/usr/bin/last-train is in last-align 921-1.
This file is owned by root:root, with mode 0o755.
The actual contents of the file can be viewed below.
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# Copyright 2015 Martin C. Frith
import gzip
import math, optparse, os, random, signal, subprocess, sys, tempfile
def myOpen(fileName): # faster than fileinput
if fileName == "-":
return sys.stdin
if fileName.endswith(".gz"):
return gzip.open(fileName)
return open(fileName)
def randomSample(things, sampleSize):
"""Randomly get sampleSize things (or all if fewer)."""
reservoir = [] # "reservoir sampling" algorithm
for i, x in enumerate(things):
if i < sampleSize:
reservoir.append(x)
else:
r = random.randrange(i + 1)
if r < sampleSize:
reservoir[r] = x
return reservoir
def writeWords(outFile, words):
outFile.write(" ".join(words) + "\n")
def seqInput(fileNames):
if not fileNames:
fileNames = ["-"]
for name in fileNames:
f = myOpen(name)
seqType = 0
for line in f:
if seqType == 0:
if line[0] == ">":
seqType = 1
seq = []
elif line[0] == "@":
seqType = 2
lineType = 1
elif seqType == 1: # fasta
if line[0] == ">":
yield "".join(seq), ""
seq = []
else:
seq.append(line.rstrip())
elif seqType == 2: # fastq
if lineType == 1:
seq = line.rstrip()
elif lineType == 3:
yield seq, line.rstrip()
lineType = (lineType + 1) % 4
if seqType == 1: yield "".join(seq), ""
f.close()
def isGoodChunk(chunk):
for i in chunk:
for j in i[3]:
if j not in "Nn":
return True
return False
def chunkInput(opts, sequences):
chunkCount = 0
chunk = []
wantedLength = opts.sample_length
for i, x in enumerate(sequences):
seq, qual = x
if all(i in "Nn" for i in seq): continue
seqLength = len(seq)
beg = 0
while beg < seqLength:
length = min(wantedLength, seqLength - beg)
end = beg + length
segment = i, beg, end, seq[beg:end], qual[beg:end]
chunk.append(segment)
wantedLength -= length
if not wantedLength:
if isGoodChunk(chunk):
yield chunk
chunkCount += 1
chunk = []
wantedLength = opts.sample_length
beg = end
if chunk and chunkCount < opts.sample_number:
yield chunk
def writeSegment(outfile, segment):
if not segment: return
i, beg, end, seq, qual = segment
name = str(i) + ":" + str(beg)
if qual:
outfile.write("@" + name + "\n")
outfile.write(seq)
outfile.write("\n+\n")
outfile.write(qual)
else:
outfile.write(">" + name + "\n")
outfile.write(seq)
outfile.write("\n")
def getSeqSample(opts, queryFiles, outfile):
sequences = seqInput(queryFiles)
chunks = chunkInput(opts, sequences)
sample = randomSample(chunks, opts.sample_number)
sample.sort()
x = None
for chunk in sample:
for y in chunk:
if x and y[0] == x[0] and y[1] == x[2]:
x = x[0], x[1], y[2], x[3] + y[3], x[4] + y[4]
else:
writeSegment(outfile, x)
x = y
writeSegment(outfile, x)
def versionFromHeader(lines):
for line in lines:
words = line.split()
if "version" in words:
return words[-1]
raise Exception("couldn't read the version")
def scaleFromHeader(lines):
for line in lines:
for i in line.split():
if i.startswith("t="):
return float(i[2:])
raise Exception("couldn't read the scale")
def scoreMatrixFromHeader(lines):
matrix = []
for line in lines:
w = line.split()
if len(w) > 2 and len(w[1]) == 1:
matrix.append(w[1:])
elif matrix:
break
return matrix
def scaledMatrix(matrix, scaleIncrease):
return matrix[0:1] + [i[0:1] + [int(j) * scaleIncrease for j in i[1:]]
for i in matrix[1:]]
def countsFromLastOutput(lines, opts):
matrix = []
# use +1 pseudocounts as a kludge to mitigate numerical problems:
matches = 1.0
deletes = 2.0 # 1 open + 1 extension
inserts = 2.0 # 1 open + 1 extension
delOpens = 1.0
insOpens = 1.0
alignments = 0 # no pseudocount here
for line in lines:
if line[0] == "s":
strand = line.split()[4] # slow?
if line[0] == "c":
c = map(float, line.split()[1:])
if not matrix:
matrixSize = int(math.sqrt(len(c) - 10))
matrix = [[1.0] * matrixSize for i in range(matrixSize)]
identities = sum(c[i * matrixSize + i] for i in range(matrixSize))
alignmentLength = c[-10] + c[-9] + c[-8]
if 100 * identities > opts.pid * alignmentLength: continue
for i in range(matrixSize):
for j in range(matrixSize):
if strand == "+" or opts.S == "0":
matrix[i][j] += c[i * matrixSize + j]
else:
matrix[-1-i][-1-j] += c[i * matrixSize + j]
matches += c[-10]
deletes += c[-9]
inserts += c[-8]
delOpens += c[-7]
insOpens += c[-6]
alignments += 1
gapCounts = matches, deletes, inserts, delOpens, insOpens, alignments
return matrix, gapCounts
def scoreFromProb(scale, prob):
if prob > 0: logProb = math.log(prob)
else: logProb = -800 # exp(-800) is exactly zero, on my computer
return int(round(scale * logProb))
def costFromProb(scale, prob):
return -scoreFromProb(scale, prob)
def guessAlphabet(matrixSize):
if matrixSize == 4: return "ACGT"
if matrixSize == 20: return "ACDEFGHIKLMNPQRSTVWY"
raise Exception("can't handle unusual alphabets")
def matrixWithLetters(matrix):
alphabet = guessAlphabet(len(matrix))
return [alphabet] + [[a] + i for a, i in zip(alphabet, matrix)]
def writeMatrixHead(outFile, prefix, alphabet, formatString):
writeWords(outFile, [prefix + " "] + [formatString % k for k in alphabet])
def writeMatrixBody(outFile, prefix, alphabet, matrix, formatString):
for i, j in zip(alphabet, matrix):
writeWords(outFile, [prefix + i] + [formatString % k for k in j])
def writeCountMatrix(outFile, matrix, prefix):
alphabet = guessAlphabet(len(matrix))
writeMatrixHead(outFile, prefix, alphabet, "%-14s")
writeMatrixBody(outFile, prefix, alphabet, matrix, "%-14s")
def writeProbMatrix(outFile, matrix, prefix):
alphabet = guessAlphabet(len(matrix))
writeMatrixHead(outFile, prefix, alphabet, "%-14s")
writeMatrixBody(outFile, prefix, alphabet, matrix, "%-14g")
def writeScoreMatrix(outFile, matrix, prefix):
alphabet = guessAlphabet(len(matrix))
writeMatrixHead(outFile, prefix, alphabet, "%6s")
writeMatrixBody(outFile, prefix, alphabet, matrix, "%6s")
def writeMatrixWithLetters(outFile, matrix, prefix):
head = matrix[0]
tail = matrix[1:]
left = [i[0] for i in tail]
body = [i[1:] for i in tail]
writeMatrixHead(outFile, prefix, head, "%6s")
writeMatrixBody(outFile, prefix, left, body, "%6s")
def matProbsFromCounts(counts, opts):
r = range(len(counts))
if opts.revsym: # add complement (reverse strand) substitutions
counts = [[counts[i][j] + counts[-1-i][-1-j] for j in r] for i in r]
if opts.matsym: # symmetrize the substitution matrix
counts = [[counts[i][j] + counts[j][i] for j in r] for i in r]
identities = sum(counts[i][i] for i in r)
total = sum(map(sum, counts))
probs = [[j / total for j in i] for i in counts]
print "# substitution percent identity: %g" % (100 * identities / total)
print
print "# count matrix (query letters = columns, reference letters = rows):"
writeCountMatrix(sys.stdout, counts, "# ")
print
print "# probability matrix (query letters = columns, reference letters = rows):"
writeProbMatrix(sys.stdout, probs, "# ")
print
return probs
def gapProbsFromCounts(counts, opts):
matches, deletes, inserts, delOpens, insOpens, alignments = counts
if not alignments: raise Exception("no alignments")
gaps = deletes + inserts
gapOpens = delOpens + insOpens
denominator = matches + gapOpens + (alignments + 1) # +1 pseudocount
if opts.gapsym:
delOpenProb = gapOpens / denominator / 2
insOpenProb = gapOpens / denominator / 2
delExtendProb = (gaps - gapOpens) / gaps
insExtendProb = (gaps - gapOpens) / gaps
else:
delOpenProb = delOpens / denominator
insOpenProb = insOpens / denominator
delExtendProb = (deletes - delOpens) / deletes
insExtendProb = (inserts - insOpens) / inserts
print "# aligned letter pairs:", matches
print "# deletes:", deletes
print "# inserts:", inserts
print "# delOpens:", delOpens
print "# insOpens:", insOpens
print "# alignments:", alignments
print "# mean delete size: %g" % (deletes / delOpens)
print "# mean insert size: %g" % (inserts / insOpens)
print "# delOpenProb: %g" % delOpenProb
print "# insOpenProb: %g" % insOpenProb
print "# delExtendProb: %g" % delExtendProb
print "# insExtendProb: %g" % insExtendProb
print
delCloseProb = 1 - delExtendProb
insCloseProb = 1 - insExtendProb
firstDelProb = delOpenProb * delCloseProb
firstInsProb = insOpenProb * insCloseProb
# If we define "an alignment" to mean "a set of indistinguishable
# paths", then:
#delExtendProb += firstDelProb
#insExtendProb += firstInsProb
# Else, this ensures gap existence cost >= 0:
delExtendProb = max(delExtendProb, firstDelProb)
insExtendProb = max(insExtendProb, firstInsProb)
delExistProb = firstDelProb / delExtendProb
insExistProb = firstInsProb / insExtendProb
return delExistProb, insExistProb, delExtendProb, insExtendProb
def scoreFromLetterProbs(scale, pairProb, prob1, prob2):
probRatio = pairProb / (prob1 * prob2)
return scoreFromProb(scale, probRatio)
def matScoresFromProbs(scale, probs):
rowProbs = map(sum, probs)
colProbs = map(sum, zip(*probs))
return [[scoreFromLetterProbs(scale, j, x, y) for j, y in zip(i, colProbs)]
for i, x in zip(probs, rowProbs)]
def gapCostsFromProbs(scale, probs):
delExistProb, insExistProb, delExtendProb, insExtendProb = probs
delExistCost = costFromProb(scale, delExistProb)
insExistCost = costFromProb(scale, insExistProb)
delExtendCost = costFromProb(scale, delExtendProb)
insExtendCost = costFromProb(scale, insExtendProb)
if delExtendCost == 0: delExtendCost = 1
if insExtendCost == 0: insExtendCost = 1
return delExistCost, insExistCost, delExtendCost, insExtendCost
def writeLine(out, *things):
out.write(" ".join(map(str, things)) + "\n")
def writeGapCosts(gapCosts, out):
delExistCost, insExistCost, delExtendCost, insExtendCost = gapCosts
writeLine(out, "#last -a", delExistCost)
writeLine(out, "#last -A", insExistCost)
writeLine(out, "#last -b", delExtendCost)
writeLine(out, "#last -B", insExtendCost)
def printGapCosts(gapCosts):
delExistCost, insExistCost, delExtendCost, insExtendCost = gapCosts
print "# delExistCost:", delExistCost
print "# insExistCost:", insExistCost
print "# delExtendCost:", delExtendCost
print "# insExtendCost:", insExtendCost
print
def tryToMakeChildProgramsFindable():
myDir = os.path.dirname(__file__)
srcDir = os.path.join(myDir, os.pardir, "src")
# put srcDir first, to avoid getting older versions of LAST:
os.environ["PATH"] = srcDir + os.pathsep + os.environ["PATH"]
def readLastalProgName(lastdbIndexName):
bitsPerInt = "32"
with open(lastdbIndexName + ".prj") as f:
for line in f:
if line.startswith("integersize="):
bitsPerInt = line.split("=")[1].strip()
return "lastal8" if bitsPerInt == "64" else "lastal"
def fixedLastalArgs(opts, lastalProgName):
x = [lastalProgName, "-j7"]
if opts.D: x.append("-D" + opts.D)
if opts.E: x.append("-E" + opts.E)
if opts.s: x.append("-s" + opts.s)
if opts.S: x.append("-S" + opts.S)
if opts.C: x.append("-C" + opts.C)
if opts.T: x.append("-T" + opts.T)
if opts.m: x.append("-m" + opts.m)
if opts.k: x.append("-k" + opts.k)
if opts.P: x.append("-P" + opts.P)
if opts.Q: x.append("-Q" + opts.Q)
if opts.verbose: x.append("-" + "v" * opts.verbose)
return x
def doTraining(opts, args):
tryToMakeChildProgramsFindable()
lastalProgName = readLastalProgName(args[0])
scaleIncrease = 20 # while training, up-scale the scores by this amount
x = fixedLastalArgs(opts, lastalProgName)
if opts.r: x.append("-r" + opts.r)
if opts.q: x.append("-q" + opts.q)
if opts.p: x.append("-p" + opts.p)
if opts.a: x.append("-a" + opts.a)
if opts.b: x.append("-b" + opts.b)
if opts.A: x.append("-A" + opts.A)
if opts.B: x.append("-B" + opts.B)
x += args
y = ["last-split", "-n"]
p = subprocess.Popen(x, stdout=subprocess.PIPE)
q = subprocess.Popen(y, stdin=p.stdout, stdout=subprocess.PIPE)
lastalVersion = versionFromHeader(q.stdout)
externalScale = scaleFromHeader(q.stdout)
internalScale = externalScale * scaleIncrease
if opts.Q:
externalMatrix = scoreMatrixFromHeader(q.stdout)
internalMatrix = scaledMatrix(externalMatrix, scaleIncrease)
oldParameters = []
print "# lastal version:", lastalVersion
print "# maximum percent identity:", opts.pid
print "# scale of score parameters:", externalScale
print "# scale used while training:", internalScale
print
while True:
print "#", " ".join(x)
print
sys.stdout.flush()
matCounts, gapCounts = countsFromLastOutput(q.stdout, opts)
gapProbs = gapProbsFromCounts(gapCounts, opts)
gapCosts = gapCostsFromProbs(internalScale, gapProbs)
printGapCosts(gapCosts)
if opts.Q:
if gapCosts in oldParameters: break
oldParameters.append(gapCosts)
else:
matProbs = matProbsFromCounts(matCounts, opts)
matScores = matScoresFromProbs(internalScale, matProbs)
print "# score matrix (query letters = columns, reference letters = rows):"
writeScoreMatrix(sys.stdout, matScores, "# ")
print
parameters = gapCosts, matScores
if parameters in oldParameters: break
oldParameters.append(parameters)
internalMatrix = matrixWithLetters(matScores)
x = fixedLastalArgs(opts, lastalProgName)
x.append("-p-")
x += args
p = subprocess.Popen(x, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
writeGapCosts(gapCosts, p.stdin)
writeMatrixWithLetters(p.stdin, internalMatrix, "")
p.stdin.close()
# in python2.6, the next line must come after p.stdin.close()
q = subprocess.Popen(y, stdin=p.stdout, stdout=subprocess.PIPE)
gapCosts = gapCostsFromProbs(externalScale, gapProbs)
writeGapCosts(gapCosts, sys.stdout)
if opts.s: writeLine(sys.stdout, "#last -s", opts.s)
if opts.S: writeLine(sys.stdout, "#last -S", opts.S)
if not opts.Q:
matScores = matScoresFromProbs(externalScale, matProbs)
externalMatrix = matrixWithLetters(matScores)
print "# score matrix (query letters = columns, reference letters = rows):"
writeMatrixWithLetters(sys.stdout, externalMatrix, "")
def lastTrain(opts, args):
if opts.sample_number:
random.seed(math.pi)
refName = args[0]
queryFiles = args[1:]
try:
with tempfile.NamedTemporaryFile(delete=False) as f:
getSeqSample(opts, queryFiles, f)
doTraining(opts, [refName, f.name])
finally:
os.remove(f.name)
else:
doTraining(opts, args)
if __name__ == "__main__":
signal.signal(signal.SIGPIPE, signal.SIG_DFL) # avoid silly error message
usage = "%prog [options] lastdb-name sequence-file(s)"
description = "Try to find suitable score parameters for aligning the given sequences."
op = optparse.OptionParser(usage=usage, description=description)
op.add_option("-v", "--verbose", action="count",
help="show more details of intermediate steps")
og = optparse.OptionGroup(op, "Training options")
og.add_option("--revsym", action="store_true",
help="force reverse-complement symmetry")
og.add_option("--matsym", action="store_true",
help="force symmetric substitution matrix")
og.add_option("--gapsym", action="store_true",
help="force insertion/deletion symmetry")
og.add_option("--pid", type="float", default=100, help=
"skip alignments with > PID% identity (default: %default)")
og.add_option("--sample-number", type="int", default=500, metavar="N",
help="number of random sequence samples (default: %default)")
og.add_option("--sample-length", type="int", default=2000, metavar="L",
help="length of each sample (default: %default)")
op.add_option_group(og)
og = optparse.OptionGroup(op, "Initial parameter options")
og.add_option("-r", metavar="SCORE",
help="match score (default: 6 if Q>0, else 5)")
og.add_option("-q", metavar="COST",
help="mismatch cost (default: 18 if Q>0, else 5)")
og.add_option("-p", metavar="NAME", help="match/mismatch score matrix")
og.add_option("-a", metavar="COST",
help="gap existence cost (default: 21 if Q>0, else 15)")
og.add_option("-b", metavar="COST",
help="gap extension cost (default: 9 if Q>0, else 3)")
og.add_option("-A", metavar="COST", help="insertion existence cost")
og.add_option("-B", metavar="COST", help="insertion extension cost")
op.add_option_group(og)
og = optparse.OptionGroup(op, "Alignment options")
og.add_option("-D", metavar="LENGTH",
help="query letters per random alignment (default: 1e6)")
og.add_option("-E", metavar="EG2",
help="maximum expected alignments per square giga")
og.add_option("-s", metavar="STRAND", help=
"0=reverse, 1=forward, 2=both (default: 2 if DNA, else 1)")
og.add_option("-S", metavar="NUMBER", default="1", help=
"score matrix applies to forward strand of: " +
"0=reference, 1=query (default: %default)")
og.add_option("-C", metavar="COUNT", help=
"omit gapless alignments in COUNT others with > score-per-length")
og.add_option("-T", metavar="NUMBER",
help="type of alignment: 0=local, 1=overlap (default: 0)")
og.add_option("-m", metavar="COUNT", help=
"maximum initial matches per query position (default: 10)")
og.add_option("-k", metavar="STEP", help="use initial matches starting at "
"every STEP-th position in each query (default: 1)")
og.add_option("-P", metavar="THREADS",
help="number of parallel threads")
og.add_option("-Q", metavar="NUMBER",
help="input format: 0=fasta, 1=fastq-sanger")
op.add_option_group(og)
(opts, args) = op.parse_args()
if len(args) < 1:
op.error("I need a lastdb index and query sequences")
if not opts.sample_number and (len(args) < 2 or "-" in args):
op.error("sorry, can't use stdin when --sample-number=0")
if not opts.p and (not opts.Q or opts.Q == "0"):
if not opts.r: opts.r = "5"
if not opts.q: opts.q = "5"
if not opts.a: opts.a = "15"
if not opts.b: opts.b = "3"
try: lastTrain(opts, args)
except KeyboardInterrupt: pass # avoid silly error message
except Exception, e:
prog = os.path.basename(sys.argv[0])
sys.exit(prog + ": error: " + str(e))
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