def main():
from optparse import OptionParser
logging.basicConfig(level=logging.DEBUG,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
parser = OptionParser("usage: %prog [options] <bam> <out.bedpe>")
parser.add_option('--min-fragment-length', dest="min_fragment_length",
type="int", default=0)
parser.add_option('--max-fragment-length', dest="max_fragment_length",
type="int", default=1000)
parser.add_option('--max-samples', dest="max_samples",
type="int", default=None)
parser.add_option('-o', dest="output_file", default=None)
options, args = parser.parse_args()
input_bam_file = args[0]
bamfh = pysam.Samfile(input_bam_file, "rb")
isizedist = InsertSizeDistribution.from_bam(bamfh, options.min_fragment_length,
options.max_fragment_length,
options.max_samples)
bamfh.close()
if options.output_file is not None:
f = open(options.output_file, "w")
else:
f = sys.stdout
isizedist.to_file(f)
if options.output_file is not None:
f.close()
logging.info("Insert size samples=%d mean=%f std=%f median=%d mode=%d" %
(isizedist.n, isizedist.mean(), isizedist.std(),
isizedist.percentile(50.0), isizedist.mode()))
def main():
from optparse import OptionParser
logging.basicConfig(level=logging.DEBUG,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
parser = OptionParser("usage: %prog [options] <in.txt> <out.txt> <isizedist.txt>")
parser.add_option("--min-isize-prob", dest="min_isize_prob",
type="float", default=0.01)
options, args = parser.parse_args()
input_file = args[0]
output_file = args[1]
isize_dist_file = args[2]
# read insert size distribution
isize_dist = InsertSizeDistribution.from_file(open(isize_dist_file))
resolve_discordant_reads(input_file, output_file, isize_dist,
options.min_isize_prob,
tmp_dir=".")
def main():
import sys
calc_chimera_pvalues(sys.argv[1], sys.argv[2], int(sys.argv[3]), int(sys.argv[4]))
return
from optparse import OptionParser
logging.basicConfig(level=logging.DEBUG,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
parser = OptionParser("usage: %prog [options] <in.txt> <bowtie.log> <isizedist.txt>")
parser.add_option("--min-isize-prob", dest="min_isize_prob",
type="float", default=0.01)
options, args = parser.parse_args()
input_file = args[0]
bowtie_log_file = args[1]
isize_dist_file = args[2]
# read insert size distribution
isize_dist = InsertSizeDistribution.from_file(open(isize_dist_file))
calc_percent_discordant_reads(input_file, bowtie_log_file, isize_dist,
min_isize_prob=options.min_isize_prob,
tmp_dir=".")
def main():
from optparse import OptionParser
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
parser = OptionParser(
"usage: %prog [options] <in.txt> <out.txt> <isizedist.txt>")
parser.add_option("--min-isize-prob",
dest="min_isize_prob",
type="float",
default=0.01)
options, args = parser.parse_args()
input_file = args[0]
output_file = args[1]
isize_dist_file = args[2]
# read insert size distribution
isize_dist = InsertSizeDistribution.from_file(open(isize_dist_file))
resolve_discordant_reads(input_file,
output_file,
isize_dist,
options.min_isize_prob,
tmp_dir=".")
def main():
from optparse import OptionParser
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
parser = OptionParser("usage: %prog [options] <bam> <out.bedpe>")
parser.add_option('--min-fragment-length',
dest="min_fragment_length",
type="int",
default=0)
parser.add_option('--max-fragment-length',
dest="max_fragment_length",
type="int",
default=1000)
parser.add_option('--max-samples',
dest="max_samples",
type="int",
default=None)
parser.add_option('-o', dest="output_file", default=None)
options, args = parser.parse_args()
input_bam_file = args[0]
bamfh = pysam.Samfile(input_bam_file, "rb")
isizedist = InsertSizeDistribution.from_bam(bamfh,
options.min_fragment_length,
options.max_fragment_length,
options.max_samples)
bamfh.close()
if options.output_file is not None:
f = open(options.output_file, "w")
else:
f = sys.stdout
isizedist.to_file(f)
if options.output_file is not None:
f.close()
logging.info("Insert size samples=%d mean=%f std=%f median=%d mode=%d" %
(isizedist.n, isizedist.mean(), isizedist.std(),
isizedist.percentile(50.0), isizedist.mode()))
def discordant_reads_to_breakpoints(index_dir, isize_dist_file, input_bam_file,
output_file, trim_bp, max_read_length,
homology_mismatches):
"""
homology_mismatches: number of mismatches to tolerate while computing
homology between chimeric breakpoint sequence and "wildtype" sequence
trim_bp: when selecting the best matching exon for each read, we
account for spurious overlap into adjacent exons by trimming the
read by 'trim_bp'
"""
# read insert size distribution
isize_dist = InsertSizeDistribution.from_file(open(isize_dist_file))
# open BAM alignment file
bamfh = pysam.Samfile(input_bam_file, "rb")
# build a lookup table to get genomic intervals from transcripts
logging.debug("Reading gene information")
gene_file = os.path.join(index_dir, config.GENE_FEATURE_FILE)
tid_tx_map = build_tid_tx_map(bamfh,
gene_file,
rname_prefix=config.GENE_REF_PREFIX)
# open the reference sequence fasta file
ref_fasta_file = os.path.join(index_dir, config.ALIGN_INDEX + ".fa")
ref_fa = pysam.Fastafile(ref_fasta_file)
# iterate through read pairs
outfh = open(output_file, "w")
logging.debug("Parsing discordant reads")
for r5p, r3p in parse_gene_discordant_reads(bamfh):
# store pertinent read information in lightweight structure called
# DiscordantRead object. this departs from SAM format into a
# custom read format
dr5p = DiscordantRead.from_read(r5p)
dr3p = DiscordantRead.from_read(r3p)
# get gene information
tx5p = tid_tx_map[r5p.rname]
tx3p = tid_tx_map[r3p.rname]
# given the insert size find the highest probability
# exon junction breakpoint between the two transcripts
isize_prob, breakpoints = \
choose_best_breakpoints(r5p, r3p, tx5p, tx3p,
trim_bp, isize_dist)
# extract the sequence of the breakpoint along with the
# number of homologous bases at the breakpoint between
# chimera and wildtype genes
for breakpoint in breakpoints:
exon_num_5p, tx_end_5p, exon_num_3p, tx_start_3p = breakpoint
breakpoint_seq_5p, breakpoint_seq_3p, homology_left, homology_right = \
extract_breakpoint_sequence(config.GENE_REF_PREFIX + tx5p.tx_name, tx_end_5p,
config.GENE_REF_PREFIX + tx3p.tx_name, tx_start_3p,
ref_fa, max_read_length,
homology_mismatches)
# write breakpoint information for each read to a file
fields = [
tx5p.tx_name,
0,
tx_end_5p,
tx3p.tx_name,
tx_start_3p,
tx3p.tx_end,
r5p.rname, # name
isize_prob, # score
tx5p.strand,
tx3p.strand, # strand 1, strand 2
# user defined fields
exon_num_5p,
exon_num_3p,
breakpoint_seq_5p,
breakpoint_seq_3p,
homology_left,
homology_right
]
fields.append('|'.join(map(str, dr5p.to_list())))
fields.append('|'.join(map(str, dr3p.to_list())))
print >> outfh, '\t'.join(map(str, fields))
# cleanup
ref_fa.close()
outfh.close()
bamfh.close()
return config.JOB_SUCCESS
def run_chimerascan(runconfig):
"""
main function for running the chimerascan pipeline
"""
# print a welcome message
title_string = "Running chimerascan version %s" % (__version__)
logging.info(title_string)
logging.info("-" * len(title_string))
# validate run configuration
config_passed = runconfig.check_config()
if not config_passed:
logging.error("Invalid run configuration, aborting.")
return config.JOB_ERROR
# create output dir if it does not exist
if not os.path.exists(runconfig.output_dir):
os.makedirs(runconfig.output_dir)
logging.info("Created output directory: %s" % (runconfig.output_dir))
# create log dir if it does not exist
log_dir = os.path.join(runconfig.output_dir, config.LOG_DIR)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
logging.debug("Created directory for log files: %s" % (log_dir))
# create tmp dir if it does not exist
tmp_dir = os.path.join(runconfig.output_dir, config.TMP_DIR)
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
logging.debug("Created directory for tmp files: %s" % (tmp_dir))
# write the run config to a file
xmlstring = runconfig.to_xml()
runconfig_xml_file = os.path.join(runconfig.output_dir,
config.RUNCONFIG_XML_FILE)
logging.info("Writing run configuration to XML file: %s" %
(runconfig_xml_file))
fh = open(runconfig_xml_file, "w")
print >> fh, xmlstring
fh.close()
# mask biotypes and references
mask_biotypes = set()
if runconfig.mask_biotypes_file:
logging.info("Reading biotypes mask file")
mask_biotypes.update(
[line.strip() for line in open(runconfig.mask_biotypes_file)])
logging.info("\tread biotypes: %s" % (','.join(sorted(mask_biotypes))))
mask_rnames = set()
if runconfig.mask_rnames_file:
logging.info("Reading references mask file")
mask_rnames.update(
[line.strip() for line in open(runconfig.mask_rnames_file)])
logging.info("\tread references: %s" % (','.join(sorted(mask_rnames))))
# read transcripts
logging.info("Reading transcript features")
transcript_file = os.path.join(runconfig.index_dir,
config.TRANSCRIPT_FEATURE_FILE)
transcripts = list(TranscriptFeature.parse(open(transcript_file)))
logging.info("\tread %d transcripts" % (len(transcripts)))
# setup alignment indexes
genome_index = os.path.join(runconfig.index_dir, config.GENOME_INDEX)
transcriptome_index = os.path.join(runconfig.index_dir,
config.TRANSCRIPTOME_INDEX)
max_transcriptome_hits_file = os.path.join(runconfig.index_dir,
config.MAX_MULTIMAPPING_FILE)
max_transcriptome_hits = int(
open(max_transcriptome_hits_file).next().strip())
# detect read length
original_read_length = detect_read_length(runconfig.fastq_files[0])
# minimum fragment length cannot be smaller than the trimmed read length
trimmed_read_length = (original_read_length - runconfig.trim5 -
runconfig.trim3)
min_fragment_length = max(runconfig.min_fragment_length,
trimmed_read_length)
#
# Process and inspect the FASTQ files, performing several alterations
# to the reads:
#
# 1) rename them from long string to numbers to save space throughout
# the pipeline. also store mapping from read numbers to full names
# in a separate file
# 2) ensure the "/1" and "/2" suffixes exist to denote paired reads
# 3) convert quality scores to sanger format
#
converted_fastq_files = [
os.path.join(tmp_dir, fq) for fq in config.CONVERTED_FASTQ_FILES
]
read_name_file = os.path.join(tmp_dir, config.READ_NAME_TXT_FILE)
msg = "Processing FASTQ files"
skip = all(
up_to_date(cfq, fq)
for cfq, fq in zip(converted_fastq_files, runconfig.fastq_files))
skip = skip and up_to_date(read_name_file, runconfig.fastq_files[0])
if skip:
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
converted_fastq_prefix = \
os.path.join(tmp_dir, config.CONVERTED_FASTQ_PREFIX)
try:
retcode = process_input_reads(runconfig.fastq_files,
converted_fastq_prefix,
quals=runconfig.quals,
trim5=runconfig.trim5,
trim3=runconfig.trim3)
if retcode != config.JOB_SUCCESS:
logging.error("%s step failed" % (msg))
return config.JOB_ERROR
except Exception as e:
logging.info("Cleaning up after error %s" % (str(e)))
for fq in converted_fastq_files:
if os.path.isfile(fq):
os.remove(fq)
#
# Transcriptome alignment step
#
# Align to transcriptome in paired-end mode, trying to resolve as many
# reads as possible.
#
transcriptome_bam_file = os.path.join(tmp_dir,
config.TRANSCRIPTOME_BAM_FILE)
transcriptome_unaligned_path = os.path.join(
tmp_dir, config.TRANSCRIPTOME_UNALIGNED_PATH)
transcriptome_unaligned_fastq_files = tuple(
os.path.join(tmp_dir, fq)
for fq in config.TRANSCRIPTOME_UNALIGNED_FASTQ_FILES)
msg = "Aligning paired-end reads to transcriptome"
if (all(
up_to_date(transcriptome_bam_file, fq)
for fq in converted_fastq_files) and all(
up_to_date(a, b)
for a, b in zip(transcriptome_unaligned_fastq_files,
converted_fastq_files))):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
log_file = os.path.join(log_dir, config.TRANSCRIPTOME_LOG_FILE)
retcode = bowtie2_align_transcriptome_pe(
transcriptome_index=transcriptome_index,
genome_index=genome_index,
transcript_file=transcript_file,
fastq_files=converted_fastq_files,
unaligned_path=transcriptome_unaligned_path,
bam_file=transcriptome_bam_file,
log_file=log_file,
library_type=runconfig.library_type,
min_fragment_length=min_fragment_length,
max_fragment_length=runconfig.max_fragment_length,
max_transcriptome_hits=max_transcriptome_hits,
num_processors=runconfig.num_processors)
# cleanup if job failed
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(transcriptome_bam_file):
os.remove(transcriptome_bam_file)
for f in transcriptome_unaligned_fastq_files:
if os.path.exists(f):
os.remove(f)
return config.JOB_ERROR
#
# Sort transcriptome reads by position
#
msg = "Sorting transcriptome reads"
sorted_transcriptome_bam_file = os.path.join(
runconfig.output_dir, config.SORTED_TRANSCRIPTOME_BAM_FILE)
if (up_to_date(sorted_transcriptome_bam_file, transcriptome_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
sorted_aligned_bam_prefix = os.path.splitext(
sorted_transcriptome_bam_file)[0]
pysam.sort("-m", str(int(1e9)), transcriptome_bam_file,
sorted_aligned_bam_prefix)
#
# Index BAM file
#
msg = "Indexing BAM file"
sorted_transcriptome_bam_index_file = sorted_transcriptome_bam_file + ".bai"
if (up_to_date(sorted_transcriptome_bam_index_file,
sorted_transcriptome_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
pysam.index(sorted_transcriptome_bam_file)
#
# Get insert size distribution
#
isize_dist_file = os.path.join(runconfig.output_dir,
config.ISIZE_DIST_FILE)
msg = "Profiling insert size distribution"
if up_to_date(isize_dist_file, transcriptome_bam_file):
logging.info("[SKIPPED] %s" % msg)
isize_dist = InsertSizeDistribution.from_file(
open(isize_dist_file, "r"))
else:
logging.info(msg)
bamfh = pysam.Samfile(sorted_transcriptome_bam_file, "rb")
isize_dist = InsertSizeDistribution.from_genome_bam(
bamfh,
transcripts,
min_isize=min_fragment_length,
max_isize=runconfig.max_fragment_length,
max_samples=config.ISIZE_MAX_SAMPLES)
bamfh.close()
# if not enough samples, use a normal distribution instead
# of the empirical distribution
if isize_dist.n < config.ISIZE_MIN_SAMPLES:
logging.warning("Not enough fragments to sample insert size "
"distribution empirically. Using mean=%d "
"stdev=%f instead" %
(runconfig.isize_mean, runconfig.isize_stdev))
isize_dist = InsertSizeDistribution.from_random(
runconfig.isize_mean,
runconfig.isize_stdev,
min_isize=runconfig.min_fragment_length,
max_isize=runconfig.max_fragment_length,
samples=config.ISIZE_MAX_SAMPLES)
isize_dist.to_file(open(isize_dist_file, "w"))
#
# Determine ideal segment length automatically
#
# log insert size statistics
logging.info("Insert size samples=%d mean=%f std=%f median=%d mode=%d" %
(isize_dist.n, isize_dist.mean(), isize_dist.std(),
isize_dist.isize_at_percentile(50.0), isize_dist.mode()))
# choose a segment length to optimize mapping
optimal_isize = isize_dist.isize_at_percentile(
DEFAULT_FRAG_SIZE_SENSITIVITY)
logging.info("Determining soft-clipped segment length")
logging.debug("\tInsert size at %f percent of distribution is %d" %
(DEFAULT_FRAG_SIZE_SENSITIVITY, optimal_isize))
optimal_segment_length = int(round(optimal_isize / 3.0))
logging.debug("\tOptimal segment length is %d/3.0 = %d" %
(optimal_isize, optimal_segment_length))
segment_length = min(optimal_segment_length, trimmed_read_length)
segment_length = max(config.MIN_SEGMENT_LENGTH, segment_length)
logging.debug(
"\tAfter adjusting for min %d and read length %d, final segment length is %d"
% (config.MIN_SEGMENT_LENGTH, trimmed_read_length, segment_length))
if runconfig.segment_length is not None:
logging.debug(
"\tOverriding auto segment length and using segment length of %d" %
(runconfig.segment_length))
segment_length = runconfig.segment_length
#
# Genome alignment step
#
# Align any unaligned transcriptome reads to genome in paired-end mode.
# Resolve as many reads as possible.
#
genome_bam_file = os.path.join(tmp_dir, config.GENOME_BAM_FILE)
genome_unaligned_path = os.path.join(tmp_dir, config.GENOME_UNALIGNED_PATH)
genome_unaligned_fastq_files = tuple(
os.path.join(tmp_dir, fq)
for fq in config.GENOME_UNALIGNED_FASTQ_FILES)
msg = "Realigning unaligned paired-end reads to genome"
if (all(up_to_date(genome_bam_file, fq) for fq in converted_fastq_files)
and all(
up_to_date(a, b) for a, b in zip(genome_unaligned_fastq_files,
converted_fastq_files))):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
log_file = os.path.join(log_dir, config.GENOME_LOG_FILE)
retcode = bowtie2_align_pe(
index=genome_index,
fastq_files=transcriptome_unaligned_fastq_files,
unaligned_path=genome_unaligned_path,
bam_file=genome_bam_file,
log_file=log_file,
library_type=runconfig.library_type,
min_fragment_length=min_fragment_length,
max_fragment_length=runconfig.max_fragment_length,
max_hits=max_transcriptome_hits,
num_processors=runconfig.num_processors)
# cleanup if job failed
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(genome_bam_file):
os.remove(genome_bam_file)
for f in genome_unaligned_fastq_files:
if os.path.exists(f):
os.remove(f)
return config.JOB_ERROR
#
# Realignment step
#
# trim and realign all the initially unaligned reads in order to
# increase sensitivity to detect reads spanning fusion junctions
#
realigned_bam_file = os.path.join(tmp_dir, config.REALIGNED_BAM_FILE)
realigned_log_file = os.path.join(log_dir, config.REALIGNED_LOG_FILE)
msg = "Trimming and realigning initially unmapped reads"
if (all(
up_to_date(realigned_bam_file, fq)
for fq in genome_unaligned_fastq_files)
and up_to_date(realigned_bam_file, isize_dist_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
retcode = bowtie2_align_pe_sr(index=transcriptome_index,
transcript_file=transcript_file,
fastq_files=genome_unaligned_fastq_files,
bam_file=realigned_bam_file,
log_file=realigned_log_file,
tmp_dir=tmp_dir,
segment_length=segment_length,
max_hits=max_transcriptome_hits,
num_processors=runconfig.num_processors)
if retcode != config.JOB_SUCCESS:
if os.path.exists(realigned_bam_file):
os.remove(realigned_bam_file)
return config.JOB_ERROR
#
# Find discordant reads
#
# iterate through realigned reads and divide them into groups of
# concordant, discordant within a gene (isoforms), discordant
# between different genes, and discordant in the genome
#
paired_bam_file = os.path.join(tmp_dir, config.PAIRED_BAM_FILE)
discordant_bam_file = os.path.join(tmp_dir, config.DISCORDANT_BAM_FILE)
unpaired_bam_file = os.path.join(tmp_dir, config.UNPAIRED_BAM_FILE)
unmapped_bam_file = os.path.join(tmp_dir, config.UNMAPPED_BAM_FILE)
multimap_bam_file = os.path.join(tmp_dir, config.MULTIMAP_BAM_FILE)
unresolved_bam_file = os.path.join(tmp_dir, config.UNRESOLVED_BAM_FILE)
output_files = (paired_bam_file, discordant_bam_file, unpaired_bam_file,
unmapped_bam_file, multimap_bam_file, unresolved_bam_file)
msg = "Classifying concordant and discordant read pairs"
if (all(up_to_date(f, realigned_bam_file) for f in output_files)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
retcode = find_discordant_fragments(
transcripts=transcripts,
input_bam_file=realigned_bam_file,
paired_bam_file=paired_bam_file,
discordant_bam_file=discordant_bam_file,
unpaired_bam_file=unpaired_bam_file,
unmapped_bam_file=unmapped_bam_file,
multimap_bam_file=multimap_bam_file,
unresolved_bam_file=unresolved_bam_file,
max_isize=runconfig.max_fragment_length,
max_multihits=runconfig.max_multihits,
library_type=runconfig.library_type)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
for f in output_files:
if os.path.exists(f):
os.remove(f)
return config.JOB_ERROR
#
# Convert discordant transcriptome reads to genome coordinates
#
discordant_genome_bam_file = os.path.join(
tmp_dir, config.DISCORDANT_GENOME_BAM_FILE)
msg = "Converting discordant transcriptome hits to genomic coordinates"
if (up_to_date(discordant_genome_bam_file, discordant_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
discordant_genome_sam_file = os.path.join(
tmp_dir, config.DISCORDANT_GENOME_SAM_FILE)
retcode = transcriptome_to_genome(
genome_index,
transcripts,
input_file=discordant_bam_file,
output_file=discordant_genome_sam_file,
library_type=runconfig.library_type,
input_sam=False,
output_sam=True)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(discordant_genome_sam_file):
os.remove(discordant_genome_sam_file)
return config.JOB_ERROR
retcode = sam_to_bam(discordant_genome_sam_file,
discordant_genome_bam_file)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(discordant_genome_bam_file):
os.remove(discordant_genome_bam_file)
return config.JOB_ERROR
if os.path.exists(discordant_genome_sam_file):
os.remove(discordant_genome_sam_file)
#
# Sort discordant reads by position
#
msg = "Sorting discordant BAM file"
sorted_discordant_genome_bam_file = os.path.join(
tmp_dir, config.SORTED_DISCORDANT_GENOME_BAM_FILE)
if (up_to_date(sorted_discordant_genome_bam_file,
discordant_genome_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
bam_prefix = os.path.splitext(sorted_discordant_genome_bam_file)[0]
pysam.sort("-m", str(int(1e9)), discordant_genome_bam_file, bam_prefix)
#
# Index BAM file
#
msg = "Indexing discordant BAM file"
sorted_discordant_bam_index_file = sorted_discordant_genome_bam_file + ".bai"
if (up_to_date(sorted_discordant_bam_index_file,
sorted_discordant_genome_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
pysam.index(sorted_discordant_genome_bam_file)
#
# Convert unpaired transcriptome reads to genome coordinates
#
unpaired_genome_bam_file = os.path.join(tmp_dir,
config.UNPAIRED_GENOME_BAM_FILE)
msg = "Converting unpaired transcriptome hits to genomic coordinates"
if (up_to_date(unpaired_genome_bam_file, unpaired_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
unpaired_genome_sam_file = os.path.join(
tmp_dir, config.UNPAIRED_GENOME_SAM_FILE)
retcode = transcriptome_to_genome(genome_index,
transcripts,
input_file=unpaired_bam_file,
output_file=unpaired_genome_sam_file,
library_type=runconfig.library_type,
input_sam=False,
output_sam=True)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(unpaired_genome_sam_file):
os.remove(unpaired_genome_sam_file)
return config.JOB_ERROR
retcode = sam_to_bam(unpaired_genome_sam_file,
unpaired_genome_bam_file)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(unpaired_genome_bam_file):
os.remove(unpaired_genome_bam_file)
return config.JOB_ERROR
if os.path.exists(unpaired_genome_sam_file):
os.remove(unpaired_genome_sam_file)
#
# Sort unpaired reads by position
#
msg = "Sorting unpaired BAM file"
sorted_unpaired_genome_bam_file = os.path.join(
tmp_dir, config.SORTED_UNPAIRED_GENOME_BAM_FILE)
if (up_to_date(sorted_unpaired_genome_bam_file, unpaired_genome_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
bam_prefix = os.path.splitext(sorted_unpaired_genome_bam_file)[0]
pysam.sort("-m", str(int(1e9)), unpaired_genome_bam_file, bam_prefix)
#
# Index BAM file
#
msg = "Indexing unpaired BAM file"
sorted_unpaired_bam_index_file = sorted_unpaired_genome_bam_file + ".bai"
if (up_to_date(sorted_unpaired_bam_index_file,
sorted_unpaired_genome_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
pysam.index(sorted_unpaired_genome_bam_file)
#
# Cluster discordant reads into chimera candidates
#
cluster_file = os.path.join(tmp_dir, config.DISCORDANT_CLUSTER_FILE)
cluster_shelve_file = \
os.path.join(tmp_dir, config.DISCORDANT_CLUSTER_SHELVE_FILE)
sorted_discordant_genome_cluster_bam_file = \
os.path.join(runconfig.output_dir,
config.SORTED_DISCORDANT_GENOME_CLUSTER_BAM_FILE)
input_files = (sorted_discordant_genome_bam_file,
sorted_unpaired_genome_bam_file)
output_files = (cluster_file, cluster_shelve_file,
sorted_discordant_genome_cluster_bam_file)
msg = "Clustering discordant reads"
skip = True
for input_file in input_files:
for output_file in output_files:
skip = skip and up_to_date(output_file, input_file)
if skip:
logging.info("[SKIPPED] %s" % (msg))
else:
logging.debug(msg)
retcode = cluster_discordant_reads(
discordant_bam_file=sorted_discordant_genome_bam_file,
unpaired_bam_file=sorted_unpaired_genome_bam_file,
concordant_bam_file=sorted_transcriptome_bam_file,
output_bam_file=sorted_discordant_genome_cluster_bam_file,
cluster_file=cluster_file,
cluster_shelve_file=cluster_shelve_file)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
for f in output_files:
if os.path.exists(f):
os.remove(f)
#
# Pair discordant clusters
#
cluster_pair_file = \
os.path.join(tmp_dir, config.DISCORDANT_CLUSTER_PAIR_FILE)
msg = "Pairing discordant clusters"
output_files = (cluster_pair_file, )
if up_to_date(cluster_pair_file,
sorted_discordant_genome_cluster_bam_file):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.debug(msg)
retcode = pair_discordant_clusters(
discordant_bam_file=sorted_discordant_genome_cluster_bam_file,
cluster_pair_file=cluster_pair_file,
tmp_dir=tmp_dir)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
for f in output_files:
if os.path.exists(f):
os.remove(f)
#
# Perform realignment across putative fusion breakpoints
#
breakpoint_bam_file = os.path.join(tmp_dir, config.BREAKPOINT_BAM_FILE)
msg = "Realigning to find breakpoint-spanning reads"
input_files = (sorted_discordant_genome_bam_file,
sorted_unpaired_genome_bam_file, cluster_shelve_file,
cluster_pair_file)
output_files = (breakpoint_bam_file, )
skip = True
for inp in input_files:
for outp in output_files:
if not up_to_date(outp, inp):
skip = False
if skip:
logging.info("[SKIPPED] %s" % (msg))
else:
logging.debug(msg)
retcode = realign_across_breakpoints(
index_dir=runconfig.index_dir,
discordant_bam_file=sorted_discordant_genome_bam_file,
unpaired_bam_file=sorted_unpaired_genome_bam_file,
cluster_shelve_file=cluster_shelve_file,
cluster_pair_file=cluster_pair_file,
breakpoint_bam_file=breakpoint_bam_file,
log_dir=log_dir,
tmp_dir=tmp_dir,
num_processors=runconfig.num_processors,
local_anchor_length=runconfig.local_anchor_length,
local_multihits=runconfig.local_multihits)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
for f in output_files:
if os.path.exists(f):
os.remove(f)
#
# Nominate breakpoint spanning reads (split reads)
#
spanning_sam_file = os.path.join(tmp_dir, config.SPANNING_SAM_FILE)
spanning_bam_file = os.path.join(tmp_dir, config.SPANNING_BAM_FILE)
spanning_cluster_pair_file = os.path.join(
tmp_dir, config.SPANNING_CLUSTER_PAIR_FILE)
msg = "Processing breakpoint-spanning alignments"
input_files = (breakpoint_bam_file, cluster_shelve_file, cluster_pair_file)
output_files = (spanning_bam_file, spanning_cluster_pair_file)
skip = True
for inp in input_files:
for outp in output_files:
if not up_to_date(outp, inp):
skip = False
if skip:
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
retcode = process_spanning_alignments(
cluster_shelve_file=cluster_shelve_file,
cluster_pair_file=cluster_pair_file,
bam_file=breakpoint_bam_file,
output_sam_file=spanning_sam_file,
output_cluster_pair_file=spanning_cluster_pair_file,
local_anchor_length=runconfig.local_anchor_length)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
for f in output_files:
if os.path.exists(f):
os.remove(f)
retcode = sam_to_bam(spanning_sam_file, spanning_bam_file)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(spanning_bam_file):
os.remove(spanning_bam_file)
return config.JOB_ERROR
if os.path.exists(spanning_sam_file):
os.remove(spanning_sam_file)
#
# Sort unpaired reads by position
#
msg = "Sorting spanning BAM file"
sorted_spanning_bam_file = os.path.join(runconfig.output_dir,
config.SORTED_SPANNING_BAM_FILE)
if (up_to_date(sorted_spanning_bam_file, spanning_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
bam_prefix = os.path.splitext(sorted_spanning_bam_file)[0]
pysam.sort("-m", str(int(1e9)), spanning_bam_file, bam_prefix)
#
# Index BAM file
#
msg = "Indexing spanning BAM file"
sorted_spanning_bam_index_file = sorted_spanning_bam_file + ".bai"
if (up_to_date(sorted_spanning_bam_index_file, sorted_spanning_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
pysam.index(sorted_spanning_bam_file)
#
# Write chimera file
#
unfiltered_chimera_bedpe_file = os.path.join(
runconfig.output_dir, config.UNFILTERED_CHIMERA_BEDPE_FILE)
msg = "Writing unfiltered chimeras to file %s" % (
unfiltered_chimera_bedpe_file)
if (up_to_date(unfiltered_chimera_bedpe_file, spanning_cluster_pair_file)
and up_to_date(unfiltered_chimera_bedpe_file,
cluster_shelve_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
retcode = write_output(transcripts,
cluster_shelve_file=cluster_shelve_file,
cluster_pair_file=spanning_cluster_pair_file,
read_name_file=read_name_file,
output_file=unfiltered_chimera_bedpe_file,
annotation_source="ensembl")
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(unfiltered_chimera_bedpe_file):
os.remove(unfiltered_chimera_bedpe_file)
#
# Filter chimeras
#
chimera_bedpe_file = os.path.join(runconfig.output_dir,
config.CHIMERA_BEDPE_FILE)
msg = "Filtering chimeras"
if (up_to_date(chimera_bedpe_file, unfiltered_chimera_bedpe_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
retcode = filter_chimeras(
input_file=unfiltered_chimera_bedpe_file,
output_file=chimera_bedpe_file,
filter_num_frags=runconfig.filter_num_frags,
filter_allele_fraction=runconfig.filter_allele_fraction,
mask_biotypes=mask_biotypes,
mask_rnames=mask_rnames)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(chimera_bedpe_file):
os.remove(chimera_bedpe_file)
#
# Cleanup
#
if not runconfig.keep_tmp:
logging.info("Cleaning up temporary files")
shutil.rmtree(tmp_dir)
#
# Done
#
logging.info("Finished run.")
return config.JOB_SUCCESS
def nominate_chimeras(index_dir, isize_dist_file, input_file, output_file,
trim_bp, max_read_length, homology_mismatches):
# read insert size distribution
isize_dist = InsertSizeDistribution.from_file(open(isize_dist_file))
# build a lookup table to get genomic intervals from transcripts
logging.debug("Reading gene information")
gene_file = os.path.join(index_dir, config.GENE_FEATURE_FILE)
tx_name_gene_map = build_tx_name_gene_map(gene_file, rname_prefix=None)
#genome_tx_trees = build_genome_tx_trees(gene_file)
# open the reference sequence fasta file
ref_fasta_file = os.path.join(index_dir, config.ALIGN_INDEX + ".fa")
ref_fa = pysam.Fastafile(ref_fasta_file)
# keep track of mapping from breakpoint sequence to breakpoint id
# this requires storing all breakpoint sequences in memory which is
# potentially expensive. TODO: investigate whether this should be
# moved to a separate sort-update-sort procedure
breakpoint_seq_name_map = {}
breakpoint_num = 1
# group discordant read pairs by gene
logging.debug("Parsing discordant reads")
chimera_num = 1
outfh = open(output_file, "w")
for tx_name_5p, tx_name_3p, frags in parse_discordant_bedpe_by_transcript_pair(open(input_file)):
# get gene information
tx5p = tx_name_gene_map[tx_name_5p]
tx3p = tx_name_gene_map[tx_name_3p]
# bin fragments into putative breakpoints
breakpoint_dict = collections.defaultdict(lambda: [])
for dr5p,dr3p in frags:
# given the insert size find the highest probability
# exon junction breakpoint between the two transcripts
isize_prob, breakpoints = \
choose_best_breakpoints(dr5p, dr3p, tx5p, tx3p,
trim_bp, isize_dist)
for breakpoint in breakpoints:
breakpoint_dict[breakpoint].append((dr5p, dr3p))
# iterate through breakpoints and build chimera candidates
for breakpoint,frags in breakpoint_dict.iteritems():
exon_num_5p, tx_end_5p, exon_num_3p, tx_start_3p = breakpoint
breakpoint_seq_5p, breakpoint_seq_3p, homology_left, homology_right = \
extract_breakpoint_sequence(config.GENE_REF_PREFIX + tx5p.tx_name, tx_end_5p,
config.GENE_REF_PREFIX + tx3p.tx_name, tx_start_3p,
ref_fa, max_read_length,
homology_mismatches)
tx3p_length = sum((end - start) for start,end in tx3p.exons)
# get unique breakpoint id based on sequence
breakpoint_seq = breakpoint_seq_5p + breakpoint_seq_3p
if breakpoint_seq in breakpoint_seq_name_map:
breakpoint_name = breakpoint_seq_name_map[breakpoint_seq]
else:
breakpoint_name = "B%07d" % (breakpoint_num)
breakpoint_seq_name_map[breakpoint_seq] = breakpoint_name
breakpoint_num += 1
# write gene, breakpoint, and raw reads to a file and follow the
# BEDPE format
gene_name_5p = '_'.join(tx5p.gene_name.split())
gene_name_3p = '_'.join(tx3p.gene_name.split())
fields = [tx5p.tx_name, 0, tx_end_5p, # chrom1, start1, end1
tx3p.tx_name, tx_start_3p, tx3p_length, # chrom2, start2, end2
"C%07d" % (chimera_num), # name
1.0, # pvalue
tx5p.strand, tx3p.strand, # strand1, strand2
gene_name_5p, gene_name_3p, # gene names
# exon interval information
'%d-%d' % (0, exon_num_5p),
'%d-%d' % (exon_num_3p, len(tx3p.exons)),
# breakpoint information
breakpoint_name,
breakpoint_seq_5p, breakpoint_seq_3p,
homology_left, homology_right,
# fragments
frags_to_encomp_string(frags),
# spanning reads
None]
print >>outfh, '\t'.join(map(str, fields))
chimera_num += 1
outfh.close()
ref_fa.close()
return config.JOB_SUCCESS
def run_chimerascan(runconfig):
"""
main function for running the chimerascan pipeline
"""
# print a welcome message
title_string = "Running chimerascan version %s" % (__version__)
logging.info(title_string)
logging.info("-" * len(title_string))
# validate run configuration
config_passed = runconfig.check_config()
if not config_passed:
logging.error("Invalid run configuration, aborting.")
return config.JOB_ERROR
# create output dir if it does not exist
if not os.path.exists(runconfig.output_dir):
os.makedirs(runconfig.output_dir)
logging.info("Created output directory: %s" % (runconfig.output_dir))
# create log dir if it does not exist
log_dir = os.path.join(runconfig.output_dir, config.LOG_DIR)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
logging.debug("Created directory for log files: %s" % (log_dir))
# create tmp dir if it does not exist
tmp_dir = os.path.join(runconfig.output_dir, config.TMP_DIR)
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
logging.debug("Created directory for tmp files: %s" % (tmp_dir))
# write the run config to a file
xmlstring = runconfig.to_xml()
runconfig_xml_file = os.path.join(runconfig.output_dir, config.RUNCONFIG_XML_FILE)
logging.info("Writing run configuration to XML file: %s" % (runconfig_xml_file))
fh = open(runconfig_xml_file, "w")
print >>fh, xmlstring
fh.close()
# mask biotypes and references
mask_biotypes = set()
if runconfig.mask_biotypes_file:
logging.info("Reading biotypes mask file")
mask_biotypes.update([line.strip() for line in open(runconfig.mask_biotypes_file)])
logging.info("\tread biotypes: %s" % (','.join(sorted(mask_biotypes))))
mask_rnames = set()
if runconfig.mask_rnames_file:
logging.info("Reading references mask file")
mask_rnames.update([line.strip() for line in open(runconfig.mask_rnames_file)])
logging.info("\tread references: %s" % (','.join(sorted(mask_rnames))))
# read transcripts
logging.info("Reading transcript features")
transcript_file = os.path.join(runconfig.index_dir, config.TRANSCRIPT_FEATURE_FILE)
transcripts = list(TranscriptFeature.parse(open(transcript_file)))
logging.info("\tread %d transcripts" % (len(transcripts)))
# setup alignment indexes
genome_index = os.path.join(runconfig.index_dir, config.GENOME_INDEX)
transcriptome_index = os.path.join(runconfig.index_dir, config.TRANSCRIPTOME_INDEX)
max_transcriptome_hits_file = os.path.join(runconfig.index_dir,
config.MAX_MULTIMAPPING_FILE)
max_transcriptome_hits = int(open(max_transcriptome_hits_file).next().strip())
# detect read length
original_read_length = detect_read_length(runconfig.fastq_files[0])
# minimum fragment length cannot be smaller than the trimmed read length
trimmed_read_length = (original_read_length - runconfig.trim5 - runconfig.trim3)
min_fragment_length = max(runconfig.min_fragment_length, trimmed_read_length)
#
# Process and inspect the FASTQ files, performing several alterations
# to the reads:
#
# 1) rename them from long string to numbers to save space throughout
# the pipeline. also store mapping from read numbers to full names
# in a separate file
# 2) ensure the "/1" and "/2" suffixes exist to denote paired reads
# 3) convert quality scores to sanger format
#
converted_fastq_files = [os.path.join(tmp_dir, fq)
for fq in config.CONVERTED_FASTQ_FILES]
read_name_file = os.path.join(tmp_dir, config.READ_NAME_TXT_FILE)
msg = "Processing FASTQ files"
skip = all(up_to_date(cfq, fq) for cfq,fq in
zip(converted_fastq_files, runconfig.fastq_files))
skip = skip and up_to_date(read_name_file, runconfig.fastq_files[0])
if skip:
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
converted_fastq_prefix = \
os.path.join(tmp_dir, config.CONVERTED_FASTQ_PREFIX)
try:
retcode = process_input_reads(runconfig.fastq_files,
converted_fastq_prefix,
quals=runconfig.quals,
trim5=runconfig.trim5,
trim3=runconfig.trim3)
if retcode != config.JOB_SUCCESS:
logging.error("%s step failed" % (msg))
return config.JOB_ERROR
except Exception as e:
logging.info("Cleaning up after error %s" % (str(e)))
for fq in converted_fastq_files:
if os.path.isfile(fq):
os.remove(fq)
#
# Transcriptome alignment step
#
# Align to transcriptome in paired-end mode, trying to resolve as many
# reads as possible.
#
transcriptome_bam_file = os.path.join(tmp_dir, config.TRANSCRIPTOME_BAM_FILE)
transcriptome_unaligned_path = os.path.join(tmp_dir, config.TRANSCRIPTOME_UNALIGNED_PATH)
transcriptome_unaligned_fastq_files = tuple(os.path.join(tmp_dir, fq) for fq in config.TRANSCRIPTOME_UNALIGNED_FASTQ_FILES)
msg = "Aligning paired-end reads to transcriptome"
if (all(up_to_date(transcriptome_bam_file, fq) for fq in converted_fastq_files) and
all(up_to_date(a,b) for a,b in zip(transcriptome_unaligned_fastq_files, converted_fastq_files))):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
log_file = os.path.join(log_dir, config.TRANSCRIPTOME_LOG_FILE)
retcode = bowtie2_align_transcriptome_pe(transcriptome_index=transcriptome_index,
genome_index=genome_index,
transcript_file=transcript_file,
fastq_files=converted_fastq_files,
unaligned_path=transcriptome_unaligned_path,
bam_file=transcriptome_bam_file,
log_file=log_file,
library_type=runconfig.library_type,
min_fragment_length=min_fragment_length,
max_fragment_length=runconfig.max_fragment_length,
max_transcriptome_hits=max_transcriptome_hits,
num_processors=runconfig.num_processors)
# cleanup if job failed
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(transcriptome_bam_file):
os.remove(transcriptome_bam_file)
for f in transcriptome_unaligned_fastq_files:
if os.path.exists(f):
os.remove(f)
return config.JOB_ERROR
#
# Sort transcriptome reads by position
#
msg = "Sorting transcriptome reads"
sorted_transcriptome_bam_file = os.path.join(runconfig.output_dir,
config.SORTED_TRANSCRIPTOME_BAM_FILE)
if (up_to_date(sorted_transcriptome_bam_file, transcriptome_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
sorted_aligned_bam_prefix = os.path.splitext(sorted_transcriptome_bam_file)[0]
pysam.sort("-m", str(int(1e9)), transcriptome_bam_file, sorted_aligned_bam_prefix)
#
# Index BAM file
#
msg = "Indexing BAM file"
sorted_transcriptome_bam_index_file = sorted_transcriptome_bam_file + ".bai"
if (up_to_date(sorted_transcriptome_bam_index_file, sorted_transcriptome_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
pysam.index(sorted_transcriptome_bam_file)
#
# Get insert size distribution
#
isize_dist_file = os.path.join(runconfig.output_dir,
config.ISIZE_DIST_FILE)
msg = "Profiling insert size distribution"
if up_to_date(isize_dist_file, transcriptome_bam_file):
logging.info("[SKIPPED] %s" % msg)
isize_dist = InsertSizeDistribution.from_file(open(isize_dist_file, "r"))
else:
logging.info(msg)
bamfh = pysam.Samfile(sorted_transcriptome_bam_file, "rb")
isize_dist = InsertSizeDistribution.from_genome_bam(bamfh, transcripts,
min_isize=min_fragment_length,
max_isize=runconfig.max_fragment_length,
max_samples=config.ISIZE_MAX_SAMPLES)
bamfh.close()
# if not enough samples, use a normal distribution instead
# of the empirical distribution
if isize_dist.n < config.ISIZE_MIN_SAMPLES:
logging.warning("Not enough fragments to sample insert size "
"distribution empirically. Using mean=%d "
"stdev=%f instead" %
(runconfig.isize_mean,
runconfig.isize_stdev))
isize_dist = InsertSizeDistribution.from_random(runconfig.isize_mean,
runconfig.isize_stdev,
min_isize=runconfig.min_fragment_length,
max_isize=runconfig.max_fragment_length,
samples=config.ISIZE_MAX_SAMPLES)
isize_dist.to_file(open(isize_dist_file, "w"))
#
# Determine ideal segment length automatically
#
# log insert size statistics
logging.info("Insert size samples=%d mean=%f std=%f median=%d mode=%d" %
(isize_dist.n, isize_dist.mean(), isize_dist.std(),
isize_dist.isize_at_percentile(50.0), isize_dist.mode()))
# choose a segment length to optimize mapping
optimal_isize = isize_dist.isize_at_percentile(DEFAULT_FRAG_SIZE_SENSITIVITY)
logging.info("Determining soft-clipped segment length")
logging.debug("\tInsert size at %f percent of distribution is %d" %
(DEFAULT_FRAG_SIZE_SENSITIVITY, optimal_isize))
optimal_segment_length = int(round(optimal_isize / 3.0))
logging.debug("\tOptimal segment length is %d/3.0 = %d" % (optimal_isize, optimal_segment_length))
segment_length = min(optimal_segment_length, trimmed_read_length)
segment_length = max(config.MIN_SEGMENT_LENGTH, segment_length)
logging.debug("\tAfter adjusting for min %d and read length %d, final segment length is %d" %
(config.MIN_SEGMENT_LENGTH, trimmed_read_length, segment_length))
if runconfig.segment_length is not None:
logging.debug("\tOverriding auto segment length and using segment length of %d" % (runconfig.segment_length))
segment_length = runconfig.segment_length
#
# Genome alignment step
#
# Align any unaligned transcriptome reads to genome in paired-end mode.
# Resolve as many reads as possible.
#
genome_bam_file = os.path.join(tmp_dir, config.GENOME_BAM_FILE)
genome_unaligned_path = os.path.join(tmp_dir, config.GENOME_UNALIGNED_PATH)
genome_unaligned_fastq_files = tuple(os.path.join(tmp_dir, fq) for fq in config.GENOME_UNALIGNED_FASTQ_FILES)
msg = "Realigning unaligned paired-end reads to genome"
if (all(up_to_date(genome_bam_file, fq) for fq in converted_fastq_files) and
all(up_to_date(a,b) for a,b in zip(genome_unaligned_fastq_files, converted_fastq_files))):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
log_file = os.path.join(log_dir, config.GENOME_LOG_FILE)
retcode = bowtie2_align_pe(index=genome_index,
fastq_files=transcriptome_unaligned_fastq_files,
unaligned_path=genome_unaligned_path,
bam_file=genome_bam_file,
log_file=log_file,
library_type=runconfig.library_type,
min_fragment_length=min_fragment_length,
max_fragment_length=runconfig.max_fragment_length,
max_hits=max_transcriptome_hits,
num_processors=runconfig.num_processors)
# cleanup if job failed
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(genome_bam_file):
os.remove(genome_bam_file)
for f in genome_unaligned_fastq_files:
if os.path.exists(f):
os.remove(f)
return config.JOB_ERROR
#
# Realignment step
#
# trim and realign all the initially unaligned reads in order to
# increase sensitivity to detect reads spanning fusion junctions
#
realigned_bam_file = os.path.join(tmp_dir, config.REALIGNED_BAM_FILE)
realigned_log_file = os.path.join(log_dir, config.REALIGNED_LOG_FILE)
msg = "Trimming and realigning initially unmapped reads"
if (all(up_to_date(realigned_bam_file, fq) for fq in genome_unaligned_fastq_files) and
up_to_date(realigned_bam_file, isize_dist_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
retcode = bowtie2_align_pe_sr(index=transcriptome_index,
transcript_file=transcript_file,
fastq_files=genome_unaligned_fastq_files,
bam_file=realigned_bam_file,
log_file=realigned_log_file,
tmp_dir=tmp_dir,
segment_length=segment_length,
max_hits=max_transcriptome_hits,
num_processors=runconfig.num_processors)
if retcode != config.JOB_SUCCESS:
if os.path.exists(realigned_bam_file):
os.remove(realigned_bam_file)
return config.JOB_ERROR
#
# Find discordant reads
#
# iterate through realigned reads and divide them into groups of
# concordant, discordant within a gene (isoforms), discordant
# between different genes, and discordant in the genome
#
paired_bam_file = os.path.join(tmp_dir, config.PAIRED_BAM_FILE)
discordant_bam_file = os.path.join(tmp_dir, config.DISCORDANT_BAM_FILE)
unpaired_bam_file = os.path.join(tmp_dir, config.UNPAIRED_BAM_FILE)
unmapped_bam_file = os.path.join(tmp_dir, config.UNMAPPED_BAM_FILE)
multimap_bam_file = os.path.join(tmp_dir, config.MULTIMAP_BAM_FILE)
unresolved_bam_file = os.path.join(tmp_dir, config.UNRESOLVED_BAM_FILE)
output_files = (paired_bam_file, discordant_bam_file, unpaired_bam_file,
unmapped_bam_file, multimap_bam_file, unresolved_bam_file)
msg = "Classifying concordant and discordant read pairs"
if (all(up_to_date(f, realigned_bam_file) for f in output_files)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
retcode = find_discordant_fragments(transcripts=transcripts,
input_bam_file=realigned_bam_file,
paired_bam_file=paired_bam_file,
discordant_bam_file=discordant_bam_file,
unpaired_bam_file=unpaired_bam_file,
unmapped_bam_file=unmapped_bam_file,
multimap_bam_file=multimap_bam_file,
unresolved_bam_file=unresolved_bam_file,
max_isize=runconfig.max_fragment_length,
max_multihits=runconfig.max_multihits,
library_type=runconfig.library_type)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
for f in output_files:
if os.path.exists(f):
os.remove(f)
return config.JOB_ERROR
#
# Convert discordant transcriptome reads to genome coordinates
#
discordant_genome_bam_file = os.path.join(tmp_dir, config.DISCORDANT_GENOME_BAM_FILE)
msg = "Converting discordant transcriptome hits to genomic coordinates"
if (up_to_date(discordant_genome_bam_file, discordant_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
discordant_genome_sam_file = os.path.join(tmp_dir, config.DISCORDANT_GENOME_SAM_FILE)
retcode = transcriptome_to_genome(genome_index, transcripts,
input_file=discordant_bam_file,
output_file=discordant_genome_sam_file,
library_type=runconfig.library_type,
input_sam=False,
output_sam=True)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(discordant_genome_sam_file):
os.remove(discordant_genome_sam_file)
return config.JOB_ERROR
retcode = sam_to_bam(discordant_genome_sam_file, discordant_genome_bam_file)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(discordant_genome_bam_file):
os.remove(discordant_genome_bam_file)
return config.JOB_ERROR
if os.path.exists(discordant_genome_sam_file):
os.remove(discordant_genome_sam_file)
#
# Sort discordant reads by position
#
msg = "Sorting discordant BAM file"
sorted_discordant_genome_bam_file = os.path.join(tmp_dir, config.SORTED_DISCORDANT_GENOME_BAM_FILE)
if (up_to_date(sorted_discordant_genome_bam_file, discordant_genome_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
bam_prefix = os.path.splitext(sorted_discordant_genome_bam_file)[0]
pysam.sort("-m", str(int(1e9)), discordant_genome_bam_file, bam_prefix)
#
# Index BAM file
#
msg = "Indexing discordant BAM file"
sorted_discordant_bam_index_file = sorted_discordant_genome_bam_file + ".bai"
if (up_to_date(sorted_discordant_bam_index_file, sorted_discordant_genome_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
pysam.index(sorted_discordant_genome_bam_file)
#
# Convert unpaired transcriptome reads to genome coordinates
#
unpaired_genome_bam_file = os.path.join(tmp_dir, config.UNPAIRED_GENOME_BAM_FILE)
msg = "Converting unpaired transcriptome hits to genomic coordinates"
if (up_to_date(unpaired_genome_bam_file, unpaired_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
unpaired_genome_sam_file = os.path.join(tmp_dir, config.UNPAIRED_GENOME_SAM_FILE)
retcode = transcriptome_to_genome(genome_index, transcripts,
input_file=unpaired_bam_file,
output_file=unpaired_genome_sam_file,
library_type=runconfig.library_type,
input_sam=False,
output_sam=True)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(unpaired_genome_sam_file):
os.remove(unpaired_genome_sam_file)
return config.JOB_ERROR
retcode = sam_to_bam(unpaired_genome_sam_file, unpaired_genome_bam_file)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(unpaired_genome_bam_file):
os.remove(unpaired_genome_bam_file)
return config.JOB_ERROR
if os.path.exists(unpaired_genome_sam_file):
os.remove(unpaired_genome_sam_file)
#
# Sort unpaired reads by position
#
msg = "Sorting unpaired BAM file"
sorted_unpaired_genome_bam_file = os.path.join(tmp_dir, config.SORTED_UNPAIRED_GENOME_BAM_FILE)
if (up_to_date(sorted_unpaired_genome_bam_file, unpaired_genome_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
bam_prefix = os.path.splitext(sorted_unpaired_genome_bam_file)[0]
pysam.sort("-m", str(int(1e9)), unpaired_genome_bam_file, bam_prefix)
#
# Index BAM file
#
msg = "Indexing unpaired BAM file"
sorted_unpaired_bam_index_file = sorted_unpaired_genome_bam_file + ".bai"
if (up_to_date(sorted_unpaired_bam_index_file, sorted_unpaired_genome_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
pysam.index(sorted_unpaired_genome_bam_file)
#
# Cluster discordant reads into chimera candidates
#
cluster_file = os.path.join(tmp_dir, config.DISCORDANT_CLUSTER_FILE)
cluster_shelve_file = \
os.path.join(tmp_dir, config.DISCORDANT_CLUSTER_SHELVE_FILE)
sorted_discordant_genome_cluster_bam_file = \
os.path.join(runconfig.output_dir,
config.SORTED_DISCORDANT_GENOME_CLUSTER_BAM_FILE)
input_files = (sorted_discordant_genome_bam_file,
sorted_unpaired_genome_bam_file)
output_files = (cluster_file, cluster_shelve_file,
sorted_discordant_genome_cluster_bam_file)
msg = "Clustering discordant reads"
skip = True
for input_file in input_files:
for output_file in output_files:
skip = skip and up_to_date(output_file, input_file)
if skip:
logging.info("[SKIPPED] %s" % (msg))
else:
logging.debug(msg)
retcode = cluster_discordant_reads(discordant_bam_file=sorted_discordant_genome_bam_file,
unpaired_bam_file=sorted_unpaired_genome_bam_file,
concordant_bam_file=sorted_transcriptome_bam_file,
output_bam_file=sorted_discordant_genome_cluster_bam_file,
cluster_file=cluster_file,
cluster_shelve_file=cluster_shelve_file)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
for f in output_files:
if os.path.exists(f):
os.remove(f)
#
# Pair discordant clusters
#
cluster_pair_file = \
os.path.join(tmp_dir, config.DISCORDANT_CLUSTER_PAIR_FILE)
msg = "Pairing discordant clusters"
output_files = (cluster_pair_file,)
if up_to_date(cluster_pair_file, sorted_discordant_genome_cluster_bam_file):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.debug(msg)
retcode = pair_discordant_clusters(discordant_bam_file=sorted_discordant_genome_cluster_bam_file,
cluster_pair_file=cluster_pair_file,
tmp_dir=tmp_dir)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
for f in output_files:
if os.path.exists(f):
os.remove(f)
#
# Perform realignment across putative fusion breakpoints
#
breakpoint_bam_file = os.path.join(tmp_dir, config.BREAKPOINT_BAM_FILE)
msg = "Realigning to find breakpoint-spanning reads"
input_files = (sorted_discordant_genome_bam_file,
sorted_unpaired_genome_bam_file,
cluster_shelve_file,
cluster_pair_file)
output_files = (breakpoint_bam_file,)
skip = True
for inp in input_files:
for outp in output_files:
if not up_to_date(outp, inp):
skip = False
if skip:
logging.info("[SKIPPED] %s" % (msg))
else:
logging.debug(msg)
retcode = realign_across_breakpoints(index_dir=runconfig.index_dir,
discordant_bam_file=sorted_discordant_genome_bam_file,
unpaired_bam_file=sorted_unpaired_genome_bam_file,
cluster_shelve_file=cluster_shelve_file,
cluster_pair_file=cluster_pair_file,
breakpoint_bam_file=breakpoint_bam_file,
log_dir=log_dir,
tmp_dir=tmp_dir,
num_processors=runconfig.num_processors,
local_anchor_length=runconfig.local_anchor_length,
local_multihits=runconfig.local_multihits)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
for f in output_files:
if os.path.exists(f):
os.remove(f)
#
# Nominate breakpoint spanning reads (split reads)
#
spanning_sam_file = os.path.join(tmp_dir, config.SPANNING_SAM_FILE)
spanning_bam_file = os.path.join(tmp_dir, config.SPANNING_BAM_FILE)
spanning_cluster_pair_file = os.path.join(tmp_dir, config.SPANNING_CLUSTER_PAIR_FILE)
msg = "Processing breakpoint-spanning alignments"
input_files = (breakpoint_bam_file,
cluster_shelve_file,
cluster_pair_file)
output_files = (spanning_bam_file,
spanning_cluster_pair_file)
skip = True
for inp in input_files:
for outp in output_files:
if not up_to_date(outp, inp):
skip = False
if skip:
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
retcode = process_spanning_alignments(cluster_shelve_file=cluster_shelve_file,
cluster_pair_file=cluster_pair_file,
bam_file=breakpoint_bam_file,
output_sam_file=spanning_sam_file,
output_cluster_pair_file=spanning_cluster_pair_file,
local_anchor_length=runconfig.local_anchor_length)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
for f in output_files:
if os.path.exists(f):
os.remove(f)
retcode = sam_to_bam(spanning_sam_file, spanning_bam_file)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(spanning_bam_file):
os.remove(spanning_bam_file)
return config.JOB_ERROR
if os.path.exists(spanning_sam_file):
os.remove(spanning_sam_file)
#
# Sort unpaired reads by position
#
msg = "Sorting spanning BAM file"
sorted_spanning_bam_file = os.path.join(runconfig.output_dir, config.SORTED_SPANNING_BAM_FILE)
if (up_to_date(sorted_spanning_bam_file, spanning_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
bam_prefix = os.path.splitext(sorted_spanning_bam_file)[0]
pysam.sort("-m", str(int(1e9)), spanning_bam_file, bam_prefix)
#
# Index BAM file
#
msg = "Indexing spanning BAM file"
sorted_spanning_bam_index_file = sorted_spanning_bam_file + ".bai"
if (up_to_date(sorted_spanning_bam_index_file, sorted_spanning_bam_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
pysam.index(sorted_spanning_bam_file)
#
# Write chimera file
#
unfiltered_chimera_bedpe_file = os.path.join(runconfig.output_dir,
config.UNFILTERED_CHIMERA_BEDPE_FILE)
msg = "Writing unfiltered chimeras to file %s" % (unfiltered_chimera_bedpe_file)
if (up_to_date(unfiltered_chimera_bedpe_file, spanning_cluster_pair_file) and
up_to_date(unfiltered_chimera_bedpe_file, cluster_shelve_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
retcode = write_output(transcripts,
cluster_shelve_file=cluster_shelve_file,
cluster_pair_file=spanning_cluster_pair_file,
read_name_file=read_name_file,
output_file=unfiltered_chimera_bedpe_file,
annotation_source="ensembl")
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(unfiltered_chimera_bedpe_file):
os.remove(unfiltered_chimera_bedpe_file)
#
# Filter chimeras
#
chimera_bedpe_file = os.path.join(runconfig.output_dir, config.CHIMERA_BEDPE_FILE)
msg = "Filtering chimeras"
if (up_to_date(chimera_bedpe_file, unfiltered_chimera_bedpe_file)):
logging.info("[SKIPPED] %s" % (msg))
else:
logging.info(msg)
retcode = filter_chimeras(input_file=unfiltered_chimera_bedpe_file,
output_file=chimera_bedpe_file,
filter_num_frags=runconfig.filter_num_frags,
filter_allele_fraction=runconfig.filter_allele_fraction,
mask_biotypes=mask_biotypes,
mask_rnames=mask_rnames)
if retcode != config.JOB_SUCCESS:
logging.error("[FAILED] %s" % (msg))
if os.path.exists(chimera_bedpe_file):
os.remove(chimera_bedpe_file)
#
# Cleanup
#
if not runconfig.keep_tmp:
logging.info("Cleaning up temporary files")
shutil.rmtree(tmp_dir)
#
# Done
#
logging.info("Finished run.")
return config.JOB_SUCCESS
def nominate_chimeras(index_dir, isize_dist_file, input_file, output_file,
trim_bp, max_read_length, homology_mismatches):
# read insert size distribution
isize_dist = InsertSizeDistribution.from_file(open(isize_dist_file))
# build a lookup table to get genomic intervals from transcripts
logging.debug("Reading transcript information")
transcript_feature_file = os.path.join(index_dir,
config.TRANSCRIPT_FEATURE_FILE)
transcripts = list(TranscriptFeature.parse(open(transcript_feature_file)))
tx_id_map = build_transcript_map(transcripts)
# open the reference sequence fasta file
ref_fasta_file = os.path.join(index_dir, config.TRANSCRIPTOME_FASTA_FILE)
ref_fa = pysam.Fastafile(ref_fasta_file)
# keep track of mapping from breakpoint sequence to breakpoint id
# this requires storing all breakpoint sequences in memory which is
# potentially expensive. TODO: investigate whether this should be
# moved to a separate sort-update-sort procedure
breakpoint_seq_name_map = {}
breakpoint_num = 1
# group discordant read pairs by gene
logging.debug("Parsing discordant reads")
chimera_num = 1
outfh = open(output_file, "w")
for tx_id_5p, tx_id_3p, frags in parse_discordant_bedpe_by_transcript_pair(
open(input_file)):
# get gene information
tx5p = tx_id_map[tx_id_5p]
tx3p = tx_id_map[tx_id_3p]
# bin fragments into putative breakpoints
breakpoint_dict = collections.defaultdict(lambda: [])
for dr5p, dr3p in frags:
# given the insert size find the highest probability
# exon junction breakpoint between the two transcripts
isize_prob, breakpoints = \
choose_best_breakpoints(dr5p, dr3p, tx5p, tx3p,
trim_bp, isize_dist)
for breakpoint in breakpoints:
breakpoint_dict[breakpoint].append((dr5p, dr3p))
# iterate through breakpoints and build chimera candidates
for breakpoint, frags in breakpoint_dict.iteritems():
exon_num_5p, tx_end_5p, exon_num_3p, tx_start_3p = breakpoint
breakpoint_seq_5p, breakpoint_seq_3p, homology_left, homology_right = \
extract_breakpoint_sequence(tx_id_5p, tx_end_5p,
tx_id_3p, tx_start_3p,
ref_fa, max_read_length,
homology_mismatches)
tx3p_length = sum((end - start) for start, end in tx3p.exons)
# get unique breakpoint id based on sequence
breakpoint_seq = breakpoint_seq_5p + breakpoint_seq_3p
if breakpoint_seq in breakpoint_seq_name_map:
breakpoint_name = breakpoint_seq_name_map[breakpoint_seq]
else:
breakpoint_name = "B%07d" % (breakpoint_num)
breakpoint_seq_name_map[breakpoint_seq] = breakpoint_name
breakpoint_num += 1
# write gene, breakpoint, and raw reads to a file and follow the
# BEDPE format
gene_names_5p = ",".join(
sorted(set(["_".join(x.split()) for x in tx5p.gene_names])))
gene_names_3p = ",".join(
sorted(set(["_".join(x.split()) for x in tx3p.gene_names])))
fields = [
tx5p.tx_id,
0,
tx_end_5p, # chrom1, start1, end1
tx3p.tx_id,
tx_start_3p,
tx3p_length, # chrom2, start2, end2
"C%07d" % (chimera_num), # name
1.0, # pvalue
tx5p.strand,
tx3p.strand, # strand1, strand2
gene_names_5p,
gene_names_3p, # gene names
# exon interval information
'%d-%d' % (0, exon_num_5p),
'%d-%d' % (exon_num_3p, len(tx3p.exons)),
# breakpoint information
breakpoint_name,
breakpoint_seq_5p,
breakpoint_seq_3p,
homology_left,
homology_right,
# fragments
frags_to_encomp_string(frags),
# spanning reads
None
]
print >> outfh, '\t'.join(map(str, fields))
chimera_num += 1
outfh.close()
ref_fa.close()
return config.JOB_SUCCESS
def discordant_reads_to_breakpoints(index_dir, isize_dist_file,
input_bam_file, output_file,
trim_bp, max_read_length,
homology_mismatches):
"""
homology_mismatches: number of mismatches to tolerate while computing
homology between chimeric breakpoint sequence and "wildtype" sequence
trim_bp: when selecting the best matching exon for each read, we
account for spurious overlap into adjacent exons by trimming the
read by 'trim_bp'
"""
# read insert size distribution
isize_dist = InsertSizeDistribution.from_file(open(isize_dist_file))
# open BAM alignment file
bamfh = pysam.Samfile(input_bam_file, "rb")
# build a lookup table to get genomic intervals from transcripts
logging.debug("Reading gene information")
gene_file = os.path.join(index_dir, config.GENE_FEATURE_FILE)
tid_tx_map = build_tid_tx_map(bamfh, gene_file,
rname_prefix=config.GENE_REF_PREFIX)
# open the reference sequence fasta file
ref_fasta_file = os.path.join(index_dir, config.ALIGN_INDEX + ".fa")
ref_fa = pysam.Fastafile(ref_fasta_file)
# iterate through read pairs
outfh = open(output_file, "w")
logging.debug("Parsing discordant reads")
for r5p,r3p in parse_gene_discordant_reads(bamfh):
# store pertinent read information in lightweight structure called
# DiscordantRead object. this departs from SAM format into a
# custom read format
dr5p = DiscordantRead.from_read(r5p)
dr3p = DiscordantRead.from_read(r3p)
# get gene information
tx5p = tid_tx_map[r5p.rname]
tx3p = tid_tx_map[r3p.rname]
# given the insert size find the highest probability
# exon junction breakpoint between the two transcripts
isize_prob, breakpoints = \
choose_best_breakpoints(r5p, r3p, tx5p, tx3p,
trim_bp, isize_dist)
# extract the sequence of the breakpoint along with the
# number of homologous bases at the breakpoint between
# chimera and wildtype genes
for breakpoint in breakpoints:
exon_num_5p, tx_end_5p, exon_num_3p, tx_start_3p = breakpoint
breakpoint_seq_5p, breakpoint_seq_3p, homology_left, homology_right = \
extract_breakpoint_sequence(config.GENE_REF_PREFIX + tx5p.tx_name, tx_end_5p,
config.GENE_REF_PREFIX + tx3p.tx_name, tx_start_3p,
ref_fa, max_read_length,
homology_mismatches)
# write breakpoint information for each read to a file
fields = [tx5p.tx_name, 0, tx_end_5p,
tx3p.tx_name, tx_start_3p, tx3p.tx_end,
r5p.rname, # name
isize_prob, # score
tx5p.strand, tx3p.strand, # strand 1, strand 2
# user defined fields
exon_num_5p, exon_num_3p,
breakpoint_seq_5p, breakpoint_seq_3p,
homology_left, homology_right]
fields.append('|'.join(map(str, dr5p.to_list())))
fields.append('|'.join(map(str, dr3p.to_list())))
print >>outfh, '\t'.join(map(str, fields))
# cleanup
ref_fa.close()
outfh.close()
bamfh.close()
return config.JOB_SUCCESS
