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# --------------------------------------------------------------------------
# OpenMS -- Open-Source Mass Spectrometry
# --------------------------------------------------------------------------
# Copyright The OpenMS Team -- Eberhard Karls University Tuebingen,
# ETH Zurich, and Freie Universitaet Berlin 2002-2018.
#
# This software is released under a three-clause BSD license:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of any author or any participating institution
# may be used to endorse or promote products derived from this software
# without specific prior written permission.
# For a full list of authors, refer to the file AUTHORS.
# --------------------------------------------------------------------------
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL ANY OF THE AUTHORS OR THE CONTRIBUTING
# INSTITUTIONS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# --------------------------------------------------------------------------
# $Maintainer: Chris Bielow $
# $Authors: Chris Bielow $
# --------------------------------------------------------------------------
import re
import random
import math
import sys
import argparse
## holds FASTA header + sequence
class FASTAEntry:
pass
## grab entries from FASTA file
def nextEntry(fileobj):
entry = FASTAEntry()
entry.header = fileobj.readline()
entry.sequence = ""
for line in fileobj:
if '>' == line[0]:
yield entry
entry.header = line
entry.sequence = ""
else:
entry.sequence += line
yield entry
## sample abundance from Gaussian in log space
def sampleAbundance(mu=3, sigma=1):
return math.exp(random.gauss(mu, sigma))
def main(argv):
## we use ArgumentParser, which requires 2.7
if sys.version_info < (2, 7):
raise "This script requires python 2.7 or greater"
## add weight filtering functionality if BioPython is available
try:
from Bio.SeqUtils.ProtParam import ProteinAnalysis
has_biopython = 1
except :
has_biopython = 0
parser = argparse.ArgumentParser(description='Add abundance to FASTA files.')
parser.add_argument('infile', type=argparse.FileType('r'), help='Input FASTA file')
parser.add_argument('outfile', type=argparse.FileType('w'), help='Output FASTA file')
parser.add_argument('--mu', dest='mu', action='store', default=3, help='mean of gaussian in log space')
parser.add_argument('--sigma', dest='sigma', action='store', default=1, help='sd of gaussian in log space')
parser.add_argument('--sample', dest='sample', action='store', default=0, help='Number of entries to keep (for sampling a bigger FASTA file)')
parser.add_argument('--random', dest='random', action='store_true', help='Randomly shuffle entries before sampling (only if --sample is given). If not given, the first \'X\' samples are used.')
if (has_biopython):
parser.add_argument('--weight_low', dest='weight_low', action='store', default=0, help='minimum molecular weight of protein')
parser.add_argument('--weight_up', dest='weight_up', action='store', default=0, help='Maximum molecular weight of protein (use 0 for unlimited)')
else:
print "Warning: protein weight filtering not supported, as BioPython module is not installed."
## argument parsing
args = parser.parse_args()
fileobj = args.infile
fileoutobj = args.outfile
sample_size = int(args.sample)
sample_random = bool(args.random)
if (has_biopython):
weight_low = float(args.weight_low)
weight_up = float(args.weight_up)
if (weight_up <= 0): weight_up = sys.float_info.max
## list of final entries
fasta_entries = []
for entry in nextEntry(fileobj):
header = entry.header
## check if it contains 'intensity'?
rep = re.compile(r"\[# *(.*) *#\]")
m = rep.search(header)
header_new = ""
other = []
if (m):
header_new = header.replace(m.group(0), "") ## delete meta
for element in m.group(1).split(','):
#print "element:", element
if (element.find("intensity") == -1):
other.append(element)
else:
header_new = header ## nothing to replace
## create new metainfo array
i = "intensity=" + str(sampleAbundance(float(args.mu), float(args.sigma)))
other.append(i)
entry.header = header_new.rstrip() + "[# " + (", ").join(other) + " #]"
if (has_biopython):
sequence = "".join(entry.sequence.split("\n"))
##
## BioPython does not like some AA letters - they need replacement
##
## replace "U" (Selenocystein) with "C" (Cystein)
sequence = sequence.replace("U","C")
## replace "X" (unknown) with "P" (Proline) [arbitrary choice - but weight of 115 is very close to averagine]
sequence = sequence.replace("X","P")
## replace "B" (Asparagine or aspartic acid) with "N" (Asparagine)
sequence = sequence.replace("B","N")
## replace "Z" (Glutamine or glutamic acid) with "Q" (Glutamine)
sequence = sequence.replace("Z","Q")
## replace "Z" (Glutamine or glutamic acid) with "Q" (Glutamine)
sequence = sequence.replace("Z","Q")
## replace "J" (Leucine or Isoleucine) with "L" (Leucine)
sequence = sequence.replace("J","L")
analysed_seq = ProteinAnalysis(sequence)
weight = analysed_seq.molecular_weight()
if (not(weight_low <= weight and weight <= weight_up)):
continue
fasta_entries.append(entry.header + "\n" + entry.sequence)
## only read to sample size (the rest is thrown away anyways)
if (sample_size > 0 and not(sample_random)):
if (len(fasta_entries) >= sample_size):
break
## select subset (if required)
if (sample_size > 0):
indices = range(0,len(fasta_entries))
## random sampling only makes sense if we take a subset
if (sample_random and sample_size < len(fasta_entries)):
random.shuffle(indices)
indices = [indices[i] for i in range(0,sample_size)]
fasta_entries = [fasta_entries[i] for i in indices]
## write to file
for entry in fasta_entries:
fileoutobj.write(entry)
if __name__ == "__main__":
main(sys.argv)
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