] + x[1]) / 2 - 150,
(x[2] + x[1]) / 2 + 150) + x[3:]
for x in stream if "FERR=" in x[3]
and float(ferr.search(x[3]).groups()[0]) <= pval),
fields=stream.fields)
##############################
processed['deconv'] = {}
for name in names['tests']:
logfile.write(name[1] + " deconvolution.\n")
logfile.flush()
if len(names['controls']) < 2:
ctrl = (name, names['controls'][0])
macsbed = processed['macs'][ctrl] + "_peaks.bed"
else:
macsbed = intersect_many_bed(ex, [
processed['macs'][(name, x)] + "_peaks.bed"
for x in names['controls']
],
via=via)
deconv = run_deconv(ex,
merged_wig[name[1]],
macsbed,
assembly.chrmeta,
options['read_extension'],
script_path,
via=via)
peak_list[name] = unique_filename_in() + ".bed"
trbed = track(deconv['peaks']).read()
with track(peak_list[name], chrmeta=chrlist,
fields=trbed.fields) as bedfile:
bedfile.write(fusion(_filter_deconv(trbed, 0.65)))
ex.add(deconv['peaks'],
description=set_file_descr(name[1] + '_peaks.sql',
type='sql',
step='deconvolution',
groupId=name[0]))
ex.add(deconv['profile'],
description=set_file_descr(name[1] + '_deconv.sql',
type='sql',
step='deconvolution',
groupId=name[0]))
bigwig = unique_filename_in()
try:
convert(deconv['profile'], (bigwig, "bigWig"))
ex.add(bigwig,
description=set_file_descr(name[1] + '_deconv.bw',
type='bigWig',
ucsc='1',
step='deconvolution',
groupId=name[0]))
except OSError as e:
logfile.write(str(e))
logfile.flush()
ex.add(deconv['pdf'],
description=set_file_descr(name[1] + '_deconv.pdf',
type='pdf',
step='deconvolution',
groupId=name[0]))
processed['deconv'][name] = deconv
##############################
def _join_macs(stream, xlsl, _f):
def _macs_row(_s):
for _p in _s:
for _n in _p[3].split("|"):
if len(xlsl) == 1:
nb = int(
_n.split(";")[0][13:]) if _n[:3] == "ID=" else int(
_n[10:])
yield _p + xlsl[0][nb - 1][1:]
else:
nb = _n.split(
";")[0][13:] if _n[:3] == "ID=" else _n[10:]
nb = nb.split(":")
yield _p + xlsl[int(nb[1])][int(nb[0]) - 1][1:]
return FeatureStream(_macs_row(stream), fields=_f)
##############################
peakfile_list = []
for name, plist in peak_list.iteritems():
ptrack = track(plist,
chrmeta=chrlist,
fields=["chr", "start", "end", "name", "score"])
peakfile = unique_filename_in()
xlsh, xlsl = parse_MACS_xls([
processed['macs'][(name, _c)] + "_peaks.xls"
for _c in names['controls']
])
try:
###### if assembly doesn't have annotations, we skip the "getNearestFeature" but still go through "_join_macs"
assembly.gene_track()
_fields = ['chr','start','end','name','score','gene','location_type','distance']\
+["MACS_%s"%h for h in xlsh[1:5]]+xlsh[5:]
peakout = track(peakfile,
format='txt',
chrmeta=chrlist,
fields=_fields)
peakout.make_header("#" + "\t".join([
'chromosome', 'start', 'end', 'info', 'peak_height', 'gene(s)',
'location_type', 'distance'
] + _fields[8:]))
for chrom in assembly.chrnames:
_feat = assembly.gene_track(chrom)
peakout.write(_join_macs(
getNearestFeature(ptrack.read(selection=chrom), _feat),
xlsl, _fields),
mode='append')
except ValueError:
_fields = ['chr', 'start', 'end', 'name', 'score'
] + ["MACS_%s" % h for h in xlsh[1:5]] + xlsh[5:]
peakout = track(peakfile,
format='txt',
chrmeta=chrlist,
fields=_fields)
peakout.make_header("#" + "\t".join(
['chromosome', 'start', 'end', 'info', 'peak_height'] +
_fields[8:]))
for chrom in assembly.chrnames:
peakout.write(_join_macs(ptrack.read(selection=chrom), xlsl,
_fields),
mode='append')
peakout.close()
gzipfile(ex, peakfile)
peakfile_list.append(
track(peakfile + ".gz", format='txt', fields=_fields))
ex.add(peakfile + ".gz",
description=set_file_descr(name[1] + '_annotated_peaks.txt.gz',
type='text',
step='annotation',
groupId=name[0]))
stracks = [
track(wig, info={'name': name + "_" + st})
for name, wigdict in merged_wig.iteritems()
for st, wig in wigdict.iteritems()
]
tablefile = unique_filename_in()
with open(tablefile, "w") as _tf:
_pnames = [
"MACS_%s_vs_%s" % (_s[1], _c[1]) if _c[1] else "MACS_%s" % _s[1]
for _s in names['tests'] for _c in names['controls']
]
_tf.write("\t".join([
'#chromosome',
'start',
'end',
] + _pnames + [s.name for s in stracks]) + "\n")
#### need to do something about peak origin (split names, write to separate columns?)
for chrom in assembly.chrnames:
pk_lst = [
apply(pt.read(chrom, fields=['chr', 'start', 'end', 'name']),
'name',
lambda __n, _n=npt: "%s:%i" % (__n, _n))
for npt, pt in enumerate(peakfile_list)
]
features = fusion(
concatenate(pk_lst,
fields=['chr', 'start', 'end', 'name'],
remove_duplicates=True,
group_by=['chr', 'start', 'end']))
sread = [sig.read(chrom) for sig in stracks]
quantifs = score_by_feature(sread, features, method='sum')
nidx = quantifs.fields.index('name')
_ns = len(tests)
_nc = len(controls)
with open(tablefile, "a") as _tf:
for row in quantifs:
pcols = [''] * _ns * _nc
_rnsplit = row[nidx].split(":")
_n1 = _rnsplit[0]
_k = 0
while (_k < len(_rnsplit) - 1 - int(_nc > 1)):
if _nc > 1:
_k += 2
_n2 = _rnsplit[_k - 1]
_n = _rnsplit[_k].split("|")
pcols[int(_n[0]) * _nc + int(_n2)] = _n1
else:
_k += 1
_n = _rnsplit[_k].split("|")
pcols[int(_n[0])] = _n1
_n1 = "|".join(_n[1:])
_tf.write("\t".join(
str(tt)
for tt in row[:nidx] + tuple(pcols) + row[nidx + 1:]) +
"\n")
gzipfile(ex, tablefile)
ex.add(tablefile + ".gz",
description=set_file_descr('Combined_peak_quantifications.txt.gz',
type='text',
step='summary'))
if run_meme:
from bbcflib.motif import parallel_meme
logfile.write("Starting MEME.\n")
logfile.flush()
processed['meme'] = parallel_meme(
ex,
assembly,
peak_list.values(),
name=peak_list.keys(),
chip=True,
meme_args=['-meme-nmotifs', '4', '-meme-mod', 'zoops'],
via=via)
return processed
def c4seq_workflow(ex,
job,
primers_dict,
assembly,
c4_url=None,
script_path='',
logfile=sys.stdout,
via='lsf'):
'''
Main
* open the 4C-seq minilims and create execution
* 0. get/create the library
* 1. if necessary, calculate the density file from the bam file (mapseq.parallel_density_sql)
* 2. calculate the count per fragment for each denstiy file with gfminer:score_by_feature to calculate)
'''
mapseq_files = job.files
### outputs
processed = {'lib': {}, 'density': {}, '4cseq': {}}
processed['4cseq'] = {
'density_files': {},
'countsPerFrag': {},
'countsPerFrag_grp': {},
'norm': {},
'norm_grp': {},
'profileCorrection': {},
'profileCorrection_grp': {},
'smooth_grp': {},
'domainogram_grp': {},
'bricks2frags': {}
}
# was 'smoothFrag': {}, 'domainogram': {}}
regToExclude = {}
new_libs = []
### options
run_domainogram = {}
before_profile_correction = {}
if not job.options.get('viewpoints_chrs', False):
out_chromosomes = ','.join([ch for ch in assembly.chrnames])
else:
out_chromosomes = ','.join([
primers_dict.get(group['name'], {}).get('baitcoord').split(':')[0]
for gid, group in job.groups.iteritems()
])
print "out_chromosomes=" + out_chromosomes + "\n"
sizeExt = job.options.get('norm_reg', 1000000)
print "region considered for normalisation: mid viewpoint +/-" + str(
sizeExt) + 'bps'
### do it
for gid, group in job.groups.iteritems():
run_domainogram[gid] = group.get('run_domainogram', False)
if isinstance(run_domainogram[gid], basestring):
run_domainogram[gid] = (run_domainogram[gid].lower()
in ['1', 'true', 'on', 't'])
before_profile_correction[gid] = group.get('before_profile_correction',
False)
if isinstance(before_profile_correction[gid], basestring):
before_profile_correction[gid] = (
before_profile_correction[gid].lower()
in ['1', 'true', 'on', 't'])
processed['lib'][gid] = get_libForGrp(ex,
group,
assembly,
new_libs,
gid,
c4_url,
via=via)
#reffile='/archive/epfl/bbcf/data/DubouleDaan/library_Nla_30bps/library_Nla_30bps_segmentInfos.bed'
processed['4cseq']['density_files'][gid] = {}
regToExclude[gid] = primers_dict.get(group['name'],
{}).get('regToExclude',
"").replace('\r', '')
# if no regToExclude defined, set it as mid_baitCoord +/-5kb
if len(regToExclude[gid]) == 0:
baitcoord_mid = int(0.5 * (int(
primers_dict.get(group['name'], {}).get('baitcoord').split(':')
[1].split('-')[0]) + int(
primers_dict.get(group['name'], {}).get('baitcoord').split(
':')[1].split('-')[1])))
regToExclude[gid] = primers_dict.get(
group['name'], {}).get('baitcoord').split(':')[0] + ':' + str(
baitcoord_mid - 5000) + '-' + str(baitcoord_mid + 5000)
#print(';'.join([k+"="+v for k,v in primers_dict.get(group['name'],{}).iteritems()]))
print(primers_dict.get(group['name'], {}))
print "regToExclude[" + str(gid) + "]=" + regToExclude[gid]
for rid, run in group['runs'].iteritems():
libname = mapseq_files[gid][rid]['libname']
if job.options.get('merge_strands') != 0 or not (
'wig' in mapseq_files[gid][rid]):
density_file = parallel_density_sql(
ex,
mapseq_files[gid][rid]['bam'],
assembly.chrmeta,
nreads=mapseq_files[gid][rid]['stats']["total"],
merge=0,
read_extension=mapseq_files[gid][rid]['stats']
['read_length'],
convert=False,
via=via)
density_file += "merged.sql"
ex.add(density_file,
description=set_file_descr("density_file_" + libname +
".sql",
groupId=gid,
step="density",
type="sql",
view='admin',
gdv="1"))
else:
density_file = mapseq_files[gid][rid]['wig']['merged']
#density_files.append(density_file)
processed['4cseq']['density_files'][gid][rid] = density_file
# back to grp level!
# not anymore:
# processed['density'][gid] = merge_sql(ex, density_files, via=via)
processed['4cseq']['countsPerFrag'] = density_to_countsPerFrag(
ex, processed, job.groups, assembly, regToExclude, script_path, via)
## access per gid+rid
futures_norm = {}
countsPerFrags_bedGraph = {}
futures_merged_raw = {}
for gid, group in job.groups.iteritems():
futures_norm[gid] = {}
countsPerFrags_bedGraph[gid] = {}
processed['4cseq']['norm'][gid] = {}
for rid, run in group['runs'].iteritems():
normfile = unique_filename_in()
touch(ex, normfile)
resfile = unique_filename_in() + ".bedGraph"
resfiles = processed['4cseq']['countsPerFrag'][gid][
rid] # _all.sql
convert(resfiles[3], resfile)
countsPerFrags_bedGraph[gid][rid] = resfile
print "call normFrags: infiles=" + resfile + ", normfile=" + normfile + "baitCoord=" + primers_dict[
group['name']][
'baitcoord'] + ", sizeExt=sizeExt, name=" + group[
'name'] + "rep_" + str(
rid) + "regToExclude=" + regToExclude[gid] + "\n"
futures_norm[gid][rid] = normFrags.nonblocking(
ex,
resfile,
normfile,
baitCoord=primers_dict[group['name']]['baitcoord'],
sizeExt=sizeExt,
name=group['name'] + "rep_" + str(rid),
regToExclude=regToExclude[gid],
script_path=script_path,
via=via)
processed['4cseq']['norm'][gid][rid] = normfile
if len(group) > 1:
## merge replicates before normalisation.
mergefile = unique_filename_in()
touch(ex, mergefile)
titleName = group['name'] + "_raw_mergedRep"
print "gid=" + group['name']
print "call mergeRep for replicates before normalisation: infiles=" + ",".join(
[
res_rid for rid, res_rid in
countsPerFrags_bedGraph[gid].iteritems()
]
) + ", mergedfile=" + mergefile + ", regToExclude=" + regToExclude[
gid] + "\n"
futures_merged_raw[gid] = mergeRep.nonblocking(
ex,
",".join([
res_rid for rid, res_rid in
countsPerFrags_bedGraph[gid].iteritems()
]),
mergefile,
regToExclude[gid],
name=titleName,
script_path=script_path,
via=via,
memory=8)
processed['4cseq']['countsPerFrag_grp'][gid] = mergefile
else:
futures_merged_raw[gid] = None
processed['4cseq']['countsPerFrag_grp'][
gid] = countsPerFrags_bedGraph[gid][
0] #if no replicates, then the file we want is the 1st one
print "***** profile correction / sample + merge normalised data"
futures_merged = {} # per gid
futures_profcor = {} # per gid, per rid
for gid, group in job.groups.iteritems():
## run profile correction per run then merge them
futures_profcor[gid] = {}
processed['4cseq']['profileCorrection'][gid] = {}
for rid, run in group['runs'].iteritems():
# wait for normalisation of all replicates to be finished
futures_norm[gid][rid].wait(
) ## normalised files, per grp, per rep
normfile = processed['4cseq']['norm'][gid][rid]
file1 = unique_filename_in() #track file
touch(ex, file1)
file2 = unique_filename_in() #report file
touch(ex, file2)
file3 = unique_filename_in() #table file
touch(ex, file3)
print "call profileCorrection: normfile=" + normfile + ", baitCoord=" + primers_dict[
group['name']]['baitcoord'] + ", name=" + group[
'name'] + ", file1=" + file1 + ", file2=" + file2 + ", file3= " + file3 + "\n"
futures_profcor[gid][rid] = profileCorrection.nonblocking(
ex,
normfile,
primers_dict[group['name']]['baitcoord'],
group['name'],
file1,
file2,
file3,
script_path,
via=via)
processed['4cseq']['profileCorrection'][gid][rid] = [
file1, file2, file3
]
## merge replicates before profile correction. Needs all normalisation for the given grp to be finished, this is why it comes after the rid loop.
if len(group) > 1:
mergefile = unique_filename_in()
touch(ex, mergefile)
titleName = group['name'] + "_norm_mergedRep"
print "gid=" + group['name']
print "call mergeRep: infiles=" + ",".join([
res_rid for rid, res_rid in processed['4cseq']['norm']
[gid].iteritems()
]) + ", mergedfile=" + mergefile + ", regToExclude=" + regToExclude[
gid] + "\n"
futures_merged[gid] = mergeRep.nonblocking(
ex,
",".join([
res_rid for rid, res_rid in processed['4cseq']['norm']
[gid].iteritems()
]),
mergefile,
regToExclude[gid],
name=titleName,
script_path=script_path,
via=via,
memory=8)
processed['4cseq']['norm_grp'][gid] = mergefile
else:
futures_merged[gid] = None
processed['4cseq']['norm_grp'][gid] = processed['4cseq']['norm'][
gid][
0] ##if no replicates, then the file we want is the 1st one
print "***** merge profile corrected data"
futures_profcor_merged = {} # per gid
for gid, group in job.groups.iteritems():
processed['4cseq']['profileCorrection_grp'][gid] = {}
for rid, run in group['runs'].iteritems():
futures_profcor[gid][rid].wait(
) ## wait for ProfileCorrection to be finished
## merge replicates after profile correction
if len(group) > 1:
mergefile = unique_filename_in()
touch(ex, mergefile)
titleName = group['name'] + "_ProfCor_mergedRep"
pcfiles = [
processed['4cseq']['profileCorrection'][gid][rid][0]
for rid, res_rid in processed['4cseq']['profileCorrection']
[gid].iteritems()
]
print "call mergeRep (for PC tables): infiles=" + ",".join(
pcfiles
) + ", mergedfile=" + mergefile + ", regToExclude=" + regToExclude[
gid] + "\n"
futures_profcor_merged[gid] = mergeRep.nonblocking(
ex,
",".join(pcfiles),
mergefile,
regToExclude[gid],
name=titleName,
script_path=script_path,
via=via,
memory=8)
processed['4cseq']['profileCorrection_grp'][gid] = mergefile
else:
futures_profcor_merged[gid] = None
processed['4cseq']['profileCorrection_grp'][gid] = processed[
'4cseq']['profileCorrection'][gid][
0] ##if no replicates, then the file we want is the 1st one
print "***** smooth data"
futures_smoothed = {}
for gid, group in job.groups.iteritems():
file1 = unique_filename_in()
touch(ex, file1)
file2 = unique_filename_in()
touch(ex, file2)
file3 = unique_filename_in()
touch(ex, file3)
nFragsPerWin = group['window_size']
futures_merged_raw[gid].wait(
) ## wait for merging of raw_grp to be completed
futures_smoothed[gid] = (smoothFragFile.nonblocking(
ex,
processed['4cseq']['countsPerFrag_grp'][gid],
nFragsPerWin,
group['name'],
file1,
regToExclude[gid],
script_path=script_path,
via=via,
memory=6), )
futures_merged[gid].wait(
) ## wait for merging of norm_grp to be completed
futures_smoothed[gid] += (smoothFragFile.nonblocking(
ex,
processed['4cseq']['norm_grp'][gid],
nFragsPerWin,
group['name'] + "_norm",
file2,
regToExclude[gid],
script_path=script_path,
via=via,
memory=6), )
futures_profcor_merged[gid].wait(
) # wait for the merging of profile corrected data to be done
futures_smoothed[gid] += (smoothFragFile.nonblocking(
ex,
processed['4cseq']['profileCorrection_grp'][gid],
nFragsPerWin,
group['name'] + "_fromProfileCorrected",
file3,
regToExclude[gid],
script_path=script_path,
via=via,
memory=6), )
processed['4cseq']['smooth_grp'][gid] = [
file1, file2, file3
] #[smoothed_file_before_Norm, smoothed file before PC, smoothed file after PC]
print "***** Domainograms"
futures_domainograms = {}
for gid, group in job.groups.iteritems():
grName = job.groups[gid]['name']
if run_domainogram[gid]:
regCoord = regToExclude[gid] or primers_dict[grName]['baitcoord']
if before_profile_correction[gid]:
futures_domainograms[gid] = runDomainogram.nonblocking(
ex,
processed['4cseq']['norm_grp'][gid],
grName,
regCoord=regCoord,
skip=1,
script_path=script_path,
via=via,
memory=15)
else:
futures_domainograms[gid] = runDomainogram.nonblocking(
ex,
processed['4cseq']['profileCorrection_grp'][gid],
grName,
regCoord=regCoord.split(':')[0],
skip=1,
script_path=script_path,
via=via,
memory=15)
## prepare tar files for domainogram results (if any)
## and create "BRICKS to frags" files
print "***** BRICKS to Frags"
futures_BRICKS2Frags = {}
for gid, f in futures_domainograms.iteritems():
if run_domainogram[gid]: # if domainogram has been run
resFiles = []
logFile = f.wait()
start = False
tarname = job.groups[gid]['name'] + "_domainogram.tar.gz"
res_tar = tarfile.open(tarname, "w:gz")
futures_BRICKS2Frags[gid] = []
processed['4cseq']['bricks2frags'][gid] = []
if logFile is None: continue
with open(logFile) as f:
for s in f:
s = s.strip()
if '####resfiles####' in s:
start = True
elif start and "RData" not in s:
resFiles.append(s)
res_tar.add(s)
if start and "foundBRICKS" in s:
bricks2fragsfile = unique_filename_in() + ".bedGraph"
touch(ex, bricks2fragsfile)
futures_BRICKS2Frags[gid] += [
BRICKSToFrag.nonblocking(
ex,
s,
processed['4cseq']['norm_grp'][gid],
bricks2fragsfile,
script_path=script_path,
via=via,
memory=4)
]
processed['4cseq']['bricks2frags'][gid] += [
bricks2fragsfile
]
res_tar.close()
processed['4cseq']['domainogram_grp'][gid] = resFiles + [tarname]
############### prepare tables for global results
print "***** combine results into tables "
allNames = []
allFiles = []
allRegToExclude = []
for gid, group in job.groups.iteritems():
for rid, run in group['runs'].iteritems():
allNames += [
group['name'] + "_rep" + str(rid) + "_norm",
group['name'] + "_rep" + str(rid) + "_fit"
]
allFiles += [processed['4cseq']['profileCorrection'][gid][rid][2]]
allRegToExclude += [regToExclude[gid]]
tablePC = unique_filename_in() + ".txt"
print("***will call makeTable with:")
print(",".join(allFiles))
print("resfile=" + tablePC)
print(",".join(allNames))
touch(ex, tablePC)
#regToExclude[gid]
futures_tables = (makeTable.nonblocking(
ex,
",".join(allFiles),
tablePC,
",".join(allNames),
idCols="4,5",
all_regToExclude=','.join(allRegToExclude),
script_path=script_path,
via=via,
memory=8), )
# wait for all smoothing to be done
for gid, fg in futures_smoothed.iteritems():
for f in fg:
f.wait()
## make Table raw/smoothed_raw
print("** make Table raw/smoothed_raw")
allNames = []
allFiles = []
allRegToExclude = []
for gid, group in job.groups.iteritems():
futures_merged_raw[gid].wait()
allNames += [group['name'] + "_raw", group['name'] + "_rawSmoothed"]
allFiles += [
processed['4cseq']['countsPerFrag_grp'][gid],
processed['4cseq']['smooth_grp'][gid][0]
]
allRegToExclude += ['NA', regToExclude[gid]]
tableSmoothedRaw_grp = unique_filename_in() + ".txt"
touch(ex, tableSmoothedRaw_grp)
futures_tables += (makeTable.nonblocking(
ex,
",".join(allFiles),
tableSmoothedRaw_grp,
",".join(allNames),
idCols="4",
out_chromosomes=out_chromosomes,
all_regToExclude=','.join(allRegToExclude),
script_path=script_path,
via=via,
memory=8), )
## make Table norm/smoothed_norm before PC
print("** make Table norm/smoothed_norm befor PC")
allNames = []
allFiles = []
allRegToExclude = []
for gid, group in job.groups.iteritems():
allNames += [group['name'] + "_norm", group['name'] + "_smoothed"]
allFiles += [
processed['4cseq']['norm_grp'][gid],
processed['4cseq']['smooth_grp'][gid][1]
]
allRegToExclude += [regToExclude[gid], regToExclude[gid]]
tableSmoothed_grp = unique_filename_in() + ".txt"
touch(ex, tableSmoothed_grp)
futures_tables += (makeTable.nonblocking(
ex,
",".join(allFiles),
tableSmoothed_grp,
",".join(allNames),
idCols="4",
out_chromosomes=out_chromosomes,
all_regToExclude=','.join(allRegToExclude),
script_path=script_path,
via=via,
memory=8), )
## make Table norm/smoothed_norm after PC
print("** make Table norm/smoothed_norm after PC")
allNames = []
allFiles = []
allRegToExclude = []
for gid, group in job.groups.iteritems():
allNames += [group['name'] + "_normPC", group['name'] + "_smoothedPC"]
allFiles += [
processed['4cseq']['profileCorrection_grp'][gid],
processed['4cseq']['smooth_grp'][gid][2]
]
allRegToExclude += [regToExclude[gid], regToExclude[gid]]
tableSmoothedPC_grp = unique_filename_in() + ".txt"
touch(ex, tableSmoothedPC_grp)
futures_tables += (makeTable.nonblocking(
ex,
",".join(allFiles),
tableSmoothedPC_grp,
",".join(allNames),
idCols="4",
out_chromosomes=out_chromosomes,
all_regToExclude=','.join(allRegToExclude),
script_path=script_path,
via=via,
memory=8), )
## combine BRICKS2Frags files
allNames = []
allFiles = []
for gid, fg in futures_BRICKS2Frags.iteritems():
for f in fg:
f.wait()
allNames += [job.groups[gid]['name'] + "_BRICKSpval"]
cat_bricks2frags = unique_filename_in() + ".txt"
print ','.join(processed['4cseq']['bricks2frags'][gid])
cat_bricks2frags = cat(processed['4cseq']['bricks2frags'][gid],
out=cat_bricks2frags)
allFiles += [cat_bricks2frags]
for gid, fg in futures_smoothed.iteritems():
for f in fg:
f.wait()
tableBRICKS2Frags = unique_filename_in() + ".txt"
touch(ex, tableBRICKS2Frags)
futures_tables += (makeTable.nonblocking(ex,
",".join(allFiles),
tableBRICKS2Frags,
",".join(allNames),
idCols="4",
out_chromosomes=out_chromosomes,
defVal="NA",
script_path=script_path,
via=via,
memory=8), )
for f in futures_tables:
f.wait()
################ Add everything to minilims below!
step = "density"
for gid in processed['4cseq']['density_files'].keys():
for rid, sql in processed['4cseq']['density_files'][gid].iteritems():
fname = "density_file_" + job.groups[gid][
'name'] + "_merged_rep" + str(rid)
ex.add(sql,
description=set_file_descr(fname + ".sql",
groupId=gid,
step=step,
type="sql",
gdv="1"))
wig = unique_filename_in() + ".bw"
convert(sql, wig)
ex.add(wig,
description=set_file_descr(fname + ".bw",
groupId=gid,
step=step,
type="bigWig",
ucsc="1"))
step = "counts_per_frag" #was _norm_counts_per_frags # before normalisation process, per replicate
for gid in processed['4cseq']['countsPerFrag'].keys():
for rid, resfiles in processed['4cseq']['countsPerFrag'][
gid].iteritems():
fname = "meanScorePerFeature_" + job.groups[gid][
'name'] + "_rep" + str(rid)
ex.add(resfiles[1],
description=set_file_descr(fname + ".sql",
groupId=gid,
step=step,
type="sql",
view="admin",
gdv='1'))
#gzipfile(ex,resfiles[0])
#ex.add( resfiles[0]+".gz", description=set_file_descr( fname+".bed.gz",
# groupId=gid,step=step,type="bed",view="admin" ))
fname = "segToFrag_" + job.groups[gid]['name'] + "_rep" + str(rid)
ex.add(resfiles[3],
description=set_file_descr(
fname + "_all.sql",
groupId=gid,
step=step,
type="sql",
comment="all informative frags - null included"))
trsql = track(resfiles[3])
bwig = unique_filename_in() + ".bw"
trwig = track(bwig, chrmeta=trsql.chrmeta)
trwig.write(
trsql.read(fields=['chr', 'start', 'end', 'score'],
selection={'score': (0.01, sys.maxint)}))
trwig.close()
ex.add(
bwig,
set_file_descr(fname + ".bw",
groupId=gid,
step=step,
type="bigWig",
ucsc='1'))
## add segToFrags before normalisation
futures_merged_raw[gid].wait()
trbedgraph = track(removeNA(
processed['4cseq']['countsPerFrag_grp'][gid]),
format='bedgraph')
bwig = unique_filename_in() + ".bw"
trwig = track(bwig, chrmeta=assembly.chrmeta)
trwig.write(
trbedgraph.read(fields=['chr', 'start', 'end', 'score'],
selection={'score': (0.01, sys.maxint)}))
trwig.close()
fname = "segToFrag_" + job.groups[gid]['name']
ex.add(bwig,
description=set_file_descr(
fname + ".bw",
groupId=gid,
step=step,
type="bigWig",
comment="segToFrag file before normalisation"))
step = "norm_counts_per_frags" # after new normalisation process, combined replicates
for gid, resfile in processed['4cseq']['norm_grp'].iteritems():
fname = "normalised_scorePerFeature_" + job.groups[gid]['name']
gzipfile(ex, resfile)
ex.add(resfile + ".gz",
description=set_file_descr(fname + ".bedGraph.gz",
groupId=gid,
step=step,
type="bedGraph",
ucsc='1'))
# norm files, per replicates (might be removed)
for gid, dict_gid in processed['4cseq']['norm'].iteritems():
for rid, resfile in dict_gid.iteritems():
fname = "normalised_scorePerFeature_" + job.groups[gid][
'name'] + "_rep" + str(rid)
gzipfile(ex, resfile)
ex.add(resfile + ".gz",
description=set_file_descr(fname + ".bedGraph.gz",
groupId=gid,
step=step,
type="bedGraph",
ucsc='1',
gdv='1'))
step = "profile_correction" # Profile corrected data, combined replicates
for gid, profileCorrectedFile in processed['4cseq'][
'profileCorrection_grp'].iteritems():
fname = "segToFrag_" + job.groups[gid]['name'] + "_profileCorrected"
gzipfile(ex, profileCorrectedFile)
ex.add(profileCorrectedFile + ".gz",
description=set_file_descr(fname + ".bedGraph.gz",
groupId=gid,
step=step,
type="bedGraph",
ucsc='1',
gdv='1'))
# Profile corrected, per replicate (might be removed)
for gid, dict_gid in processed['4cseq']['profileCorrection'].iteritems():
for rid, resfiles in dict_gid.iteritems():
# profileCorrectedFile = resfiles[0]
reportProfileCorrection = resfiles[1]
fname = "segToFrag_" + job.groups[gid][
'name'] + "_profileCorrected_rep" + str(rid)
# gzipfile(ex,profileCorrectedFile)
# ex.add( profileCorrectedFile+".gz",
# description=set_file_descr(fname+".bedGraph.gz",groupId=gid,step=step,type="bedGraph",ucsc='1',gdv='1'))
ex.add(reportProfileCorrection,
description=set_file_descr(fname + ".pdf",
groupId=gid,
step=step,
type="pdf"))
step = "smoothing"
for gid, resfiles in processed['4cseq']['smooth_grp'].iteritems():
rawSmoothFile = resfiles[0]
smoothFile = resfiles[1]
afterProfileCorrection = resfiles[2]
nFrags = str(job.groups[gid]['window_size'])
## smoothed file before normalisation
fname = "segToFrag_" + job.groups[gid][
'name'] + "_smoothed_" + nFrags + "FragsPerWin.bedGraph.gz"
gzipfile(ex, rawSmoothFile)
ex.add(rawSmoothFile + ".gz",
description=set_file_descr(fname,
groupId=gid,
step=step,
type="bedGraph",
ucsc='1',
gdv='1'))
## smoothed file after normalisation, before Profile correction
fname = "segToFrag_" + job.groups[gid][
'name'] + "_norm_smoothed_" + nFrags + "FragsPerWin.bedGraph.gz"
gzipfile(ex, smoothFile)
ex.add(smoothFile + ".gz",
description=set_file_descr(fname,
groupId=gid,
step=step,
type="bedGraph",
ucsc='1',
gdv='1'))
## smoothed file after normalisation, after Profile correction
fname = "segToFrag_" + job.groups[gid][
'name'] + "_profileCorrected_smoothed_" + nFrags + "FragsPerWin.bedGraph.gz"
gzipfile(ex, afterProfileCorrection)
ex.add(afterProfileCorrection + ".gz",
description=set_file_descr(fname,
groupId=gid,
step=step,
type="bedGraph",
ucsc='1',
gdv='1'))
step = "domainograms"
for gid, resfiles in processed['4cseq']['domainogram_grp'].iteritems():
tarFile = resfiles.pop()
fname = job.groups[gid]['name'] + "_domainogram.tar.gz"
ex.add(tarFile,
description=set_file_descr(fname,
groupId=gid,
step=step,
type="tgz"))
for s in resfiles:
if s[-8:] == "bedGraph":
gzipfile(ex, s)
s += ".gz"
ex.add(s,
description=set_file_descr(s,
groupId=gid,
step=step,
type="bedGraph",
ucsc="1",
gdv="1"))
step = "combined_results"
gzipfile(ex, tableSmoothedRaw_grp)
ex.add(tableSmoothedRaw_grp + ".gz",
description=set_file_descr(
"table_segToFrags_smoothed_combined_replicates.txt.gz",
step=step,
type="txt"))
gzipfile(ex, tableSmoothed_grp)
ex.add(tableSmoothed_grp + ".gz",
description=set_file_descr(
"table_normalised_smoothed_combined_replicates.txt.gz",
step=step,
type="txt"))
gzipfile(ex, tableSmoothedPC_grp)
ex.add(tableSmoothedPC_grp + ".gz",
description=set_file_descr(
"table_profileCorrected_smoothed_combined_replicates.txt.gz",
step=step,
type="txt"))
gzipfile(ex, tablePC)
ex.add(tablePC + ".gz",
description=set_file_descr(
"table_normalised_fit_per_replicates.txt.gz",
step=step,
type="txt"))
gzipfile(ex, tableBRICKS2Frags)
ex.add(tableBRICKS2Frags + ".gz",
description=set_file_descr(
"table_frags_in_BRICKS_combined_replicates.txt.gz",
step=step,
type="txt"))
return processed
def parallel_meme( ex, assembly, regions, name=None, chip=False, meme_args=None, via='lsf' ):
"""Fetches sequences, then calls ``meme`` on them and finally saves the results in the repository.
"""
if meme_args is None: meme_args = []
if not(isinstance(regions,list)): regions = [regions]
if not(isinstance(name,list)): name = [name or '_']
futures = {}
fasta_files = {}
background = assembly.statistics(unique_filename_in(),frequency=True)
# genomeRef = assembly.untar_genome_fasta()
for i,n in enumerate(name):
(fasta, size) = assembly.fasta_from_regions( regions[i], ex=ex )
tmpfile = unique_filename_in()
outdir = unique_filename_in()
if chip:
futures[n] = (outdir, memechip.nonblocking( ex, fasta, outdir, background,
args=meme_args, via=via,
stderr=tmpfile, memory=6 ))
else:
futures[n] = (outdir, meme.nonblocking( ex, fasta, outdir, background,
maxsize=(size*3)/2, args=meme_args,
via=via, stderr=tmpfile, memory=6 ))
fasta_files[n] = fasta
all_res = {}
for n,f in futures.iteritems():
f[1].wait()
meme_out = f[0]
archive = unique_filename_in()
tgz = tarfile.open(archive, "w:gz")
tgz.add( meme_out, arcname=n[1]+"_meme",
exclude=lambda x: os.path.basename(x) in [fasta_files[n],background] )
tgz.close()
ex.add( archive, description=set_file_descr(n[1]+"_meme.tgz",
step='meme', type='tar',
groupId=n[0]) )
gzipfile(ex,fasta_files[n],args=["-f"])
ex.add( fasta_files[n]+".gz",
description=set_file_descr(n[1]+"_sites.fa.gz",
step='meme', type='fasta',
groupId=n[0]) )
if not(chip) and os.path.exists(os.path.join(meme_out, "meme.xml")):
meme_res = parse_meme_xml( ex, os.path.join(meme_out, "meme.xml"),
assembly.chrmeta )
if os.path.exists(os.path.join(meme_out, "meme.html")):
ex.add( os.path.join(meme_out, "meme.html"),
description=set_file_descr(n[1]+"_meme.html",
step='meme', type='html',
groupId=n[0]) )
ex.add( meme_res['sql'], description=set_file_descr(n[1]+"_meme_sites.sql",
step='meme', type='sql',
groupId=n[0]) )
for i,motif in enumerate(meme_res['matrices'].keys()):
ex.add( meme_res['matrices'][motif],
description=set_file_descr(n[1]+"_meme_"+motif+".txt",
step='meme', type='txt',
groupId=n[0]) )
ex.add( os.path.join(meme_out, "logo"+str(i+1)+".png"),
description=set_file_descr(n[1]+"_meme_"+motif+".png",
step='meme', type='png',
groupId=n[0]) )
all_res[n] = meme_res
return all_res