def main(argv=None):
settings = process_command_line(argv)
if not os.path.exists(settings.dirout):
os.makedirs(settings.dirout)
if settings.genes_gff:
try:
pos_feat_list, all_features = RILseq.read_gtf(
open(settings.genes_gff), settings.feature, settings.identifier)
except IOError:
settings.genes_gff = None
gcounts = {}
lib_order = []
fastq_1_list = list(RILseq.flat_list(settings.fastq_1))
fastq_2_list = list(RILseq.flat_list(settings.fastq_2))
for i, r1_name in enumerate(RILseq.flat_list(settings.fastq_1)):
try:
r2_name = fastq_2_list[i]
except IndexError:
r2_name = None
outhead = r1_name.rsplit('.', 1)[0]
libname = outhead.rsplit('/',1)[-1]
outhead = '%s_bwa'%libname
bamname = RILseq.run_bwa(
settings.bwa_exec, r1_name, r2_name,
settings.dirout, outhead, settings.allowed_mismatches,
settings.genome_fasta, settings.params_aln, settings.sampe_params,
settings.samse_params, settings.samtools_cmd)
samfile = pysam.Samfile(bamname)
if settings.genes_gff:
lib_order.append(libname)
gcounts[libname] = RILseq.count_features(
pos_feat_list, samfile, settings.overlap,
rev=settings.reverse_complement)
if settings.create_wig:
outwigs = [open("%s/%s_coverage.wig"%(settings.dirout, fastq.split("_cutadapt")[0]), 'w')
for fastq in fastq_1_list]
coverage = RILseq.generate_wig(
samfile, rev=settings.reverse_complement, first_pos=False)
RILseq.print_wiggle(
coverage, "%s_single_fragments_coverage"%libname,
"%s single fragments coverage"%libname, outwigs[i])
# Print the table of counts
if settings.genes_gff:
outtables = [open("%s/%s_counts.txt"%(settings.dirout, fastq.split("_cutadapt")[0]), 'w')
for fastq in fastq_1_list]
for i, r1_name in enumerate(fastq_1_list):
outt = csv.writer(outtables[i], delimiter='\t')
outt.writerow(['Gene name'] + lib_order)
for g in sorted(list(all_features)):
row_out = [g]
for libn in lib_order:
row_out.append(gcounts[libn][g])
outt.writerow(row_out)
return 0 # success
def main(argv=None):
settings = process_command_line(argv)
if not os.path.exists(settings.dirout):
os.makedirs(settings.dirout)
outwig = open("%s/%s_coverage.wig"%(settings.dirout, settings.outhead), 'w')
if settings.genes_gff:
try:
pos_feat_list, all_features = RILseq.read_gtf(
open(settings.genes_gff), settings.feature, settings.identifier)
except IOError:
settings.genes_gff = None
gcounts = {}
lib_order = []
fastq_2_list = list(RILseq.flat_list(settings.fastq_2))
for i, r1_name in enumerate(RILseq.flat_list(settings.fastq_1)):
try:
r2_name = fastq_2_list[i]
except IndexError:
r2_name = None
outhead = r1_name.rsplit('.', 1)[0]
libname = outhead.rsplit('/',1)[-1]
outhead = '%s_bwa'%libname
bamname = RILseq.run_bwa(
settings.bwa_exec, r1_name, r2_name,
settings.dirout, outhead, settings.allowed_mismatches,
settings.genome_fasta, settings.params_aln, settings.sampe_params,
settings.samse_params, settings.samtools_cmd,
processors=settings.processors)
samfile = pysam.Samfile(bamname)
if settings.genes_gff:
lib_order.append(libname)
gcounts[libname] = RILseq.count_features(
pos_feat_list, samfile, settings.overlap,
rev=settings.reverse_complement)
coverage = RILseq.generate_wig(
samfile, rev=settings.reverse_complement, first_pos=False)
RILseq.print_wiggle(
coverage, "%s_single_fragments_coverage"%libname,
"%s single fragments coverage"%libname, outwig)
# Print the table of counts
if settings.genes_gff:
outtable = open(
"%s/%s_counts.txt"%(settings.dirout, settings.outhead), 'w')
outt = csv.writer(outtable, delimiter='\t')
outt.writerow(['Gene name'] + lib_order)
for g in sorted(list(all_features)):
row_out = [g]
for libn in lib_order:
row_out.append(gcounts[libn][g])
outt.writerow(row_out)
return 0 # success
def main(argv=None):
settings = process_command_line(argv)
try:
pos_feat_list, all_features = RILseq.read_gtf(
open(settings.genes_gff), settings.feature, settings.identifier)
except IOError:
return 1
lib_order = []
all_counts = {}
if settings.singles:
settings.only_first = True
settings.only_second = False
for r1_name in RILseq.flat_list(settings.reads_files):
sys.stderr.write('%s\n'%str(r1_name))
lib_order.append(r1_name)
all_counts[r1_name] = count_features(
pos_feat_list, open(r1_name), settings.overlap, length=25,
ignore_first=settings.only_second,
ignore_second=settings.only_first, count_singles=settings.singles)
outt = csv.writer(sys.stdout, delimiter='\t')
if not settings.quiet:
outt.writerow(['Gene name'] + lib_order)
for g in sorted(list(all_features)):
if settings.quiet and g.startswith('~'):
continue
row_out = [g]
for libn in lib_order:
row_out.append(all_counts[libn][g])
outt.writerow(row_out)
# application code here, like:
# run(settings, args)
return 0 # success
def main(argv=None):
settings = process_command_line(argv)
# Read the transcripts if given
if settings.transcripts:
trans_dict = RILseq.read_transcripts(settings.transcripts)
else:
trans_dict = None
# Get the ends of the reads from the bam files
# sys.stderr.write('%s\n'%str(settings.bamfiles))
if settings.all_reads:
try:
outall = open(settings.all_reads, 'w')
except IOError:
outall = None
elif settings.add_all_reads:
outall = sys.stdout
else:
outall = None
for bf in RILseq.flat_list(settings.bamfiles):
bfin = pysam.Samfile(bf)
outhead = bf.rsplit('.', 1)[0]
libname = outhead.rsplit('/',1)[-1]
fsq1name = "%s/%s_ends_1.fastq"%(settings.dirout, libname)
fsq2name = "%s/%s_ends_2.fastq"%(settings.dirout, libname)
if settings.skip_mapping:
fsq1 = open(os.devnull, 'w')
fsq2 = fsq1
else:
fsq1 = open(fsq1name, 'w')
fsq2 = open(fsq2name, 'w')
single_mapped = RILseq.get_unmapped_reads(
bfin, fsq1, fsq2, settings.length, settings.maxG,
rev=settings.reverse_complement, all_reads=True,
dust_thr=settings.dust_thr)
reads_in = []
# Map the fastq files to the genome
for fqname in (fsq1name, fsq2name):
bamheadname = fqname.rsplit('.',1)[0].rsplit('/',1)[-1]
if settings.skip_mapping:
bamname = "%s/%s.bam"%(settings.dirout, bamheadname)
else:
bamname = RILseq.run_bwa(
settings.bwa_exec, fqname, None,
settings.dirout, bamheadname, settings.max_mismatches,
settings.genome_fasta, settings.params_aln,
'', settings.samse_params,
settings.samtools_cmd, processors=settings.processors)
bamin = pysam.Samfile(bamname)
reads_in.append(RILseq.read_bam_file(
bamin, bamin.references, settings.allowed_mismatches))
RILseq.write_reads_table(
sys.stdout, reads_in[0], reads_in[1], bfin.references,
settings.distance, not settings.keep_circular,
trans_dict, write_single=outall, single_mapped=single_mapped,
max_NM=settings.allowed_mismatches)
return 0 # success
def main(argv=None):
settings = process_command_line(argv)
try:
pos_feat_list, all_features = RILseq.read_gtf(open(settings.genes_gff),
settings.feature,
settings.identifier)
except IOError:
return 1
lib_order = []
all_counts = {}
if settings.singles:
settings.only_first = True
settings.only_second = False
for r1_name in RILseq.flat_list(settings.reads_files):
sys.stderr.write('%s\n' % str(r1_name))
lib_order.append(r1_name)
all_counts[r1_name] = count_features(pos_feat_list,
open(r1_name),
settings.overlap,
length=25,
ignore_first=settings.only_second,
ignore_second=settings.only_first,
count_singles=settings.singles)
outt = csv.writer(sys.stdout, delimiter='\t')
if not settings.quiet:
outt.writerow(['Gene name'] + lib_order)
for g in sorted(list(all_features)):
if settings.quiet and g.startswith('~'):
continue
row_out = [g]
for libn in lib_order:
row_out.append(all_counts[libn][g])
outt.writerow(row_out)
# application code here, like:
# run(settings, args)
return 0 # success
def main(argv=None):
settings = process_command_line(argv)
# Read the read names and positions
read_5ps = {}
read_3ps = {}
read_genes = {}
genome = {}
gsize = {}
for sr in SeqIO.parse(settings.genome, 'fasta'):
genome[sr.id] = sr.seq
gsize[sr.id] = len(sr.seq)
if len(settings.EC_chrlist) >= 2:
chr_dict = dict(zip(
settings.EC_chrlist.split(',')[0::2],
settings.EC_chrlist.split(',')[1::2]))
else:
chr_dict = {}
if settings.summary:
sig_reads = RILseq.read_significant_reads(
settings.summary, chr_dict, gname=settings.gene_name)
for line in csv.reader(open(settings.list_reads), delimiter='\t'):
# skip single
if len(line) > 7 and line[7]=="single":
continue
if settings.summary:
if (int(line[4])-1, line[5], line[3]) not in\
sig_reads[(int(line[1])-1, line[2], line[0])]:
continue
read_5ps[line[6]] = [int(line[1])-1, line[2], line[0]]
read_3ps[line[6]] = [int(line[4])-1, line[5], line[3]]
# read_genes[line[6]] = [line[0], line[1]]
# Read the bam files and return the long sequences
r1_seqs = {}
r2_seqs = {}
for bamfile in list(RILseq.flat_list(settings.bamfiles)):
r1s, r2s = get_reads_seqs(
pysam.Samfile(bamfile), read_5ps.keys(), rev=settings.reverse)
r1_seqs.update(r1s)
r2_seqs.update(r2s)
# For each read find the overlap, if exists and find the fusion point
outer = csv.writer(sys.stdout, delimiter='\t')
print 'track name="%s" description="%s" visibility=4 itemRgb="On" useScore=0'%(
settings.track_name, settings.track_desc)
# Because I'm lazy, the code is written so r1 is the 3' end of the fragment
for rname in set(r2_seqs.keys()):
if rname in r1_seqs:
r2seq = r2_seqs[rname]
r1seq = r1_seqs[rname]
else: # single-end
r2seq = r2_seqs[rname]
r1seq = ''
s1, overlap, s2 = find_overlap(r2seq, r1seq)
side_5p_len = extend_alignment(
s1+overlap+s2, read_5ps[rname][0], 0, False, read_5ps[rname][1],
genome[read_5ps[rname][2]])
side_3p_len = extend_alignment(
s1+overlap+s2, 0, read_3ps[rname][0], True, read_3ps[rname][1],
genome[read_3ps[rname][2]])
# Write each of the sides to the output file
score=0
if settings.rand_score:
score=random.randint(0, 1000)
if read_5ps[rname][1] == '+':
gfrom = max(0, read_5ps[rname][0])
gto = min(gsize[read_5ps[rname][2]], read_5ps[rname][0]+side_5p_len)
outer.writerow([
read_5ps[rname][2], gfrom, gto, "%s_5p"%rname, score, '+',
gfrom, gto, settings.pos_first])
elif read_5ps[rname][1] == '-':
gfrom = max(0, read_5ps[rname][0]-side_5p_len+1)
gto = min(gsize[read_5ps[rname][2]], read_5ps[rname][0]+1)
outer.writerow([
read_5ps[rname][2], gfrom, gto, "%s_5p"%rname, score, '-',
gfrom, gto,settings.rev_first])
if read_3ps[rname][1] == '+':
gfrom = max(0, read_3ps[rname][0]-side_3p_len+1)
gto = min(gsize[read_3ps[rname][2]], read_3ps[rname][0]+1)
outer.writerow([
read_3ps[rname][2], gfrom, gto,"%s_3p"%rname, score, '+',
gfrom, gto, settings.pos_second])
elif read_3ps[rname][1] == '-':
gfrom = max(0, read_3ps[rname][0])
gto = min(gsize[read_3ps[rname][2]], read_3ps[rname][0]+side_3p_len)
outer.writerow([
read_3ps[rname][2], gfrom, gto, "%s_3p"%rname, score, '-',
gfrom, gto, settings.rev_second])
return 0 # success
def main(argv=None):
settings = process_command_line(argv)
# Read the read names and positions
read_5ps = {}
read_3ps = {}
read_genes = {}
genome = {}
gsize = {}
for sr in SeqIO.parse(settings.genome, 'fasta'):
genome[sr.id] = sr.seq
gsize[sr.id] = len(sr.seq) # genome size dictionary - {chr:size}
if len(settings.BC_chrlist) >= 2:
chr_dict = dict(zip(
settings.BC_chrlist.split(',')[0::2],
settings.BC_chrlist.split(',')[1::2]))
# create dictionary of {'COLI-K12' : 'chr'}
else:
chr_dict = {}
if settings.summary: # only reads from the significant interactions in -s param, also can enter a specific gene.
sig_reads = RILseq.read_significant_reads(
settings.summary, chr_dict, gname=settings.gene_name)
for line in csv.reader(open(settings.list_reads), delimiter='\t'):
# skip single
if len(line) > 7 and line[7]=="single":
continue
if settings.summary:
if (int(line[4])-1, line[5], line[3]) not in\
sig_reads[(int(line[1])-1, line[2], line[0])]:
# skip if (coord_2, strand_2, chrom_2) not in the (coord_2, strand_2, chrom_2) of the significant reads.
continue
read_5ps[line[6]] = [int(line[1])-1, line[2], line[0]] # {read_id : [coord_1(0-based), strand_1, chrom_1]}
read_3ps[line[6]] = [int(line[4])-1, line[5], line[3]] # {read_id : [coord_2(0-based), strand_2, chrom_2]}
# read_genes[line[6]] = [line[0], line[1]]
# Read the bam files and return the long sequences
r1_seqs = {}
r2_seqs = {}
for bamfile in list(RILseq.flat_list(settings.bamfiles)): # flat multiple lists into one list.
r1s, r2s = get_reads_seqs(
pysam.AlignmentFile(bamfile, 'rb'), read_5ps.keys(), rev=settings.reverse)
r1_seqs.update(r1s)
r2_seqs.update(r2s)
# For each read find the overlap, if exists and find the fusion point
outer = csv.writer(sys.stdout, delimiter='\t')
print ('track name="%s" description="%s" visibility=4 itemRgb="On" useScore=0'%(
settings.track_name, settings.track_desc))
# Because I'm lazy, the code is written so r1 is the 3' end of the fragment
for rname in set(r2_seqs.keys()):
if rname in r1_seqs:
r2seq = r2_seqs[rname]
r1seq = r1_seqs[rname]
else: # single-end
r2seq = r2_seqs[rname]
r1seq = ''
# r2seq, r1seq are the sequences from the bam files for paired end
s1, overlap, s2 = find_overlap(r2seq, r1seq)
# print here
print(s1+overlap+s2)
print(read_5ps[rname][0])
print(read_3ps[rname][0])
side_5p_len = extend_alignment(
s1+overlap+s2, read_5ps[rname][0], 0, False, read_5ps[rname][1],
genome[read_5ps[rname][2]])
side_3p_len = extend_alignment(
s1+overlap+s2, 0, read_3ps[rname][0], True, read_3ps[rname][1],
genome[read_3ps[rname][2]])
# Write each of the sides to the output file
score=0
if settings.rand_score:
score=random.randint(0, 1000)
if read_5ps[rname][1] == '+':
gfrom = max(0, read_5ps[rname][0])
gto = min(gsize[read_5ps[rname][2]], read_5ps[rname][0]+side_5p_len)
outer.writerow([
read_5ps[rname][2], gfrom, gto, "%s_5p"%rname, score, '+',
gfrom, gto, settings.pos_first])
elif read_5ps[rname][1] == '-':
gfrom = max(0, read_5ps[rname][0]-side_5p_len+1)
gto = min(gsize[read_5ps[rname][2]], read_5ps[rname][0]+1)
outer.writerow([
read_5ps[rname][2], gfrom, gto, "%s_5p"%rname, score, '-',
gfrom, gto, settings.rev_first])
if read_3ps[rname][1] == '+':
gfrom = max(0, read_3ps[rname][0]-side_3p_len+1)
gto = min(gsize[read_3ps[rname][2]], read_3ps[rname][0]+1)
outer.writerow([
read_3ps[rname][2], gfrom, gto,"%s_3p"%rname, score, '+',
gfrom, gto, settings.pos_second])
elif read_3ps[rname][1] == '-':
gfrom = max(0, read_3ps[rname][0])
gto = min(gsize[read_3ps[rname][2]], read_3ps[rname][0]+side_3p_len)
outer.writerow([
read_3ps[rname][2], gfrom, gto, "%s_3p"%rname, score, '-',
gfrom, gto, settings.rev_second])
return 0 # success
def main(argv=None):
settings = process_command_line(argv)
# Read the transcripts if given
if settings.transcripts:
trans_dict = RILseq.read_transcripts(settings.transcripts)
else:
trans_dict = None
# Get the ends of the reads from the bam files
# sys.stderr.write('%s\n'%str(settings.bamfiles))
if settings.all_reads:
try:
outall = open(settings.all_reads, 'w')
except IOError:
outall = None
elif settings.add_all_reads:
outall = sys.stdout
else:
outall = None
for bf in RILseq.flat_list(settings.bamfiles):
bfin = pysam.Samfile(bf)
outhead = bf.rsplit('.', 1)[0]
libname = outhead.rsplit('/', 1)[-1]
fsq1name = "%s/%s_ends_1.fastq" % (settings.dirout, libname)
fsq2name = "%s/%s_ends_2.fastq" % (settings.dirout, libname)
if settings.skip_mapping:
fsq1 = open(os.devnull, 'w')
fsq2 = fsq1
else:
fsq1 = open(fsq1name, 'w')
fsq2 = open(fsq2name, 'w')
single_mapped = RILseq.get_unmapped_reads(
bfin,
fsq1,
fsq2,
settings.length,
settings.maxG,
rev=settings.reverse_complement,
all_reads=True,
dust_thr=settings.dust_thr)
reads_in = []
# Map the fastq files to the genome
for fqname in (fsq1name, fsq2name):
bamheadname = fqname.rsplit('.', 1)[0].rsplit('/', 1)[-1]
if settings.skip_mapping:
bamname = "%s/%s.bam" % (settings.dirout, bamheadname)
else:
bamname = RILseq.run_bwa(settings.bwa_exec,
fqname,
None,
settings.dirout,
bamheadname,
settings.max_mismatches,
settings.genome_fasta,
settings.params_aln,
'',
settings.samse_params,
settings.samtools_cmd,
processors=settings.processors)
bamin = pysam.Samfile(bamname)
reads_in.append(
RILseq.read_bam_file(bamin, bamin.references,
settings.allowed_mismatches))
RILseq.write_reads_table(sys.stdout,
reads_in[0],
reads_in[1],
bfin.references,
settings.distance,
not settings.keep_circular,
trans_dict,
write_single=outall,
single_mapped=single_mapped,
max_NM=settings.allowed_mismatches)
return 0 # success
