def alignerSubRoutine(folderName, referenceFile, queryFile, mummerLink,
header):
#alignerRobot.useMummerAlign(mummerLink, folderName, header, referenceFile, queryFile)
numberOfFiles = houseKeeper.globalParallelFileNum
bindir = os.path.abspath(os.path.dirname(sys.argv[0]))
command = bindir + "/finisherSCCoreLib/fasta-splitter.pl --n-parts " + str(
numberOfFiles) + " " + folderName + queryFile
os.system(command)
os.system("cp *.fasta " + folderName)
os.system("rm *.fasta ")
workerList = []
for dummyI in range(1, numberOfFiles + 1):
indexOfMum = ""
if dummyI < 10:
indexOfMum = "0" + str(dummyI)
else:
indexOfMum = str(dummyI)
outputName, referenceName, queryName, specialName = header + indexOfMum, referenceFile, queryFile[
0:-6] + ".part-" + indexOfMum + ".fasta", header + indexOfMum
workerList.append([outputName, referenceName, queryName, specialName])
alignerRobot.useMummerAlignBatch(mummerLink,
folderName,
workerList,
houseKeeper.globalParallel,
specialForRaw=False,
refinedVersion=False)
alignerRobot.combineMultipleCoorMum(True, mummerLink, folderName, header,
header + "Out", numberOfFiles)
def alignerSubRoutine(folderName ,referenceFile, queryFile, mummerLink, header ):
#alignerRobot.useMummerAlign(mummerLink, folderName, header, referenceFile, queryFile)
numberOfFiles = 20
bindir = os.path.abspath(os.path.dirname(sys.argv[0]))
command = bindir + "/finisherSCCoreLib/fasta-splitter.pl --n-parts " + str(numberOfFiles) + " " + folderName + queryFile
os.system(command)
os.system("cp *.fasta " + folderName )
os.system("rm *.fasta ")
workerList = []
for dummyI in range(1, numberOfFiles + 1):
indexOfMum = ""
if dummyI < 10:
indexOfMum = "0" + str(dummyI)
else:
indexOfMum = str(dummyI)
outputName, referenceName, queryName, specialName= header+indexOfMum, referenceFile,queryFile[0:-6]+".part-"+ indexOfMum + ".fasta" , header + indexOfMum
workerList.append([outputName, referenceName, queryName, specialName])
alignerRobot.useMummerAlignBatch(mummerLink, folderName, workerList, houseKeeper.globalParallel ,specialForRaw = False, refinedVersion = False)
alignerRobot.combineMultipleCoorMum( True, mummerLink, folderName, header,header +"Out", numberOfFiles)
def generateAbundanceGraph(folderName, mummerLink):
print "generateAbundanceGraph"
"""
1. Find your favorite mappers to map read back
a. MUMmer, Bowtie, bbmap, any that works V
b. And then write a short parser to parse the results V
"""
numberOfFiles = 20
workerList = []
for dummyI in range(1, numberOfFiles + 1):
indexOfMum = ""
if dummyI < 10:
indexOfMum = "0" + str(dummyI)
else:
indexOfMum = str(dummyI)
"""
"outGapFillRefine"+indexOfMum , "smaller_improvedContig.fasta", "relatedReads_Double.part-" + indexOfMum + ".fasta", "fromMumRefine" + indexOfMum
"""
outputName, referenceName, queryName, specialName = (
"outAbun" + indexOfMum,
"improved3.fasta",
"raw_reads.part-" + indexOfMum + ".fasta",
"outAbun" + indexOfMum,
)
workerList.append([outputName, referenceName, queryName, specialName])
if True:
alignerRobot.useMummerAlignBatch(mummerLink, folderName, workerList, houseKeeper.globalParallel, False)
"""
command = mummerLink + "nucmer --maxmatch --nosimplify -p " + folderName + "out " + folderName + "improved3.fasta "+folderName+"raw_reads.part-" + indexOfMum + ".fasta"
os.system(command)
command = mummerLink + "show-coords -r " + folderName + "out.delta > " + folderName + "fromMumAbun" + indexOfMum
os.system(command)
"""
dataList = []
for i in range(1, 1 + numberOfFiles):
if i < 10:
indexOfMum = "0" + str(i)
else:
indexOfMum = str(i)
dataList = dataList + alignerRobot.extractMumData(folderName, "outAbun" + str(indexOfMum) + "Out")
"""
2. Calculate count on the abundances
a. Aggregate by taking average [put weights on bin along contigs]
b. Inheritance and a subclass
"""
lenDic = IORobot.obtainLength(folderName, "improved3.fasta")
readLenDic = IORobot.obtainLength(folderName, "raw_reads.fasta")
myCountDic = {}
for eachitem in lenDic:
myCountDic[eachitem] = [0 for i in range(lenDic[eachitem])]
thres = 30
lenSum = 0
extraDataList = []
print "len(dataList)", len(dataList)
if not abunHouseKeeper.abunGlobalAvoidrefine:
myCountDic = evaluateCoverage(dataList, lenDic, readLenDic, folderName, mummerLink, True)
extraDataList = alignerRobot.extractMumData(folderName, "abunMissOut")
else:
extraDataList = []
dataList = dataList + extraDataList
myCountDic = evaluateCoverage(dataList, lenDic, readLenDic, folderName, mummerLink, False)
with open(folderName + "myCountDic.json", "w") as f:
json.dump(myCountDic, f)
return myCountDic
def evaluateCoverage(dataList, lenDic, readLenDic, folderName, mummerLink, continueFilter):
myCountDic = {}
for eachitem in lenDic:
myCountDic[eachitem] = 0
dataList.sort(key=itemgetter(-1))
ctkk, ctbase = 0, 0
toAddBackDic = copy.deepcopy(readLenDic)
for key, items in groupby(dataList, itemgetter(-1)):
maxMatch = -1
bestname = ""
for eachitem in items:
ct = eachitem[6] / 100.0 * eachitem[4]
if ct > maxMatch:
maxMatch = ct
bestname = eachitem[-2]
myCountDic[bestname] += readLenDic[key]
ctkk = ctkk + 1
ctbase = ctbase + readLenDic[key]
toAddBackDic[key] = -1
cttot = 0
for eachitem in readLenDic:
cttot = cttot + readLenDic[eachitem]
print "Missed coverage ", (cttot - ctbase) / (4.7 * pow(10, 6))
print "percentage miss read", (len(readLenDic) - ctkk) / (1.0 * len(readLenDic))
toAddReadList = []
for eachitem in toAddBackDic:
if toAddBackDic[eachitem] >= 0:
toAddReadList.append(eachitem)
"""
This part need the most parallelism because it is most intense with -l 10
split V, workerList V , combine
"""
if continueFilter:
numberOfFiles = 20
IORobot.putListToFileO(folderName, "raw_reads.fasta", "selected_raw", toAddReadList)
bindir = os.path.abspath(os.path.dirname(sys.argv[0]))
command = (
bindir
+ "/finisherSCCoreLib/fasta-splitter.pl --n-parts "
+ str(numberOfFiles)
+ " "
+ folderName
+ "selected_raw.fasta"
)
os.system(command)
workerList = []
for dummyI in range(1, numberOfFiles + 1):
indexOfMum = ""
if dummyI < 10:
indexOfMum = "0" + str(dummyI)
else:
indexOfMum = str(dummyI)
outputName, referenceName, queryName, specialName = (
"outAbunRefine" + indexOfMum,
"improved3.fasta",
"selected_raw.part-" + indexOfMum + ".fasta",
"abunMissOut" + indexOfMum,
)
workerList.append([outputName, referenceName, queryName, specialName])
alignerRobot.useMummerAlignBatch(
mummerLink, folderName, workerList, houseKeeper.globalParallel, specialForRaw=True, refinedVersion=True
)
alignerRobot.combineMultipleCoorMum(True, mummerLink, folderName, "outAbunRefine", "abunMissOut", numberOfFiles)
for i in range(len(myCountDic)):
eachitem = "Segkk" + str(i)
print eachitem, myCountDic[eachitem] / (1.0 * lenDic[eachitem])
myCountDic[eachitem] = myCountDic[eachitem] / (1.0 * lenDic[eachitem])
return myCountDic
def getAllAssociatedReads(folderName, mummerLink,forFastaName):
'''
Input : relatedReads.fasta, raw_reads.fasta
Output : all_associated_reads.fasta
Algorithm :
a) Get all the associated reads
b) Loop for N=1 times : ==> this correspond 4 reads to link between the bridge in total
i) Align the raws and tmp_seedReads
ii) Put the new reads into the SeedReads
'''
header, referenceFile, queryFile = "seedReads", forFastaName + ".fasta" , "raw_reads.fasta"
gapFiller.formRelatedReadsFile(folderName, mummerLink, "improved3")
command = "cp " + folderName + "relatedReads.fasta " + folderName + referenceFile
os.system(command)
N = abunHouseKeeper.abunGlobalReadSearchDepth
print "N: ", N
if N >0 :
for trial in range(N):
print "trial", trial
numberOfFiles = houseKeeper.globalParallelFileNum
if True:
workerList = []
for dummyI in range(1, numberOfFiles + 1):
indexOfMum = ""
if dummyI < 10:
indexOfMum = "0" + str(dummyI)
else:
indexOfMum = str(dummyI)
outputName, referenceName, queryName, specialName= header+indexOfMum, referenceFile, "raw_reads.part-"+ indexOfMum + ".fasta", header + indexOfMum
workerList.append([outputName, referenceName, queryName, specialName])
alignerRobot.useMummerAlignBatch(mummerLink, folderName, workerList, houseKeeper.globalParallel ,False)
dataList = []
for i in range(1, 1+numberOfFiles):
if i < 10:
indexOfMum = "0" + str(i)
else:
indexOfMum = str(i)
dataList = dataList+ alignerRobot.extractMumData(folderName, header+ str(indexOfMum)+"Out")
filterList = []
lenDicRR = IORobot.obtainLength(folderName, queryFile)
print "len(dataList)", len(dataList)
for eachitem in dataList:
if checkSatisfy(eachitem, lenDicRR):
filterList.append(eachitem)
filterList.sort(key=itemgetter(-1))
newReads = []
for key, items in groupby(filterList, itemgetter(-1)):
newReads.append(key)
f = open(folderName + forFastaName + ".txt", 'w')
for eachitem in newReads:
f.write(eachitem + "\n")
f.close()
command = "perl -ne 'if(/^>(\S+)/){$c=$i{$1}}$c?print:chomp;$i{$_}=1 if @ARGV' " + folderName + forFastaName + ".txt " + folderName + "raw_reads.fasta > " + folderName + forFastaName + ".fasta"
os.system(command)
else:
os.system("cp " + folderName + "relatedReads.fasta " + folderName + forFastaName + ".fasta")
def formReadContigStringGraph(folderName,
mummerLink,
contigFilename,
readsetFilename,
optTypeFileHeader,
graphName,
needAlignment=True):
'''
Input : all_associated_reads.fasta, improved3.fasta
Output : (G) String Graph linking the reads and contigs
Algorithm:
a) Form double reads and contigs V
b) Mummer the data and extract dataList three times V
c) Use the subroutine to output a graph V
d) Output the graph to a file phasing_String_graph.graph V
'''
G = []
IORobot.writeToFile_Double1(folderName, contigFilename + ".fasta",
contigFilename + "_Double.fasta", "contig")
IORobot.writeToFile_Double1(folderName, readsetFilename + ".fasta",
readsetFilename + "_Double.fasta", "reads")
header, referenceFile, queryFile = optTypeFileHeader + "CC", contigFilename + "_Double.fasta", contigFilename + "_Double.fasta"
#if needAlignment:
# alignerRobot.useMummerAlign(mummerLink, folderName, header, referenceFile, queryFile)
if needAlignment:
alignerRobot.useMummerAlignBatch(
mummerLink, folderName, [[header, referenceFile, queryFile, ""]],
houseKeeper.globalParallel)
lenDicCC = IORobot.obtainLength(folderName,
contigFilename + "_Double.fasta")
dataListCC = alignerRobot.extractMumData(folderName, header + "Out")
dataListCC = abunHouseKeeper.filterData(dataListCC, lenDicCC)
header, referenceFile, queryFile = optTypeFileHeader + "RR", readsetFilename + "_Double.fasta", readsetFilename + "_Double.fasta"
lenDicRR = IORobot.obtainLength(folderName,
readsetFilename + "_Double.fasta")
if not abunHouseKeeper.abunGlobalRRDisable:
if needAlignment:
alignerSubRoutine(folderName, referenceFile, queryFile, mummerLink,
header)
dataListRR = alignerRobot.extractMumData(folderName, header + "Out")
dataListRR = abunHouseKeeper.filterData(dataListRR, lenDicRR)
dataListRR = abunHouseKeeper.filterDataIdentical(dataListRR, lenDicRR)
else:
dataListRR = []
header, referenceFile, queryFile = optTypeFileHeader + "CR", contigFilename + "_Double.fasta", readsetFilename + "_Double.fasta"
if needAlignment:
alignerSubRoutine(folderName, referenceFile, queryFile, mummerLink,
header)
lenDicCR = dict(lenDicCC.items() + lenDicRR.items())
dataListCR = alignerRobot.extractMumData(folderName, header + "Out")
dataListCR = abunHouseKeeper.filterData(dataListCR, lenDicCR)
numberOfNodes = len(lenDicCR)
G = graphLib.seqGraph(numberOfNodes)
N1, N2 = len(lenDicCC), len(lenDicRR)
print "N1, N2, numberOfNodes: ", N1, N2, numberOfNodes
'''
e.g. of dataListCC[0], dataListRR[0], dataListCR[0]
[1, 520, 2913194, 2913716, 520, 523, 99.05, 'Contig0_d', 'Contig2_d']
[1, 1383, 1253, 2603, 1383, 1351, 82.39, 'Read0_d', 'Read1705_p']
[1, 718, 4334, 5074, 718, 741, 91.91, 'Contig0_d', 'Read1018_d']
'''
addDataToList(dataListCC, G, 0, 0, 'C', 'C')
addDataToList(dataListRR, G, N1, N1, 'R', 'R')
addDataToList(dataListCR, G, 0, N1, 'C', 'R')
Gnew = formExtraEdges(folderName, optTypeFileHeader, contigFilename, G, N1)
Gnew.saveToFile(folderName, graphName)
print "len(Gnew.graphNodesList)", len(Gnew.graphNodesList)
def getAllAssociatedReads(folderName, mummerLink,forFastaName):
'''
Input : relatedReads.fasta, raw_reads.fasta
Output : all_associated_reads.fasta
Algorithm :
a) Get all the associated reads
b) Loop for N=1 times : ==> this correspond 4 reads to link between the bridge in total
i) Align the raws and tmp_seedReads
ii) Put the new reads into the SeedReads
'''
header, referenceFile, queryFile = "seedReads", forFastaName + ".fasta" , "raw_reads.fasta"
command = "cp " + folderName + "relatedReads.fasta " + folderName + referenceFile
os.system(command)
N = abunHouseKeeper.abunGlobalReadSearchDepth
print "N: ", N
if N >0 :
for trial in range(N):
print "trial", trial
numberOfFiles = 20
if True:
workerList = []
for dummyI in range(1, numberOfFiles + 1):
indexOfMum = ""
if dummyI < 10:
indexOfMum = "0" + str(dummyI)
else:
indexOfMum = str(dummyI)
outputName, referenceName, queryName, specialName= header+indexOfMum, referenceFile, "raw_reads.part-"+ indexOfMum + ".fasta", header + indexOfMum
workerList.append([outputName, referenceName, queryName, specialName])
alignerRobot.useMummerAlignBatch(mummerLink, folderName, workerList, houseKeeper.globalParallel ,False)
dataList = []
for i in range(1, 1+numberOfFiles):
if i < 10:
indexOfMum = "0" + str(i)
else:
indexOfMum = str(i)
dataList = dataList+ alignerRobot.extractMumData(folderName, header+ str(indexOfMum)+"Out")
filterList = []
lenDicRR = IORobot.obtainLength(folderName, queryFile)
print "len(dataList)", len(dataList)
for eachitem in dataList:
if checkSatisfy(eachitem, lenDicRR):
filterList.append(eachitem)
filterList.sort(key=itemgetter(-1))
newReads = []
for key, items in groupby(filterList, itemgetter(-1)):
newReads.append(key)
f = open(folderName + forFastaName + ".txt", 'w')
for eachitem in newReads:
f.write(eachitem + "\n")
f.close()
command = "perl -ne 'if(/^>(\S+)/){$c=$i{$1}}$c?print:chomp;$i{$_}=1 if @ARGV' " + folderName + forFastaName + ".txt " + folderName + "raw_reads.fasta > " + folderName + forFastaName + ".fasta"
os.system(command)
else:
os.system("cp " + folderName + "relatedReads.fasta " + folderName + forFastaName + ".fasta")
def generateAbundanceGraph(folderName, mummerLink):
print "generateAbundanceGraph"
'''
1. Find your favorite mappers to map read back
a. MUMmer, Bowtie, bbmap, any that works V
b. And then write a short parser to parse the results V
'''
numberOfFiles = 20
workerList = []
for dummyI in range(1, numberOfFiles + 1):
indexOfMum = ""
if dummyI < 10:
indexOfMum = "0" + str(dummyI)
else:
indexOfMum = str(dummyI)
'''
"outGapFillRefine"+indexOfMum , "smaller_improvedContig.fasta", "relatedReads_Double.part-" + indexOfMum + ".fasta", "fromMumRefine" + indexOfMum
'''
outputName, referenceName, queryName, specialName= "outAbun"+indexOfMum, "improved3.fasta", "raw_reads.part-"+ indexOfMum + ".fasta", "outAbun" + indexOfMum
workerList.append([outputName, referenceName, queryName, specialName])
if True:
alignerRobot.useMummerAlignBatch(mummerLink, folderName, workerList, houseKeeper.globalParallel ,False)
'''
command = mummerLink + "nucmer --maxmatch --nosimplify -p " + folderName + "out " + folderName + "improved3.fasta "+folderName+"raw_reads.part-" + indexOfMum + ".fasta"
os.system(command)
command = mummerLink + "show-coords -r " + folderName + "out.delta > " + folderName + "fromMumAbun" + indexOfMum
os.system(command)
'''
dataList = []
for i in range(1, 1+numberOfFiles):
if i < 10:
indexOfMum = "0" + str(i)
else:
indexOfMum = str(i)
dataList = dataList+ alignerRobot.extractMumData(folderName, "outAbun"+ str(indexOfMum)+"Out")
'''
2. Calculate count on the abundances
a. Aggregate by taking average [put weights on bin along contigs]
b. Inheritance and a subclass
'''
lenDic = IORobot.obtainLength(folderName, "improved3.fasta")
readLenDic = IORobot.obtainLength(folderName , "raw_reads.fasta")
myCountDic = {}
for eachitem in lenDic:
myCountDic[eachitem] = [0 for i in range(lenDic[eachitem])]
thres = 30
lenSum = 0
extraDataList= []
print "len(dataList)", len(dataList)
if not abunHouseKeeper.abunGlobalAvoidrefine:
myCountDic = evaluateCoverage(dataList, lenDic, readLenDic, folderName, mummerLink, True)
extraDataList = alignerRobot.extractMumData(folderName, "abunMissOut" )
else:
extraDataList = []
dataList = dataList + extraDataList
myCountDic = evaluateCoverage(dataList, lenDic, readLenDic, folderName, mummerLink,False)
with open(folderName + 'myCountDic.json', 'w') as f:
json.dump(myCountDic, f)
return myCountDic
def evaluateCoverage(dataList, lenDic, readLenDic, folderName,mummerLink, continueFilter):
myCountDic = {}
for eachitem in lenDic:
myCountDic[eachitem] = 0
dataList.sort(key = itemgetter(-1))
ctkk, ctbase = 0, 0
toAddBackDic = copy.deepcopy(readLenDic)
for key, items in groupby(dataList, itemgetter(-1)):
maxMatch = -1
bestname = ""
for eachitem in items:
ct = eachitem[6]/100.0 * eachitem[4]
if ct > maxMatch:
maxMatch = ct
bestname = eachitem[-2]
myCountDic[bestname] += readLenDic[key]
ctkk = ctkk + 1
ctbase = ctbase + readLenDic[key]
toAddBackDic[key] = -1
cttot = 0
for eachitem in readLenDic:
cttot = cttot + readLenDic[eachitem]
print "Missed coverage ", (cttot - ctbase)/(4.7*pow(10, 6))
print "percentage miss read", (len(readLenDic) - ctkk)/(1.0*len(readLenDic))
toAddReadList = []
for eachitem in toAddBackDic:
if toAddBackDic[eachitem] >= 0 :
toAddReadList.append(eachitem)
'''
This part need the most parallelism because it is most intense with -l 10
split V, workerList V , combine
'''
if continueFilter:
numberOfFiles= 20
IORobot.putListToFileO(folderName, "raw_reads.fasta" , "selected_raw", toAddReadList)
bindir = os.path.abspath(os.path.dirname(sys.argv[0]))
command = bindir + "/finisherSCCoreLib/fasta-splitter.pl --n-parts " + str(numberOfFiles) + " " + folderName + "selected_raw.fasta"
os.system(command)
workerList = []
for dummyI in range(1, numberOfFiles + 1):
indexOfMum = ""
if dummyI < 10:
indexOfMum = "0" + str(dummyI)
else:
indexOfMum = str(dummyI)
outputName, referenceName, queryName, specialName= "outAbunRefine"+indexOfMum, "improved3.fasta", "selected_raw.part-"+ indexOfMum + ".fasta", "abunMissOut" + indexOfMum
workerList.append([outputName, referenceName, queryName, specialName])
alignerRobot.useMummerAlignBatch(mummerLink, folderName, workerList, houseKeeper.globalParallel ,specialForRaw = True, refinedVersion = True)
alignerRobot.combineMultipleCoorMum( True, mummerLink, folderName, "outAbunRefine", "abunMissOut", numberOfFiles)
for i in range(len(myCountDic)):
eachitem = "Segkk"+str(i)
print eachitem , myCountDic[eachitem]/(1.0*lenDic[eachitem])
myCountDic[eachitem] = myCountDic[eachitem]/(1.0*lenDic[eachitem])
return myCountDic
def evaluateCoverage(dataList, lenDic, readLenDic, folderName, mummerLink,
continueFilter, contigFilename):
'''
not sure if that is the right documentation...
Input : string_graph_3, improved3.fasta, raw_reads.fasta
Output : string_graph_4 with weights [need a data structure to store the weight on node]
Algorithm :
1. Find your favorite mappers to map read back
a. MUMmer, Bowtie, bbmap, any that works V
b. And then write a short parser to parse the results V
2. Calculate count on the abundances
a. Aggregate by taking average [put weights on bin along contigs]
b. Inheritance and a subclass
3. Find your favorite graphical tool to display
a. Use a javascript library [halfviz should just work ! put weight on edge ]
'''
myCountDic = {}
for eachitem in lenDic:
myCountDic[eachitem] = 0
dataList.sort(key=itemgetter(-1))
ctkk, ctbase = 0, 0
toAddBackDic = copy.deepcopy(readLenDic)
for key, items in groupby(dataList, itemgetter(-1)):
maxMatch = -1
bestname = ""
for eachitem in items:
ct = eachitem[6] / 100.0 * eachitem[4]
if ct > maxMatch:
maxMatch = ct
bestname = eachitem[-2]
myCountDic[bestname] += readLenDic[key]
ctkk = ctkk + 1
ctbase = ctbase + readLenDic[key]
toAddBackDic[key] = -1
cttot = 0
for eachitem in readLenDic:
cttot = cttot + readLenDic[eachitem]
print "Missed coverage ", (cttot - ctbase) / (4.7 * pow(10, 6))
print "percentage miss read", (len(readLenDic) - ctkk) / (1.0 *
len(readLenDic))
toAddReadList = []
for eachitem in toAddBackDic:
if toAddBackDic[eachitem] >= 0:
toAddReadList.append(eachitem)
'''
This part need the most parallelism because it is most intense with -l 10
split V, workerList V , combine
'''
if continueFilter:
numberOfFiles = houseKeeper.globalParallelFileNum
IORobot.putListToFileO(folderName, "raw_reads.fasta", "selected_raw",
toAddReadList)
bindir = os.path.abspath(os.path.dirname(sys.argv[0]))
command = bindir + "/finisherSCCoreLib/fasta-splitter.pl --n-parts " + str(
numberOfFiles) + " " + folderName + "selected_raw.fasta"
os.system(command)
workerList = []
for dummyI in range(1, numberOfFiles + 1):
indexOfMum = ""
if dummyI < 10:
indexOfMum = "0" + str(dummyI)
else:
indexOfMum = str(dummyI)
outputName, referenceName, queryName, specialName = "outAbunRefine" + indexOfMum, contigFilename + ".fasta", "selected_raw.part-" + indexOfMum + ".fasta", "abunMissOut" + indexOfMum
workerList.append(
[outputName, referenceName, queryName, specialName])
alignerRobot.useMummerAlignBatch(mummerLink,
folderName,
workerList,
houseKeeper.globalParallel,
specialForRaw=True,
refinedVersion=True)
alignerRobot.combineMultipleCoorMum(True, mummerLink, folderName,
"outAbunRefine", "abunMissOut",
numberOfFiles)
for eachitem in lenDic:
#eachitem = "Segkk"+str(i)
print eachitem, myCountDic[eachitem] / (1.0 * lenDic[eachitem])
myCountDic[eachitem] = myCountDic[eachitem] / (1.0 * lenDic[eachitem])
return myCountDic
