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illumina.py
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illumina.py
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#!/usr/bin/env python3
"""
Utilities for demultiplexing Illumina data.
"""
__author__ = "dpark@broadinstitute.org"
__commands__ = []
import argparse
import logging
import os
import os.path
import re
import gc
import csv
import json
import sqlite3, itertools
import shutil
import subprocess
import tempfile
import xml.etree.ElementTree
from collections import defaultdict
import concurrent.futures
import arrow
import util.cmd
import util.file
import util.misc
import tools.picard
from util.illumina_indices import IlluminaIndexReference, IlluminaBarcodeHelper
log = logging.getLogger(__name__)
# =========================
# *** illumina_demux ***
# =========================
def parser_illumina_demux(parser=argparse.ArgumentParser()):
parser.add_argument('inDir', help='Illumina BCL directory (or tar.gz of BCL directory). This is the top-level run directory.')
parser.add_argument('lane', help='Lane number.', type=int)
parser.add_argument('outDir', help='Output directory for BAM files.')
parser.add_argument('--outMetrics',
help='Output ExtractIlluminaBarcodes metrics file. Default is to dump to a temp file.',
default=None)
parser.add_argument('--commonBarcodes',
help='''Write a TSV report of all barcode counts, in descending order.
Only applicable for read structures containing "B"''',
default=None)
parser.add_argument('--max_barcodes',
help='''Cap the commonBarcodes report length to this size (default: %(default)s)''',
default=10000, type=int)
parser.add_argument('--sampleSheet',
default=None,
help='''Override SampleSheet. Input tab or CSV file w/header and four named columns:
barcode_name, library_name, barcode_sequence_1, barcode_sequence_2.
Default is to look for a SampleSheet.csv in the inDir.''')
parser.add_argument('--runInfo',
default=None,
dest="runinfo",
help='''Override RunInfo. Input xml file.
Default is to look for a RunInfo.xml file in the inDir.''')
parser.add_argument('--flowcell', help='Override flowcell ID (default: read from RunInfo.xml).', default=None)
parser.add_argument('--read_structure',
help='Override read structure (default: read from RunInfo.xml).',
default=None)
parser.add_argument('--append_run_id',
help='If specified, output filenames will include the flowcell ID and lane number.',
action='store_true')
parser.add_argument('--out_meta_by_sample',
help='Output json metadata by sample',
default=None)
parser.add_argument('--out_meta_by_filename',
help='Output json metadata by bam file basename',
default=None)
parser.add_argument('--out_runinfo',
help='Output json metadata about the run',
default=None)
for opt in tools.picard.ExtractIlluminaBarcodesTool.option_list:
if opt not in ('read_structure', 'num_processors'):
parser.add_argument('--' + opt,
help='Picard ExtractIlluminaBarcodes ' + opt.upper() + ' (default: %(default)s)',
default=tools.picard.ExtractIlluminaBarcodesTool.defaults.get(opt))
for opt in tools.picard.IlluminaBasecallsToSamTool.option_list:
if opt == 'adapters_to_check':
parser.add_argument('--' + opt,
nargs='*',
help='Picard IlluminaBasecallsToSam ' + opt.upper() + ' (default: %(default)s)',
default=tools.picard.IlluminaBasecallsToSamTool.defaults.get(opt))
elif opt in ('read_structure', 'num_processors'):
pass
else:
parser.add_argument('--' + opt,
help='Picard IlluminaBasecallsToSam ' + opt.upper() + ' (default: %(default)s)',
default=tools.picard.IlluminaBasecallsToSamTool.defaults.get(opt))
parser.add_argument('--JVMmemory',
help='JVM virtual memory size (default: %(default)s)',
default=tools.picard.IlluminaBasecallsToSamTool.jvmMemDefault)
util.cmd.common_args(parser, (('threads', tools.picard.IlluminaBasecallsToSamTool.defaults['num_processors']), ('loglevel', None), ('version', None), ('tmp_dir', None)))
util.cmd.attach_main(parser, main_illumina_demux)
return parser
def main_illumina_demux(args):
''' Read Illumina runs & produce BAM files, demultiplexing to one bam per sample, or
for simplex runs, a single bam will be produced bearing the flowcell ID.
Wraps together Picard's ExtractBarcodes (for multiplexed samples) and IlluminaBasecallsToSam
while handling the various required input formats. Also can
read Illumina BCL directories, tar.gz BCL directories.
'''
# prepare
illumina = IlluminaDirectory(args.inDir)
illumina.load()
if args.runinfo:
runinfo = RunInfo(args.runinfo)
else:
runinfo = illumina.get_RunInfo()
if args.flowcell:
flowcell = args.flowcell
else:
flowcell = runinfo.get_flowcell()
if args.run_start_date:
run_date = args.run_start_date
else:
run_date = runinfo.get_rundate_american()
if args.read_structure:
read_structure = args.read_structure
else:
read_structure = runinfo.get_read_structure()
if args.append_run_id:
run_id = "{}.{}".format(flowcell, args.lane)
else:
run_id = None
if args.sampleSheet:
samples = SampleSheet(args.sampleSheet, only_lane=args.lane, append_run_id=run_id)
else:
samples = illumina.get_SampleSheet(only_lane=args.lane, append_run_id=run_id)
link_locs=False
# For HiSeq-4000/X runs, If Picard's CheckIlluminaDirectory is
# called with LINK_LOCS=true, symlinks with absolute paths
# may be created, pointing from tile-specific *.locs to the
# single s.locs file in the Intensities directory.
# These links may break if the run directory is moved.
# We should begin by removing broken links, if present,
# and call CheckIlluminaDirectory ourselves if a 's.locs'
# file is present, but only if the directory check fails
# since link_locs=true tries to create symlinks even if they
# (or the files) already exist
try:
tools.picard.CheckIlluminaDirectoryTool().execute(
illumina.get_BCLdir(),
args.lane,
read_structure,
link_locs=link_locs
)
except subprocess.CalledProcessError as e:
log.warning("CheckIlluminaDirectory failed for %s", illumina.get_BCLdir())
if os.path.exists(os.path.join(illumina.get_intensities_dir(), "s.locs")):
# recurse to remove broken links in directory
log.info("This run has an 's.locs' file; checking for and removing broken per-tile symlinks...")
broken_links = util.file.find_broken_symlinks(illumina.get_intensities_dir())
if len(broken_links):
for lpath in broken_links:
log.info("Removing broken symlink: %s", lpath)
os.unlink(lpath)
# call CheckIlluminaDirectory with LINK_LOCS=true
link_locs=True
log.info("Checking run directory with Picard...")
tools.picard.CheckIlluminaDirectoryTool().execute(
illumina.get_BCLdir(),
args.lane,
read_structure,
link_locs=link_locs
)
else:
log.error("CheckIlluminaDirectory failed for %s", illumina.get_BCLdir())
multiplexed_samples = True if 'B' in read_structure else False
if multiplexed_samples:
assert samples is not None, "This looks like a multiplexed run since 'B' is in the read_structure: a SampleSheet must be given."
else:
assert samples==None, "A SampleSheet may not be provided unless 'B' is present in the read_structure"
if args.commonBarcodes:
log.warning("--commonBarcodes was set but 'B' is not present in the read_structure; emitting an empty file.")
util.file.touch(args.commonBarcodes)
# B in read structure indicates barcoded multiplexed samples
if multiplexed_samples:
# Picard ExtractIlluminaBarcodes
extract_input = util.file.mkstempfname('.txt', prefix='.'.join(['barcodeData', flowcell, str(args.lane)]))
barcodes_tmpdir = tempfile.mkdtemp(prefix='extracted_barcodes-')
samples.make_barcodes_file(extract_input)
out_metrics = (args.outMetrics is None) and util.file.mkstempfname('.metrics.txt') or args.outMetrics
picardOpts = dict((opt, getattr(args, opt)) for opt in tools.picard.ExtractIlluminaBarcodesTool.option_list
if hasattr(args, opt) and getattr(args, opt) != None)
picardOpts['read_structure'] = read_structure
tools.picard.ExtractIlluminaBarcodesTool().execute(
illumina.get_BCLdir(),
args.lane,
extract_input,
barcodes_tmpdir,
out_metrics,
picardOptions=picardOpts,
JVMmemory=args.JVMmemory)
if args.commonBarcodes:
barcode_lengths = re.findall(r'(\d+)B',read_structure)
try:
barcode1_len=int(barcode_lengths[0])
except IndexError:
barcode1_len = 0
try:
barcode2_len=int(barcode_lengths[1])
except IndexError:
barcode2_len = 0
# this step can take > 2 hours on a large high-output flowcell
# so kick it to the background while we demux
#count_and_sort_barcodes(barcodes_tmpdir, args.commonBarcodes)
executor = concurrent.futures.ProcessPoolExecutor()
executor.submit(count_and_sort_barcodes, barcodes_tmpdir, args.commonBarcodes, barcode1_len, barcode2_len, truncateToLength=args.max_barcodes, threads=util.misc.sanitize_thread_count(args.threads))
# Picard IlluminaBasecallsToSam
basecalls_input = util.file.mkstempfname('.txt', prefix='.'.join(['library_params', flowcell, str(args.lane)]))
samples.make_params_file(args.outDir, basecalls_input)
picardOpts = dict((opt, getattr(args, opt)) for opt in tools.picard.IlluminaBasecallsToSamTool.option_list
if hasattr(args, opt) and getattr(args, opt) != None)
picardOpts['run_start_date'] = run_date
picardOpts['read_structure'] = read_structure
if args.threads:
picardOpts['num_processors'] = args.threads
if not picardOpts.get('sequencing_center') and runinfo:
picardOpts['sequencing_center'] = runinfo.get_machine()
if picardOpts.get('sequencing_center'):
picardOpts["sequencing_center"] = util.file.string_to_file_name(picardOpts["sequencing_center"])
if args.out_runinfo:
with open(args.out_runinfo, 'wt') as outf:
json.dump({
'sequencing_center':picardOpts['sequencing_center'],
'run_start_date':runinfo.get_rundate_iso(),
'read_structure':picardOpts['read_structure'],
'indexes':str(samples.indexes),
'run_id':runinfo.get_run_id(),
'lane':str(args.lane),
'flowcell':str(runinfo.get_flowcell()),
'lane_count':str(runinfo.get_lane_count()),
'surface_count':str(runinfo.get_surface_count()),
'swath_count':str(runinfo.get_swath_count()),
'tile_count':str(runinfo.get_tile_count()),
'total_tile_count':str(runinfo.tile_count()),
'sequencer_model':runinfo.get_machine_model(),
}, outf, indent=2)
# manually garbage collect to make sure we have as much RAM free as possible
gc.collect()
if multiplexed_samples:
tools.picard.IlluminaBasecallsToSamTool().execute(
illumina.get_BCLdir(),
barcodes_tmpdir,
flowcell,
args.lane,
basecalls_input,
picardOptions=picardOpts,
JVMmemory=args.JVMmemory)
# organize samplesheet metadata as json
sample_meta = list(samples.get_rows())
for row in sample_meta:
row['lane'] = str(args.lane)
if args.out_meta_by_sample:
with open(args.out_meta_by_sample, 'wt') as outf:
json.dump(dict((r['sample'],r) for r in sample_meta), outf, indent=2)
if args.out_meta_by_filename:
with open(args.out_meta_by_filename, 'wt') as outf:
json.dump(dict((r['run'],r) for r in sample_meta), outf, indent=2)
else:
tools.picard.IlluminaBasecallsToSamTool().execute_single_sample(
illumina.get_BCLdir(),
os.path.join(args.outDir,flowcell+".bam"),
flowcell,
args.lane,
flowcell,
picardOptions=picardOpts,
JVMmemory=args.JVMmemory)
# clean up
if multiplexed_samples:
if args.commonBarcodes:
log.info("waiting for commonBarcodes output to finish...")
executor.shutdown(wait=True)
os.unlink(extract_input)
os.unlink(basecalls_input)
shutil.rmtree(barcodes_tmpdir)
illumina.close()
log.info("illumina_demux complete")
return 0
__commands__.append(('illumina_demux', parser_illumina_demux))
# ==========================
# *** flowcell_metadata ***
# ==========================
def parser_flowcell_metadata(parser=argparse.ArgumentParser()):
parser.add_argument('outMetadataFile', help='path of file to which metadata will be written.')
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('--inDir', dest="in_dir", help='Illumina BCL directory (or tar.gz of BCL directory). This is the top-level run directory.', default=None)
group.add_argument('--runInfo',
default=None,
dest="run_info",
help='''RunInfo.xml file.''')
group.add_argument('--flowcellID', dest="flowcell_id", help='flowcell ID (default: read from RunInfo.xml).', default=None)
util.cmd.common_args(parser, (('threads', tools.picard.IlluminaBasecallsToSamTool.defaults['num_processors']), ('loglevel', None), ('version', None), ('tmp_dir', None)))
util.cmd.attach_main(parser, main_flowcell_metadata)
return parser
def main_flowcell_metadata(args):
''' Writes run metadata to a file
'''
if args.flowcell_id:
machine_matches = RunInfo.get_machines_for_flowcell_id(args.flowcell_id)
machine_match = None
if len(machine_matches)>1:
raise LookupError("Multiple sequencers found for flowcell ID: %s" % " ".join([m["machine"] for m in machine_matches]))
if len(machine_matches)==0:
raise LookupError("No sequencers found for flowcell ID '%s' " % args.flowcell_id)
machine_match = machine_matches[0]
if args.run_info:
runinfo = RunInfo(args.run_info)
machine_match = runinfo.infer_sequencer_model()
if args.in_dir:
illumina = IlluminaDirectory(args.in_dir)
illumina.load()
runinfo = illumina.get_RunInfo()
machine_match = runinfo.infer_sequencer_model()
with open(args.outMetadataFile,"w") as outf:
for k,v in machine_match.items():
if type(v)==str and len(v)>0 or type(v)!=str:
outline = "{k}\t{v}\n".format(k=k,v=v)
print(outline,end="")
outf.writelines([outline])
__commands__.append(('flowcell_metadata', parser_flowcell_metadata))
# ==========================
# *** lane_metrics ***
# ==========================
def parser_lane_metrics(parser=argparse.ArgumentParser()):
parser.add_argument('inDir', help='Illumina BCL directory (or tar.gz of BCL directory). This is the top-level run directory.')
parser.add_argument('outPrefix', help='''Prefix path to the *.illumina_lane_metrics and *.illumina_phasing_metrics files.''')
parser.add_argument('--read_structure',
help='Override read structure (default: read from RunInfo.xml).',
default=None)
parser.add_argument('--JVMmemory',
help='JVM virtual memory size (default: %(default)s)',
default=tools.picard.ExtractIlluminaBarcodesTool.jvmMemDefault)
util.cmd.common_args(parser, (('loglevel', None), ('version', None), ('tmp_dir', None)))
util.cmd.attach_main(parser, main_lane_metrics)
return parser
def main_lane_metrics(args):
'''
Write out lane metrics to a tsv file.
'''
# prepare
illumina = IlluminaDirectory(args.inDir)
illumina.load()
if args.read_structure:
read_structure = args.read_structure
else:
read_structure = illumina.get_RunInfo().get_read_structure()
# Picard CollectIlluminaLaneMetrics
output_dir = os.path.dirname(os.path.realpath(args.outPrefix))
output_prefix = os.path.basename(os.path.realpath(args.outPrefix))
picardOpts = dict((opt, getattr(args, opt)) for opt in tools.picard.CollectIlluminaLaneMetricsTool.option_list
if hasattr(args, opt) and getattr(args, opt) != None)
picardOpts['read_structure'] = read_structure
tools.picard.CollectIlluminaLaneMetricsTool().execute(
illumina.path,
output_dir,
output_prefix,
picardOptions=picardOpts,
JVMmemory=args.JVMmemory)
illumina.close()
return 0
__commands__.append(('lane_metrics', parser_lane_metrics))
# ==========================
# *** common_barcodes ***
# ==========================
def parser_common_barcodes(parser=argparse.ArgumentParser()):
parser.add_argument('inDir', help='Illumina BCL directory (or tar.gz of BCL directory). This is the top-level run directory.')
parser.add_argument('lane', help='Lane number.', type=int)
parser.add_argument('outSummary', help='''Path to the summary file (.tsv format). It includes several columns:
(barcode1, likely_index_name1, barcode2, likely_index_name2, count),
where likely index names are either the exact match index name for the barcode
sequence, or those Hamming distance of 1 away.''')
parser.add_argument('--truncateToLength',
help='If specified, only this number of barcodes will be returned. Useful if you only want the top N barcodes.',
type=int,
default=None)
parser.add_argument('--omitHeader',
help='If specified, a header will not be added to the outSummary tsv file.',
action='store_true')
parser.add_argument('--includeNoise',
help='If specified, barcodes with periods (".") will be included.',
action='store_true')
parser.add_argument('--outMetrics',
help='Output ExtractIlluminaBarcodes metrics file. Default is to dump to a temp file.',
default=None)
parser.add_argument('--sampleSheet',
default=None,
help='''Override SampleSheet. Input tab or CSV file w/header and four named columns:
barcode_name, library_name, barcode_sequence_1, barcode_sequence_2.
Default is to look for a SampleSheet.csv in the inDir.''')
parser.add_argument('--flowcell', help='Override flowcell ID (default: read from RunInfo.xml).', default=None)
parser.add_argument('--read_structure',
help='Override read structure (default: read from RunInfo.xml).',
default=None)
for opt in tools.picard.ExtractIlluminaBarcodesTool.option_list:
if opt not in ('read_structure', 'num_processors'):
parser.add_argument('--' + opt,
help='Picard ExtractIlluminaBarcodes ' + opt.upper() + ' (default: %(default)s)',
default=tools.picard.ExtractIlluminaBarcodesTool.defaults.get(opt))
parser.add_argument('--JVMmemory',
help='JVM virtual memory size (default: %(default)s)',
default=tools.picard.ExtractIlluminaBarcodesTool.jvmMemDefault)
util.cmd.common_args(parser, (('threads',None), ('loglevel', None), ('version', None), ('tmp_dir', None)))
util.cmd.attach_main(parser, main_common_barcodes)
return parser
def main_common_barcodes(args):
'''
Extract Illumina barcodes for a run and write a TSV report
of the barcode counts in descending order
'''
# prepare
illumina = IlluminaDirectory(args.inDir)
illumina.load()
if args.flowcell:
flowcell = args.flowcell
else:
flowcell = illumina.get_RunInfo().get_flowcell()
if args.read_structure:
read_structure = args.read_structure
else:
read_structure = illumina.get_RunInfo().get_read_structure()
if args.sampleSheet:
samples = SampleSheet(args.sampleSheet, only_lane=args.lane)
else:
samples = illumina.get_SampleSheet(only_lane=args.lane)
# Picard ExtractIlluminaBarcodes
barcode_file = util.file.mkstempfname('.txt', prefix='.'.join(['barcodeData', flowcell, str(args.lane)]))
barcodes_tmpdir = tempfile.mkdtemp(prefix='extracted_barcodes-')
samples.make_barcodes_file(barcode_file)
out_metrics = (args.outMetrics is None) and util.file.mkstempfname('.metrics.txt') or args.outMetrics
picardOpts = dict((opt, getattr(args, opt)) for opt in tools.picard.ExtractIlluminaBarcodesTool.option_list
if hasattr(args, opt) and getattr(args, opt) != None)
picardOpts['read_structure'] = read_structure
tools.picard.ExtractIlluminaBarcodesTool().execute(
illumina.get_BCLdir(),
args.lane,
barcode_file,
barcodes_tmpdir,
out_metrics,
picardOptions=picardOpts,
JVMmemory=args.JVMmemory)
barcode_lengths = re.findall(r'(\d+)B',read_structure)
try:
barcode1_len=int(barcode_lengths[0])
except IndexError:
barcode1_len = 0
try:
barcode2_len=int(barcode_lengths[1])
except IndexError:
barcode2_len = 0
count_and_sort_barcodes(barcodes_tmpdir, args.outSummary, barcode1_len, barcode2_len, args.truncateToLength, args.includeNoise, args.omitHeader, args.threads)
# clean up
os.unlink(barcode_file)
shutil.rmtree(barcodes_tmpdir)
illumina.close()
return 0
__commands__.append(('common_barcodes', parser_common_barcodes))
def count_and_sort_barcodes(barcodes_dir, outSummary, barcode1_len=8, barcode2_len=8, truncateToLength=None, includeNoise=False, omitHeader=False, threads=None):
# collect the barcode file paths for all tiles
tile_barcode_files = [os.path.join(barcodes_dir, filename) for filename in os.listdir(barcodes_dir)]
# count all of the barcodes present in the tile files
log.info("reading barcodes in all tile files")
with util.file.CountDB() as reduce_db:
barcodefile_tempfile_tuples = [(tile_barcode_file,util.file.mkstempfname('sqlite_.db')) for tile_barcode_file in tile_barcode_files]
# scatter tile-specific barcode files among workers to store barcode counts in SQLite
workers = util.misc.sanitize_thread_count(threads)
with concurrent.futures.ProcessPoolExecutor(max_workers=workers) as executor:
futures = [executor.submit(util.file.count_occurrences_in_tsv_sqlite_backed, tf, bf, include_noise=includeNoise) for bf,tf in barcodefile_tempfile_tuples]
for future in concurrent.futures.as_completed(futures):
tmp_db, barcode_file = future.result()
log.debug("done reading barcodes from %s; adding to total...",barcode_file)
# gather and reduce counts from separate SQLite databases into one
reduce_db.add_counts_from_other_db(tmp_db)
os.unlink(tmp_db)
illumina_reference = IlluminaIndexReference()
log.info("Number of barcodes seen %s",reduce_db.get_num_IDS())
# write the barcodes and their corresponding counts
with open(outSummary, 'w') as tsvfile:
log.info("sorting counts...")
log.info("writing output...")
writer = csv.writer(tsvfile, delimiter='\t')
# write the header unless the user has specified not to do so
if not omitHeader:
writer.writerow(("Barcode1", "Likely_Index_Names1", "Barcode2", "Likely_Index_Names2", "Count"))
for num_processed,row in enumerate(reduce_db.get_counts_descending()):
if truncateToLength and num_processed>truncateToLength:
break
barcode,count = row
writer.writerow((barcode[:barcode1_len], ",".join([x for x in illumina_reference.guess_index(barcode[:barcode1_len], distance=1)] or ["Unknown"]),
barcode[barcode1_len:len(barcode)], ",".join([x for x in illumina_reference.guess_index(barcode[barcode1_len:len(barcode)], distance=1)] or ["Unknown"]),
count))
if num_processed%50000==0:
log.debug("written %s barcode summaries to output file",num_processed)
log.info("done")
# ======================================
# *** guess_low-abundance_barcodes ***
# ======================================
def parser_guess_barcodes(parser=argparse.ArgumentParser()):
parser.add_argument('in_barcodes', help='The barcode counts file produced by common_barcodes.')
parser.add_argument('in_picard_metrics', help='The demultiplexing read metrics produced by Picard.')
parser.add_argument('out_summary_tsv', help='''Path to the summary file (.tsv format). It includes several columns:
(sample_name, expected_barcode_1, expected_barcode_2,
expected_barcode_1_name, expected_barcode_2_name,
expected_barcodes_read_count, guessed_barcode_1,
guessed_barcode_2, guessed_barcode_1_name,
guessed_barcode_2_name, guessed_barcodes_read_count,
match_type),
where the expected values are those used by Picard during demultiplexing
and the guessed values are based on the barcodes seen among the data.''')
group = parser.add_mutually_exclusive_group()
group.add_argument('--readcount_threshold',
default=None,
type=int,
help='''If specified, guess barcodes for samples with fewer than this many reads.''')
group.add_argument('--sample_names',
nargs='*',
help='If specified, only guess barcodes for these sample names.',
type=str,
default=None)
parser.add_argument('--outlier_threshold',
help='threshold of how far from unbalanced a sample must be to be considered an outlier.',
type=float,
default=0.775)
parser.add_argument('--expected_assigned_fraction',
help='The fraction of reads expected to be assigned. An exception is raised if fewer than this fraction are assigned.',
type=float,
default=0.7)
group2 = parser.add_mutually_exclusive_group()
group2.add_argument('--number_of_negative_controls',
help='If specified, the number of negative controls in the pool, for calculating expected number of reads in the rest of the pool.',
type=int)
group2.add_argument('--neg_control_prefixes',
nargs='+',
help='If specified, the sample name prefixes assumed for counting negative controls. Case-insensitive.',
type=str,
default=["neg", "water", "NTC", "H2O"])
parser.add_argument('--rows_limit',
default=1000,
type=int,
help='''The number of rows to use from the in_barcodes.''')
util.cmd.common_args(parser, (('loglevel', None), ('version', None), ('tmp_dir', None)))
util.cmd.attach_main(parser, main_guess_barcodes, split_args=True)
return parser
def main_guess_barcodes(in_barcodes,
in_picard_metrics,
out_summary_tsv,
sample_names,
outlier_threshold,
expected_assigned_fraction,
number_of_negative_controls,
readcount_threshold,
rows_limit,
neg_control_prefixes):
"""
Guess the barcode value for a sample name,
based on the following:
- a list is made of novel barcode pairs seen in the data, but not in the picard metrics
- for the sample in question, get the most abundant novel barcode pair where one of the
barcodes seen in the data matches one of the barcodes in the picard metrics (partial match)
- if there are no partial matches, get the most abundant novel barcode pair
Limitations:
- If multiple samples share a barcode with multiple novel barcodes, disentangling them
is difficult or impossible
The names of samples to guess are selected:
- explicitly by name, passed via argument, OR
- explicitly by read count threshold, OR
- automatically (if names or count threshold are omitted)
based on basic outlier detection of deviation from an assumed-balanced pool with
some number of negative controls
"""
bh = util.illumina_indices.IlluminaBarcodeHelper(in_barcodes, in_picard_metrics, rows_limit)
guessed_barcodes = bh.find_uncertain_barcodes(sample_names=sample_names,
outlier_threshold=outlier_threshold,
expected_assigned_fraction=expected_assigned_fraction,
number_of_negative_controls=number_of_negative_controls,
readcount_threshold=readcount_threshold,
neg_control_prefixes=neg_control_prefixes)
bh.write_guessed_barcodes(out_summary_tsv, guessed_barcodes)
__commands__.append(('guess_barcodes', parser_guess_barcodes))
# ============================
# *** IlluminaDirectory ***
# ============================
class IlluminaDirectory(object):
''' A class that handles Illumina data directories
'''
def __init__(self, uri):
self.uri = uri
self.path = None
self.tempDir = None
self.runinfo = None
self.samplesheet = None
def __enter__(self):
self.load()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.close()
return 0
def load(self):
if self.path is None:
if '://' in self.uri:
raise NotImplementedError('boto s3 download here uri -> tarball')
# tarball = util.file.mkstempfname('.tar.gz')
# # TODO: download here, uri -> tarball
# self._extract_tarball(tarball)
# os.unlink(tarball)
else:
if os.path.isdir(self.uri):
self.path = self.uri
else:
self._extract_tarball(self.uri)
self._fix_path()
def _fix_path(self):
assert self.path is not None
# this is not the correct root-level directory
# sometimes this points to one level up
while True:
if os.path.isdir(os.path.join(self.path, 'Data', 'Intensities', 'BaseCalls')):
# found it! self.path is correct
break
else:
subdirs = list(os.path.join(self.path, x) for x in os.listdir(self.path)
if os.path.isdir(os.path.join(self.path, x)))
if len(subdirs) == 1:
# follow the rabbit hole
self.path = subdirs[0]
else:
# don't know where to go now!
raise Exception('cannot find Data/Intensities/BaseCalls/ inside %s (%s)' % (self.uri, self.path))
def _extract_tarball(self, tarfile):
self.tempDir = tempfile.mkdtemp(prefix='IlluminaDirectory-')
self.path = self.tempDir
util.file.extract_tarball(tarfile, self.tempDir)
def close(self):
if self.tempDir:
shutil.rmtree(self.tempDir)
self.tempDir = None
def get_RunInfo(self):
if self.runinfo is None:
runinfo_file = os.path.join(self.path, 'RunInfo.xml')
util.file.check_paths(runinfo_file)
self.runinfo = RunInfo(runinfo_file)
return self.runinfo
def get_SampleSheet(self, only_lane=None, append_run_id=None):
if self.samplesheet is None:
samplesheet_file = os.path.join(self.path, 'SampleSheet.csv')
util.file.check_paths(samplesheet_file)
self.samplesheet = SampleSheet(samplesheet_file, only_lane=only_lane, append_run_id=append_run_id)
return self.samplesheet
def get_intensities_dir(self):
return os.path.join(self.path, 'Data', 'Intensities')
def get_BCLdir(self):
return os.path.join(self.get_intensities_dir(), 'BaseCalls')
# ==================
# *** RunInfo ***
# ==================
class RunInfo(object):
''' A class that reads the RunInfo.xml file emitted by Illumina
MiSeq and HiSeq machines.
'''
def __init__(self, xml_fname):
self.fname = xml_fname
self.root = xml.etree.ElementTree.parse(xml_fname).getroot()
def get_fname(self):
return self.fname
def get_run_id(self):
return self.root[0].attrib['Id']
def get_flowcell_raw(self):
return self.root[0].find('Flowcell').text
def get_flowcell(self):
fc = self.get_flowcell_raw()
# slice in the case where the ID has a prefix of zeros
if re.match(r"^0+-", fc):
if '-' in fc:
# miseq often adds a bunch of leading zeros and a dash in front
fc = "-".join(fc.split('-')[1:])
# >=5 to avoid an exception here: https://github.com/broadinstitute/picard/blob/2.17.6/src/main/java/picard/illumina/IlluminaBasecallsToSam.java#L510
# <= 15 to limit the bytes added to each bam record
assert len(fc) >= 5,"The flowcell ID must be five or more characters in length"
if len(fc) > 15:
log.warning("The provided flowcell ID is longer than 15 characters. Is that correct?")
return fc
def _get_rundate_obj(self):
"""
Access the text of the <Date> node in the RunInfo.xml file
and returns an arrow date object.
"""
rundate = self.root[0].find('Date').text
# possible formats found in RunInfo.xml:
# "170712" (YYMMDD)
# "20170712" (YYYYMMDD)
# "6/27/2018 4:59:20 PM" (M/D/YYYY h:mm:ss A)
# "2021-04-21T20:48:39Z" (YYYY-MM-DDTHH:mm:ssZ) [seen on NextSeq 2000]
datestring_formats = [
"YYMMDD",
"YYYYMMDD",
"M/D/YYYY h:mm:ss A",
"YYYY-MM-DDTHH:mm:ssZ"
]
for datestring_format in datestring_formats:
try:
date_parsed = arrow.get(rundate, datestring_format)
return date_parsed
except arrow.parser.ParserError:
pass
raise arrow.parser.ParserError("The date string seen in RunInfo.xml ('%s') did not match known Illumina formats: %s" % (rundate,datestring_formats) )
def get_rundate_american(self):
return str(self._get_rundate_obj().format("MM/DD/YYYY"))
def get_rundate_iso(self):
return str(self._get_rundate_obj().format("YYYY-MM-DD"))
def get_machine(self):
return self.root[0].find('Instrument').text
def get_read_structure(self):
reads = []
for x in self.root[0].find('Reads').findall('Read'):
order = int(x.attrib['Number'])
read = x.attrib['NumCycles'] + (x.attrib['IsIndexedRead'] == 'Y' and 'B' or 'T')
reads.append((order, read))
return ''.join([r for _, r in sorted(reads)])
def num_reads(self):
return sum(1 for x in self.root[0].find('Reads').findall('Read') if x.attrib['IsIndexedRead'] == 'N')
def get_lane_count(self):
layout = self.root[0].find('FlowcellLayout')
return int(layout.attrib['LaneCount'])
def get_surface_count(self):
layout = self.root[0].find('FlowcellLayout')
return int(layout.attrib['SurfaceCount'])
def get_swath_count(self):
layout = self.root[0].find('FlowcellLayout')
return int(layout.attrib['SwathCount'])
def get_tile_count(self):
layout = self.root[0].find('FlowcellLayout')
return int(layout.attrib['TileCount'])
def get_section_count(self):
layout = self.root[0].find('FlowcellLayout')
# not ever flowcell type has sections but some do (ex. NextSeq 550 does)
# return 1 in the event it's not listed in the RunInfo.xml file
return int(layout.attrib.get('SectionPerLane',1))
def tile_count(self):
lane_count = self.get_lane_count()
surface_count = self.get_surface_count()
swath_count = self.get_swath_count()
tile_count = self.get_tile_count()
section_count = self.get_section_count()
total_tile_count = lane_count*surface_count*swath_count*tile_count*section_count
return total_tile_count
def machine_model_from_tile_count(self):
"""
Return machine name and lane count based on tile count
Machine names aim to conform to the NCBI SRA controlled
vocabulary for Illumina sequencers available here:
https://www.ncbi.nlm.nih.gov/viewvc/v1/trunk/sra/doc/SRA_1-5/SRA.common.xsd?view=co&content-type=text%2Fplain
"""
tc = self.tile_count()
machine=None
if tc == 2:
log.info("Detected %s tiles, interpreting as MiSeq nano run.",tc)
machine = {"machine":"Illumina MiSeq","lane_count":1}
elif tc == 8:
log.info("Detected %s tiles, interpreting as MiSeq micro run.",tc)
machine = {"machine":"Illumina MiSeq","lane_count":1}
elif tc == 16:
log.info("Detected %s tiles, interpreting as iSeq run.",tc)
machine = {"machine":"Illumina iSeq 100","lane_count":1}
elif tc == 28:
log.info("Detected %s tiles, interpreting as MiSeq run.",tc)
machine = {"machine":"Illumina MiSeq","lane_count":1}
elif tc == 38:
log.info("Detected %s tiles, interpreting as MiSeq run.",tc)
machine = {"machine":"Illumina MiSeq","lane_count":1}
elif tc == 128:
log.info("Detected %s tiles, interpreting as HiSeq2k run.",tc)
machine = {"machine":"Illumina HiSeq 2500","lane_count":2}
elif tc == 132:
# NextSeq P2 kit can be used on either NextSeq 1000 or 2000
# so we cannot know which from the tile count alone
log.info("Detected %s tiles, interpreting as NextSeq 1000/2000 P2 run.",tc)
machine = {"machine":"NextSeq 1000/2000","lane_count":1}
elif tc == 264:
log.info("Detected %s tiles, interpreting as NextSeq 2000 P3 run.",tc)
machine = {"machine":"NextSeq 2000","lane_count":2}
elif tc == 288:
# NextSeq 550 is a NextSeq 500 that can also read arrays.
# Since we cannot tell them apart based on tile count, we call it the 550
log.info("Detected %s tiles, interpreting as NextSeq 550 (mid-output) run.",tc)
machine = {"machine":"NextSeq 550","lane_count":4}
elif tc == 624:
log.info("Detected %s tiles, interpreting as Illumina NovaSeq 6000 run.",tc)
machine = {"machine":"Illumina NovaSeq 6000","lane_count":2}
elif tc == 768:
# HiSeq 2000 and 2500 have the same number of tiles
# Defaulting to the newer HiSeq 2500
log.info("Detected %s tiles, interpreting as HiSeq2500 run.",tc)
machine = {"machine":"Illumina HiSeq 2500","lane_count":8}
elif tc == 864:
# NextSeq 550 is a NextSeq 500 that can also read arrays.
# Since we cannot tell them apart based on tile count, we call it the 550
log.info("Detected %s tiles, interpreting as NextSeq 550 (high-output) run.",tc)
machine = {"machine":"NextSeq 550","lane_count":4}
elif tc == 896:
log.info("Detected %s tiles, interpreting as HiSeq4k run.",tc)
machine = {"machine":"Illumina HiSeq 4000","lane_count":8}
elif tc == 1408:
log.info("Detected %s tiles, interpreting as Illumina NovaSeq 6000 run.",tc)
machine = {"machine":"Illumina NovaSeq 6000","lane_count":2}
elif tc == 3744:
log.info("Detected %s tiles, interpreting as Illumina NovaSeq 6000 run.",tc)
machine = {"machine":"Illumina NovaSeq 6000","lane_count":4}
elif tc > 3744:
log.info("Tile count: %s tiles (unknown instrument type).",tc)
return machine
def get_flowcell_chemistry(self):
guessed_sequencer = self.infer_sequencer_model()
return guessed_sequencer["chemistry"]
def get_flowcell_lane_count(self):
guessed_sequencer = self.infer_sequencer_model()
try:
return self.get_lane_count()
except Exception as e:
return guessed_sequencer.get("lane_count",None)
def get_machine_model(self):
guessed_sequencer = self.infer_sequencer_model()
return guessed_sequencer["machine"]
@classmethod
def get_machines_for_flowcell_id(cls, fcid):
sequencer_by_fcid = []
for key in cls.flowcell_to_machine_model_and_chemistry:
if re.search(key,fcid):
sequencer_by_fcid.append(cls.flowcell_to_machine_model_and_chemistry[key])
return sequencer_by_fcid
def infer_sequencer_model(self):
fcid = self.get_flowcell_raw()
sequencer_by_tile_count = self.machine_model_from_tile_count()
sequencers_by_fcid = self.get_machines_for_flowcell_id(fcid)
if len(sequencers_by_fcid)>1:
raise LookupError("Multiple sequencers possible: %s",fcid)
print("self.tile_count()",self.tile_count())
# always return sequencer model based on flowcell ID, if we can
if len(sequencers_by_fcid)>0:
if sequencer_by_tile_count is not None and sequencers_by_fcid[0]["machine"]!=sequencer_by_tile_count["machine"]:
log.warning("Sequencer type inferred from flowcell ID: %s does not match sequencer inferred from tile count: %s; is this a new machine type?" % (sequencers_by_fcid[0]["machine"], sequencer_by_tile_count["machine"]))
return sequencers_by_fcid[0]
# otherwise return based on tile count if we can
elif sequencer_by_tile_count is not None:
log.warning("Sequencer type unknown flowcell ID: %s, yet sequencer type was inferred for tile count: %s; is this a new flowcell ID pattern?" % (fcid, self.tile_count()))
return sequencer_by_tile_count
# otherwise we do not know
else:
log.warning("Tile count: %s and flowcell ID: %s are both novel; is this a new machine type?" % (self.tile_count(), fcid))
return {"machine":"UNKNOWN","lane_count":self.get_lane_count()}
# Machine names aim to conform to the NCBI SRA controlled
# vocabulary for Illumina sequencers available here:
# https://www.ncbi.nlm.nih.gov/viewvc/v1/trunk/sra/doc/SRA_1-5/SRA.common.xsd?view=co&content-type=text%2Fplain
flowcell_to_machine_model_and_chemistry = {
r'[A-Z,0-9]{5}AAXX':{
"machine": "Illumina Genome Analyzer IIx",
"chemistry": "All",
"lane_count": 8,
"note": ""
},
r'[A-Z,0-9]{5}ABXX':{
"machine": "Illumina HiSeq 2000",
"chemistry": "V2 Chemistry",
"lane_count": 8,
"note": ""
},
r'[A-Z,0-9]{5}ACXX':{
"machine": "Illumina HiSeq 2000",
"chemistry": "V3 Chemistry",
"lane_count": 8,