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"""
Command line object for Jackhmmer Multiple Sequence Alignment generation
"""
__author__ = "Felix Simkovic"
__date__ = "01 June 2016"
__version__ = "0.13.3"
from Bio.Application import _Argument
from Bio.Application import _Option
from Bio.Application import _Switch
from Bio.Application import AbstractCommandline
[docs]class JackhmmerCommandline(AbstractCommandline):
"""
Command line object for Jackhmmer [#]_ alignment generation
http://hmmer.org/
Jackhmmer is an algorithm that uses iterative searches a protein sequence
against a protein sequence database to find sequence homologs.
.. [#] Johnson L. S., Eddy S. R., Portugaly E. (2010). Hidden Markov
Model Speed Heuristic and Iterative HMM Search Procedure. BMC Bioinformatics 11, 431.
Examples
--------
To generate a Multiple Sequence Alignment, use:
>>> from conkit.applications import JackhmmerCommandline
>>> jackhmmer_cline = JackhmmerCommandline(
... input="test.fasta", database="uniref100.fasta"
... )
>>> print(jackhmmer_cline)
jackhmmer test.fasta uniref100.fasta
You would typically run the command line with :func:`jackhmmer_cline` or via
the :mod:`~subprocess` module.
"""
def __init__(self, cmd="jackhmmer", **kwargs):
self.parameters = [
_Option(["-N", "niterations"], "set maximum number of iterations [default: 5]", equate=False),
# Options directing output
_Option(["-o", "output"], "direct output to file <f>, not stdout", filename=True, equate=False),
_Option(["-A", "msa_hits"], "save multiple alignment of hits to file <f>", filename=True, equate=False),
_Option(
["--tblout", "per_sequence_hits"],
"save parseable table of per-sequence hits to file <f>",
filename=True,
equate=False,
),
_Option(
["--domtblout", "per_domain_hits"],
"save parseable table of per-domain hits to file <f>",
filename=True,
equate=False,
),
_Option(
["--chkhmm", "hmm_checkpoints"],
"save HMM checkpoints to files <f>-<iteration>.hmm",
filename=True,
equate=False,
),
_Option(
["--chkali", "alignment_checkpoints"],
"save alignment checkpoints to files <f>-<iteration>.sto",
filename=True,
equate=False,
),
_Switch(["--acc", "accession"], "prefer accessions over names in output"),
_Switch(["--noali", "no_alignment"], "don't output alignments, so output is smaller"),
_Switch(["--notextw", "notextw"], "unlimit ASCII text output line width"),
_Switch(["--textw", "textw"], "set max width of ASCII text output lines [default: 120] (n>=120)"),
# Options controlling scoring system in first iteration
_Option(["--popen", "gap_open_probability"], "gap open probability", equate=False),
_Option(["--pextend", "gap_extend_probability"], "gap extend probability", equate=False),
_Option(
["--mx", "matrix_choice"], "substitution score matrix choice (of some built-in matrices)", equate=False
),
_Option(
["--mxfile", "matrix_option"],
"read substitution score matrix from file <f>",
filename=True,
equate=False,
),
# Options controlling reporting thresholds
_Option(
["-E", "evalue"],
"report sequences <= this E-value threshold in output [default: 10.0] (x>0)",
equate=False,
),
_Option(["-T", "score_threshold"], "report sequences >= this score threshold in output", equate=False),
_Option(
["--domE", "domain_evalue"],
"report domains <= this E-value threshold in output [default: 10.0] (x>0)",
equate=False,
),
_Option(
["--domT", "domain_score_threshold"], "report domains >= this score cutoff in output", equate=False
),
# Options controlling significance thresholds for inclusion in next round
_Option(
["--incE", "inclusion_evalue"],
"consider sequences <= this E-value threshold as significant",
equate=False,
),
_Option(
["--incT", "inclusion_score_threshold"],
"consider sequences >= this score threshold as significant",
equate=False,
),
_Option(
["--incdomE", "inclusion_domain_evalue"],
"consider domains <= this E-value threshold as significant",
equate=False,
),
_Option(
["--incdomT", "inclusion_domain_score_threshold"],
"consider domains >= this score threshold as significant",
equate=False,
),
# Options controlling acceleration heuristics
_Switch(["--max", "no_heuristics"], "Turn all heuristic filters off (less speed, more power)"),
_Option(
["--F1", "stage1_threshold"],
"Stage 1 (MSV) threshold: promote hits w/ P <= F1 [default: 0.02]",
equate=False,
),
_Option(
["--F2", "stage2_threshold"],
"Stage 2 (Vit) threshold: promote hits w/ P <= F2 [default: 1e-3]",
equate=False,
),
_Option(
["--F3", "stage3_threshold"],
"Stage 3 (Fwd) threshold: promote hits w/ P <= F3 [default: 1e-5]",
equate=False,
),
_Switch(["--nobias", "nobias"], "turn off composition bias filter"),
# Options controlling model construction after first iteration
_Switch(["--fast", "fast"], "assign cols w/ >= symfrac residues as consensus"),
_Switch(["--hand", "hand"], "manual construction (requires reference annotation)"),
_Option(["--symfrac", "symfrac"], "sets sym fraction controlling --fast construction", equate=False),
_Option(["--fragthres", "fragthres"], "if L <= x*alen, tag sequence as a fragment", equate=False),
# Options controlling relative weights in models after first iteration
_Switch(["--wpb", "henikoff_pb_weights"], "Henikoff position-based weights [default]"),
_Switch(["--wgsc", "GSC_weights"], "Gerstein/Sonnhammer/Chothia tree weights"),
_Switch(["--wblosum", "henikoff_sf_weights"], "Henikoff simple filter weights"),
_Switch(["--wnone", "no_weight"], "don't do any relative weighting; set all to 1"),
_Option(
["--wid", "wblosum_cutoff"],
"for --wblosum: set identity cutoff [default: 0.62] (0<=x<=1)",
equate=False,
),
# Options controlling effective seq number in models after first iteration
_Switch(["--eent", "eent"], "adjust eff seq # to achieve relative entropy target [default]"),
_Switch(["--eclust", "ecluse"], "eff seq # is # of single linkage clusters"),
_Switch(["--enone", "enone"], "no effective seq # weighting: just use nseq"),
_Option(["--eset", "eset"], "set eff seq # for all models to <x>", equate=False),
_Option(["--ere", "ere"], "for --eent: set minimum rel entropy/position to <x>", equate=False),
_Option(["--esigma", "esigma"], "for --eent: set sigma param to <x> [default: 45.0]", equate=False),
_Option(
["--eid", "eid"], "for --eclust: set fractional identity cutoff to <x> [default: 0.62]", equate=False
),
# Options controlling prior strategy in models after first iteration
_Switch(["--pnone", "pnone"], "don't use any prior; parameters are frequencies"),
_Switch(["--plaplace", "plaplace"], "use a Laplace +1 prior"),
# Options controlling E value calibration
_Option(["--EmL", "eml"], "length of sequences for MSV Gumbel mu fit [default: 200] (n>0)", equate=False),
_Option(["--EmN", "emn"], "number of sequences for MSV Gumbel mu fit [default: 200] (n>0)", equate=False),
_Option(
["--EvL", "evl"], "length of sequences for Viterbi Gumbel mu fit [default: 200] (n>0)", equate=False
),
_Option(
["--EvN", "evn"], "number of sequences for Viterbi Gumbel mu fit [default: 200] (n>0)", equate=False
),
_Option(
["--EfL", "efl"], "length of sequences for Forward exp tail tau fit [default: 100] (n>0)", equate=False
),
_Option(
["--EfN", "efn"], "number of sequences for Forward exp tail tau fit [default: 200] (n>0)", equate=False
),
_Option(
["--Eft", "eft"], "tail mass for Forward exponential tail tau fit [default: 0.04] (0<x<1)", equate=False
),
# Other expert options
_Switch(["--nonull2", "nonull2"], "turn off biased composition score corrections"),
_Option(["-Z", "ncomparison"], "set # of comparisons done, for E-value calculation", equate=False),
_Option(["--domZ", "domz"], "set # of significant seqs, for domain E-value calculation", equate=False),
_Option(
["--seed", "seed"], "set RNG seed to <n> (if 0: one-time arbitrary seed) [default: 42]", equate=False
),
_Option(
["--qformat", "qformat"], "assert query <seqfile> is in format <s>: no autodetection", equate=False
),
_Option(
["--tformat", "tformat"], "assert target < seqdb > is in format < s >>: no autodetection", equate=False
),
_Option(["--cpu", "cpu"], "number of parallel CPU workers to use for multithreads", equate=False),
# Required arguments
_Argument(["input"], "sequence containing file", filename=True, is_required=True),
_Argument(["database"], "sequence database", filename=True, is_required=True),
]
AbstractCommandline.__init__(self, cmd, **kwargs)
