/usr/lib/python2.7/dist-packages/GenomicConsensus/consensus.py is in python-pbgenomicconsensus 2.1.0-1.
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# Copyright (c) 2011-2013, Pacific Biosciences of California, Inc.
#
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * 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 Pacific Biosciences nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY
# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY PACIFIC BIOSCIENCES AND ITS
# 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 PACIFIC BIOSCIENCES OR
# ITS CONTRIBUTORS 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.
#################################################################################
# Author: David Alexander
import numpy as np
__all__ = [ "Consensus",
"QuiverConsensus",
"ArrowConsensus",
"totalLength",
"areContiguous",
"join" ]
class Consensus(object):
"""
A multiple sequence consensus corresponding to a
(reference/scaffold) coordinate region
"""
def __init__(self, refWindow, sequence, confidence):
assert (len(sequence) ==
len(confidence))
self.refWindow = refWindow
self.sequence = sequence
self.confidence = confidence
def __cmp__(self, other):
return cmp(self.refWindow, other.refWindow)
#
# Functions for calling the consensus for regions of inadequate
# coverage
#
@classmethod
def nAsConsensus(cls, refWin, referenceSequence):
length = len(referenceSequence)
seq = np.empty(length, dtype="S1")
seq.fill("N")
conf = np.zeros(length, dtype=np.uint8)
return cls(refWin, seq.tostring(), conf)
@classmethod
def referenceAsConsensus(cls, refWin, referenceSequence):
conf = np.zeros(len(referenceSequence), dtype=np.uint8)
return cls(refWin, referenceSequence, conf)
@classmethod
def lowercaseReferenceAsConsensus(cls, refWin, referenceSequence):
conf = np.zeros(len(referenceSequence), dtype=np.uint8)
return cls(refWin, referenceSequence.lower(), conf)
@classmethod
def noCallConsensus(cls, noCallStyle, refWin, refSequence):
d = { "nocall" : cls.nAsConsensus,
"reference" : cls.referenceAsConsensus,
"lowercasereference" : cls.lowercaseReferenceAsConsensus}
factory = d[noCallStyle]
return factory(refWin, refSequence)
class QuiverConsensus(Consensus):
"""
A QuiverConsensus object carries an additional field, `mms`, which
is the ConsensusCore MultiReadMutationScorer object, which can be
used to perform some post-hoc analyses (diploid, sample mixture, etc)
"""
def __init__(self, refWindow, sequence, confidence, mms=None):
super(QuiverConsensus, self).__init__(refWindow, sequence, confidence)
self.mms = mms
class ArrowConsensus(Consensus):
"""
An ArrowConsensus object carries an additional field, `ai`, which
is the ConsensusCore2 abstract integrator object, which can be used
to perform some post-hoc analyses (diploid, sample mixture, etc)
"""
def __init__(self, refWindow, sequence, confidence, ai=None):
super(ArrowConsensus, self).__init__(refWindow, sequence, confidence)
self.ai = ai
def totalLength(consensi):
"""
Total length of reference/scaffold coordinate windows
"""
return sum(cssChunk.refWindow[2] - cssChunk.refWindow[1]
for cssChunk in consensi)
def areContiguous(refWindows):
"""
Predicate that determines whether the reference/scaffold windows
are contiguous.
"""
lastEnd = None
lastId = None
for refWin in refWindows:
id, start, end = refWin
if ((lastId is not None and id != lastId) or
(lastEnd is not None and start != lastEnd)):
return False
lastEnd = end
lastId = id
return True
def join(consensi):
"""
[Consensus] -> Consensus
String together all the consensus objects into a single consensus.
Will raise a ValueError if the reference windows are not
contiguous.
"""
assert len(consensi) >= 1
sortedConsensi = sorted(consensi)
if not areContiguous([cssChunk.refWindow for cssChunk in sortedConsensi]):
raise ValueError, "Consensus chunks must be contiguous"
joinedRefWindow = (sortedConsensi[0].refWindow[0],
sortedConsensi[0].refWindow[1],
sortedConsensi[-1].refWindow[2])
joinedSeq = "".join([cssChunk.sequence for cssChunk in sortedConsensi])
joinedConfidence = np.concatenate([cssChunk.confidence for cssChunk in sortedConsensi])
return Consensus(joinedRefWindow,
joinedSeq,
joinedConfidence)
#
# Naming convention for consensus contigs
#
def consensusContigName(referenceName, algorithmName):
return "%s|%s" % (referenceName, algorithmName)
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