def sample_genome_like_peaks(in_peaks, out_files):
"""Sample from the genome, keeping the sample widths the same as peaks"""
out_sample, out_locations = out_files[:2]
peak_lengths = array(
'i', (stop - start
for chrom, start, stop, strand in readBedLines(open(in_peaks))))
if len(peak_lengths) == 0:
raise RuntimeError("Peaks file %s is empty!" % in_peaks)
wb_genome = worldbase(cfg.get('DEFAULT', 'worldbase_genome'))
s = sampling.sample_genome(
wb_genome,
peak_lengths,
sampleSize=cfg.getint('motifs', 'motif_significance_sample_size'),
excludeRepeat=cfg.getboolean('motifs', 'sampling_exclude_repeats'),
excludeN=cfg.getboolean('motifs', 'sampling_exclude_N'),
ignoreCharacters='_',
weighted=True)
with open(out_sample, 'w') as outfile:
with open(out_locations, 'w') as outlocations:
for index, line in enumerate(s):
outfile.write('>%s\n%s\n' % (index, line))
outlocations.write('\t'.join([
line.id,
str(line.start),
str(line.stop),
str(index), '0', '+' if line.orientation == 1 else '-'
]) + '\n')
def sample_genome_like_peaks(in_peaks, out_files):
"""Sample from the genome, keeping the sample widths the same as peaks"""
out_sample, out_locations = out_files[:2]
peak_lengths = array('i', (stop - start for chrom, start, stop, strand in
readBedLines(open(in_peaks))))
if len(peak_lengths) == 0:
raise RuntimeError("Peaks file %s is empty!" % in_peaks)
wb_genome = worldbase(cfg.get('DEFAULT', 'worldbase_genome'))
s = sampling.sample_genome(wb_genome, peak_lengths,
sampleSize=cfg.getint('motifs',
'motif_significance_sample_size'),
excludeRepeat=cfg.getboolean('motifs',
'sampling_exclude_repeats'),
excludeN=cfg.getboolean('motifs',
'sampling_exclude_N'),
ignoreCharacters='_', weighted=True)
with open(out_sample, 'w') as outfile:
with open(out_locations, 'w') as outlocations:
for index, line in enumerate(s):
outfile.write('>%s\n%s\n' % (index, line))
outlocations.write('\t'.join([line.id, str(line.start),
str(line.stop), str(index), '0',
'+' if line.orientation == 1 else '-']) + '\n')
def trim_regex(in_fastq, out_fastq, trim_pattern):
"""Search the reads for a regex, and trim everything matching the pattern
and all succeeding sequence.
"""
pattern = re.compile(trim_pattern)
with gzip.open(in_fastq) as infile:
with gzip.open(out_fastq, 'w') as outfile:
for header, seq, qual in parseFastq(infile):
matches = [m.span() for m in pattern.finditer(seq)]
if len(matches) > 0:
# match to re found--
# trim the right-most hit and add the trimmed sequence to the read ID
m = matches[-1]
header = seq[m[0]:] + '_' + header
seq = seq[:m[0]]
qual = qual[:m[0]]
if len(matches) > 0 or not cfg.getboolean('filtering', 'require_regex'):
if len(seq) >= 10: # TODO: add adjustable min length
outfile.write('@%s\n%s\n+%s\n%s\n' % (header, seq,
header, qual))
def trim_regex(in_fastq, out_fastq, trim_pattern):
"""Search the reads for a regex, and trim everything matching the pattern
and all succeeding sequence.
"""
pattern = re.compile(trim_pattern)
with gzip.open(in_fastq) as infile:
with gzip.open(out_fastq, 'w') as outfile:
for header, seq, qual in parseFastq(infile):
matches = [m.span() for m in pattern.finditer(seq)]
if len(matches) > 0:
# match to re found--
# trim the right-most hit and add the trimmed sequence to the read ID
m = matches[-1]
header = seq[m[0]:] + '_' + header
seq = seq[:m[0]]
qual = qual[:m[0]]
if len(matches) > 0 or not cfg.getboolean(
'filtering', 'require_regex'):
if len(seq) >= 10: # TODO: add adjustable min length
outfile.write('@%s\n%s\n+%s\n%s\n' %
(header, seq, header, qual))
from Bio import SeqIO
from ruffus import (transform, follows, collate, files, split, merge,
suffix, mkdir, jobs_limit, output_from)
from ruffus.task import active_if
from hts_waterworks.utils.ruffus_utils import (sys_call, main_logger as log,
main_mutex as log_mtx)
from hts_waterworks.bootstrap import cfg
from hts_waterworks.utils.common import parseFastq
# filtering
original_reads = '*.fastq'
prev_output = original_reads
prev_suffix = '.fastq'
@active_if(cfg.getboolean('filtering', 'convert_sanger_to_illumina'))
@transform(prev_output, suffix(prev_suffix), '.fastq_illumina')
def convert_fastq(in_fastq, out_fastq):
'convert sanger fastq format (phred-33) to illumina format (phred-64)'
base_out = os.path.splitext(out_fastq)[0]
records = SeqIO.parse(in_fastq, "fastq")
with open(base_out, 'w') as outfile:
SeqIO.write(records, outfile, "fastq-illumina")
check_call('gzip %s' % base_out, shell=True)
if cfg.getboolean('filtering', 'convert_sanger_to_illumina'):
prev_output = convert_fastq
prev_suffix = ''
@active_if(cfg.getboolean('filtering', 'clip_adapter'))
with open(in_fasta) as infile:
seqs = list(parseFastaLines(infile))
if len(seqs) <= cfg.get('motifs', 'motif_chunk_size'):
num_chunks = 1
else:
num_chunks = cfg.get('motifs', 'motif_num_chunks')
# get a random sample of peaks
for i in xrange(num_chunks):
with open(name + '.small_sample.%s.fasta' % i, 'w') as outfile:
subset = random.sample(seqs, min(len(seqs),
cfg.getint('motifs', 'motif_chunk_size')))
outfile.writelines('>%s\n%s\n' % (s[0].strip(), s[1].strip())
for s in subset)
# motif discovery
@active_if(cfg.getboolean('motifs', 'run_meme'))
@jobs_limit(cfg.getint('DEFAULT', 'max_throttled_jobs'), 'throttled')
@transform(motif_select_random_seqs,
#suffix('.fasta'), '.meme.discovered.motifs')
#regex(r'(.*(?=_around).*(?=top).*).fasta$'),
regex(r'(.*(?=top).*).fasta$'),
r'\1.meme.discovered.motifs')
def discover_meme_motifs(in_fasta, out_motifs):
"""Discover sequence motifs in peaks by running meme"""
cmd = 'meme %s %s -oc %s_meme_out ' % (in_fasta,
cfg.get('motifs', 'meme_params'),
out_motifs)
#if 'top' in in_fasta and 'around' in in_fasta:
sys_call(cmd)
motifs = sequence_motif.parseMemeMotifs('%s_meme_out/meme.txt' % out_motifs)
pickle.dump(motifs, open(out_motifs, 'w'))
+ mapping.all_mappers_output
+ mapping.all_mappers_raw_reads,
suffix(""),
".clipped.sorted",
)
def clip_and_sort_peaks(in_bed, out_sorted):
"""Sort the bed file and constrain bed regions to chromosome sizes"""
with tempfile.NamedTemporaryFile() as tmp_clipped:
cmd = "bedClip %s %s.chrom.sizes %s" % (in_bed, genome_path(), tmp_clipped.name)
sys_call(cmd)
# cmd = 'bedSort %s %s' % (out_clipped, out_sorted)
cmd = r"sort -t $'\t' -k 1,1 -k 2,2n -S 2G %s > %s" % (tmp_clipped.name, out_sorted)
sys_call(cmd)
@active_if(cfg.getboolean("visualization", "uniquefy_track"))
@transform(
[clip_and_sort_peaks] + mapping.all_mappers_output,
suffix(""),
".unique",
cfg.getint("visualization", "uniquefy_track_max_reads"),
)
def bed_uniquefy(in_bed, out_bed, max_reads):
"Given a sorted bed file, remove tags that are on the same start, strand"
with open(in_bed) as infile:
with open(out_bed, "w") as outfile:
prev_start, prev_chrom = None, None
plus_seen, minus_seen = 0, 0
for line in infile:
fields = line.split("\t")
chrom, start, stop = fields[:3]
def uniquefy_downsample_reads(in_files, out_files):
"""Uniquefy sequence reads then downsample so the total unique tag count in
treatment and control is the same. This may generate many downsampled datasets.
"""
# WARNING: this is a circular dependency. It has to be included at runtime
# Top-level import will cause this module to load only 1/2 way
# we import here because we need to call this function directly,
# and not just when using ruffus
from hts_waterworks.visualize import bed_uniquefy
if not cfg.getboolean('peaks', 'downsample_reads'):
with log_mtx:
log.debug('NOT downsampling the sequence reads!')
else:
in_treat, in_control = in_files
out_treat_template = re.sub(r'mapped_reads$',
'matched_size_%s.mapped_reads', in_treat)
out_control_template = re.sub(r'mapped_reads$',
'matched_size_%s.mapped_reads', in_control)
if out_treat_template == in_treat:
raise RuntimeError('regex substitution failed from %s to %s' % (
in_treat, out_treat_template))
if out_control_template == in_control:
raise RuntimeError('regex substitution failed from %s to %s' % (
in_control, out_control_template))
tmp_t_sorted = tempfile.NamedTemporaryFile(delete=False).name
tmp_c_sorted = tempfile.NamedTemporaryFile(delete=False).name
tmp_t_unique = tempfile.NamedTemporaryFile(delete=False).name
tmp_c_unique = tempfile.NamedTemporaryFile(delete=False).name
# sort the reads
bed_clip_and_sort(in_treat, tmp_t_sorted)
bed_clip_and_sort(in_control, tmp_c_sorted)
# uniquefy the reads
bed_uniquefy(tmp_t_sorted, tmp_t_unique,
cfg.getint('visualization', 'uniquefy_track_max_reads'))
bed_uniquefy(tmp_c_sorted, tmp_c_unique,
cfg.getint('visualization', 'uniquefy_track_max_reads'))
total_treat = sum(1 for l in open(tmp_t_unique))
total_control = sum(1 for l in open(tmp_c_unique))
if total_treat == total_control:
with log_mtx:
log.debug('No downsampling required-- tag counts identical')
else:
# downsample num_down_sample times
for i in xrange(cfg.getint('peaks', 'num_down_samples')):
out_treat = out_treat_template % i
out_control = out_control_template % i
if total_treat > total_control:
# reduce number of treatment reads
inds_to_keep = set(random.sample(xrange(total_treat),
total_control))
in_orig, out_orig = tmp_c_unique, out_control
in_subset, out_subset = tmp_t_unique, out_treat
else:
# reduce number of control reads
inds_to_keep = set(random.sample(xrange(total_control),
total_treat))
in_orig, out_orig = tmp_t_unique, out_treat
in_subset, out_subset = tmp_c_unique, out_control
sys_call('cp %s %s' % (in_orig, out_orig))
# subset the tags
with open(in_subset) as infile:
with open(out_subset, 'w') as outfile:
outfile.writelines(line for i, line in enumerate(infile)
if i in inds_to_keep)
for f in [tmp_t_sorted, tmp_t_unique, tmp_c_sorted, tmp_c_unique]:
os.unlink(f)
import hts_waterworks.call_peaks as call_peaks
import hts_waterworks.mapping as mapping
@transform('%s.*.gtfgenes' % cfg.get('DEFAULT', 'genome'), suffix('.gtfgenes'), '_genes')
def convert_gtf_genes_to_bed(in_gtf, out_gene_pred):
"""convert gtf genes to UCSC's genePred format"""
sys_call('gtfToGenePred %s %s' % (in_gtf, out_gene_pred), file_log=False)
@transform('%s.*.gff3genes' % cfg.get('DEFAULT', 'genome'), suffix('.gff3genes'), '_genes')
def convert_gff3_genes_to_bed(in_gff3, out_gene_pred):
"""convert gff3 genes to UCSC's genePred format"""
sys_call('gff3ToGenePred %s %s' % (in_gff3, out_gene_pred), file_log=False)
@active_if(cfg.getboolean('genes','download_refseq'))
@files(None, '%s.refseq_genes' % cfg.get('DEFAULT', 'genome'))
def get_refseq_genes(_, out_genes):
"""Download refseq genes from UCSC and reformat as BED"""
url = 'http://hgdownload.cse.ucsc.edu/goldenPath/%s/database/refGene.txt.gz'
url = url % cfg.get('DEFAULT', 'genome')
sys_call('wget -N -P . %s' % url)
sys_call('gunzip -f refGene.txt.gz')
sys_call('mv refGene.txt %s' % out_genes)
@transform([get_refseq_genes, convert_gtf_genes_to_bed,
convert_gff3_genes_to_bed],
suffix('_genes'), '_genes.all')
def refseq_genes_to_bed(in_genes, out_bed):
"""convert refseq genes file to BED format"""
@active_if(cfg.getint('PAS-Seq', 'min_read_count') > 0)
@jobs_limit(cfg.getint('DEFAULT', 'max_throttled_jobs'), 'throttled')
@transform(mapping.all_mappers_output, suffix('.mapped_reads'),
'.overlap.mapped_reads', cfg.getint('PAS-Seq', 'min_read_count'))
def remove_nonoverlapping_reads(in_bed, out_bed, min_read_count):
"""
Remove mapped reads that don't overlap with at least *min_read_count* reads
"""
cmd = "intersectBed -wa -c -a %s -b %s | awk '$(NF) >= %s' |" \
r"cut -f 1,2,3,4,5,6 > %s" % (in_bed, in_bed, min_read_count + 1,
out_bed)
sys_call(cmd, file_log=False)
@active_if(cfg.getboolean('PAS-Seq', 'merge_adjacent_reads'))
#@split(mapping.all_mappers_output, regex('(.*).mapped_reads$'),
@split(remove_nonoverlapping_reads, regex('(.*).mapped_reads$'),
[r'\1.merged.mapped_reads', r'\1.merged.pileup_reads'],
cfg.getint('PAS-Seq', 'merge_window_width'),
cfg.getint('PAS-Seq', 'merge_num_iterations'),
r'\1.merged.mapped_reads', r'\1.merged.pileup_reads',
cfg.getint('PAS-Seq', 'min_read_count'))
def merge_adjacent_reads(in_bed, out_pattern, window_width, iterations,
out_merged, out_pileup, min_read_count):
"""Reassign read ends to a weighted average of adjacent reads"""
# helper functions for parsing bed files
filter_lines = lambda l: l.strip() and (not l.startswith('#') or \
l.startswith('"'))
read_bed_lines = lambda infile: itertools.ifilter(filter_lines, infile)
import shutil
from ruffus import (transform, follows, collate, files, split, merge,
add_inputs, regex, suffix, mkdir, jobs_limit, output_from)
from ruffus.task import active_if
from hts_waterworks.utils.ruffus_utils import (sys_call, main_logger as log,
main_mutex as log_mtx)
from hts_waterworks.bootstrap import cfg, get_chrom_sizes, genome_path
import hts_waterworks.mapping as mapping
import hts_waterworks.clip_seq as clip_seq
from hts_waterworks.utils.common import (bedCommentFilter, readBedLines,
parse_ucsc_range)
@active_if(cfg.getboolean('peaks', 'run_macs'))
@collate(mapping.all_mappers_output, regex(r'(.+)\.treat(.*)\.mapped_reads'),
add_inputs(r'\1.control\2.mapped_reads'), r'\1.treat\2.macs.peaks',
cfg.getfloat('peaks', 'max_FDR'))
def run_macs(in_files, out_peaks, max_fdr):
"""Call peak with MACS (v1.3).
Apply a maximum FDR threshold and treat centers as peak summits
"""
in_treat, in_control = in_files[0]
matches = re.search(r'(.*\.treat)(.*)\.mapped_reads', in_treat).groups()
name = matches[0] + matches[1] + '.macs.peaks'
max_fdr = cfg.getfloat('peaks', 'max_FDR')
cmd = 'macs -t %s -c %s --name=%s %s' % (in_treat, in_control, name,
cfg.get('peaks', 'macs_params'))
sys_call(cmd)
def uniquefy_downsample_reads(in_files, out_files):
"""Uniquefy sequence reads then downsample so the total unique tag count in
treatment and control is the same. This may generate many downsampled datasets.
"""
# WARNING: this is a circular dependency. It has to be included at runtime
# Top-level import will cause this module to load only 1/2 way
# we import here because we need to call this function directly,
# and not just when using ruffus
from hts_waterworks.visualize import bed_uniquefy
if not cfg.getboolean('peaks', 'downsample_reads'):
with log_mtx:
log.debug('NOT downsampling the sequence reads!')
else:
in_treat, in_control = in_files
out_treat_template = re.sub(r'mapped_reads$',
'matched_size_%s.mapped_reads', in_treat)
out_control_template = re.sub(r'mapped_reads$',
'matched_size_%s.mapped_reads', in_control)
if out_treat_template == in_treat:
raise RuntimeError('regex substitution failed from %s to %s' % (
in_treat, out_treat_template))
if out_control_template == in_control:
raise RuntimeError('regex substitution failed from %s to %s' % (
in_control, out_control_template))
tmp_t_sorted = tempfile.NamedTemporaryFile(delete=False).name
tmp_c_sorted = tempfile.NamedTemporaryFile(delete=False).name
tmp_t_unique = tempfile.NamedTemporaryFile(delete=False).name
tmp_c_unique = tempfile.NamedTemporaryFile(delete=False).name
# sort the reads
bed_clip_and_sort(in_treat, tmp_t_sorted)
bed_clip_and_sort(in_control, tmp_c_sorted)
# uniquefy the reads
bed_uniquefy(tmp_t_sorted, tmp_t_unique,
cfg.getint('visualization', 'uniquefy_track_max_reads'))
bed_uniquefy(tmp_c_sorted, tmp_c_unique,
cfg.getint('visualization', 'uniquefy_track_max_reads'))
total_treat = sum(1 for l in open(tmp_t_unique))
total_control = sum(1 for l in open(tmp_c_unique))
if total_treat == total_control:
with log_mtx:
log.debug('No downsampling required-- tag counts identical')
else:
# downsample num_down_sample times
for i in xrange(cfg.getint('peaks', 'num_down_samples')):
out_treat = out_treat_template % i
out_control = out_control_template % i
if total_treat > total_control:
# reduce number of treatment reads
inds_to_keep = set(random.sample(xrange(total_treat),
total_control))
in_orig, out_orig = tmp_c_unique, out_control
in_subset, out_subset = tmp_t_unique, out_treat
else:
# reduce number of control reads
inds_to_keep = set(random.sample(xrange(total_control),
total_treat))
in_orig, out_orig = tmp_t_unique, out_treat
in_subset, out_subset = tmp_c_unique, out_control
sys_call('cp %s %s' % (in_orig, out_orig))
# subset the tags
with open(in_subset) as infile:
with open(out_subset, 'w') as outfile:
outfile.writelines(line for i, line in enumerate(infile)
if i in inds_to_keep)
for f in [tmp_t_sorted, tmp_t_unique, tmp_c_sorted, tmp_c_unique]:
os.unlink(f)
from ruffus import (transform, follows, files, split, merge, add_inputs,
regex, suffix, jobs_limit, mkdir)
from ruffus.task import active_if
from pygr import worldbase, cnestedlist, seqdb
import pybedtools
from hts_waterworks.utils.ruffus_utils import (sys_call, main_logger as log,
main_mutex as log_mtx)
from hts_waterworks.bootstrap import (genome_path, get_genome, cfg,
get_chrom_sizes)
import hts_waterworks.preprocessing as preprocessing
#: the references to map against for this run (genome, transcriptome, etc)
reference_genomes = [genome_path()]
if cfg.getboolean('mapping', 'map_to_transcriptome'):
reference_genomes.append('*_genes.transcriptome.fasta')
@follows(mkdir('mapped'))
def make_mapping_dir():
pass
@active_if(cfg.getboolean('mapping', 'map_to_transcriptome'))
@split('*_genes', regex(r'(.*)_genes$'),
[r'\1_genes.transcriptome.fasta',
r'\1_genes.transcriptome.seqdb',
r'\1_genes.transcriptome.msa'])
def make_transcriptome(in_genes, out_files):
"""Splice UTR's and exons from gene annotations into a transcriptome.
Creates a fasta-file of resulting genes and a gene to genome alignment.
if len(seqs) <= cfg.get('motifs', 'motif_chunk_size'):
num_chunks = 1
else:
num_chunks = cfg.get('motifs', 'motif_num_chunks')
# get a random sample of peaks
for i in xrange(num_chunks):
with open(name + '.small_sample.%s.fasta' % i, 'w') as outfile:
subset = random.sample(
seqs,
min(len(seqs), cfg.getint('motifs', 'motif_chunk_size')))
outfile.writelines('>%s\n%s\n' % (s[0].strip(), s[1].strip())
for s in subset)
# motif discovery
@active_if(cfg.getboolean('motifs', 'run_meme'))
@jobs_limit(cfg.getint('DEFAULT', 'max_throttled_jobs'), 'throttled')
@transform(
motif_select_random_seqs,
#suffix('.fasta'), '.meme.discovered.motifs')
#regex(r'(.*(?=_around).*(?=top).*).fasta$'),
regex(r'(.*(?=top).*).fasta$'),
r'\1.meme.discovered.motifs')
def discover_meme_motifs(in_fasta, out_motifs):
"""Discover sequence motifs in peaks by running meme"""
cmd = 'meme %s %s -oc %s_meme_out ' % (
in_fasta, cfg.get('motifs', 'meme_params'), out_motifs)
#if 'top' in in_fasta and 'around' in in_fasta:
sys_call(cmd)
motifs = sequence_motif.parseMemeMotifs('%s_meme_out/meme.txt' %
out_motifs)
@transform('%s.*.gtfgenes' % cfg.get('DEFAULT', 'genome'), suffix('.gtfgenes'),
'_genes')
def convert_gtf_genes_to_bed(in_gtf, out_gene_pred):
"""convert gtf genes to UCSC's genePred format"""
sys_call('gtfToGenePred %s %s' % (in_gtf, out_gene_pred), file_log=False)
@transform('%s.*.gff3genes' % cfg.get('DEFAULT', 'genome'),
suffix('.gff3genes'), '_genes')
def convert_gff3_genes_to_bed(in_gff3, out_gene_pred):
"""convert gff3 genes to UCSC's genePred format"""
sys_call('gff3ToGenePred %s %s' % (in_gff3, out_gene_pred), file_log=False)
@active_if(cfg.getboolean('genes', 'download_refseq'))
@files(None, '%s.refseq_genes' % cfg.get('DEFAULT', 'genome'))
def get_refseq_genes(_, out_genes):
"""Download refseq genes from UCSC and reformat as BED"""
url = 'http://hgdownload.cse.ucsc.edu/goldenPath/%s/database/refGene.txt.gz'
url = url % cfg.get('DEFAULT', 'genome')
sys_call('wget -N -P . %s' % url)
sys_call('gunzip -f refGene.txt.gz')
sys_call('mv refGene.txt %s' % out_genes)
@transform(
[get_refseq_genes, convert_gtf_genes_to_bed, convert_gff3_genes_to_bed],
suffix('_genes'), '_genes.all')
def refseq_genes_to_bed(in_genes, out_bed):
"""convert refseq genes file to BED format"""
from ruffus import (transform, follows, files, split, merge, add_inputs,
regex, suffix, jobs_limit, mkdir)
from ruffus.task import active_if
from pygr import worldbase, cnestedlist, seqdb
import pybedtools
from hts_waterworks.utils.ruffus_utils import (sys_call, main_logger as log,
main_mutex as log_mtx)
from hts_waterworks.bootstrap import (genome_path, get_genome, cfg,
get_chrom_sizes)
import hts_waterworks.preprocessing as preprocessing
#: the references to map against for this run (genome, transcriptome, etc)
reference_genomes = [genome_path()]
if cfg.getboolean('mapping', 'map_to_transcriptome'):
reference_genomes.append('*_genes.transcriptome.fasta')
@follows(mkdir('mapped'))
def make_mapping_dir():
pass
@active_if(cfg.getboolean('mapping', 'map_to_transcriptome'))
@split('*_genes', regex(r'(.*)_genes$'),
[r'\1_genes.transcriptome.fasta',
r'\1_genes.transcriptome.seqdb',
r'\1_genes.transcriptome.msa'])
def make_transcriptome(in_genes, out_files):
"""Splice UTR's and exons from gene annotations into a transcriptome.
Creates a fasta-file of resulting genes and a gene to genome alignment.
@jobs_limit(cfg.get('DEFAULT', 'max_throttled_jobs'), 'throttled')
@follows(bootstrap.get_chrom_sizes)
@transform(call_peaks.all_peak_caller_functions + [pas_seq.remove_terminal_exon] + [clip_seq.search_genome_consensus] + mapping.all_mappers_output + mapping.all_mappers_raw_reads,
suffix(''), '.clipped.sorted')
def clip_and_sort_peaks(in_bed, out_sorted):
"""Sort the bed file and constrain bed regions to chromosome sizes"""
with tempfile.NamedTemporaryFile() as tmp_clipped:
cmd = 'bedClip %s %s.chrom.sizes %s' % (in_bed, genome_path(),
tmp_clipped.name)
sys_call(cmd)
#cmd = 'bedSort %s %s' % (out_clipped, out_sorted)
cmd = r"sort -t $'\t' -k 1,1 -k 2,2n -S 2G %s > %s" % (tmp_clipped.name, out_sorted)
sys_call(cmd)
@active_if(cfg.getboolean('visualization', 'uniquefy_track'))
@transform([clip_and_sort_peaks] + mapping.all_mappers_output, suffix(''),
'.unique',
cfg.getint('visualization', 'uniquefy_track_max_reads'))
def bed_uniquefy(in_bed, out_bed, max_reads):
'Given a sorted bed file, remove tags that are on the same start, strand'
with open(in_bed) as infile:
with open(out_bed, 'w') as outfile:
prev_start, prev_chrom = None, None
plus_seen, minus_seen = 0, 0
for line in infile:
fields = line.split('\t')
chrom, start, stop = fields[:3]
if prev_start is None or prev_start != start or \
prev_chrom != chrom:
prev_start, prev_chrom = start, chrom
from ruffus import (transform, follows, collate, files, split, merge, suffix,
mkdir, jobs_limit, output_from)
from ruffus.task import active_if
from hts_waterworks.utils.ruffus_utils import (sys_call, main_logger as log,
main_mutex as log_mtx)
from hts_waterworks.bootstrap import cfg
from hts_waterworks.utils.common import parseFastq
# filtering
original_reads = '*.fastq'
prev_output = original_reads
prev_suffix = '.fastq'
@active_if(cfg.getboolean('filtering', 'convert_sanger_to_illumina'))
@transform(prev_output, suffix(prev_suffix), '.fastq_illumina')
def convert_fastq(in_fastq, out_fastq):
'convert sanger fastq format (phred-33) to illumina format (phred-64)'
base_out = os.path.splitext(out_fastq)[0]
records = SeqIO.parse(in_fastq, "fastq")
with open(base_out, 'w') as outfile:
SeqIO.write(records, outfile, "fastq-illumina")
check_call('gzip %s' % base_out, shell=True)
if cfg.getboolean('filtering', 'convert_sanger_to_illumina'):
prev_output = convert_fastq
prev_suffix = ''
