def test_aniblastall_concordance(self):
"""ANIblastall results concordant with JSpecies."""
# Perform ANIblastall on the input directory contents
outdir = os.path.join(self.outdir, "blastall")
os.makedirs(outdir, exist_ok=True)
fragfiles, fraglengths = anib.fragment_fasta_files(
self.infiles, outdir, self.fragsize
)
jobgraph = anib.make_job_graph(
self.infiles, fragfiles, anib.make_blastcmd_builder("ANIblastall", outdir)
)
assert_equal(0, run_mp.run_dependency_graph(jobgraph))
results = anib.process_blast(
outdir, self.orglengths, fraglengths, mode="ANIblastall"
)
result_pid = results.percentage_identity
result_pid.to_csv(os.path.join(self.outdir, "pyani_aniblastall.tab"), sep="\t")
# Compare JSpecies output to results
result_pid = result_pid.sort_index(axis=0).sort_index(axis=1) * 100.0
diffmat = result_pid.values - self.target["ANIb"].values
aniblastall_diff = pd.DataFrame(
diffmat, index=result_pid.index, columns=result_pid.columns
)
aniblastall_diff.to_csv(
os.path.join(self.outdir, "pyani_aniblastall_diff.tab"), sep="\t"
)
assert_less(aniblastall_diff.abs().values.max(), self.tolerance["ANIblastall"])
def test_aniblastall_concordance(self):
"""Check ANIblastall results are concordant with JSpecies."""
# Perform ANIblastall on the input directory contents
outdir = self.outdir / "blastall"
outdir.mkdir(exist_ok=True)
fragfiles, fraglengths = anib.fragment_fasta_files(
self.infiles, outdir, self.fragsize)
jobgraph = anib.make_job_graph(
self.infiles, fragfiles,
anib.make_blastcmd_builder("ANIblastall", outdir))
self.assertEqual(0, run_mp.run_dependency_graph(jobgraph))
results = anib.process_blast(outdir,
self.orglengths,
fraglengths,
mode="ANIblastall")
result_pid = results.percentage_identity
result_pid.to_csv(self.outdir / "pyani_aniblastall.tab", sep="\t")
# Compare JSpecies output to results
result_pid = result_pid.sort_index(axis=0).sort_index(axis=1) * 100.0
diffmat = result_pid.values - self.target["ANIb"].values
aniblastall_diff = pd.DataFrame(diffmat,
index=result_pid.index,
columns=result_pid.columns)
aniblastall_diff.to_csv(self.outdir / "pyani_aniblastall_diff.tab",
sep="\t")
self.assertLess(aniblastall_diff.abs().values.max(),
self.tolerance["ANIblastall"])
def test_aniblastall_concordance(
paths_concordance_fna,
path_concordance_jspecies,
tolerance_anib_hi,
fragment_length,
tmp_path,
):
"""Check ANIblastall results are concordant with JSpecies."""
# Get lengths of input genomes
orglengths = pyani_files.get_sequence_lengths(paths_concordance_fna)
# Perform ANIblastall on the input directory contents
fragfiles, fraglengths = anib.fragment_fasta_files(
paths_concordance_fna, tmp_path, fragment_length
)
jobgraph = anib.make_job_graph(
paths_concordance_fna,
fragfiles,
anib.make_blastcmd_builder("ANIblastall", tmp_path),
)
assert 0 == run_mp.run_dependency_graph(jobgraph) # Jobs must run correctly
# Process BLAST output
result_pid = anib.process_blast(
tmp_path, orglengths, fraglengths, mode="ANIblastall"
).percentage_identity
# Compare JSpecies output to results
result_pid = (result_pid.sort_index(axis=0).sort_index(axis=1) * 100.0).values
tgt_pid = parse_jspecies(path_concordance_jspecies)["ANIb"].values
assert result_pid - tgt_pid == pytest.approx(0, abs=tolerance_anib_hi)
def test_aniblastall_concordance():
"""Test concordance of ANIblastall method with JSpecies output."""
# Make/check output directory
mode = "ANIblastall"
outdirname = delete_and_remake_outdir(mode)
# Get dataframes of JSpecies output
aniblastall_jspecies = parse_table(JSPECIES_OUTFILE, 'ANIb')
# Identify our input files, and the total lengths of each organism seq
infiles = pyani_files.get_fasta_files(INDIRNAME)
org_lengths = pyani_files.get_sequence_lengths(infiles)
# Test ANIblastall concordance:
# Make fragments
fragfiles, fraglengths = anib.fragment_fasta_files(infiles, outdirname,
pyani_config.FRAGSIZE)
# Build jobgraph
jobgraph = anib.make_job_graph(
infiles, fragfiles,
anib.make_blastcmd_builder("ANIblastall", outdirname))
print("\nJobgraph:\n", jobgraph)
print("\nJob 0:\n", jobgraph[0].script)
# Run jobgraph with multiprocessing
run_dependency_graph(jobgraph)
print("Ran multiprocessing jobs")
# Process BLAST; the pid data is in anib_data[1]
aniblastall_data = anib.process_blast(outdirname,
org_lengths,
fraglengths,
mode="ANIblastall")
aniblastall_pid = \
aniblastall_data.percentage_identity.sort_index(axis=0).\
sort_index(axis=1) * 100.
index, columns = aniblastall_pid.index, aniblastall_pid.columns
diffmat = aniblastall_pid.as_matrix() - aniblastall_jspecies.as_matrix()
aniblastall_diff = pd.DataFrame(diffmat, index=index, columns=columns)
# Write dataframes to file, for reference
aniblastall_pid.to_csv(os.path.join(outdirname, 'ANIblastall_pid.tab'),
sep='\t')
aniblastall_jspecies.to_csv(os.path.join(outdirname,
'ANIblastall_jspecies.tab'),
sep='\t')
aniblastall_diff.to_csv(os.path.join(outdirname, 'ANIblastall_diff.tab'),
sep='\t')
print("ANIblastall concordance test output placed in %s" % outdirname)
print("ANIblastall PID:\n", aniblastall_pid)
print("ANIblastall JSpecies:\n", aniblastall_jspecies)
print("ANIblastall diff:\n", aniblastall_diff)
# We'd like the absolute difference reported to be < ANIBLASTALL_THRESHOLD
max_diff = aniblastall_diff.abs().values.max()
print("Maximum difference for ANIblastall: %e" % max_diff)
assert_less(max_diff, ANIB_THRESHOLD)
def test_anib_concordance():
"""Test concordance of ANIb method with JSpecies output.
This may take some time. Please be patient.
"""
# Make/check output directory
mode = "ANIb"
outdirname = make_outdir(mode)
# Get dataframes of JSpecies output
anib_jspecies = parse_table(JSPECIES_OUTFILE, 'ANIb')
# Identify our input files, and the total lengths of each organism seq
infiles = pyani_files.get_fasta_files(INDIRNAME)
org_lengths = pyani_files.get_sequence_lengths(infiles)
# Test ANIb concordance:
# Make fragments
fragfiles, fraglengths = anib.fragment_FASTA_files(infiles, outdirname,
pyani_config.FRAGSIZE)
# Build databases
cmdlist = anib.generate_blastdb_commands(infiles, outdirname,
pyani_config.MAKEBLASTDB_DEFAULT,
mode="ANIb")
multiprocessing_run(cmdlist)
# Run pairwise BLASTN
cmdlist = anib.generate_blastn_commands(fragfiles, outdirname,
pyani_config.BLASTN_DEFAULT,
mode="ANIb")
multiprocessing_run(cmdlist, verbose=False)
# Process BLAST; the pid data is in anib_data[1]
anib_data = anib.process_blast(outdirname, org_lengths, fraglengths,
mode="ANIb")
anib_pid = anib_data[1].sort(axis=0).sort(axis=1) * 100.
index, columns = anib_pid.index, anib_pid.columns
diffmat = anib_pid.as_matrix() - anib_jspecies.as_matrix()
anib_diff = pd.DataFrame(diffmat, index=index, columns=columns)
# Write dataframes to file, for reference
anib_pid.to_csv(os.path.join(outdirname,
'ANIb_pid.tab'),
sep='\t')
anib_jspecies.to_csv(os.path.join(outdirname,
'ANIb_jspecies.tab'),
sep='\t')
anib_diff.to_csv(os.path.join(outdirname,
'ANIb_diff.tab'),
sep='\t')
print "ANIb concordance test output placed in %s" % outdirname
print anib_pid, anib_jspecies, anib_diff
# We'd like the absolute difference reported to be < ANIB_THRESHOLD
max_diff = anib_diff.abs().values.max()
print "Maximum difference for ANIb: %e" % max_diff
assert_less(max_diff, ANIB_THRESHOLD)
def test_anib_concordance():
"""Test concordance of ANIb method with JSpecies output.
This may take some time. Please be patient.
"""
# Make/check output directory
mode = "ANIb"
outdirname = make_outdir(mode)
# Get dataframes of JSpecies output
anib_jspecies = parse_table(JSPECIES_OUTFILE, 'ANIb')
# Identify our input files, and the total lengths of each organism seq
infiles = pyani_files.get_fasta_files(INDIRNAME)
org_lengths = pyani_files.get_sequence_lengths(infiles)
# Test ANIb concordance:
# Make fragments
fragfiles, fraglengths = anib.fragment_FASTA_files(infiles, outdirname,
pyani_config.FRAGSIZE)
# Build databases
cmdlist = anib.generate_blastdb_commands(infiles,
outdirname,
pyani_config.MAKEBLASTDB_DEFAULT,
mode="ANIb")
multiprocessing_run(cmdlist)
# Run pairwise BLASTN
cmdlist = anib.generate_blastn_commands(fragfiles,
outdirname,
pyani_config.BLASTN_DEFAULT,
mode="ANIb")
multiprocessing_run(cmdlist, verbose=False)
# Process BLAST; the pid data is in anib_data[1]
anib_data = anib.process_blast(outdirname,
org_lengths,
fraglengths,
mode="ANIb")
anib_pid = anib_data[1].sort(axis=0).sort(axis=1) * 100.
index, columns = anib_pid.index, anib_pid.columns
diffmat = anib_pid.as_matrix() - anib_jspecies.as_matrix()
anib_diff = pd.DataFrame(diffmat, index=index, columns=columns)
# Write dataframes to file, for reference
anib_pid.to_csv(os.path.join(outdirname, 'ANIb_pid.tab'), sep='\t')
anib_jspecies.to_csv(os.path.join(outdirname, 'ANIb_jspecies.tab'),
sep='\t')
anib_diff.to_csv(os.path.join(outdirname, 'ANIb_diff.tab'), sep='\t')
print "ANIb concordance test output placed in %s" % outdirname
print anib_pid, anib_jspecies, anib_diff
# We'd like the absolute difference reported to be < ANIB_THRESHOLD
max_diff = anib_diff.abs().values.max()
print "Maximum difference for ANIb: %e" % max_diff
assert_less(max_diff, ANIB_THRESHOLD)
def unified_anib(indirname,User_ID):
# Build BLAST databases and run pairwise BLASTN
# Fraglengths does not get reused with BLASTN
os.mkdir(indirname+'{0}_out/'.format(User_ID))
os.system("chmod 777 {0}".format(indirname+'{0}_out'.format(User_ID)))
logging.basicConfig(level=logging.DEBUG, filename="/home/linproject/Workspace/LIN_log/logfile_{0}".format(User_ID),
filemode="a+", format="%(asctime)-15s %(levelname)-8s %(message)s")
infiles = pyani_files.get_fasta_files(indirname)
org_lengths = pyani_files.get_sequence_lengths(infiles)
fragsize = pyani_config.FRAGSIZE
filestems = pyani_config.ANIB_FILESTEMS
filenames = os.listdir(indirname)
for fname in filenames:
if ' ' in os.path.abspath(fname):
logging.error("File or directory '%s' contains whitespace" % fname)
logging.error("This will cause issues with MUMmer and BLAST")
logging.error("(exiting)")
sys.exit(1)
fragfiles, fraglengths = anib.fragment_FASTA_files(infiles, indirname+'{0}_out/'.format(User_ID), fragsize)
# Export fragment lengths as JSON, in case we re-run BLASTALL with
# --skip_blastn
with open(os.path.join(indirname+'{0}_out/'.format(User_ID), 'fraglengths.json'), 'w') as outfile:
json.dump(fraglengths, outfile)
# Which executables are we using?
format_exe = pyani_config.FORMATDB_DEFAULT
blast_exe = pyani_config.BLASTALL_DEFAULT
# Run BLAST database-building and executables from a jobgraph
logging.info("Creating job dependency graph")
jobgraph = anib.make_job_graph(infiles, fragfiles, indirname+'{0}_out/'.format(User_ID), format_exe, blast_exe, 'ANIblastall')
logging.info("Running jobs with multiprocessing")
logging.info("Running job dependency graph")
cumval = run_mp.run_dependency_graph(jobgraph, verbose=False,
logger=logging)
if 0 < cumval:
logging.warning("At least one BLAST run failed. " +
"%s may fail." % 'ANIblastall')
else:
logging.info("All multiprocessing jobs complete.")
# Process pairwise BLASTN output
logging.info("Processing pairwise %s BLAST output." % 'ANIblastall')
try:
data = anib.process_blast(indirname+'{0}_out/'.format(User_ID), org_lengths,
fraglengths=fraglengths, mode='ANIblastall')
except ZeroDivisionError:
logging.error("One or more BLAST output files has a problem.")
if 0 < cumval:
logging.error("This is possibly due to BLASTN run failure, " +
"please investigate")
else:
logging.error("This is possibly due to ara BLASTN comparison " +
"being too distant for use.")
logging.error(last_exception())
return data[1]
def test_blastdir_processing(self):
"""parses directory of .blast_tab output."""
orglengths = pyani_files.get_sequence_lengths(self.infnames)
fraglengths = anib.get_fraglength_dict(self.fragfiles)
# ANIb
result = anib.process_blast(self.anibdir,
orglengths,
fraglengths,
mode="ANIb")
assert_frame_equal(
result.percentage_identity.sort_index(1).sort_index(),
self.anibtgt.sort_index(1).sort_index())
# ANIblastall
result = anib.process_blast(self.aniblastalldir,
orglengths,
fraglengths,
mode="ANIblastall")
assert_frame_equal(
result.percentage_identity.sort_index(1).sort_index(),
self.aniblastalltgt.sort_index(1).sort_index())
def test_parse_blastdir(anib_output_dir):
"""Parse directory of BLAST+ output."""
orglengths = pyani_files.get_sequence_lengths(anib_output_dir.infiles)
fraglengths = anib.get_fraglength_dict(anib_output_dir.fragfiles)
result = anib.process_blast(anib_output_dir.blastdir,
orglengths,
fraglengths,
mode="ANIb")
assert_frame_equal(
result.percentage_identity.sort_index(1).sort_index(),
anib_output_dir.blastresult.sort_index(1).sort_index(),
)
def test_anib_concordance(self):
"""ANIb results concordant with JSpecies.
We expect ANIb results to be quite different, as the BLASTN
algorithm changed substantially between BLAST and BLAST+
"""
# Perform ANIb on the input directory contents
outdir = os.path.join(self.outdir, "blastn")
os.makedirs(outdir, exist_ok=True)
fragfiles, fraglengths = anib.fragment_fasta_files(
self.infiles, outdir, self.fragsize)
jobgraph = anib.make_job_graph(
self.infiles, fragfiles,
anib.make_blastcmd_builder("ANIb", outdir))
assert_equal(0, run_mp.run_dependency_graph(jobgraph))
results = anib.process_blast(outdir,
self.orglengths,
fraglengths,
mode="ANIb")
result_pid = results.percentage_identity
result_pid.to_csv(os.path.join(self.outdir, "pyani_anib.tab"),
sep="\t")
# Compare JSpecies output to results. We do this in two blocks,
# masked according to whether the expected result is greater than
# 90% identity, or less than that threshold.
# The complete difference matrix is written to output, though
result_pid = result_pid.sort_index(axis=0).sort_index(axis=1) * 100.0
lo_result = result_pid.mask(result_pid >= 90).fillna(0)
hi_result = result_pid.mask(result_pid < 90).fillna(0)
lo_target = self.target["ANIb"].mask(
self.target["ANIb"] >= 90).fillna(0)
hi_target = self.target["ANIb"].mask(
self.target["ANIb"] < 90).fillna(0)
lo_diffmat = lo_result.as_matrix() - lo_target.as_matrix()
hi_diffmat = hi_result.as_matrix() - hi_target.as_matrix()
diffmat = result_pid.as_matrix() - self.target["ANIb"].as_matrix()
lo_diff = pd.DataFrame(lo_diffmat,
index=result_pid.index,
columns=result_pid.columns)
hi_diff = pd.DataFrame(hi_diffmat,
index=result_pid.index,
columns=result_pid.columns)
anib_diff = pd.DataFrame(diffmat,
index=result_pid.index,
columns=result_pid.columns)
anib_diff.to_csv(os.path.join(self.outdir, "pyani_anib_diff.tab"),
sep="\t")
assert_less(lo_diff.abs().values.max(), self.tolerance["ANIb_lo"])
assert_less(hi_diff.abs().values.max(), self.tolerance["ANIb_hi"])
def test_anib_concordance(
paths_concordance_fna,
path_concordance_jspecies,
tolerance_anib_hi,
tolerance_anib_lo,
threshold_anib_lo_hi,
fragment_length,
tmp_path,
):
"""Check ANIb results are concordant with JSpecies.
We expect ANIb results to be quite different, as the BLASTN
algorithm changed substantially between BLAST and BLAST+ (the
megaBLAST algorithm is now the default for BLASTN)
"""
# Get lengths of input genomes
orglengths = pyani_files.get_sequence_lengths(paths_concordance_fna)
# Build and run BLAST jobs
fragfiles, fraglengths = anib.fragment_fasta_files(
paths_concordance_fna, tmp_path, fragment_length
)
jobgraph = anib.make_job_graph(
paths_concordance_fna, fragfiles, anib.make_blastcmd_builder("ANIb", tmp_path)
)
assert 0 == run_mp.run_dependency_graph(jobgraph) # Jobs must run correctly
# Process BLAST output
result_pid = anib.process_blast(
tmp_path, orglengths, fraglengths, mode="ANIb"
).percentage_identity
# Compare JSpecies output to results. We do this in two blocks,
# masked according to whether the expected result is greater than
# a threshold separating "low" from "high" identity comparisons.
result_pid = result_pid.sort_index(axis=0).sort_index(axis=1) * 100.0
lo_result = result_pid.mask(result_pid >= threshold_anib_lo_hi).fillna(0).values
hi_result = result_pid.mask(result_pid < threshold_anib_lo_hi).fillna(0).values
tgt_pid = parse_jspecies(path_concordance_jspecies)["ANIb"]
lo_target = tgt_pid.mask(tgt_pid >= threshold_anib_lo_hi).fillna(0).values
hi_target = tgt_pid.mask(tgt_pid < threshold_anib_lo_hi).fillna(0).values
assert (lo_result - lo_target, hi_result - hi_target) == (
pytest.approx(0, abs=tolerance_anib_lo),
pytest.approx(0, abs=tolerance_anib_hi),
)
def test_anib_concordance(self):
"""ANIb results concordant with JSpecies.
We expect ANIb results to be quite different, as the BLASTN
algorithm changed substantially between BLAST and BLAST+
"""
# Perform ANIb on the input directory contents
outdir = os.path.join(self.outdir, "blastn")
os.makedirs(outdir, exist_ok=True)
fragfiles, fraglengths = anib.fragment_fasta_files(
self.infiles, outdir, self.fragsize
)
jobgraph = anib.make_job_graph(
self.infiles, fragfiles, anib.make_blastcmd_builder("ANIb", outdir)
)
assert_equal(0, run_mp.run_dependency_graph(jobgraph))
results = anib.process_blast(outdir, self.orglengths, fraglengths, mode="ANIb")
result_pid = results.percentage_identity
result_pid.to_csv(os.path.join(self.outdir, "pyani_anib.tab"), sep="\t")
# Compare JSpecies output to results. We do this in two blocks,
# masked according to whether the expected result is greater than
# 90% identity, or less than that threshold.
# The complete difference matrix is written to output, though
result_pid = result_pid.sort_index(axis=0).sort_index(axis=1) * 100.0
lo_result = result_pid.mask(result_pid >= 90).fillna(0)
hi_result = result_pid.mask(result_pid < 90).fillna(0)
lo_target = self.target["ANIb"].mask(self.target["ANIb"] >= 90).fillna(0)
hi_target = self.target["ANIb"].mask(self.target["ANIb"] < 90).fillna(0)
lo_diffmat = lo_result.values - lo_target.values
hi_diffmat = hi_result.values - hi_target.values
diffmat = result_pid.values - self.target["ANIb"].values
lo_diff = pd.DataFrame(
lo_diffmat, index=result_pid.index, columns=result_pid.columns
)
hi_diff = pd.DataFrame(
hi_diffmat, index=result_pid.index, columns=result_pid.columns
)
anib_diff = pd.DataFrame(
diffmat, index=result_pid.index, columns=result_pid.columns
)
anib_diff.to_csv(os.path.join(self.outdir, "pyani_anib_diff.tab"), sep="\t")
assert_less(lo_diff.abs().values.max(), self.tolerance["ANIb_lo"])
assert_less(hi_diff.abs().values.max(), self.tolerance["ANIb_hi"])
def unified_anib(infiles, org_lengths):
"""Calculate ANIb for files in input directory.
- infiles - paths to each input file
- org_lengths - dictionary of input sequence lengths, keyed by sequence
Calculates ANI by the ANIb method, as described in Goris et al. (2007)
Int J Syst Evol Micr 57: 81-91. doi:10.1099/ijs.0.64483-0. There are
some minor differences depending on whether BLAST+ or legacy BLAST
(BLASTALL) methods are used.
All FASTA format files (selected by suffix) in the input directory are
used to construct BLAST databases, placed in the output directory.
Each file's contents are also split into sequence fragments of length
options.fragsize, and the multiple FASTA file that results written to
the output directory. These are BLASTNed, pairwise, against the
databases.
The BLAST output is interrogated for all fragment matches that cover
at least 70% of the query sequence, with at least 30% nucleotide
identity over the full length of the query sequence. This is an odd
choice and doesn't correspond to the twilight zone limit as implied by
Goris et al. We persist with their definition, however. Only these
qualifying matches contribute to the total aligned length, and total
aligned sequence identity used to calculate ANI.
The results are processed to give matrices of aligned sequence length
(aln_lengths.tab), similarity error counts (sim_errors.tab), ANIs
(perc_ids.tab), and minimum aligned percentage (perc_aln.tab) of
each genome, for each pairwise comparison. These are written to the
output directory in plain text tab-separated format.
"""
logger.info("Running %s", args.method)
blastdir = os.path.join(args.outdirname, ALIGNDIR[args.method])
logger.info("Writing BLAST output to %s", blastdir)
# Build BLAST databases and run pairwise BLASTN
if not args.skip_blastn:
# Make sequence fragments
logger.info("Fragmenting input files, and writing to %s",
args.outdirname)
# Fraglengths does not get reused with BLASTN
fragfiles, fraglengths = anib.fragment_fasta_files(
infiles, blastdir, args.fragsize)
# Export fragment lengths as JSON, in case we re-run with --skip_blastn
with open(os.path.join(blastdir, 'fraglengths.json'), 'w') as outfile:
json.dump(fraglengths, outfile)
# Which executables are we using?
#if args.method == "ANIblastall":
# format_exe = args.formatdb_exe
# blast_exe = args.blastall_exe
#else:
# format_exe = args.makeblastdb_exe
# blast_exe = args.blastn_exe
# Run BLAST database-building and executables from a jobgraph
logger.info("Creating job dependency graph")
jobgraph = anib.make_job_graph(
infiles, fragfiles,
anib.make_blastcmd_builder(args.method, blastdir))
#jobgraph = anib.make_job_graph(infiles, fragfiles, blastdir,
# format_exe, blast_exe, args.method,
# jobprefix=args.jobprefix)
if args.scheduler == 'multiprocessing':
logger.info("Running jobs with multiprocessing")
logger.info("Running job dependency graph")
cumval = run_mp.run_dependency_graph(jobgraph, logger=logger)
if 0 < cumval:
logger.warning(
"At least one BLAST run failed. " + "%s may fail.",
args.method)
else:
logger.info("All multiprocessing jobs complete.")
else:
run_sge.run_dependency_graph(jobgraph, logger=logger)
logger.info("Running jobs with SGE")
else:
# Import fragment lengths from JSON
if args.method == "ANIblastall":
with open(os.path.join(blastdir, 'fraglengths.json'),
'rU') as infile:
fraglengths = json.load(infile)
else:
fraglengths = None
logger.warning("Skipping BLASTN runs (as instructed)!")
# Process pairwise BLASTN output
logger.info("Processing pairwise %s BLAST output.", args.method)
try:
data = anib.process_blast(blastdir,
org_lengths,
fraglengths=fraglengths,
mode=args.method)
except ZeroDivisionError:
logger.error("One or more BLAST output files has a problem.")
if not args.skip_blastn:
if 0 < cumval:
logger.error("This is possibly due to BLASTN run failure, " +
"please investigate")
else:
logger.error("This is possibly due to a BLASTN comparison " +
"being too distant for use.")
logger.error(last_exception())
if not args.nocompress:
logger.info("Compressing/deleting %s", blastdir)
compress_delete_outdir(blastdir)
# Return processed BLAST data
return data
def unified_anib(args: Namespace, infiles: List[Path],
org_lengths: Dict[str, int]) -> pyani_tools.ANIResults:
"""Calculate ANIb for files in input directory.
:param args: Namespace of command-line options
:param logger: logging object
:param infiles: iterable of paths to each input file
:param org_lengths: dict of input sequence lengths
keyed by sequence name
Calculates ANI by the ANIb method, as described in Goris et al. (2007)
Int J Syst Evol Micr 57: 81-91. doi:10.1099/ijs.0.64483-0. There are
some minor differences depending on whether BLAST+ or legacy BLAST
(BLASTALL) methods are used.
All FASTA format files (selected by suffix) in the input directory are
used to construct BLAST databases, placed in the output directory.
Each file's contents are also split into sequence fragments of length
options.fragsize, and the multiple FASTA file that results written to
the output directory. These are BLASTNed, pairwise, against the
databases.
The BLAST output is interrogated for all fragment matches that cover
at least 70% of the query sequence, with at least 30% nucleotide
identity over the full length of the query sequence. This is an odd
choice and doesn't correspond to the twilight zone limit as implied by
Goris et al. We persist with their definition, however. Only these
qualifying matches contribute to the total aligned length, and total
aligned sequence identity used to calculate ANI.
The results are processed to give matrices of aligned sequence length
(aln_lengths.tab), similarity error counts (sim_errors.tab), ANIs
(perc_ids.tab), and minimum aligned percentage (perc_aln.tab) of
each genome, for each pairwise comparison. These are written to the
output directory in plain text tab-separated format.
"""
logger = logging.getLogger(__name__)
logger.info("Running %s", args.method)
blastdir = args.outdirname / ALIGNDIR[args.method]
logger.info("Writing BLAST output to %s", blastdir)
# Build BLAST databases and run pairwise BLASTN
cumval, fraglengths = run_blast(args, logger, infiles, blastdir)
# Process pairwise BLASTN output
logger.info("Processing pairwise %s BLAST output.", args.method)
try:
data = anib.process_blast(blastdir,
org_lengths,
fraglengths=fraglengths,
mode=args.method)
except ZeroDivisionError:
logger.error("One or more BLAST output files has a problem.")
if not args.skip_blastn:
if cumval > 0:
logger.error(
"This is possibly due to BLASTN run failure, please investigate",
exc_info=True,
)
else:
logger.error(
"This is possibly due to a BLASTN comparison being too distant for use.",
exc_info=True,
)
if not args.nocompress:
logger.info("Compressing/deleting %s", blastdir)
compress_delete_outdir(blastdir, logger)
# Return processed BLAST data
return data
def unified_anib(infiles, org_lengths):
"""Calculate ANIb for files in input directory.
- infiles - paths to each input file
- org_lengths - dictionary of input sequence lengths, keyed by sequence
Calculates ANI by the ANIb method, as described in Goris et al. (2007)
Int J Syst Evol Micr 57: 81-91. doi:10.1099/ijs.0.64483-0. There are
some minor differences depending on whether BLAST+ or legacy BLAST
(BLASTALL) methods are used.
All FASTA format files (selected by suffix) in the input directory are
used to construct BLAST databases, placed in the output directory.
Each file's contents are also split into sequence fragments of length
options.fragsize, and the multiple FASTA file that results written to
the output directory. These are BLASTNed, pairwise, against the
databases.
The BLAST output is interrogated for all fragment matches that cover
at least 70% of the query sequence, with at least 30% nucleotide
identity over the full length of the query sequence. This is an odd
choice and doesn't correspond to the twilight zone limit as implied by
Goris et al. We persist with their definition, however. Only these
qualifying matches contribute to the total aligned length, and total
aligned sequence identity used to calculate ANI.
The results are processed to give matrices of aligned sequence length
(aln_lengths.tab), similarity error counts (sim_errors.tab), ANIs
(perc_ids.tab), and minimum aligned percentage (perc_aln.tab) of
each genome, for each pairwise comparison. These are written to the
output directory in plain text tab-separated format.
"""
logger.info("Running %s" % args.method)
# Build BLAST databases and run pairwise BLASTN
if not args.skip_blastn:
# Make sequence fragments
logger.info("Fragmenting input files, and writing to %s" %
args.outdirname)
# Fraglengths does not get reused with BLASTN
fragfiles, fraglengths = anib.fragment_FASTA_files(infiles,
args.outdirname,
args.fragsize)
# Export fragment lengths as JSON, in case we re-run BLASTALL with
# --skip_blastn
if args.method == "ANIblastall":
with open(os.path.join(args.outdirname,
'fraglengths.json'), 'w') as outfile:
json.dump(fraglengths, outfile)
# Which executables are we using?
if args.method == "ANIblastall":
format_exe = args.formatdb_exe
blast_exe = args.blastall_exe
else:
format_exe = args.makeblastdb_exe
blast_exe = args.blastn_exe
# Run BLAST database-building and executables from a jobgraph
logger.info("Creating job dependency graph")
jobgraph = anib.make_job_graph(infiles, fragfiles, args.outdirname,
format_exe, blast_exe, args.method)
if args.scheduler == 'multiprocessing':
logger.info("Running jobs with multiprocessing")
logger.info("Running job dependency graph")
cumval = run_mp.run_dependency_graph(jobgraph, verbose=args.verbose,
logger=logger)
if 0 < cumval:
logger.warning("At least one BLAST run failed. " +
"%s may fail." % args.method)
else:
logger.info("All multiprocessing jobs complete.")
else:
run_sge.run_dependency_graph(jobgraph, verbose=args.verbose,
logger=logger)
logger.info("Running jobs with SGE")
else:
# Import fragment lengths from JSON
if args.method == "ANIblastall":
with open(os.path.join(args.outdirname, 'fraglengths.json'),
'rU') as infile:
fraglengths = json.load(infile)
else:
fraglengths = None
logger.warning("Skipping BLASTN runs (as instructed)!")
# Process pairwise BLASTN output
logger.info("Processing pairwise %s BLAST output." % args.method)
try:
data = anib.process_blast(args.outdirname, org_lengths,
fraglengths=fraglengths, mode=args.method)
except ZeroDivisionError:
logger.error("One or more BLAST output files has a problem.")
if not args.skip_blastn:
if 0 < cumval:
logger.error("This is possibly due to BLASTN run failure, " +
"please investigate")
else:
logger.error("This is possibly due to a BLASTN comparison " +
"being too distant for use.")
logger.error(last_exception())
return data