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trans-splicing-fasta-to-fusions-pipe.py
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import sys, csv, subprocess, os, argparse
from datetime import date
from statistics import median
from collections import Counter
import copy
import math
# import time
# import json
import numpy
def binarySearch(arr, t):
if t <= arr[0]: return arr[0]
if t >= arr[-1]: return arr[-1]
i, j, mid = 0, len(arr)-1, 0
while i < j:
mid = int((i + j)/2)
if arr[mid] == t: return arr[mid]
elif t < arr[mid]:
if mid > 0 and t > arr[mid-1]:
if abs(arr[mid]-t) < abs(arr[mid-1]-t): return arr[mid]
else: return arr[mid-1]
j = mid
else:
if mid < len(arr)-1 and t < arr[mid+1]:
if abs(arr[mid]-t) < abs(arr[mid+1]-t): return arr[mid]
else: return arr[mid+1]
i = mid + 1
def grouper(iterable, key):
prev = None
group = []
for item in iterable:
if prev is None or item[key] - prev[key] <= 15:
group.append(item)
else:
yield group
group = [item]
prev = item
if group:
yield group
#Removing all repetitive region references
parser = argparse.ArgumentParser(description='fusion caller parse options', usage='python 8-7-flair-to-fusions-pipe.py -i flair.aligned.bam -o outputPrefix -b buffer -a path to annotations')
parser.add_argument('-o', '--output', action='store', dest='o', default=date.today().strftime("%d-%m-%Y"), help='output file name base (default: date)')
parser.add_argument('-r', '--reads', action='store', dest='r', default="", help='.fa or fq file')
parser.add_argument('-m', '--bedFile', action='store', dest='m', default="", help='.bed file')
parser.add_argument('-f', '--flair', action='store', dest='f', default="/private/home/cafelton/flair-new/flair.py", help='flair path')
parser.add_argument('-g', '--genome', action='store', dest='g', default="/private/groups/brookslab/reference_sequence/GRCh38.primary_assembly.genome.fa", help='path to genome')
#parser.add_argument('-x', '--minimap', action='store', dest='x', default="/private/groups/brookslab/bin/minimap2", help='path to minimap')
parser.add_argument('-k', '--remapSize', action='store', dest='k', default=0, type=int, help='size of area to remap - only remaps if this is specified')
parser.add_argument('-t', '--transcriptome', action='store', dest='t', default="/private/groups/brookslab/reference_annotations/gencode.v37.annotation.gtf", help='path to transcriptome (.gtf)')
parser.add_argument('-n', '--spliceJunctions', action='store', dest='n', default="/private/groups/brookslab/cafelton/fusions-code/intropolis.liftover.hg38.junctions.sorted.txt", help='path to splice junction file (.txt)')
parser.add_argument('-b', '--buffer', action='store', dest='b', default=50000, help='length of buffer for combining nearby regions')
parser.add_argument('-l', '--readSupport', action='store', dest='l', default=3, help='number of reads required to call fusion')
parser.add_argument('-a', '--anno', action='store', dest='a', default=os.path.dirname(__file__) + '/gencode.v37.annotation-short.gtf', help='path to anno.gtf')
parser.add_argument('-p', '--bedProcess', action='store_true', dest='p', help='whether to take .bam and convert to .bed and process (True = assume existing processed .bam)')
parser.add_argument('-s', '--samConvert', action='store_true', dest='s', help='whether to convert .bam to .sam or (True = convert .bam (from fq prefix) to .sam)')
parser.add_argument('-y', '--includeMito', action='store_true', dest='y', help='whether to include fusions that are in the mitochondria (True=include)')
# parser.add_argument('-q', '--geneCov', action='store_true', dest='q', help='whether to filter out fusions ')
# parser.add_argument('-v', '--fastqCov', action='store_true', dest='v', help='whether to include fusions that are in the mitochondria (True=include)')
parser.add_argument('-w', '--removePromiscuous', action='store_true', dest='w', help='whether to filter out promiscuous chimeric genes')
parser.add_argument('-u', '--flairAlign', action='store_true', dest='u', help='whether to run flair align (True=already aligned, dont run)')
parser.add_argument('-c', '--flairCorrect', action='store_true', dest='c', help='whether to run flair correct (True=already corrected, dont run)')
parser.add_argument('-d', '--detectFusions', action='store_true', dest='d', help='whether to detect fusions (True=already detected, dont run)')
parser.add_argument('-i', '--callIsoforms', action='store_true', dest='i', help='whether to detect fusion isoforms (True=already detected, dont run)')
parser.add_argument('-j', '--matchFusionIsos', action='store_true', dest='j', help='whether to match isoforms to fusions (True=already matched or dont want to match, dont run)')
#/private/groups/brookslab/reference_annotations/
args = parser.parse_args()
# print('buffer', args.b)
prefix = '.'.join(args.r.split('.')[:-1])
bedFileMade = False
if len(args.r) > 0 and not args.u and not args.p:
process = subprocess.Popen('python3 ' + args.f + ' align -g ' + args.g + ' -r ' + args.r + ' -o ' + prefix + '.aligned' + #+ ' -m ' + args.x,
'; bamToBed -bed12 -i ' + prefix + '.aligned.bam > ' + prefix + '.aligned.bed'
'; rm ' + prefix + '.aligned.bam ' + prefix + '.aligned.bam.bai',
stdout=subprocess.PIPE, shell=True)
print(process.communicate()[0].strip())
bedFileMade = True
if (bedFileMade or os.path.exists(prefix + '.aligned.bed') or len(args.m) > 0) and not args.c and not args.p:
correctQ = args.m if len(args.m) > 0 else prefix + '.aligned.bed'
process = subprocess.Popen('python3 ' + args.f + ' correct -g ' + args.g + ' -f ' + args.t + ' -q ' + correctQ + ' -o ' + prefix,
stdout=subprocess.PIPE, shell=True)
print(process.communicate()[0].strip())
if not args.p:
correctQ = args.m if args.c else prefix + '_all_corrected.bed'
#'python ' + os.path.dirname(os.path.realpath(__file__)) + '/standardizeBed.py' + ' -i ' + correctQ,
myCommands = ['bedtools intersect -wao -a ' + correctQ.rstrip('bed').rstrip('.') + '.bed' + ' -b ' + args.a + ' > ' + prefix + '-bedtools-genes.txt',
'python ' + os.path.dirname(os.path.realpath(__file__)) + '/bedtoolsGeneHelper.py' + ' -i ' + prefix + '-bedtools-genes.txt',
'rm ' + prefix + '-bedtools-genes.txt']
process = subprocess.Popen('; '.join(myCommands), stdout=subprocess.PIPE, shell=True)
print(process.communicate()[0].strip())
print('done with preprocessing')
filepath = '/'.join(prefix.split('/')[:-1]) + '/' if '/' in prefix else ''
outfilename = filepath + args.o + prefix.split('/')[-1]
if not args.d:
args.b = int(args.b)
meta = open(outfilename + "-meta.txt", "w")
metadata = []
bed = open(prefix + '-bedtools-genes-short.bed', 'r')
junctions = {}
count = 0
readLength = {}
last = None
c = 0
print('loading splice junctions and genes and paralogs')
for line in open(args.n, 'r'): # "/private/groups/brookslab/cafelton/fusions-code/gencode.v37.junctions.txt", 'r'):
count += 1
if count % 2 > 0:
last = line.strip()
else:
temp = numpy.fromstring(line.rstrip(), dtype=numpy.int, sep=",")
junctions[last] = temp
allGeneLoc = {}
for line in open(args.t, 'r'):
if line[0] != "#":
line = line.split('\t')
if line[2] == 'gene':
name = line[8].split('gene_name "')[1].split('"')[0]
allGeneLoc[name] = [[line[0], int(line[3]), int(line[4]), line[6]]]
elif line[2] == 'transcript':
name = line[8].split('gene_name "')[1].split('"')[0]
allGeneLoc[name].append([line[0], int(line[3]), int(line[4]), line[6]])
paralogs = {}
for line in open(os.path.dirname(os.path.realpath(__file__)) + '/paralog_clusters_with_dup.txt'):
line = line.rstrip().split('\t')
for i in line: paralogs[i] = line
clinicalF = []
for line in open("/private/groups/brookslab/cafelton/fusions-code/treehouse-clinical-fusions.txt"):
clinicalF.append(frozenset(line.strip().split('--')))
clinicalF = set(clinicalF)
potential_chimeric = {} # {read name: [entries]}
print("finding chimeric reads")
bedLines = []
buffer = args.b
maxMapQ, bedLineCount, avgMapQ = 0, 0, 0
c = 0
geneReads = {}
#GET POTEINTIAL CHIMERIC (MULTIPLE MAPPING) READS FROM BED FILE
c = 0
for line in bed:
if len(line) > 20:
c += 1
bedLineCount += 1
line = line.rstrip().split('\t')
bedLines.append(line)
readname, gene = line[3].split('--')
#geneReads is important for understanding what fraction of reads at a locus are in the fusion
if gene.split('/')[0] not in geneReads.keys():
geneReads[gene.split('/')[0]] = []
geneReads[gene.split('/')[0]].append(readname)
if '/' not in gene: gene = gene.replace('.0', '')
else: gene = gene.split('/')[0]
avgMapQ += int(line[4])
if int(line[4]) > maxMapQ: maxMapQ = int(line[4])
if readname in potential_chimeric:
if gene in potential_chimeric[readname]: potential_chimeric[readname][gene].append(line)
else: potential_chimeric[readname][gene] = [line]
else: potential_chimeric[readname] = {gene: [line]}
fusions_found = {} # {fused genes: count}
fusionReads = []
avgMapQ = avgMapQ/float(bedLineCount)
print("finding potential fusions")
c = 0
for read in potential_chimeric:
if len(potential_chimeric[read]) > 1:
c += 1
locs = list(potential_chimeric[read].keys())
locs.sort()
fusion_name = frozenset(locs)
if fusion_name not in fusions_found:
fusions_found[fusion_name] = {'mapScore':0, 'readNames':[]}
for loc in locs:
fusions_found[fusion_name][loc] = {'reads':[], 'left':[], 'right':[], 'strand':[], 'chr':[potential_chimeric[read][loc][0][0]]}
for loc in locs:
fusions_found[fusion_name][loc]['reads'] += potential_chimeric[read][loc] #This gives the whole line from the bed file
for i in potential_chimeric[read][loc]:
fusions_found[fusion_name]['mapScore'] += int(i[4])
fusions_found[fusion_name][loc]['left'].append(int(i[1]))
fusions_found[fusion_name][loc]['right'].append(int(i[2]))
fusions_found[fusion_name][loc]['strand'].append(i[5])
fusions_found[fusion_name]['readNames'].append(read)
# print('fusions', len(fusions_found.keys()), c)
#AGGREGATE AND SORT NON-GENIC REGIONS IN FUSIONS
print('condensing fusions in non-genic regions')
locsToCondense = {}
c = 0
for i in fusions_found:
locs = list(i)
for j in locs:
if len(j.split('-')) > 1 and j[:3] == 'chr':
chr, loc = j.split('-')
if chr not in locsToCondense: locsToCondense[chr] = []
locsToCondense[chr].append(int(loc))
#JOIN CLOSE REGIONS TOGETHER AND MARK THEM FOR UPDATING
updateValues = {}
for chr in locsToCondense:
lastLoc = 0
lastKey = None
locsToCondense[chr].sort()
for j in locsToCondense[chr]:
if j-lastLoc <= buffer or j-lastLoc <= 1000 and lastKey == None:
lastKey = '-'.join([chr, str(lastLoc)])
updateValues[lastKey] = lastKey
updateValues['-'.join([chr, str(j)])] = lastKey
elif j-lastLoc <= buffer or j-lastLoc <= 1000:
updateValues['-'.join([chr, str(j)])] = lastKey
else:
updateValues['-'.join([chr, str(j)])] = '-'.join([chr, str(j)])
lastKey = None
lastLoc = j
#CONDENSE NON-GENIC FUSION REGIONS INTO FEWER FUSIONS
new_fusions_found = {}
# allMatches = []
readToFusion = {}
for i in fusions_found:
locs, locsstatic = list(i), list(i)
paralocs = {}
for l in range(len(locs)):
if len(locs[l].split('-')) > 1 and locs[l][:3] == 'chr':
locs[l] = updateValues[locs[l]]
if locs[l] in paralogs:
if paralogs[locs[l]][0] not in paralocs: paralocs[paralogs[locs[l]][0]] = []
paralocs[paralogs[locs[l]][0]].append(l)
else: paralocs[locs[l]] = [l]
newparalocs = {}
for p in paralocs:
a = []
for l in paralocs[p]: a.append(locs[l])
a.sort()
if p not in a: newparalocs[a[0]] = paralocs[p]
else: newparalocs[p] = paralocs[p]
newlocs = list(newparalocs.keys())
newlocs.sort()
if len(newparalocs.keys()) <= 1:
metadata.append(['--'.join(locsstatic), 'paralogs', 'pl', fusions_found[i]['readNames']])
else:
fusionname = frozenset(newlocs)
if fusionname not in new_fusions_found.keys():
new_fusions_found[fusionname] = {}
new_fusions_found[fusionname]['mapScore'] = fusions_found[i]['mapScore']
new_fusions_found[fusionname]['readNames'] = fusions_found[i]['readNames']
# allMatches.extend(fusions_found[i]['readNames'])
for r in fusions_found[i]['readNames']: readToFusion[r] = fusionname
for l in newparalocs:
new_fusions_found[fusionname][l] = fusions_found[i][locsstatic[newparalocs[l][0]]]
for subl in newparalocs[l][1:]:
for key in ['reads', 'left', 'right', 'strand', 'chr']:
new_fusions_found[fusionname][l][key] += fusions_found[i][locsstatic[subl]][key]
else:
new_fusions_found[fusionname]['mapScore'] += fusions_found[i]['mapScore']
new_fusions_found[fusionname]['readNames'].extend(fusions_found[i]['readNames'])
# allMatches.extend(fusions_found[i]['readNames'])
for r in fusions_found[i]['readNames']: readToFusion[r] = fusionname
for l in newparalocs:
if l not in new_fusions_found[fusionname]:
new_fusions_found[fusionname][l] = fusions_found[i][locsstatic[newparalocs[l][0]]]
for subl in newparalocs[l][1:]:
for key in ['reads', 'left', 'right', 'strand', 'chr']:
new_fusions_found[fusionname][l][key] += fusions_found[i][locsstatic[subl]][key]
else:
for subl in newparalocs[l]:
for key in ['reads', 'left', 'right', 'strand', 'chr']:
new_fusions_found[fusionname][l][key] += fusions_found[i][locsstatic[subl]][key]
print('new fusions', len(new_fusions_found.keys()))
# print(new_fusions_found[frozenset(['SLC4A8','CAPN7'])])
correctQ = prefix if os.path.exists(prefix + '.bam') else prefix + '.aligned'
if args.s:
print('making sam file')
process = subprocess.Popen('samtools view -h -o ' + correctQ + '.sam ' + correctQ + '.bam',stdout=subprocess.PIPE, shell=True)
print(process.communicate()[0].strip())
correctQ = prefix if os.path.exists(prefix + '.sam') else prefix + '.aligned'
# allMatches = set(allMatches)
readLength = {}
print('processing sam file')
sam = open(correctQ + '.sam', 'r')
flagbinary = {'0':0, '2048':0, '16':1, '2064':1}
mappingLocs = {}
fgenes = []
finalchimeras = []
for line in sam:
line = line.rstrip().split('\t')
if line[0] in readToFusion.keys():
locs = []
if readToFusion[line[0]] not in mappingLocs: mappingLocs[readToFusion[line[0]]] = {}
if line[0] not in mappingLocs[readToFusion[line[0]]]: mappingLocs[readToFusion[line[0]]][line[0]] = []
i = 0
while line[5][i] not in ['M', 'D', 'I', 'S', 'H', 'X', 'P', 'N']: i += 1
if line[5][i] == 'M': locs.append(0)
else: locs.append(int(line[5][:i]))
i = -2
while line[5][i] not in ['M', 'D', 'I', 'S', 'H', 'X', 'P', 'N']: i -= 1
if line[0] not in readLength: readLength[line[0]] = len(line[9])
if line[5][-1] == 'M': locs.append(readLength[line[0]])
else: locs.append(readLength[line[0]] - int(line[5][i + 1:-1]))
currDist = 1000000000000000000000
currGene = None
for gene in list(readToFusion[line[0]]):
loc = median(new_fusions_found[readToFusion[line[0]]][gene]['left'])
locchr = max(set(new_fusions_found[readToFusion[line[0]]][gene]['chr']),
key=new_fusions_found[readToFusion[line[0]]][gene]['chr'].count)
if locchr == line[2] or currGene == None:
if abs(int(line[3]) - loc) < currDist:
currDist, currGene = abs(int(line[3]) - loc), gene
index = new_fusions_found[readToFusion[line[0]]]['readNames'].index(line[0])
if flagbinary[line[1]] == 1:
temp = locs[0]
locs[0] = readLength[line[0]] - locs[1]
locs[1] = readLength[line[0]] - temp
mappingLocs[readToFusion[line[0]]][line[0]].append([locs[0], new_fusions_found[readToFusion[line[0]]][currGene]['right'][index], currGene])
mappingLocs[readToFusion[line[0]]][line[0]].append([locs[1], new_fusions_found[readToFusion[line[0]]][currGene]['left'][index], currGene])
else:
mappingLocs[readToFusion[line[0]]][line[0]].append([locs[0], new_fusions_found[readToFusion[line[0]]][currGene]['left'][index], currGene])
mappingLocs[readToFusion[line[0]]][line[0]].append([locs[1], new_fusions_found[readToFusion[line[0]]][currGene]['right'][index], currGene])
print('filtering fusions', len(mappingLocs.keys()))
filteredFusions, fusionlocbounds = [], {}
longestfastqdist, readfastqdist = {}, {}
readcorrectedbp = {}
trueBreakpoints = {'ALK':[2, 29223529], 'EML4':[2,42264732], 'GUCY1A2':[1,106776438], 'PIWIL4':[1,94568729], 'SCAMP2':[1,74845472], 'WDR72':[2,53706075],
'RET':[1,43116583], 'CCDC6':[1,59906121], 'CHRNA6':[2,42765205], 'ERGIC2':[1,29366876], 'UBR3':[1,170061078], 'EFHD1':[2,232672444]}
# tempout = open("EML4-breakpoint-extra-exon.tsv", 'w')
# tempout.write('\t'.join(['dist of genomic mapping', 'mapping gap on read', 'genomic breakpoint location', 'extra exon?']) + '\n')
# tempout = open("drr-fusion-loc-bounds.bed", 'w')
tempout2 = open("drr-breakpoint-clusters.tsv", 'w')
for f in mappingLocs:
if len(mappingLocs[f].keys()) >= args.l:
fastqcov, genecov, tooshort, genecovfinal, shortmap, isMito, mitogene = [], {}, False, [0], 0, False, None
for l in list(f):
new_fusions_found[f][l]['chr'] = max(set(new_fusions_found[f][l]['chr']), key=new_fusions_found[f][l]['chr'].count)
genecov[l] = []
for i in range(len(new_fusions_found[f][l]['left'])):
genecov[l].append([new_fusions_found[f][l]['right'][i]-new_fusions_found[f][l]['left'][i], new_fusions_found[f][l]['left'][i], new_fusions_found[f][l]['right'][i]])
if (not(l[:3] == 'chr' and '-' in l)) and l in allGeneLoc:
if allGeneLoc[l][0][0] == 'chrM': isMito, mitogene = True, l
elif l[:4] == 'chrM': isMito, mitogene = True, l
if isMito:
metadata.append(['--'.join(list(f)), 'isMito', mitogene, list(mappingLocs[f].keys())])
continue
for l in genecov:
genecov[l].sort()
# if median([x[0] for x in genecov[l][:int(len(genecov[l])/4)]]) < 200: tooshort = True
midcov, bestcov = genecov[l][int(len(genecov[l])/2)], []
if midcov[0] < 100: tooshort, shortmap = True, midcov[0]
if not (l[:3] == 'chr' and '-' in l) and l in allGeneLoc:
for t in allGeneLoc[l][1:]:
if midcov[1] >= t[1]-50 and midcov[2] <= t[2]+50: bestcov.append(float(midcov[0])/(t[2]-t[1]))
if len(bestcov) > 0: genecovfinal.append(sum(bestcov)/len(bestcov))
else: genecovfinal.append(0)
if max(genecovfinal) > 0.95:
metadata.append(['--'.join(list(f)), 'geneCov', max(genecovfinal), list(mappingLocs[f].keys())])
continue
if tooshort:
metadata.append(['--'.join(list(f)), 'tooShortMapping', shortmap, list(mappingLocs[f].keys())])
continue
minlen, alllen = 100000000, []
for r in mappingLocs[f]:
if len(mappingLocs[f][r]) < minlen: minlen = len(mappingLocs[f][r])
possPromLocs = [[] for i in range(minlen)]
firstgene = max(set([mappingLocs[f][x][0][2] for x in mappingLocs[f]]), key=[mappingLocs[f][x][0][2] for x in mappingLocs[f]].count)
breakpoints = {}
ends = {}
for r in mappingLocs[f]:
#mappingLocs[fusion][read] = [[loc on fastq, loc on genome, gene], [etc]]
tempfastqcov = 0
for l in range(0, len(mappingLocs[f][r]), 2):
tempfastqcov += abs(mappingLocs[f][r][l+1][0]-mappingLocs[f][r][l][0])
fastqcov.append(float(tempfastqcov)/readLength[r])
mappingLocs[f][r].sort()
x = 1
while x < len(mappingLocs[f][r])-2:
#condensing adjacent mappings in the same gene
if mappingLocs[f][r][x][2] == mappingLocs[f][r][x + 1][2]:
mappingLocs[f][r] = mappingLocs[f][r][:x] + mappingLocs[f][r][x+2:]
if mappingLocs[f][r][x][2] != mappingLocs[f][r][x-1][2]:
temp = mappingLocs[f][r][x]
mappingLocs[f][r][x] = mappingLocs[f][r][x+1]
mappingLocs[f][r][x+1] = temp
x += 2
while len(mappingLocs[f][r]) > minlen:
#removing extra mappings on the edges - if A-B-A, convert to A-B - seems like a bad strategy, should remove later
if mappingLocs[f][r][0][2] == mappingLocs[f][r][-1][2]:
if abs(mappingLocs[f][r][0][0]-mappingLocs[f][r][1][0]) < abs(mappingLocs[f][r][-1][0]-mappingLocs[f][r][-2][0]):
mappingLocs[f][r] = mappingLocs[f][r][2:]
else: mappingLocs[f][r] = mappingLocs[f][r][:-2]
else: mappingLocs[f][r] = mappingLocs[f][r][:-2]
#just making sure all reads are oriented in the same way - read 1: A-B, read 2: B-A -> read1:A-B, read2: A-B
if mappingLocs[f][r][0][2] != firstgene:
mappingLocs[f][r] = mappingLocs[f][r][::-1]
for x in range(len(mappingLocs[f][r])): mappingLocs[f][r][x][0] = readLength[r] - mappingLocs[f][r][x][0]
for x in range(1, len(mappingLocs[f][r])-2, 2):
bpname, gap = frozenset([mappingLocs[f][r][x][2], mappingLocs[f][r][x + 1][2]]), abs(mappingLocs[f][r][x][0] - mappingLocs[f][r][x + 1][0])
#this is just for my testing of fastq dist corr to mapping did to breakpoint, not essential
if r in readfastqdist:
if gap > readfastqdist[r]: readfastqdist[r] = gap
else: readfastqdist[r] = gap
if bpname not in breakpoints:
breakpoints[bpname] = {'fastqdist': [], 'genomedist': []}
for y in range(2): breakpoints[bpname][mappingLocs[f][r][x+y][2]] = []
breakpoints[bpname]['fastqdist'].append(gap)
if new_fusions_found[f][mappingLocs[f][r][x][2]]['chr'] == new_fusions_found[f][mappingLocs[f][r][x + 1][2]]['chr']:
breakpoints[bpname]['genomedist'].append(abs(mappingLocs[f][r][x][1] - mappingLocs[f][r][x + 1][1]))
breakpoints[bpname][mappingLocs[f][r][x][2]].append([abs(mappingLocs[f][r][x][1]-mappingLocs[f][r][x-1][1]), gap, mappingLocs[f][r][x][1]])
breakpoints[bpname][mappingLocs[f][r][x+1][2]].append([abs(mappingLocs[f][r][x+1][1] - mappingLocs[f][r][x+2][1]), gap, mappingLocs[f][r][x+1][1]])
if r not in readcorrectedbp: readcorrectedbp[r] = {}
readcorrectedbp[r][mappingLocs[f][r][x][2]] = [mappingLocs[f][r][x][1], binarySearch(junctions[new_fusions_found[f][mappingLocs[f][r][x][2]]['chr']],mappingLocs[f][r][x][1])]
readcorrectedbp[r][mappingLocs[f][r][x+1][2]] = [mappingLocs[f][r][x+1][1], binarySearch(junctions[new_fusions_found[f][mappingLocs[f][r][x+1][2]]['chr']], mappingLocs[f][r][x+1][1])]
for x in [0, -1]:
if mappingLocs[f][r][x][2] not in ends: ends[mappingLocs[f][r][x][2]] = []
#dist of mapping on genome, dist of mapping on fastq, loc on genome
ends[mappingLocs[f][r][0][2]].append([abs(mappingLocs[f][r][0][1]-mappingLocs[f][r][1][1]), abs(mappingLocs[f][r][0][0]-mappingLocs[f][r][1][0]), mappingLocs[f][r][0][1]])
ends[mappingLocs[f][r][-1][2]].append([abs(mappingLocs[f][r][-1][1] - mappingLocs[f][r][-2][1]),abs(mappingLocs[f][r][-1][0] - mappingLocs[f][r][-2][0]),mappingLocs[f][r][-1][1]])
for l in range(len(mappingLocs[f][r])): possPromLocs[l].append(mappingLocs[f][r][l])
if median(fastqcov) < 0.8: metadata.append(['--'.join(list(f)), 'fastqCov', median(fastqcov), list(mappingLocs[f].keys())])
# print("breakpoints", breakpoints)
tooclose, fastqdistfinal, closedist, ssdist = False, 0, 0, []
geneorder = []
for bp in breakpoints:
# print(f, breakpoints[bp]['genomedist'])
if geneorder == []: geneorder = list(bp)
else:
temp = list(bp)
print(geneorder, temp)
for i in range(2):
if temp[i] in geneorder:
if geneorder.index(temp[i]) == 0: geneorder = [temp[abs(i-1)]] + geneorder
else: geneorder.append(temp[abs(i-1)])
break
# print(bp, sorted(breakpoints[bp]['fastqdist']))
if median(breakpoints[bp]['fastqdist']) > 15: fastqdistfinal = median(breakpoints[bp]['fastqdist'])
if len(breakpoints[bp]['genomedist']) > 0 and median(breakpoints[bp]['genomedist']) < args.b: tooclose, closedist = True, median(breakpoints[bp]['genomedist'])
for loc in set(breakpoints[bp].keys()) - {'fastqdist', 'genomedist'}:
#just for coloring reads by fastq dist
if loc in longestfastqdist:
if max(breakpoints[bp]['fastqdist']) > longestfastqdist[loc]: longestfastqdist[loc] = max(breakpoints[bp]['fastqdist'])
else: longestfastqdist[loc] = max(breakpoints[bp]['fastqdist'])
breakpoints[bp][loc].sort(key=lambda x: x[2])
c = 0
for group in grouper(breakpoints[bp][loc], 2):
if len(group) >= 0.1 * len(breakpoints[bp][loc]) and median([x[1] for x in group]) < 50:
tempout2.write('\t'.join(['--'.join(list(f)), loc, str(c), str(len(group)), str(median([x[2] for x in group])), str(median([x[1] for x in group]))]) +'\n') #+ '\t' + ','.join([str(x[1]) + '-' + str(x[2]) for x in group]) + '\n')
c += 1
breakpoints[bp][loc].sort()
#selecting for longest mapping length on genome
breakpoints[bp][loc] = breakpoints[bp][loc][-1*int(math.ceil(float(len(breakpoints[bp][loc]))/4)):][::-1]
breakpoints[bp][loc].sort(key=lambda x:abs(x[1])) #sorting by shortest mapping gap on fastq
# if loc == 'EML4':
# for l in breakpoints[bp][loc]:
# extraexon = 'extra' if abs(l[2]-42264730) > abs(l[2]-42264950) else 'correct'
# tempout.write('\t'.join([str(x) for x in l] + [extraexon]) + '\n')
closestSS = binarySearch(junctions[new_fusions_found[f][loc]['chr']],int(median([x[2] for x in breakpoints[bp][loc][:int(math.ceil(float(len(breakpoints[bp][loc]))/4))]])))
# print(loc, int(median([x[2] for x in breakpoints[bp][loc][:int(math.ceil(float(len(breakpoints[bp][loc]))/4))]])), closestSS, trueBreakpoints[loc] - closestSS)
ssdist.append(abs(closestSS - breakpoints[bp][loc][0][2]))
breakpoints[bp][loc] = closestSS
# if f == frozenset(['SLC25A24', 'NBPF6']): print(breakpoints[bp][loc])
if fastqdistfinal > 15: metadata.append(['--'.join(list(f)), 'fastqDist', fastqdistfinal, list(mappingLocs[f].keys())])
if tooclose: metadata.append(['--'.join(list(f)), 'tooClose', closedist, list(mappingLocs[f].keys())])
if max(ssdist) > 10: metadata.append(['--'.join(list(f)), 'ssDist', max(ssdist), list(mappingLocs[f].keys())])
distToProm, gene5 = 1000000000000000000000, None
# if 'RET' in f: print("ends", ends)
fusionlocbounds[f] = {}
for loc in ends:
ends[loc].sort(key=lambda x:abs(x[1]), reverse=True)
ends[loc] = ends[loc][:int(math.ceil(float(len(ends[loc]))/4))]
ends[loc].sort(reverse=True)
medianEnd = int(median([x[2] for x in ends[loc][:int(math.ceil(float(len(ends[loc]))/4))]]))
fusionlocbounds[f][loc] = [medianEnd]
if loc in allGeneLoc:
for t in allGeneLoc[loc][1:]:
genestart = t[1] if t[3] == '+' else t[2]
if abs(medianEnd - genestart) < distToProm:
distToProm = abs(medianEnd - genestart)
gene5 = loc
# print(f, median(fastqcov), median(genecovfinal), fastqdistfinal, tooclose, ssdist, distToProm, gene5) #if f == frozenset(['SLC25A24', 'NBPF6']):
if distToProm > 300: metadata.append(['--'.join(list(f)), 'distToProm', distToProm, list(mappingLocs[f].keys())])
if gene5 != geneorder[0]: geneorder = geneorder[::-1]
#USE THIS FOR ALL FILES EXCEPT DRR and distToProm <= 300 and fastqdistfinal <= 15
if not isMito and median(fastqcov) >= 0.8 and max(ssdist) <= 10 and median(genecovfinal) <= 0.95 and not tooshort and not tooclose:
for g in f:
fgenes.append(g)
finalchimeras.append(['--'.join(list(f)), list(mappingLocs[f].keys())])
locbreakpoints = []
for i in range(0, len(geneorder)-1):
locbreakpoints.append([geneorder[i], new_fusions_found[f][geneorder[i]]['chr'], str(breakpoints[frozenset([geneorder[i], geneorder[i+1]])][geneorder[i]])])
if geneorder[i] not in fusionlocbounds[f]: fusionlocbounds[f][geneorder[i]] = []
fusionlocbounds[f][geneorder[i]].append(breakpoints[frozenset([geneorder[i], geneorder[i+1]])][geneorder[i]])
locbreakpoints.append([geneorder[-1], new_fusions_found[f][geneorder[-1]]['chr'], str(breakpoints[frozenset([geneorder[-2], geneorder[-1]])][geneorder[-1]])])
fusionlocbounds[f][geneorder[-1]].append(breakpoints[frozenset([geneorder[-1], geneorder[-2]])][geneorder[-1]])
filteredFusions.append(['--'.join(geneorder), len(mappingLocs[f]), round(new_fusions_found[f]['mapScore']/float(len(mappingLocs[f])), 2)] + ['-'.join(x) for x in locbreakpoints])
for loc in fusionlocbounds[f]:
fusionlocbounds[f][loc].sort()
# tempout.write('\t'.join([new_fusions_found[f][loc]['chr'], str(fusionlocbounds[f][loc][0]), str(fusionlocbounds[f][loc][1]), loc]) + '\n')
else: metadata.append(['--'.join(list(f)), 'readSupport', len(mappingLocs[f].keys()), list(mappingLocs[f].keys())])
for line in finalchimeras:
thesegenes = line[0].split('--')
if fgenes.count(thesegenes[0]) > 2 or fgenes.count(thesegenes[1]) > 2:
metadata.append([line[0], 'promiscuous', str(fgenes.count(thesegenes[0])) + ',' + str(fgenes.count(thesegenes[1])), line[1]])
else:
metadata.append([line[0], 'predictedSV', str(fgenes.count(thesegenes[0])) + ',' + str(fgenes.count(thesegenes[1])),line[1]])
reads = open(outfilename + "Reads.bed", "w")
#reads.write('track name="ItemRGBDemo" description="Item RGB demonstration" visibility=2 itemRgb="On"\n')
# print(fusionlocbounds)
for i in metadata:
i[3] = ','.join(i[3])
meta.write('\t'.join([str(x) for x in i]) + '\n')#i + "\t" + str(metadata[i]) + "\n")
meta.close()
# print(longestfastqdist)
# c = 0
# for x in readcorrectedbp:
# if c < 5: print(x, readcorrectedbp[x])
# c += 1
fastqdistvsbp = [[],[]]
fusions = open(outfilename + "Fusions.tsv", "w")
filteredFusions.sort(key=lambda x:x[1], reverse=True)
c = 0
for line in filteredFusions:
fusions.write('\t'.join([str(x) for x in line]) + '\n')
for l in list(set(line[0].split('--'))):
# print(line[0].split('--'))
# print([r[3] for r in new_fusions_found[frozenset(line[0].split('--'))][l]['reads']])
for r in new_fusions_found[frozenset(line[0].split('--'))][l]['reads']:
if r[3].split('--')[1].split('/')[0] in trueBreakpoints:
# print(line)
# print(r[3].split('--')[1].split('/')[0], trueBreakpoints[r[3].split('--')[1].split('/')[0]])
# print(abs(int(r[trueBreakpoints[r[3].split('--')[1].split('/')[0]][0]])-trueBreakpoints[r[3].split('--')[1].split('/')[0]][1]), int(line[trueBreakpoints[r[3].split('--')[1].split('/')[0]][0]]))
# if abs(int(r[trueBreakpoints[r[3].split('--')[1].split('/')[0]][0]])-trueBreakpoints[r[3].split('--')[1].split('/')[0]][1]) > 160000: print(line[0], r)
fastqdistvsbp[0].append(readfastqdist[r[3].split('--')[0]])
fastqdistvsbp[1].append(abs(int(r[trueBreakpoints[r[3].split('--')[1].split('/')[0]][0]])-trueBreakpoints[r[3].split('--')[1].split('/')[0]][1]))
currGene = r[3].split('--')[1].split('/')[0]
# if int(r[1]) > fusionlocbounds[frozenset(line[0].split('--'))][currGene][0] - 150 and int(r[2]) < fusionlocbounds[frozenset(line[0].split('--'))][currGene][1] + 150:
#r[8] = str(int((250/math.log(longestfastqdist[r[3].split('--')[1].split('/')[0]] + 1)) + 5)) + ",0,255"
r[3] = '-.-'.join([line[0]] + r[3].split('--')) #+ [str(readfastqdist[r[3].split('--')[0]])])
# c += 1
# for x in [1,2]:
# if int(r[x]) == readcorrectedbp[r[3].split('-.-')[1]][r[3].split('-.-')[2].split('/')[0]][0]:
# currdiff = readcorrectedbp[r[3].split('-.-')[1]][r[3].split('-.-')[2].split('/')[0]][1] - int(r[x])
# tempsizes = r[10].split(',')
# tempstarts = r[11].split(',')
# # if c <= 10: print(x, currdiff, tempsizes, tempstarts)
# if x == 1: #left side of gene
# tempsizes[0] = str(int(tempsizes[0]) + currdiff)
# for s in range(1, len(tempstarts)-1):
# tempstarts[s] = str(int(tempstarts[s]) + currdiff)
# r[11] = ','.join(tempstarts)
# elif x == 2: #right side of gene
# tempsizes[-2] = str(int(tempsizes[-2]) + currdiff)
# r[10] = ','.join(tempsizes)
# # if c <= 10: print(tempsizes, tempstarts, r[10:12])
# r[x] = str(readcorrectedbp[r[3].split('-.-')[1]][r[3].split('-.-')[2].split('/')[0]][1])
# r[x+5] = str(readcorrectedbp[r[3].split('-.-')[1]][r[3].split('-.-')[2].split('/')[0]][1])
reads.write('\t'.join(r) + '\n')
fusions.close()
reads.close()
# import scipy.stats as sps
# import matplotlib.pyplot as plt
# # tempa, tempb = max(fastqdistvsbp[0]), max(fastqdistvsbp[1])
# # fastqdistvsbp[0] = [x/tempa for x in fastqdistvsbp[0]]
# # fastqdistvsbp[1] = [x / tempb for x in fastqdistvsbp[1]]
# # print(fastqdistvsbp)
# print(sps.spearmanr(fastqdistvsbp[0], fastqdistvsbp[1]))
# fig1 = plt.figure(1)
# f, ax = plt.subplots()
# plt.xlabel('fastq distance (norm)')
# plt.ylabel('dist to breakpoint (norm)')
# points = ax.scatter([x+1 for x in fastqdistvsbp[0]], [x+1 for x in fastqdistvsbp[1]], alpha=.2)
# plt.xscale('log')
# plt.yscale('log')
# plt.xlim([1,10000])
# plt.ylim([1,1000000])
# plt.savefig('fastq-bp-dist-corr.png', dpi=600)
# 'reads': [
# ['chr12', '51391316', '51489951', 'jointest-SLC4A8-CAPN7-0-0-0--SLC4A8/+/51391317/51515763', '60', '+', '51391316', '51489951', '255,0,0', '19', '172,82,147,136,161,189,92,158,88,147,101,175,134,246,106,162,114,162,252,', '0,49391,59559,60807,62222,66034,67242,68634,69887,70993,72297,78297,79075,79970,83025,83728,94470,97382,98383,'],
# ['chr12', '51391316', '51489951', 'jointest-SLC4A8-CAPN7-0-0-1--SLC4A8/+/51391317/51515763', '60', '+', '51391316', '51489951', '255,0,0', '19', '172,82,147,136,161,189,92,158,88,147,101,175,134,246,106,162,114,162,252,', '0,49391,59559,60807,62222,66034,67242,68634,69887,70993,72297,78297,79075,79970,83025,83728,94470,97382,98383,'],
# ['chr12', '51391316', '51489951', 'jointest-SLC4A8-CAPN7-0-0-2--SLC4A8/+/51391317/51515763', '60', '+', '51391316', '51489951', '255,0,0', '19', '172,82,147,136,161,189,92,158,88,147,101,175,134,246,106,162,114,162,252,', '0,49391,59559,60807,62222,66034,67242,68634,69887,70993,72297,78297,79075,79970,83025,83728,94470,97382,98383,'],
# ['chr12', '51391316', '51489951', 'jointest-SLC4A8-CAPN7-0-0-3--SLC4A8/+/51391317/51515763', '60', '+', '51391316', '51489951', '255,0,0', '19', '172,82,147,136,161,189,92,158,88,147,101,175,134,246,106,162,114,162,252,', '0,49391,59559,60807,62222,66034,67242,68634,69887,70993,72297,78297,79075,79970,83025,83728,94470,97382,98383,'],
# ['chr12', '51391316', '51489951', 'jointest-SLC4A8-CAPN7-0-0-4--SLC4A8/+/51391317/51515763', '60', '+', '51391316', '51489951', '255,0,0', '19', '172,82,147,136,161,189,92,158,88,147,101,175,134,246,106,162,114,162,252,', '0,49391,59559,60807,62222,66034,67242,68634,69887,70993,72297,78297,79075,79970,83025,83728,94470,97382,98383,']],
# 'CAPN7': {'reads': [
# ['chr3', '15227836', '15252916', 'jointest-SLC4A8-CAPN7-0-0-0--CAPN7/+/15206152/15252916', '60', '+', '15227836', '15252916', '255,0,0', '15', '129,86,94,147,107,121,145,100,136,76,146,63,131,93,1801,', '0,1137,2605,4682,6030,7188,12636,12917,13616,14341,17689,18895,19490,23094,23279,'],
# ['chr3', '15227836', '15252916', 'jointest-SLC4A8-CAPN7-0-0-1--CAPN7/+/15206152/15252916', '60', '+', '15227836', '15252916', '255,0,0', '15', '129,86,94,147,107,121,145,100,136,76,146,63,131,93,1801,', '0,1137,2605,4682,6030,7188,12636,12917,13616,14341,17689,18895,19490,23094,23279,'],
# ['chr3', '15227836', '15252916', 'jointest-SLC4A8-CAPN7-0-0-2--CAPN7/+/15206152/15252916', '60', '+', '15227836', '15252916', '255,0,0', '15', '129,86,94,147,107,121,145,100,136,76,146,63,131,93,1801,', '0,1137,2605,4682,6030,7188,12636,12917,13616,14341,17689,18895,19490,23094,23279,'], ['chr3', '15227836', '15252916', 'jointest-SLC4A8-CAPN7-0-0-3--CAPN7/+/15206152/15252916', '60', '+', '15227836', '15252916', '255,0,0', '15', '129,86,94,147,107,121,145,100,136,76,146,63,131,93,1801,', '0,1137,2605,4682,6030,7188,12636,12917,13616,14341,17689,18895,19490,23094,23279,'], ['chr3', '15227836', '15252916', 'jointest-SLC4A8-CAPN7-0-0-4--CAPN7/+/15206152/15252916', '60', '+', '15227836', '15252916', '255,0,0', '15', '129,86,94,147,107,121,145,100,136,76,146,63,131,93,1801,', '0,1137,2605,4682,6030,7188,12636,12917,13616,14341,17689,18895,19490,23094,23279,']]
# # fusionreads = {}
# # readToLoc = {} # We will use this later
# # for fusion in new_fusions_found:
# #
# # file:
# # Line = line.split(‘\t’)
# # fusionName = line[3].split(‘-.-’)[0]
# # Gene = line[3].split(stuff)
# # Readname = line[3].split(stuff)
# # fusionreads[fusionName] = {}
# # ** Option1: fusionreads[fusionname][gene] = [[read1name, read1start, read1end], [read2name, read2start, read2end]]
# # ** Option2: fusionreads[fusionname][gene] = {leftlocs: [read1start, read2start], rightlocs: [read1end, read2end],
# # readnames: [read1name, read2name]}
# # ** *Go
# # with option2 please
# # If
# # readname not in readToLoc: readToLoc[readname] = [fusionname]
# # # readToLoc[readname].append([readstart, readend, +/-])
# #
# # For
# # fusion in fusionreads:
# # Closest = []
# # minDistToGeneStart = []
# # For
# # gene in fusionreads[fusion]:
# # Mindist = 10000000000
# # For
# # i in [mean(leftlocs), mean(rightlocs)]: ##We'll need a much better way to get this than mean but its ok for now
# # For
# # j in geneanno[gene]: # iterate through transcripts
# # Annostart = j[0] if j[2] == ‘+’ else j[1]
# # If
# # abs(annostart - i) < mindist: mindist = abs(annostart - i)
# # minDistToGeneStart.append([mindist, gene])
# # minDistToGeneStart.sort
# # Transcribed = True if minDistToGeneStart[0][
# # 0] < 50 else False # The 50 is totally arbitrary, we should do tests to figure out a better number
# # print(fusion, transcribed, minDistToGeneStart[0][1])
# orgFusions = []
# allMatches = []
# readNames = {}
# avgQualScore = 0
# print('filtering fusions and detecting breakpoints')
# c = 0
# #print(new_fusions_found)
# for i in new_fusions_found:
# supportCount = len(new_fusions_found[i]['readNames'])
# mapScore = round((new_fusions_found[i]['mapScore']/float(supportCount * len(list(i))))/maxMapQ, 3)
# avgBreakpointAgg = 0
# isMito = False
# for loc in new_fusions_found[i]:
# if loc not in ['mapScore', 'readNames']:
# if new_fusions_found[i][loc]['chr'] == "chrM": isMito=True
# if supportCount < int(args.l): metadata.append(['--'.join(list(i)), 'readsup', str(supportCount), new_fusions_found[i]['readNames']])
# # if not ((mapScore >= avgMapQ/float(maxMapQ) or mapScore > 0.8) and mapScore > .5): metadata.append(['--'.join(list(i)), 'mapScore', str(mapScore), new_fusions_found[i]['readNames']])
# if isMito: metadata.append(['--'.join(list(i)), 'isMito', 'M', new_fusions_found[i]['readNames']])
# if (supportCount >= int(args.l) and (args.y or ('chrM' not in i and not isMito))) or i in clinicalF:
# currFusion = [i, str(supportCount), str(mapScore)]
# distTo5 = []
# locInfo = {}
# dupLocusFrac = []
# for loc in new_fusions_found[i]:
# if loc not in ['mapScore', 'readNames', 'repeatScore']:
# leftMed, rightMed = int(median(new_fusions_found[i][loc]['left'])), int(median(new_fusions_found[i][loc]['right']))
# locInfo[loc] = {'chr':new_fusions_found[i][loc]['chr'].strip('chr'), 'loc':leftMed}
# leftFracClose, rightFracClose = 0, 0
# for j in new_fusions_found[i][loc]['left']:
# if abs(j-leftMed) <= 10: leftFracClose += 1
# for j in new_fusions_found[i][loc]['right']:
# if abs(j-rightMed) <= 10: rightFracClose += 1
# leftFracClose = leftFracClose/float(supportCount)
# rightFracClose = rightFracClose/float(supportCount)
# #print(i, loc, leftFracClose, rightFracClose)
# if loc in allGeneLoc:
# if allGeneLoc[loc] != None:
# geneStart = int(allGeneLoc[loc][0][1]) if allGeneLoc[loc][0][3] == '+' else int(allGeneLoc[loc][0][2])
# else: geneStart=None
# else: geneStart=None
# #s = 'm' if Counter(new_fusions_found[i][loc]['strand']).most_common(1)[0][0] == '-' else 'p'
# if loc in geneReads.keys():
# dupLocusFrac.append(round(float(len([a for a in set(geneReads[loc]) if a in new_fusions_found[i]['readNames']])) / len(geneReads[loc]), 3))
# else:
# dupLocusFrac.append('/')
# if leftFracClose > rightFracClose:
# currFusion.append(loc + '-' + new_fusions_found[i][loc]['chr'] + '-' + str(leftMed) + '-' + str(dupLocusFrac[-1]))
# avgBreakpointAgg += leftFracClose
# locInfo[loc]['side'] = 'l'
# if geneStart: distTo5.append(abs(geneStart-rightMed))
# else: distTo5.append(None)
# else:
# currFusion.append(loc + '-' + new_fusions_found[i][loc]['chr'] + '-' + str(rightMed) + '-' + str(dupLocusFrac[-1]))
# avgBreakpointAgg += rightFracClose
# locInfo[loc]['side'] = 'r'
# if geneStart: distTo5.append(abs(geneStart-leftMed))
# else: distTo5.append(None)
# if all(isinstance(x, float) for x in dupLocusFrac):
# currFusion.insert(3, str(round(sum(dupLocusFrac)/len(dupLocusFrac), 3)))
# else: currFusion.insert(3, '/')
# avgBreakpointAgg = avgBreakpointAgg/float(len(new_fusions_found[i].keys()))
# currFusion.append(mapScore*supportCount*avgBreakpointAgg)#*(1-repeatScore))
# avgQualScore += mapScore*supportCount*avgBreakpointAgg#*(1-repeatScore)
# ####REMOVING DETERMINATION OF LEFT AND RIGHT GENE
# if None not in distTo5:
# #print(distTo5)
# if distTo5[0] < distTo5[1]:
# temp = currFusion[4]
# currFusion[4] = "3'-" + currFusion[5]
# currFusion[5] = "5'-" + temp
# else:
# currFusion[4] = "3'-" + currFusion[4]
# currFusion[5] = "5'-" + currFusion[5]
# currFusion[-1] *= -1
# if (currFusion[4].split('-')[-3] != currFusion[5].split('-')[-3] or abs(int(currFusion[4].split('-')[-2]) - int(currFusion[5].split('-')[-2])) > args.b or i in clinicalF):
# for j in new_fusions_found[i]['readNames']:
# readNames[j] = {'fusion': i, **copy.deepcopy(locInfo)}
# allMatches += new_fusions_found[i]['readNames']
# orgFusions.append(currFusion)
# else: metadata.append(['--'.join(list(i)), 'tooClose', 'tc', new_fusions_found[i]['readNames']])
# else: metadata.append(['--'.join(list(i)), 'tooClose', 'tc', new_fusions_found[i]['readNames']])
# correctQ = prefix if os.path.exists(prefix + '.bam') else prefix + '.aligned'
#
# if args.s:
# print('making sam file')
# process = subprocess.Popen('samtools view -h -o ' + correctQ + '.sam ' + correctQ + '.bam',stdout=subprocess.PIPE, shell=True)
# print(process.communicate()[0].strip())
# correctQ = prefix if os.path.exists(prefix + '.sam') else prefix + '.aligned'
# sam = open(correctQ + '.sam', 'r')
#
# allMatches = set(allMatches)
# readLength = {}
# sam = open(correctQ + '.sam', 'r')
# print('checking multi-mapping distance - searching sam file')
# readSams = {i:{'dist':[],'len':[]} for i in allMatches}
# c = 0
# allLenDiff = 0
# for line in sam:
# line = line.rstrip().split('\t')
# if line[0] in allMatches:
# c += 1
# locs = []
# i = 0
# while line[5][i] not in ['M', 'D', 'I', 'S', 'H', 'X', 'P', 'N']:
# i += 1
# if line[5][i] == 'M': locs.append(0)
# else: locs.append(int(line[5][:i]))
# i = -2
# while line[5][i] not in ['M', 'D', 'I', 'S', 'H', 'X', 'P', 'N']:
# i -= 1
# #TEMPORARY - ASSUMPTIONS
# if line[0] not in readLength:
# readLength[line[0]] = len(line[9])
# #DONE
# if line[5][-1] == 'M': locs.append(readLength[line[0]])
# else: locs.append(readLength[line[0]] - int(line[5][i+1:-1]))
# #New 10/2021
# i = 0
# last = 0
# total = 0
# while i < len(line[5]):
# if not line[5][i].isnumeric():
# if line[5][i] not in ['S', 'H', 'I', 'P']:
# total += int(line[5][last:i])
# last = i + 1
# i += 1
# fusInfo = readNames[line[0]]
# currDist = 1000000000000000000000
# currGene = None
# for loc in fusInfo.keys():
# if loc != 'fusion':
# if 'chr' + fusInfo[loc]['chr'] == line[2]:
# temp = abs(fusInfo[loc]['loc'] - int(line[3]))
# if temp < currDist:
# currGene = loc
# currDist = temp
# lenDiff=1
# if currGene != None:
# if currGene[:3] != 'chr' and currGene in allGeneLoc:
# lenDiff = total/(allGeneLoc[currGene][0][2]-allGeneLoc[currGene][0][1])
# allLenDiff += lenDiff
# #DONE
# if line[1] == '16' or line[1] == '2064':
# locs[0] = readLength[line[0]]-locs[0]
# locs[1] = readLength[line[0]] - locs[1]
# locs = locs[::-1]
# locs.append(line[2] + '-' + line[3])
# readSams[line[0]]['dist'].append(locs)
# readSams[line[0]]['len'].append(lenDiff)
# sam.close()
# c = 0
# fastqFusionLocs = {}
# keysToRemove = []
#
# for i in readSams:
# c += 1
# if len(readSams[i]['dist']) == 2:
# readSams[i]['dist'].sort()
# readSams[i]['dist'].append(readSams[i]['dist'][1][0]-readSams[i]['dist'][0][1])
# if readNames[i]['fusion'] not in fastqFusionLocs:
# fastqFusionLocs[readNames[i]['fusion']] = {'loc':[], 'len':[], 'fastqCov':[]}
# fastqFusionLocs[readNames[i]['fusion']]['loc'].append(readSams[i]['dist'])
# tempavg = sum(readSams[i]['len'])/float(len(readSams[i]['len']))
# fastqFusionLocs[readNames[i]['fusion']]['len'].append(tempavg + ((max(readSams[i]['len'])-tempavg)/2))#sum(readSams[i]['len'])/float(len(readSams[i]['len'])))
# fastqFusionLocs[readNames[i]['fusion']]['fastqCov'].append(((readSams[i]['dist'][1][1]-readSams[i]['dist'][0][0])-(readSams[i]['dist'][1][0]-readSams[i]['dist'][0][1]))/readLength[i])
# #THIS IS TEMPORARY< REMOVE FOR MORE FLEXIBILITY/3-GENE
# else: keysToRemove.append(i)
# #DONE
#
# for i in fastqFusionLocs:
# temp = [a[-1] for a in fastqFusionLocs[i]['loc']]
# temp.sort()
# fastqFusionLocs[i]['loc'].append(temp[int(len(temp) / 2)])
#
#
# # print('fusions filtered')
# reads = open(outfilename + "Reads.bed", "w")
# fusions = open(outfilename + "Fusions.tsv", "w")
# if len(orgFusions) > 0:
# avgQualScore = avgQualScore/len(orgFusions)
# orgFusions.sort(key=lambda x:abs(x[-1]), reverse=True)
# fusions.write("#name\tspanning reads\tmapping score(1 is good)\tavg frac of reads at loci in fusion\t3' breakpoint\t5' breakpoint\n")
# fusionReadLocs = {}
# orgFusionsDict = {}
#
# for i in orgFusions:
# if abs(i[-1]) > avgQualScore*.01:
# orgFusionsDict[i[0]] = i[:-1]
# fusionReadLocs[i[0]] = {i[5].split('-')[1] + '-' + i[5].split('-')[-3]:[], i[4].split('-')[1] + '-' + i[4].split('-')[-3]:[]}
#
# count = 0
# last = ""
# print("num pre final filter", len(fusionReadLocs.keys()))
# bedLinesFiltered = []
# fusionDist = {a:[] for a in list(fusionReadLocs.keys())}
# for line in bedLines:
# thisName = line[3].split('--')[0]
# if thisName in allMatches:
# fName = readNames[thisName]['fusion'] ##Maybe this is where we figure out the orientation of the fusion name
# currName = line[3].split('--')[1].split('/')[0]
# if currName[:3] == 'chr':
# #currName = currName.split('.')[0]
# for loc in readNames[thisName]:
# if loc[:3] == 'chr':
# if abs(float(currName.split('-')[1]) - float(loc.split('-')[1])) <= args.b * 3:
# currName = loc
# locName = line[3].split('--')[1].split('/')[0] if '-' not in line[3].split('--')[1].split('/')[0] else line[3].split('--')[1].split('/')[0].split('.')[0]
# if fName in fusionReadLocs:
# if locName + '-' + line[0] in fusionReadLocs[fName]:
# fusionReadLocs[fName][locName + '-' + line[0]].append([int(line[1]), int(line[2])])
# if currName in readNames[thisName].keys():
# ###DEFINITELY NEED ORIENTATION OF GENES IN FUSION BY HERE
# line[3] = '-.-'.join(['--'.join(list(fName))] + line[3].split('--'))
# if line[-2][-1] != ',':
# line[-2] += ','
# line[-1] += ','
# bedLinesFiltered.append(line)
# c = 0
# x = 0
# finalFusions = []
# almostDone = []
# adGenes = []
# for fusion in fusionReadLocs:
# fusionlist = list(fusion)
# wasWritten = False
# theseLocs = []
# SSdist = []
# readEnds = []
# counts = []
# chrs = []
# for loc in fusionReadLocs[fusion]:
# chrs.append(loc.split('-')[-1])
# temp = [list(i) for i in zip(*fusionReadLocs[fusion][loc])]
# far = True
# if len(temp) > 0:
# temp[0].sort()
# temp[1].sort()
# counts.append(min(temp[0]))
# counts.append(max(temp[1]))
# leftSS1 = binarySearch(junctions[loc.split('-')[-1]], temp[0][int(len(temp[0])/2)])
# rightSS1 = binarySearch(junctions[loc.split('-')[-1]], temp[1][int(len(temp[1]) / 2)])
# if len(temp[0]) % 2 == 0 and len(temp) > 2:
# leftSS1 = binarySearch(junctions[loc.split('-')[-1]], temp[0][int(len(temp[0]) / 2)]+1)
# rightSS1 = binarySearch(junctions[loc.split('-')[-1]], temp[1][int(len(temp[1]) / 2)]+1)
# leftDistToSS = min([abs(leftSS1-temp[0][int(len(temp[0])/2)]), abs(leftSS2-temp[0][int(len(temp[0])/2)+1])])
# rightDistToSS = min([abs(rightSS1 - temp[1][int(len(temp[1]) / 2)]), abs(rightSS2 - temp[1][int(len(temp[1]) / 2) + 1])])
# else:
# leftDistToSS = abs(leftSS1-temp[0][int(len(temp[0])/2)])
# rightDistToSS = abs(rightSS1-temp[1][int(len(temp[1])/2)])
# SSdist.append(sorted([[leftDistToSS, temp[0][int(len(temp[0])/2)]], [rightDistToSS,temp[1][int(len(temp[1])/2)]]]))
# theseLocs.append(loc.split('-')[0])
# else: far = False
# if len(counts) > 2:
# if abs(counts[0] - counts[2]) < args.b or abs(counts[0] - counts[3]) < args.b or \
# abs(counts[1] - counts[2]) < args.b or abs(counts[1] - counts[3]) < args.b:
# far = False
# fastqDist = 500
#
# if fusion in fastqFusionLocs:
# fastqDist = abs(fastqFusionLocs[fusion]['loc'][-1])
# lenDiff = str(sum(fastqFusionLocs[fusion]['len']) / len(fastqFusionLocs[fusion]['len']))
# fastqCov = str(sum(fastqFusionLocs[fusion]['fastqCov']) / len(fastqFusionLocs[fusion]['fastqCov']))
# if fastqDist >= 15: metadata.append(['--'.join(fusionlist), 'fastqDist', str(fastqDist), new_fusions_found[fusion]['readNames']])#metadata["fastqDist"] += 1
# # print(list(set(chrs)))
# if not (len(list(set(chrs))) > 1 or far): metadata.append(['--'.join(fusionlist), 'tooClose2', 'tc2', new_fusions_found[fusion]['readNames']])#metadata["tooClose2"] += 1
# if (fastqDist < 15 and (len(list(set(chrs))) > 1 or far)) or fusion in clinicalF:
# if len(SSdist) > 1:
# if SSdist[0][0][0] <= 10 and SSdist[1][0][0] <= 10:
# if theseLocs[0] in allGeneLoc:
# if allGeneLoc[theseLocs[0]] != None:
# aDistToProm = abs(SSdist[0][1][1] - int(allGeneLoc[theseLocs[0]][0][1])) if allGeneLoc[theseLocs[0]][0][3] == '+' else abs(SSdist[0][1][1] - int(allGeneLoc[theseLocs[0]][0][2]))
# else:
# aDistToProm = 1000000000000000
# else: aDistToProm = 1000000000000000
# if theseLocs[1] in allGeneLoc:
# if allGeneLoc[theseLocs[1]] != None:
# bDistToProm = abs(SSdist[1][1][1] - int(allGeneLoc[theseLocs[1]][0][1])) if allGeneLoc[theseLocs[1]][0][3] == '+' else abs(SSdist[0][1][1] - int(allGeneLoc[theseLocs[1]][0][2]))
# else:
# bDistToProm = 1000000000000000
# else: bDistToProm = 1000000000000000
# temp = [a.split('-') for a in list(orgFusionsDict[fusion][-2:])]
# if temp[0][-4] == theseLocs[0]:
# temp[0][-2] = str(SSdist[0][0][1])
# temp[1][-2] = str(SSdist[1][0][1])
# elif temp[1][-4] == theseLocs[0]:
# temp[0][-2] = str(SSdist[1][0][1])
# temp[1][-2] = str(SSdist[0][0][1])
# orgFusionsDict[-2] = '-'.join(temp[0])
# orgFusionsDict[-1] = '-'.join(temp[1])
# orgFusionsDict[fusion].append(lenDiff)
# orgFusionsDict[fusion].append(fastqCov)
# almostDone.append(orgFusionsDict[fusion])
# adGenes.append(fusionlist[0])
# adGenes.append(fusionlist[1])
# c += 1
# else: metadata.append(['--'.join(fusionlist), 'ssDist', '-'.join([str(x) for x in SSdist]), new_fusions_found[fusion]['readNames']])#metadata["ssdist"] += 1
# elif len(SSdist) == 1:
# if SSdist[0][0][0] < 10:
# c += 1
# almostDone.append(orgFusionsDict[fusion])
# adGenes.append(fusionlist[0])
# adGenes.append(fusionlist[1])
# else: metadata.append(['--'.join(fusionlist), 'ssDist', '-'.join([str(x) for x in SSdist]), new_fusions_found[fusion]['readNames']])#metadata["ssdist"] += 1
# else: metadata.append(['--'.join(fusionlist), 'ssDist', '-'.join([str(x) for x in SSdist]), new_fusions_found[fusion]['readNames']])#metadata["ssdist"] += 1
# c = 0
# freq = Counter(adGenes)
# for temp in almostDone:
# good = True
# fusion, fusionlist = temp[0], list(temp[0])
# if len(temp) > 8 and temp[0] not in clinicalF:
# if float(temp[-1]) < 0.7:
# good = False
# metadata.append(['--'.join(fusionlist), 'fastqCov', str(temp[-1]), new_fusions_found[fusion]['readNames']])
# if float(temp[-2]) > 0.9:
# good = False
# metadata.append(['--'.join(fusionlist), 'geneCov', str(temp[-2]), new_fusions_found[fusion]['readNames']])
# if args.w:
# if freq[fusionlist[0]] > 2 or freq[fusionlist[1]] > 2:
# good = False
# metadata.append(['--'.join(fusionlist), 'repeat', 'r', new_fusions_found[fusion]['readNames']])
# if good:
# finalFusions.append(temp[0])
# ##NEED TO ADD FUSION GENE ORDER HERE
# fusions.write('\t'.join(['--'.join(list(temp[0]))] + temp[1:7]) + '\n')
# c += 1
# print("final output", c)
# fusions.close()
# print('fusions written')
# for i in metadata:
# i[3] = ','.join(i[3])
# meta.write('\t'.join(i) + '\n')#i + "\t" + str(metadata[i]) + "\n")
# meta.close()
# finalFusions = set(finalFusions)
# printNames = open(outfilename + "readNames.txt", "w")
# for line in bedLinesFiltered:
# if line[3].split('-.-')[0] in finalFusions:
# reads.write("\t".join(line) + "\n")
# printNames.write(line[3].split('-.-')[1] + '\n')
# reads.close()
# printNames.close()
# print('reads written')
#
# subprocess.run(['perl', os.path.dirname(os.path.realpath(__file__)) + '/bed12ToGTF.pl'],
# stdin=open(args.o + prefix + 'Reads.bed', 'r'), stdout=open(args.o + prefix + 'Reads.gtf', 'w'))
# if int(args.k) > 0:
# readNames = []
# print('remapping reads to fusions')
# with open(outfilename + "readNames.txt", 'r') as names:
# for line in names:
# readNames.append(line.rstrip())
# readNames = set(readNames)
# #Filter reads to only the double mapped reads
# leadingChar = '@' if args.r.split('.')[-1] == 'fastq' or args.r.split('.')[-1] == 'fq' else '>'
# with open(args.r, 'r') as reads, open(outfilename + "Filtered.fa", "w") as faOut:
# writeRead = False
# c, d = 0, 0
# for line in reads:
# c += 1
# if c % 20000000 == 0: print(c, d)
# if line[0] == leadingChar:
# if line.rstrip('\n').lstrip(leadingChar) in readNames:
# writeRead = True
# d += 1
# if leadingChar == '@': faOut.write(">" + line.lstrip('@'))