def align_rna_metagenomics(
inBam,
db,
taxDb,
outReport,
dupeReport=None,
outBam=None,
dupeLca=None,
outLca=None,
sensitive=None,
JVMmemory=None,
numThreads=None,
picardOptions=None,
min_score_to_output=None,
):
'''
Align to metagenomics bwa index, mark duplicates, and generate LCA report
'''
picardOptions = picardOptions if picardOptions else []
bwa = tools.bwa.Bwa()
samtools = tools.samtools.SamtoolsTool()
bwa_opts = ['-a']
if sensitive:
bwa_opts += '-k 12 -A 1 -B 1 -O 1 -E 1'.split()
map_threshold = min_score_to_output or 30
aln_bam = util.file.mkstempfname('.bam')
bwa.mem(inBam, db, aln_bam, options=bwa_opts, min_qual=map_threshold)
tax_db = TaxonomyDb(tax_dir=taxDb, load_names=True, load_nodes=True)
if dupeReport:
aln_bam_sorted = util.file.mkstempfname('.align_namesorted.bam')
samtools.sort(aln_bam, aln_bam_sorted, args=['-n'], threads=numThreads)
sam_lca_report(tax_db,
aln_bam_sorted,
outReport=dupeReport,
outLca=dupeLca,
unique_only=False)
os.unlink(aln_bam_sorted)
aln_bam_deduped = outBam if outBam else util.file.mkstempfname(
'.align_deduped.bam')
opts = list(picardOptions)
dupe_removal_out_metrics = util.file.mkstempfname('.metrics')
pic = tools.picard.MarkDuplicatesTool()
pic.execute([aln_bam],
aln_bam_deduped,
dupe_removal_out_metrics,
picardOptions=opts,
JVMmemory=pic.jvmMemDefault)
os.unlink(aln_bam)
sam_lca_report(tax_db, aln_bam_deduped, outReport=outReport, outLca=outLca)
if not outBam:
os.unlink(aln_bam_deduped)
def bwamem_idxstats(inBam,
refFasta,
outBam=None,
outStats=None,
min_score_to_filter=None,
aligner_options=None):
''' Take reads, align to reference with BWA-MEM and perform samtools idxstats.
'''
assert outBam or outStats, "Either outBam or outStats must be specified"
if outBam is None:
bam_aligned = mkstempfname('.aligned.bam')
else:
bam_aligned = outBam
samtools = tools.samtools.SamtoolsTool()
bwa = tools.bwa.Bwa()
ref_indexed = util.file.mkstempfname('.reference.fasta')
shutil.copyfile(refFasta, ref_indexed)
bwa.index(ref_indexed)
bwa_opts = [] if aligner_options is None else aligner_options.split()
bwa.mem(inBam,
refFasta,
bam_aligned,
options=bwa_opts,
min_score_to_filter=min_score_to_filter)
if outStats is not None:
samtools.idxstats(bam_aligned, outStats)
if outBam is None:
os.unlink(bam_aligned)
def align_rna_metagenomics(
inBam,
db,
taxDb,
outReport,
dupeReport=None,
outBam=None,
dupeLca=None,
outLca=None,
sensitive=None,
JVMmemory=None,
numThreads=None,
picardOptions=None,
min_score_to_output=None,
):
"""
Align to metagenomics bwa index, mark duplicates, and generate LCA report
"""
picardOptions = picardOptions if picardOptions else []
bwa = tools.bwa.Bwa()
samtools = tools.samtools.SamtoolsTool()
bwa_opts = ["-a"]
if sensitive:
bwa_opts += "-k 12 -A 1 -B 1 -O 1 -E 1".split()
map_threshold = min_score_to_output or 30
aln_bam = util.file.mkstempfname(".bam")
bwa.mem(inBam, db, aln_bam, options=bwa_opts, min_qual=map_threshold)
tax_db = TaxonomyDb(tax_dir=taxDb, load_names=True, load_nodes=True)
if dupeReport:
aln_bam_sorted = util.file.mkstempfname(".align_namesorted.bam")
samtools.sort(aln_bam, aln_bam_sorted, args=["-n"], threads=numThreads)
sam_lca_report(tax_db, aln_bam_sorted, outReport=dupeReport, outLca=dupeLca, unique_only=False)
os.unlink(aln_bam_sorted)
aln_bam_deduped = outBam if outBam else util.file.mkstempfname(".align_deduped.bam")
opts = list(picardOptions)
dupe_removal_out_metrics = util.file.mkstempfname(".metrics")
pic = tools.picard.MarkDuplicatesTool()
pic.execute([aln_bam], aln_bam_deduped, dupe_removal_out_metrics, picardOptions=opts, JVMmemory=pic.jvmMemDefault)
os.unlink(aln_bam)
sam_lca_report(tax_db, aln_bam_deduped, outReport=outReport, outLca=outLca)
if not outBam:
os.unlink(aln_bam_deduped)
def bwamem_idxstats(inBam, refFasta, outBam=None, outStats=None):
''' Take reads, align to reference with BWA-MEM and perform samtools idxstats.
'''
if outBam is None:
bam_aligned = mkstempfname('.aligned.bam')
else:
bam_aligned = outBam
samtools = tools.samtools.SamtoolsTool()
bwa = tools.bwa.Bwa()
bwa.mem(inBam, refFasta, bam_aligned)
if outStats is not None:
samtools.idxstats(bam_aligned, outStats)
if outBam is None:
os.unlink(bam_aligned)
def bwamem_idxstats(inBam, refFasta, outBam=None, outStats=None):
''' Take reads, align to reference with BWA-MEM and perform samtools idxstats.
'''
assert outBam or outStats, "Either outBam or outStats must be specified"
if outBam is None:
bam_aligned = mkstempfname('.aligned.bam')
else:
bam_aligned = outBam
samtools = tools.samtools.SamtoolsTool()
bwa = tools.bwa.Bwa()
bwa.mem(inBam, refFasta, bam_aligned)
if outStats is not None:
samtools.idxstats(bam_aligned, outStats)
if outBam is None:
os.unlink(bam_aligned)
def align_rna_metagenomics(
inBam,
db,
taxDb,
outReport,
dupeReport=None,
outBam=None,
dupeReads=None,
outReads=None,
sensitive=None,
JVMmemory=None,
numThreads=None,
picardOptions=None,
):
'''
Align to metagenomics bwa index, mark duplicates, and generate LCA report
'''
picardOptions = picardOptions if picardOptions else []
bwa = tools.bwa.Bwa()
samtools = tools.samtools.SamtoolsTool()
bwa_opts = ['-a']
if sensitive:
bwa_opts += '-k 12 -A 1 -B 1 -O 1 -E 1'.split()
# TODO: Use bwa.mem's min_score_to_filter argument to decrease false
# positives in the output. Currently, it works by summing the alignment
# score across all alignments output by bwa for each query (reads in a
# pair, supplementary, and secondary alignments). This is not reasonable
# for reads with secondary alignments because it will be easier for those
# reads/queries to exceed the threshold given by the value of the argument.
# In this context, bwa is called using '-a' as an option and its output
# will likely include many secondary alignments. One option is to add
# another argument to bwa.mem, similar to min_score_to_filter, that sets a
# threshold on the alignment score of output alignments but only filters on
# a per-alignment level (i.e., not by summing alignment scores across all
# alignments for each query).
aln_bam = util.file.mkstempfname('.bam')
bwa.mem(inBam, db, aln_bam, options=bwa_opts)
tax_db = TaxonomyDb(tax_dir=taxDb, load_names=True, load_nodes=True)
if dupeReport:
aln_bam_sorted = util.file.mkstempfname('.align_namesorted.bam')
samtools.sort(aln_bam, aln_bam_sorted, args=['-n'], threads=numThreads)
sam_lca_report(tax_db,
aln_bam_sorted,
outReport=dupeReport,
outReads=dupeReads,
unique_only=False)
os.unlink(aln_bam_sorted)
aln_bam_deduped = outBam if outBam else util.file.mkstempfname(
'.align_deduped.bam')
opts = list(picardOptions)
dupe_removal_out_metrics = util.file.mkstempfname('.metrics')
pic = tools.picard.MarkDuplicatesTool()
pic.execute([aln_bam],
aln_bam_deduped,
dupe_removal_out_metrics,
picardOptions=opts,
JVMmemory=JVMmemory)
os.unlink(aln_bam)
aln_bam_dd_sorted = util.file.mkstempfname('.bam')
samtools.sort(aln_bam_deduped,
aln_bam_dd_sorted,
args=['-n'],
threads=numThreads)
sam_lca_report(tax_db,
aln_bam_dd_sorted,
outReport=outReport,
outReads=outReads)
if not outBam:
os.unlink(aln_bam_deduped)
def align_and_plot_coverage(
out_plot_file,
plot_format,
plot_data_style,
plot_style,
plot_width,
plot_height,
plot_dpi,
plot_title,
base_q_threshold,
mapping_q_threshold,
max_coverage_depth,
read_length_threshold,
out_summary,
in_bam,
ref_fasta,
out_bam=None,
sensitive=False,
excludeDuplicates=False,
JVMmemory=None,
picardOptions=None,
min_score_to_output=None
):
'''
Take reads, align to reference with BWA-MEM, and generate a coverage plot
'''
if out_bam is None:
bam_aligned = util.file.mkstempfname('.aligned.bam')
else:
bam_aligned = out_bam
ref_indexed = util.file.mkstempfname('.reference.fasta')
shutil.copyfile(ref_fasta, ref_indexed)
bwa = tools.bwa.Bwa()
samtools = tools.samtools.SamtoolsTool()
bwa.index(ref_indexed)
bwa_opts = []
if sensitive:
bwa_opts + "-k 12 -A 1 -B 1 -O 1 -E 1".split()
map_threshold = min_score_to_output or 30
bwa_opts + ["-T", str(map_threshold)]
aln_bam = util.file.mkstempfname('.bam')
bwa.mem(in_bam, ref_indexed, aln_bam, opts=bwa_opts)
# @haydenm says:
# For some reason (particularly when the --sensitive option is on), bwa
# doesn't listen to its '-T' flag and outputs alignments with score less
# than the '-T 30' threshold. So filter these:
aln_bam_filtered = util.file.mkstempfname('.filtered.bam')
samtools.view(["-b", "-h", "-q", str(map_threshold)], aln_bam, aln_bam_filtered)
os.unlink(aln_bam)
aln_bam_dupe_processed = util.file.mkstempfname('.filtered_dupe_processed.bam')
if excludeDuplicates:
opts = list(picardOptions)
dupe_removal_out_metrics = util.file.mkstempfname('.metrics')
tools.picard.MarkDuplicatesTool().execute(
[aln_bam_filtered], aln_bam_dupe_processed,
dupe_removal_out_metrics, picardOptions=opts,
JVMmemory=JVMmemory
)
else:
aln_bam_dupe_processed = aln_bam_filtered
samtools.sort(aln_bam_dupe_processed, bam_aligned)
os.unlink(aln_bam_filtered)
if excludeDuplicates:
os.unlink(aln_bam_dupe_processed)
samtools.index(bam_aligned)
# -- call plot function --
plot_coverage(
bam_aligned, out_plot_file, plot_format, plot_data_style, plot_style, plot_width, plot_height, plot_dpi, plot_title,
base_q_threshold, mapping_q_threshold, max_coverage_depth, read_length_threshold, excludeDuplicates, out_summary
)
# remove the output bam, unless it is needed
if out_bam is None:
os.unlink(bam_aligned)
# remove the files created by bwa index.
# The empty extension causes the original fasta file to be removed
for ext in [".amb", ".ann", ".bwt", ".bwa", ".pac", ".sa", ""]:
file_to_remove = ref_indexed + ext
if os.path.isfile(file_to_remove):
os.unlink(file_to_remove)
