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# Copyright 2009 by Cymon J. Cox. All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""Command line wrapper for the multiple alignment program MUSCLE.
"""
from __future__ import print_function
from Bio.Application import _Option, _Switch, AbstractCommandline
class MuscleCommandline(AbstractCommandline):
r"""Command line wrapper for the multiple alignment program MUSCLE.
http://www.drive5.com/muscle/
Example:
--------
>>> from Bio.Align.Applications import MuscleCommandline
>>> muscle_exe = r"C:\Program Files\Aligments\muscle3.8.31_i86win32.exe"
>>> in_file = r"C:\My Documents\unaligned.fasta"
>>> out_file = r"C:\My Documents\aligned.fasta"
>>> muscle_cline = MuscleCommandline(muscle_exe, input=in_file, out=out_file)
>>> print(muscle_cline)
"C:\Program Files\Aligments\muscle3.8.31_i86win32.exe" -in "C:\My Documents\unaligned.fasta" -out "C:\My Documents\aligned.fasta"
You would typically run the command line with muscle_cline() or via
the Python subprocess module, as described in the Biopython tutorial.
Citations:
----------
Edgar, Robert C. (2004), MUSCLE: multiple sequence alignment with high
accuracy and high throughput, Nucleic Acids Research 32(5), 1792-97.
Edgar, R.C. (2004) MUSCLE: a multiple sequence alignment method with
reduced time and space complexity. BMC Bioinformatics 5(1): 113.
Last checked against version: 3.7, briefly against 3.8
"""
def __init__(self, cmd="muscle", **kwargs):
CLUSTERING_ALGORITHMS = ["upgma", "upgmb", "neighborjoining"]
DISTANCE_MEASURES_ITER1 = ["kmer6_6", "kmer20_3", "kmer20_4", "kbit20_3",
"kmer4_6"]
DISTANCE_MEASURES_ITER2 = DISTANCE_MEASURES_ITER1 + \
["pctid_kimura", "pctid_log"]
OBJECTIVE_SCORES = ["sp", "ps", "dp", "xp", "spf", "spm"]
TREE_ROOT_METHODS = ["pseudo", "midlongestspan", "minavgleafdist"]
SEQUENCE_TYPES = ["protein", "nucleo", "auto"]
WEIGHTING_SCHEMES = ["none", "clustalw", "henikoff", "henikoffpb", "gsc", "threeway"]
self.parameters = [
# Can't use "in" as the final alias as this is a reserved word in python:
_Option(["-in", "in", "input"],
"Input filename",
filename=True,
equate=False),
_Option(["-out", "out"],
"Output filename",
filename=True,
equate=False),
_Switch(["-diags", "diags"],
"Find diagonals (faster for similar sequences)"),
_Switch(["-profile", "profile"],
"Perform a profile alignment"),
_Option(["-in1", "in1"],
"First input filename for profile alignment",
filename=True,
equate=False),
_Option(["-in2", "in2"],
"Second input filename for a profile alignment",
filename=True,
equate=False),
# anchorspacing Integer 32 Minimum spacing between
_Option(["-anchorspacing", "anchorspacing"],
"Minimum spacing between anchor columns",
checker_function=lambda x: isinstance(x, int),
equate=False),
# center Floating point [1] Center parameter.
# Should be negative.
_Option(["-center", "center"],
"Center parameter - should be negative",
checker_function=lambda x: isinstance(x, float),
equate=False),
# cluster1 upgma upgmb Clustering method.
_Option(["-cluster1", "cluster1"],
"Clustering method used in iteration 1",
checker_function=lambda x: x in CLUSTERING_ALGORITHMS,
equate=False),
# cluster2 upgmb cluster1 is used in
# neighborjoining iteration 1 and 2,
# cluster2 in later
# iterations.
_Option(["-cluster2", "cluster2"],
"Clustering method used in iteration 2",
checker_function=lambda x: x in CLUSTERING_ALGORITHMS,
equate=False),
# diaglength Integer 24 Minimum length of
# diagonal.
_Option(["-diaglength", "diaglength"],
"Minimum length of diagonal",
checker_function=lambda x: isinstance(x, int),
equate=True),
# diagmargin Integer 5 Discard this many
# positions at ends of
# diagonal.
_Option(["-diagmargin", "diagmargin"],
"Discard this many positions at ends of diagonal",
checker_function=lambda x: isinstance(x, int),
equate=False),
# distance1 kmer6_6 Kmer6_6 (amino) or Distance measure for
# kmer20_3 Kmer4_6 (nucleo) iteration 1.
# kmer20_4
# kbit20_3
# kmer4_6
_Option(["-distance1", "distance1"],
"Distance measure for iteration 1",
checker_function=lambda x: x in DISTANCE_MEASURES_ITER1,
equate=False),
# distance2 kmer6_6 pctid_kimura Distance measure for
# kmer20_3 iterations 2, 3 ...
# kmer20_4
# kbit20_3
# pctid_kimura
# pctid_log
_Option(["-distance2", "distance2"],
"Distance measure for iteration 2",
checker_function=lambda x: x in DISTANCE_MEASURES_ITER2,
equate=False),
# gapopen Floating point [1] The gap open score.
# Must be negative.
_Option(["-gapopen", "gapopen"],
"Gap open score - negative number",
checker_function=lambda x: isinstance(x, float),
equate=False),
# hydro Integer 5 Window size for
# determining whether a
# region is hydrophobic.
_Option(["-hydro", "hydro"],
"Window size for hydrophobic region",
checker_function=lambda x: isinstance(x, int),
equate=False),
# hydrofactor Floating point 1.2 Multiplier for gap
# open/close penalties in
# hydrophobic regions.
_Option(["-hydrofactor", "hydrofactor"],
"Multiplier for gap penalties in hydrophobic regions",
checker_function=lambda x: isinstance(x, float),
equate=False),
# log File name None. Log file name (delete
# existing file).
_Option(["-log", "log"],
"Log file name",
filename=True,
equate=False),
# loga File name None. Log file name (append
# to existing file).
_Option(["-loga", "loga"],
"Log file name (append to existing file)",
filename=True,
equate=False),
# maxdiagbreak Integer 1 Maximum distance
# between two diagonals
# that allows them to
# merge into one
# diagonal.
_Option(["-maxdiagbreak", "maxdiagbreak"],
"Maximum distance between two diagonals that allows "
"them to merge into one diagonal",
checker_function=lambda x: isinstance(x, int),
equate=False),
# maxhours Floating point None. Maximum time to run in
# hours. The actual time
# may exceed the
# requested limit by a
# few minutes. Decimals
# are allowed, so 1.5
# means one hour and 30
# minutes.
_Option(["-maxhours", "maxhours"],
"Maximum time to run in hours",
checker_function=lambda x: isinstance(x, float),
equate=False),
# maxiters Integer 1, 2 ... 16 Maximum number of
# iterations.
_Option(["-maxiters", "maxiters"],
"Maximum number of iterations",
checker_function=lambda x: isinstance(x, int),
equate=False),
# maxtrees Integer 1 Maximum number of new
# trees to build in
# iteration 2.
_Option(["-maxtrees", "maxtrees"],
"Maximum number of trees to build in iteration 2",
checker_function=lambda x: isinstance(x, int),
equate=False),
# minbestcolscore Floating point [1] Minimum score a column
# must have to be an
# anchor.
_Option(["-minbestcolscore", "minbestcolscore"],
"Minimum score a column must have to be an anchor",
checker_function=lambda x: isinstance(x, float),
equate=False),
# minsmoothscore Floating point [1] Minimum smoothed score
# a column must have to
# be an anchor.
_Option(["-minsmoothscore", "minsmoothscore"],
"Minimum smoothed score a column must have to "
"be an anchor",
checker_function=lambda x: isinstance(x, float),
equate=False),
# objscore sp spm Objective score used by
# ps tree dependent
# dp refinement.
# xp sp=sum-of-pairs score.
# spf spf=sum-of-pairs score
# spm (dimer approximation)
# spm=sp for < 100 seqs,
# otherwise spf
# dp=dynamic programming
# score.
# ps=average profile-
# sequence score.
# xp=cross profile score.
_Option(["-objscore", "objscore"],
"Objective score used by tree dependent refinement",
checker_function=lambda x: x in OBJECTIVE_SCORES,
equate=False),
# root1 pseudo pseudo Method used to root
_Option(["-root1", "root1"],
"Method used to root tree in iteration 1",
checker_function=lambda x: x in TREE_ROOT_METHODS,
equate=False),
# root2 midlongestspan tree; root1 is used in
# minavgleafdist iteration 1 and 2,
# root2 in later
# iterations.
_Option(["-root2", "root2"],
"Method used to root tree in iteration 2",
checker_function=lambda x: x in TREE_ROOT_METHODS,
equate=False),
# seqtype protein auto Sequence type.
# nucleo
# auto
_Option(["-seqtype", "seqtype"],
"Sequence type",
checker_function=lambda x: x in SEQUENCE_TYPES,
equate=False),
# smoothscoreceil Floating point [1] Maximum value of column
# score for smoothing
# purposes.
_Option(["-smoothscoreceil", "smoothscoreceil"],
"Maximum value of column score for smoothing",
checker_function=lambda x: isinstance(x, float),
equate=False),
# smoothwindow Integer 7 Window used for anchor
# column smoothing.
_Option(["-smoothwindow", "smoothwindow"],
"Window used for anchor column smoothing",
checker_function=lambda x: isinstance(x, int),
equate=False),
# SUEFF Floating point value 0.1 Constant used in UPGMB
# between 0 and 1. clustering. Determines
# the relative fraction
# of average linkage
# (SUEFF) vs. nearest-
# neighbor linkage (1
# SUEFF).
_Option(["-sueff", "sueff"],
"Constant used in UPGMB clustering",
checker_function=lambda x: isinstance(x, float),
equate=False),
# tree1 File name None Save tree produced in
_Option(["-tree1", "tree1"],
"Save Newick tree from iteration 1",
equate=False),
# tree2 first or second
# iteration to given file
# in Newick (Phylip-
# compatible) format.
_Option(["-tree2", "tree2"],
"Save Newick tree from iteration 2",
equate=False),
# weight1 none clustalw Sequence weighting
_Option(["-weight1", "weight1"],
"Weighting scheme used in iteration 1",
checker_function=lambda x: x in WEIGHTING_SCHEMES,
equate=False),
# weight2 henikoff scheme.
# henikoffpb weight1 is used in
# gsc iterations 1 and 2.
# clustalw weight2 is used for
# threeway tree-dependent
# refinement.
# none=all sequences have
# equal weight.
# henikoff=Henikoff &
# Henikoff weighting
# scheme.
# henikoffpb=Modified
# Henikoff scheme as used
# in PSI-BLAST.
# clustalw=CLUSTALW
# method.
# threeway=Gotoh three-
# way method.
_Option(["-weight2", "weight2"],
"Weighting scheme used in iteration 2",
checker_function=lambda x: x in WEIGHTING_SCHEMES,
equate=False),
# ################### FORMATS #######################################
# Multiple formats can be specified on the command line
# If -msf appears it will be used regardless of other formats
# specified. If -clw appears (and not -msf), clustalw format will be
# used regardless of other formats specified. If both -clw and
# -clwstrict are specified -clwstrict will be used regardless of
# other formats specified. If -fasta is specified and not -msf,
# -clw, or clwstrict, fasta will be used. If -fasta and -html are
# specified -fasta will be used. Only if -html is specified alone
# will html be used. I kid ye not.
# clw no Write output in CLUSTALW format (default is
# FASTA).
_Switch(["-clw", "clw"],
"Write output in CLUSTALW format (with a MUSCLE header)"),
# clwstrict no Write output in CLUSTALW format with the
# "CLUSTAL W (1.81)" header rather than the
# MUSCLE version. This is useful when a post-
# processing step is picky about the file
# header.
_Switch(["-clwstrict", "clwstrict"],
"Write output in CLUSTALW format with version 1.81 header"),
# fasta yes Write output in FASTA format. Alternatives
# include clw,
# clwstrict, msf and html.
_Switch(["-fasta", "fasta"],
"Write output in FASTA format"),
# html no Write output in HTML format (default is
# FASTA).
_Switch(["-html", "html"],
"Write output in HTML format"),
# msf no Write output in MSF format (default is
# FASTA).
_Switch(["-msf", "msf"],
"Write output in MSF format"),
# Phylip interleaved - undocumented as of 3.7
_Switch(["-phyi", "phyi"],
"Write output in PHYLIP interleaved format"),
# Phylip sequential - undocumented as of 3.7
_Switch(["-phys", "phys"],
"Write output in PHYLIP sequential format"),
# ################# Additional specified output files #########
_Option(["-phyiout", "phyiout"],
"Write PHYLIP interleaved output to specified filename",
filename=True,
equate=False),
_Option(["-physout", "physout"], "Write PHYLIP sequential format to specified filename",
filename=True,
equate=False),
_Option(["-htmlout", "htmlout"], "Write HTML output to specified filename",
filename=True,
equate=False),
_Option(["-clwout", "clwout"],
"Write CLUSTALW output (with MUSCLE header) to specified "
"filename",
filename=True,
equate=False),
_Option(["-clwstrictout", "clwstrictout"],
"Write CLUSTALW output (with version 1.81 header) to "
"specified filename",
filename=True,
equate=False),
_Option(["-msfout", "msfout"],
"Write MSF format output to specified filename",
filename=True,
equate=False),
_Option(["-fastaout", "fastaout"],
"Write FASTA format output to specified filename",
filename=True,
equate=False),
# ############# END FORMATS ###################################
# anchors yes Use anchor optimization in tree dependent
# refinement iterations.
_Switch(["-anchors", "anchors"],
"Use anchor optimisation in tree dependent "
"refinement iterations"),
# noanchors no Disable anchor optimization. Default is
# anchors.
_Switch(["-noanchors", "noanchors"],
"Do not use anchor optimisation in tree dependent "
"refinement iterations"),
# group yes Group similar sequences together in the
# output. This is the default. See also
# stable.
_Switch(["-group", "group"],
"Group similar sequences in output"),
# stable no Preserve input order of sequences in output
# file. Default is to group sequences by
# similarity (group).
_Switch(["-stable", "stable"],
"Do not group similar sequences in output (not supported in v3.8)"),
# ############# log-expectation profile score ######################
# One of either -le, -sp, or -sv
#
# According to the doc, spn is default and the only option for
# nucleotides: this doesn't appear to be true. -le, -sp, and -sv can
# be used and produce numerically different logs (what is going on?)
#
# spn fails on proteins
# le maybe Use log-expectation profile score (VTML240).
# Alternatives are to use sp or sv. This is
# the default for amino acid sequences.
_Switch(["-le", "le"],
"Use log-expectation profile score (VTML240)"),
# sv no Use sum-of-pairs profile score (VTML240).
# Default is le.
_Switch(["-sv", "sv"],
"Use sum-of-pairs profile score (VTML240)"),
# sp no Use sum-of-pairs protein profile score
# (PAM200). Default is le.
_Switch(["-sp", "sp"],
"Use sum-of-pairs protein profile score (PAM200)"),
# spn maybe Use sum-of-pairs nucleotide profile score
# (BLASTZ parameters). This is the only option
# for nucleotides, and is therefore the
# default.
_Switch(["-spn", "spn"],
"Use sum-of-pairs protein nucleotide profile score"),
# ############# END log-expectation profile score ######################
# quiet no Do not display progress messages.
_Switch(["-quiet", "quiet"],
"Use sum-of-pairs protein nucleotide profile score"),
# refine no Input file is already aligned, skip first
# two iterations and begin tree dependent
# refinement.
_Switch(["-refine", "refine"],
"Only do tree dependent refinement"),
# core yes in muscle, Do not catch exceptions.
# no in muscled.
_Switch(["-core", "core"],
"Catch exceptions"),
# nocore no in muscle, Catch exceptions and give an error message
# yes in muscled. if possible.
_Switch(["-nocore", "nocore"],
"Do not catch exceptions"),
# termgapsfull no Terminal gaps penalized with full penalty.
# [1] Not fully supported in this version.
#
# termgapshalf yes Terminal gaps penalized with half penalty.
# [1] Not fully supported in this version.
#
# termgapshalflonger no Terminal gaps penalized with half penalty if
# gap relative to
# longer sequence, otherwise with full
# penalty.
# [1] Not fully supported in this version.
# verbose no Write parameter settings and progress
# messages to log file.
_Switch(["-verbose", "verbose"],
"Write parameter settings and progress"),
# version no Write version string to stdout and exit.
_Switch(["-version", "version"],
"Write version string to stdout and exit"),
]
AbstractCommandline.__init__(self, cmd, **kwargs)
if __name__ == "__main__":
from Bio._utils import run_doctest
run_doctest()
|
