def run_maxcluster_cluster(predictions,
method="average",
rmsd=True,
clustering_threshold=None,
binary="maxcluster"):
"""
Compare a set of predicted structures to an experimental structure
using maxcluster.
For clustering functionality, use run_maxcluster_clustering() function.
Parameters
----------
predictions : list(str)
List of PDB files that should be compared against experiment
method : {"single", "average", "maximum", "pairs_min", "pairs_abs"}, optional (default: "average")
Clustering method (single / average / maximum linkage,
or min / absolute size neighbour pairs
clustering_threshold : float (optional, default: None)
Initial clustering threshold (maxcluster -T option)
rmsd : bool, optional (default: True)
Use RMSD-based clustering (faster)
binary : str, optional (default: "maxcluster")
Path to maxcluster binary
Returns
-------
pandas.DataFrame
Clustering result table (see parse_maxcluster_clustering
for more detailed explanation)
"""
# create a list of files for input to maxcluster
list_file = temp()
with open(list_file, "w") as f:
for pred_file in predictions:
f.write(pred_file + "\n")
method_map = {
"single": 1,
"average": 2,
"maximum": 3,
"pairs_min": 4,
"pairs_abs": 5,
}
if method not in method_map:
raise InvalidParameterError("Method must be one of the following: " +
", ".join(method_map.keys()))
cmd = [binary, "-l", list_file, "-C", str(method_map[method])]
if rmsd:
cmd += ["-rmsd"]
if clustering_threshold is not None:
cmd += ["-T", str(clustering_threshold)]
return_code, stdout, stderr = run(cmd)
return parse_maxcluster_clustering(stdout)
def run_maxcluster_compare(predictions,
experiment,
normalization_length=None,
distance_cutoff=None,
binary="maxcluster"):
"""
Compare a set of predicted structures to an experimental structure
using maxcluster.
For clustering functionality, use run_maxcluster_clustering() function.
For a high-level wrapper around this function that removes
problematic atoms and compares multiple models, please look at
evcouplings.fold.protocol.compare_models_maxcluster().
Parameters
----------
predictions : list(str)
List of PDB files that should be compared against experiment
experiment : str
Path of experimental structure PDB file. Note that the numbering
and residues in this file must agree with the predicted structure,
and that the structure may not contain duplicate atoms (multiple
models, or alternative locations for the same atom).
normalization_length : int, optional (default: None)
Use this length to normalize the Template Modeling (TM)
score (-N option of maxcluster). If None, will normalize
by length of experiment.
distance_cutoff : float, optional (default: None)
Distance cutoff for MaxSub search (-d option of maxcluster).
If None, will use maxcluster auto-calibration.
binary : str, optional (default: "maxcluster")
Path to maxcluster binary
Returns
-------
pandas.DataFrame
Comparison result table (see parse_maxcluster_comparison
for more detailed explanation)
"""
# create a list of files for input to maxcluster
list_file = temp()
with open(list_file, "w") as f:
for pred_file in predictions:
f.write(pred_file + "\n")
cmd = [binary, "-l", list_file, "-e", experiment]
# normalization length for TM score calculation
if normalization_length is not None:
cmd += ["-N", str(normalization_length)]
# distance cutoff for MaxSub search
if distance_cutoff is not None:
cmd += ["-d", str(distance_cutoff)]
return_code, stdout, stderr = run(cmd)
return parse_maxcluster_comparison(stdout)
def run_hhfilter(input_file,
output_file,
threshold=95,
columns="a2m",
binary="hhfilter"):
"""
Redundancy-reduce a sequence alignment using hhfilter
from the HHsuite alignment suite.
Parameters
----------
input_file : str
Path to input alignment in A2M/FASTA format
output_file : str
Path to output alignment (will be in A3M format)
threshold : int, optional (default: 95)
Sequence identity threshold for maximum pairwise
identity (between 0 and 100)
columns : {"first", "a2m"}, optional (default: "a2m")
Definition of match columns (based on first sequence
or upper-case columns (a2m))
binary : str
Path to hhfilter binary
Returns
-------
str
output_file
Raises
------
ResourceError
If output alignment is non-existent/empty
ValueError
Upon invalid value of columns parameter
"""
if columns not in ["first", "a2m"]:
raise ValueError("Invalid column selection: {}".format(columns))
verify_resources("Alignment file does not exist or is empty", input_file)
create_prefix_folders(output_file)
cmd = [
binary, "-i", input_file, "-o", output_file, "-id",
str(threshold), "-M", columns, "-v",
str(2)
]
return_code, stdout, stderr = run(cmd)
verify_resources(
"hhfilter returned empty alignment: "
"stdout={} stderr={} file={}".format(stdout, stderr, output_file),
output_file)
return output_file
def run_plmc(alignment, couplings_file, param_file=None,
focus_seq=None, alphabet=None, theta=None,
scale=None, ignore_gaps=False, iterations=None,
lambda_h=None, lambda_J=None, lambda_g=None,
cpu=None, binary="plmc"):
"""
Run plmc on sequence alignment and store
files with model parameters and pair couplings.
Parameters
----------
alignment : str
Path to input sequence alignment
couplings_file : str
Output path for file with evolutionary couplings
(folder will be created)
param_file : str
Output path for binary file containing model
parameters (folder will be created)
focus_seq : str, optional (default: None)
Name of focus sequence, if None, non-focus mode
will be used
alphabet : str, optional (default: None)
Alphabet for model inference. If None, standard
amino acid alphabet including gap will be used.
First character in string corresponds to gap
character (relevant for ignore_gaps).
theta : float, optional (default: None)
Sequences with pairwise identity >= theta
will be clustered and their sequence weights
downweighted as 1 / num_cluster_members.
Important: Note that plmc will be parametrized using
1 - theta. If None, default value in plmc will be used,
which corresponds to theta=0.8 (plmc setting 0.2).
scale : float, optional (default: None)
Scale weights of clusters by this value.
If None, default value in plmc (1.0) will be used
ignore_gaps : bool, optional (default: False)
Exclude gaps from parameter inference. Gap
character is first character of alphabet
parameter.
iterations : int, optional (default: None)
Maximum iterations for optimization.
lambda_h : float, optional (default: None)
l2 regularization strength on fields.
If None, plmc default will be used.
lambda_J : float, optional (default: None)
l2-regularization strength on couplings.
If None, plmc default will be used
lambda_g : float, optional (default: None)
group l1-regularization strength on couplings
If None, plmc default will be used.
cpu : Number of cores to use for running plmc.
Note that plmc has to be compiled in openmp
mode to runnable with multiple cores.
Can also be set to "max".
binary : str, optional (default: "plmc")
Path to plmc binary
Returns
-------
PlmcResult
namedtuple containing output files and
parsed fields from console output of plmc
Raises
------
ExternalToolError
"""
create_prefix_folders(couplings_file)
# Make sure input alignment exists
verify_resources(
"Alignment file does not exist", alignment
)
cmd = [
binary,
"-c", couplings_file,
]
# store eij file if explicitly requested
if param_file is not None:
create_prefix_folders(param_file)
cmd += ["-o", param_file]
# focus sequence mode and ID
if focus_seq is not None:
# TODO: for now split exclude sequence
# region from focus seq name, otherwise
# plmc does not remap names. If this
# behaviour changes in plmc, remove the
# following line.
focus_seq = focus_seq.split("/")[0]
cmd += ["-f", focus_seq]
# exclude gaps from calculation?
if ignore_gaps:
cmd += ["-g"]
# maximum number of iterations, can also be "max"
if iterations is not None:
cmd += ["-m", str(iterations)]
# set custom alphabet
# (first character is gap by default in nogap mode)
if alphabet is not None:
cmd += ["-a", alphabet]
# sequence reweighting
if theta is not None:
# transform into plmc convention (1-theta)
theta = 1.0 - theta
cmd += ["-t", str(theta)]
# cluster weight
if scale is not None:
cmd += ["-s", str(scale)]
# L2 regularization weight for fields
if lambda_h is not None:
cmd += ["-lh", str(lambda_h)]
# L2 regularization weight for pair couplings
if lambda_J is not None:
cmd += ["-le", str(lambda_J)]
# Group L1 regularization weight for pair couplings
if lambda_g is not None:
cmd += ["-lg", str(lambda_g)]
# Number of cores to use for calculation
if cpu is not None:
cmd += ["-n", str(cpu)]
# finally also add input alignment (main parameter)
cmd += [alignment]
# TODO: for now do not check returncode because sometimes
# returncode == -11 (segfault) despite successful calculation
return_code, stdout, stderr = run(cmd, check_returncode=False)
# TODO: remove this segfault-hunting output once fixed
if return_code != 0:
# if not a segfault, still raise exception
if return_code != -11:
from evcouplings.utils.system import ExternalToolError
raise ExternalToolError(
"Call failed:\ncmd={}\nreturncode={}\nstdout={}\nstderr={}".format(
cmd, return_code, stdout, stderr
)
)
print("PLMC NON-ZERO RETURNCODE:", return_code)
print(cmd)
print(" ".join(cmd))
print("stdout:", stdout)
print("stderr:", stderr)
iter_df, out_fields = parse_plmc_log(stderr)
# also check we actually calculated couplings...
if not valid_file(couplings_file):
raise ResourceError(
"plmc returned no couplings: stdout={} stderr={} file={}".format(
stdout, stderr, couplings_file
)
)
# ... and parameter file, if requested
if param_file and not valid_file(param_file):
raise ResourceError(
"plmc returned no parameter file: stdout={} stderr={} file={}".format(
stdout, stderr, param_file
)
)
return PlmcResult(
couplings_file, param_file,
iter_df, *out_fields
)
def run_jackhmmer(query,
database,
prefix,
use_bitscores,
domain_threshold,
seq_threshold,
iterations=5,
nobias=False,
cpu=None,
stdout_redirect=None,
checkpoints_hmm=False,
checkpoints_ali=False,
binary="jackhmmer"):
"""
Run jackhmmer sequence search against target database.
Refer to HMMER Userguide for explanation of these parameters.
Parameters
----------
query : str
File containing query sequence
database : str
File containing sequence database
prefix : str
Prefix path for output files. Folder structure in
the prefix will be created if not existing.
use_bitscores : bool
Use bitscore inclusion thresholds rather than E-values.
domain_threshold : int or float or str
Inclusion threshold applied on the domain level
(e.g. "1E-03" or 0.001 or 50)
seq_threshold : int or float or str
Inclusion threshold applied on the sequence level
(e.g. "1E-03" or 0.001 or 50)
iterations : int
number of jackhmmer search iterations
nobias : bool, optional (default: False)
Turn of bias correction
cpu : int, optional (default: None)
Number of CPUs to use for search. Uses all if None.
stdout_redirect : str, optional (default: None)
Redirect bulky stdout instead of storing
with rest of results (use "/dev/null" to dispose)
checkpoints_hmm : bool, optional (default: False)
Store checkpoint HMMs to prefix.<iter>.hmm
checkpoints_ali : bool, optional (default: False)
Store checkpoint alignments to prefix.<iter>.sto
binary : str (default: "jackhmmer")
Path to jackhmmer binary (put in PATH for
default to work)
Returns
-------
JackhmmerResult
namedtuple with fields corresponding to the different
output files (prefix, alignment, output, tblout, domtblout)
Raises
------
ExternalToolError, ResourceError
"""
verify_resources("Input file does not exist or is empty", query, database)
create_prefix_folders(prefix)
# store filenames of all individual results;
# these will be returned as result of the
# function.
result = JackhmmerResult(
prefix, prefix + ".sto",
prefix + ".output" if stdout_redirect is None else stdout_redirect,
prefix + ".tblout", prefix + ".domtblout")
cmd = [
binary, "-N",
str(iterations), "-o", result.output, "-A", result.alignment,
"--tblout", result.tblout, "--domtblout", result.domtblout, "--noali",
"--notextw"
]
# reporting thresholds are set accordingly to
# inclusion threshold to reduce memory footprit
if use_bitscores:
cmd += [
"-T",
str(seq_threshold), "--domT",
str(domain_threshold), "--incT",
str(seq_threshold), "--incdomT",
str(domain_threshold)
]
else:
cmd += [
"-E",
str(seq_threshold), "--domE",
str(domain_threshold), "--incE",
str(seq_threshold), "--incdomE",
str(domain_threshold)
]
# number of CPUs
if cpu is not None:
cmd += ["--cpu", str(cpu)]
# bias correction filter
if nobias:
cmd += ["--nobias"]
# save checkpoints for alignments and HMMs?
if checkpoints_ali:
cmd += ["--chkali", prefix]
if checkpoints_hmm:
cmd += ["--chkhmm", prefix]
cmd += [query, database]
return_code, stdout, stderr = run(cmd)
# also check we actually created some sort of alignment
verify_resources(
"jackhmmer returned empty alignment: "
"stdout={} stderr={} file={}".format(stdout, stderr, result.alignment),
result.alignment)
return result
def run_hmmscan(query,
database,
prefix,
use_model_threshold=True,
threshold_type="cut_ga",
use_bitscores=True,
domain_threshold=None,
seq_threshold=None,
nobias=False,
cpu=None,
stdout_redirect=None,
binary="hmmscan"):
"""
Run hmmscan of HMMs in database against sequences in query
to identify matches of these HMMs.
Refer to HMMER Userguide for explanation of these parameters.
Parameters
----------
query : str
File containing query sequence(s)
database : str
File containing HMM database (prepared with hmmpress)
prefix : str
Prefix path for output files. Folder structure in
the prefix will be created if not existing.
use_model_threshold: bool (default: True)
Use model-specific inclusion thresholds from
HMM database rather than global bitscore/E-value
thresholds (use_bitscores, domain_threshold and
seq_threshold are overriden by this flag).
threshold-type: {"cut_ga", "cut_nc", "cut_tc"} (default: "cut_ga")
Use gathering (default), noise or trusted cutoff
to define scan hits. Please refer to HMMER manual for
details.
use_bitscores : bool
Use bitscore inclusion thresholds rather than E-values.
Overriden by use_model_threshold flag.
domain_threshold : int or float or str
Inclusion threshold applied on the domain level
(e.g. "1E-03" or 0.001 or 50)
seq_threshold : int or float or str
Inclusion threshold applied on the sequence level
(e.g. "1E-03" or 0.001 or 50)
nobias : bool, optional (default: False)
Turn of bias correction
cpu : int, optional (default: None)
Number of CPUs to use for search. Uses all if None.
stdout_redirect : str, optional (default: None)
Redirect bulky stdout instead of storing
with rest of results (use "/dev/null" to dispose)
binary : str (default: "hmmscan")
Path to hmmscan binary (put in PATH for
default to work)
Returns
-------
HmmscanResult
namedtuple with fields corresponding to the different
output files (prefix, output, tblout, domtblout, pfamtblout)
Raises
------
ExternalToolError, ResourceError
"""
verify_resources("Input file does not exist or is empty", query, database)
create_prefix_folders(prefix)
result = HmmscanResult(
prefix,
prefix + ".output" if stdout_redirect is None else stdout_redirect,
prefix + ".tblout", prefix + ".domtblout", prefix + ".pfamtblout")
cmd = [
binary,
"-o",
result.output,
"--tblout",
result.tblout,
"--domtblout",
result.domtblout,
"--pfamtblout",
result.pfamtblout,
"--notextw",
"--acc",
]
# number of CPUs
if cpu is not None:
cmd += ["--cpu", str(cpu)]
# bias correction filter
if nobias:
cmd += ["--nobias"]
# either use model-specific threshold, or custom
# bitscore/E-value thresholds
if use_model_threshold:
THRESHOLD_CHOICES = ["cut_ga", "cut_nc", "cut_tc"]
if threshold_type not in THRESHOLD_CHOICES:
raise ValueError("Invalid model threshold, valid choices are: " +
", ".join(THRESHOLD_CHOICES))
cmd += ["--" + threshold_type]
else:
if seq_threshold is None or domain_threshold is None:
raise ValueError("Must define sequence- and domain-level reporting"
"thresholds, or use gathering threshold instead.")
if use_bitscores:
cmd += [
"-T",
str(seq_threshold),
"--domT",
str(domain_threshold),
]
else:
cmd += [
"-E",
str(seq_threshold),
"--domE",
str(domain_threshold),
]
cmd += [database, query]
return_code, stdout, stderr = run(cmd)
# also check we actually created a table with hits
verify_resources(
"hmmscan did not return results: "
"stdout={} stderr={} file={}".format(stdout, stderr, result.domtblout),
result.domtblout)
return result
def run_cns_13(inp_script=None,
inp_file=None,
log_file=None,
source_script=None,
binary="cns"):
"""
Run CNSsolve 1.3
Note that the user is responsible for verifying the output products
of CNS, since their paths are determined by .inp scripts and
hard to check automatically and in a general way.
Either input_script or input_file has to be specified.
Parameters
----------
inp_script : str, optional (default: None)
CNS ".inp" input script (actual commands, not file)
inp_file : str, optional (default: None)
Path to .inp input script file. Will override
inp_script if also specified.
log_file : str, optional (default: None)
Save CNS stdout output to this file
source_script : str, optional (default: None)
Script to set CNS environment variables.
This should typically point to .cns_solve_env_sh
in the CNS installation main directory (the
shell script itself needs to be edited to
contain the path of the installation)
binary : str, optional (default: "cns")
Name of CNS binary
Raises
------
ExternalToolError
If call to CNS fails
InvalidParameterError
If no input script (file or string) given
"""
# usually need to source script to set up environment for CNS
if source_script is not None:
cmd = "source {};".format(source_script)
else:
cmd = ""
cmd += binary
if inp_script is None and inp_file is None:
raise InvalidParameterError(
"Must specify either input_script or input_file")
# read input script, this is fed into CNS using stdin
if inp_file is not None:
with open(inp_file) as f:
inp_script = "".join(f.readlines())
# run and store output
return_code, stdout, stderr = run(cmd, stdin=inp_script, shell=True)
# write stdout output to log file
if log_file is not None:
with open(log_file, "w") as f:
f.write(stdout)
def run_cns(inp_script=None, inp_file=None, log_file=None, binary="cns"):
"""
Run CNSsolve 1.21 (without worrying about environment setup)
Note that the user is responsible for verifying the output products
of CNS, since their paths are determined by .inp scripts and
hard to check automatically and in a general way.
Either input_script or input_file has to be specified.
Parameters
----------
inp_script : str, optional (default: None)
CNS ".inp" input script (actual commands, not file)
inp_file : str, optional (default: None)
Path to .inp input script file. Will override
inp_script if also specified.
log_file : str, optional (default: None)
Save CNS stdout output to this file
binary : str, optional (default: "cns")
Absolute path of CNS binary
Raises
------
ExternalToolError
If call to CNS fails
InvalidParameterError
If no input script (file or string) given
"""
# make sure we have absolute path
binary = path.abspath(binary)
# extract main installation directory
cns_main_dir = binary
for i in range(3):
cns_main_dir = path.dirname(cns_main_dir)
# create environment
env = deepcopy(os.environ)
library_dir = path.join(cns_main_dir, "libraries")
module_dir = path.join(cns_main_dir, "modules")
env["CNS_SOLVE"] = cns_main_dir
env["CNS_LIB"] = library_dir
env["CNS_MODULE"] = module_dir
env["CNS_HELPLIB"] = path.join(cns_main_dir, "helplip")
for var, subdir in [
("CNS_TOPPAR", "toppar"),
("CNS_CONFDB", "confdb"),
("CNS_XTALLIB", "xtal"),
("CNS_NMRLIB", "nmr"),
("CNS_XRAYLIB", "xray"),
]:
env[var] = path.join(library_dir, subdir)
for var, subdir in [
("CNS_XTALMODULE", "xtal"),
("CNS_NMRMODULE", "nmr"),
]:
env[var] = path.join(module_dir, subdir)
if inp_script is None and inp_file is None:
raise InvalidParameterError(
"Must specify either input_script or input_file")
# read input script, this is fed into CNS using stdin
if inp_file is not None:
with open(inp_file) as f:
inp_script = "".join(f.readlines())
# run and store output
return_code, stdout, stderr = run(binary, stdin=inp_script)
# write stdout output to log file
if log_file is not None:
with open(log_file, "w") as f:
f.write(stdout)
def run_psipred(fasta_file, output_dir, binary="runpsipred"):
"""
Run psipred secondary structure prediction
psipred output file convention: run_psipred creates
output files <rootname>.ss2 and <rootname2>.horiz
in the current working directory, where <rootname>
is extracted from the basename of the input file
(e.g. /home/test/<rootname>.fa)
Parameters
----------
fasta_file : str
Input sequence file in FASTA format
output_dir : str
Directory in which output will be saved
binary : str, optional (default: "cns")
Path of psipred executable (runpsipred)
Returns
-------
ss2_file : str
Absolute path to prediction output in "VFORMAT"
horiz_file : str
Absolute path to prediction output in "HFORMAT"
Raises
------
ExternalToolError
If call to psipred fails
"""
# make sure we have absolute path
binary = path.abspath(binary)
fasta_file = path.abspath(fasta_file)
output_dir = path.abspath(output_dir)
# make sure input file is valid
verify_resources("Input FASTA file is invalid", fasta_file)
# make sure output directory exists
makedirs(output_dir)
# execute psipred;
# we have to start it from output directory so
# result files end up there (this is hardcoded
# in runpsipred)
return_code, stdout, stderr = run(
[binary, fasta_file],
working_dir=output_dir,
)
# determine where psipred will store output based
# on logic from runpsipred script
rootname, _ = path.splitext(path.basename(fasta_file))
output_prefix = path.join(output_dir, rootname)
# construct paths to output files in vertical and horizontal formats
ss2_file = output_prefix + ".ss2"
horiz_file = output_prefix + ".horiz"
# make sure we actually predicted something
verify_resources("psipred output is invalid", ss2_file, horiz_file)
return ss2_file, horiz_file
def run_hmmbuild(alignment_file,
prefix,
cpu=None,
stdout_redirect=None,
symfrac=None,
binary="hmmbuild"):
"""
Profile HMM construction from multiple sequence alignments
Refer to HMMER documentation for details.
http://eddylab.org/software/hmmer3/3.1b2/Userguide.pdf
Parameters
----------
alignment_file : str
File containing the multiple sequence alignment. Can be in
Stockholm, a2m, or clustal formats, or any other format
recognized by hmmer. Please note that ALL POSITIONS
above the symfrac cutoff will be used in HMM
construction (if the alignment contains columns that are
insertions relative to the query sequence, this may be
problematic for structure comparison)
prefix : str
Prefix path for output files. Folder structure in
the prefix will be created if not existing.
cpu : int, optional (default: None)
Number of CPUs to use for search. Uses all if None.
stdout_redirect : str, optional (default: None)
Redirect bulky stdout instead of storing
with rest of results (use "/dev/null" to dispose)
symfrac : float, optional (default: None)
range 0.0 - 1.0, HMMbuild will use columns with
> symfrac percent gaps to construct the HMM.
If None provided, HMMbuild internal default is 0.5.
(Note: this is calculated after their internal sequence
weighting is calculated)
binary : str (default: "hmmbuild")
Path to jackhmmer binary (put in PATH for
default to work)
Returns
-------
HmmbuildResult
namedtuple with fields corresponding to the different
output files (prefix, alignment, output, tblout, domtblout)
Raises
------
ExternalToolError, ResourceError
"""
verify_resources("Input file does not exist or is empty", alignment_file)
create_prefix_folders(prefix)
# store filenames of all individual results;
# these will be returned as result of the
# function.
result = HmmbuildResult(
prefix,
prefix + ".hmm",
prefix + ".output" if stdout_redirect is None else stdout_redirect,
)
cmd = [
binary,
"-o",
result.output,
]
# number of CPUs
if cpu is not None:
cmd += ["--cpu", str(cpu)]
if symfrac is not None:
cmd += ["--symfrac", str(symfrac)]
cmd += [result.hmmfile, alignment_file]
return_code, stdout, stderr = run(cmd)
# also check we actually created some sort of alignment
verify_resources(
"hmmbuild returned empty HMM profile: "
"stdout={} stderr={} file={}".format(stdout, stderr, result.hmmfile),
result.hmmfile)
return result
def run_hmmsearch(hmmfile,
database,
prefix,
use_bitscores,
domain_threshold,
seq_threshold,
nobias=False,
cpu=None,
stdout_redirect=None,
binary="hmmsearch"):
"""
Search profile(s) against a sequence database.
Refer to HMMER documentation for details.
http://eddylab.org/software/hmmer3/3.1b2/Userguide.pdf
Parameters
----------
hmmfile : str
File containing the profile(s)
database : str
File containing sequence database
prefix : str
Prefix path for output files. Folder structure in
the prefix will be created if not existing.
use_bitscores : bool
Use bitscore inclusion thresholds rather than E-values.
domain_threshold : int or float or str
Inclusion threshold applied on the domain level
(e.g. "1E-03" or 0.001 or 50)
seq_threshold : int or float or str
Inclusion threshold applied on the sequence level
(e.g. "1E-03" or 0.001 or 50)
nobias : bool, optional (default: False)
Turn of bias correction
cpu : int, optional (default: None)
Number of CPUs to use for search. Uses all if None.
stdout_redirect : str, optional (default: None)
Redirect bulky stdout instead of storing
with rest of results (use "/dev/null" to dispose)
binary : str (default: "hmmsearch")
Path to jackhmmer binary (put in PATH for
default to work)
Returns
-------
HmmsearchResult
namedtuple with fields corresponding to the different
output files (prefix, alignment, output, tblout, domtblout)
Raises
------
ExternalToolError, ResourceError
"""
verify_resources("Input file does not exist or is empty", hmmfile,
database)
create_prefix_folders(prefix)
# store filenames of all individual results;
# these will be returned as result of the
# function.
result = HmmsearchResult(
prefix, prefix + ".sto",
prefix + ".output" if stdout_redirect is None else stdout_redirect,
prefix + ".tblout", prefix + ".domtblout")
cmd = [
binary, "-o", result.output, "-A", result.alignment, "--tblout",
result.tblout, "--domtblout", result.domtblout, "--noali", "--notextw"
]
# reporting thresholds are set accordingly to
# inclusion threshold to reduce memory footprint
if use_bitscores:
cmd += [
"-T",
str(seq_threshold), "--domT",
str(domain_threshold), "--incT",
str(seq_threshold), "--incdomT",
str(domain_threshold)
]
else:
cmd += [
"-E",
str(seq_threshold), "--domE",
str(domain_threshold), "--incE",
str(seq_threshold), "--incdomE",
str(domain_threshold)
]
# number of CPUs
if cpu is not None:
cmd += ["--cpu", str(cpu)]
# bias correction filter
if nobias:
cmd += ["--nobias"]
cmd += [hmmfile, database]
return_code, stdout, stderr = run(cmd)
return result