Merge pull request #116 from J35P312/master

TIDDIT 3.7.0

Author: J35P312

setup.py

@@ -1,5 +1,9 @@
 from setuptools import setup
 import numpy
+import pyximport
+import pysam
+pyximport.install()
+
 
 try:
     from Cython.Build import cythonize
@@ -20,14 +24,16 @@
 
 setup(
     name = 'tiddit',
-    version = '3.6.1',
+    version = '3.7.0',
 
 
     url = "https://github.com/SciLifeLab/TIDDIT",
     author = "Jesper Eisfeldt",
     author_email= "[email protected]",
     ext_modules = ext_modules,
-    include_dirs=[numpy.get_include()],
+    extra_link_args=pysam.get_libraries(),
+    define_macros=pysam.get_defines(),
+    include_dirs=[numpy.get_include()]+pysam.get_include(),
     packages = ['tiddit'],
     install_requires = ['numpy','pysam'],
     entry_points = {'console_scripts': ['tiddit = tiddit.__main__:main']},

tiddit/__main__.py

@@ -17,7 +17,7 @@
 import tiddit.tiddit_contig_analysis as tiddit_contig_analysis
 
 def main():
-	version="3.6.1"
+	version="3.7.0"
 	parser = argparse.ArgumentParser("""tiddit-{}""".format(version),add_help=False)
 	parser.add_argument("--sv"	 , help="call structural variation", required=False, action="store_true")
 	parser.add_argument("--cov"        , help="generate a coverage bed file", required=False, action="store_true")
@@ -27,6 +27,7 @@ def main():
 
 		parser = argparse.ArgumentParser("""tiddit --sv --bam inputfile [-o prefix] --ref ref.fasta""")
 		parser.add_argument('--sv'       , help="call structural variation", required=False, action="store_true")
+		parser.add_argument('--force_overwrite'       , help="force the analysis and overwrite any data in the output folder", required=False, action="store_true")
 		parser.add_argument('--bam', type=str,required=True, help="coordinate sorted bam file(required)")
 		parser.add_argument('-o', type=str,default="output", help="output prefix(default=output)")
 		parser.add_argument('-i', type=int, help="paired reads maximum allowed insert size. Pairs aligning on the same chr at a distance higher than this are considered candidates for SV (default= 99.9th percentile of insert size)")
@@ -48,6 +49,7 @@ def main():
 		parser.add_argument('--fermi2', type=str,default="fermi2", help="path to fermi2 executable file (default=fermi2)")
 		parser.add_argument('--ropebwt2', type=str , default="ropebwt2", help="path to ropebwt2 executable file (default=ropebwt2)")
 		parser.add_argument('--skip_assembly', action="store_true", help="Skip running local assembly, tiddit will perform worse, but wont require fermi2, bwa, ropebwt and bwa indexed ref")
+		#parser.add_argument('--skip_index', action="store_true", help="Do not generate the csi index")
 		parser.add_argument('--p_ratio', type=float,default=0.1, help="minimum discordant pair/normal pair ratio at the breakpoint junction(default=0.1)")
 		parser.add_argument('--r_ratio', type=float,default=0.1, help="minimum split read/coverage ratio at the breakpoint junction(default=0.1)")
 		parser.add_argument('--max_coverage', type=float,default=4, help="filter call if X times higher than chromosome average coverage (default=4)")
@@ -115,10 +117,21 @@ def main():
 			i+=1
 
 		prefix=args.o
-		os.mkdir(f"{prefix}_tiddit")
-		os.mkdir(f"{prefix}_tiddit/clips")
+		try:
+			os.mkdir(f"{prefix}_tiddit")
+			os.mkdir(f"{prefix}_tiddit/clips")
+		except:
+			if args.force_overwrite:
+				pass
+			else:
+				print("Eror output folder exists")
+				quit()
 
-		pysam.index("-c","-m","6","-@",str(args.threads),bam_file_name,"{}_tiddit/{}.csi".format(args.o,sample_id))
+		#if not args.skip_index:
+		t=time.time()
+		print("Creating index")
+		pysam.index("-c","-m","4","-@",str(args.threads),bam_file_name,"{}_tiddit/{}.csi".format(args.o,sample_id))
+		print("Created index in: " + str(time.time()-t) )
 
 		min_mapq=args.q
 		max_ins_len=100000
@@ -132,7 +145,7 @@ def main():
 
 
 		t=time.time()
-		coverage_data=tiddit_signal.main(bam_file_name,args.ref,prefix,min_mapq,max_ins_len,sample_id,args.threads,args.min_contig)
+		coverage_data=tiddit_signal.main(bam_file_name,args.ref,prefix,min_mapq,max_ins_len,sample_id,args.threads,args.min_contig,False)
 		print("extracted signals in:")
 		print(t-time.time())
 
@@ -153,6 +166,8 @@ def main():
 
 		if not args.e:
 			args.e=int(library["avg_insert_size"]/2.0)
+		if not args.e:
+			args.e=50
 
 		t=time.time()
 		sv_clusters=tiddit_cluster.main(prefix,contigs,contig_length,samples,library["mp"],args.e,args.l,max_ins_len,args.min_contig,args.skip_assembly,args.r)

tiddit/tiddit_cluster.pyx

@@ -2,8 +2,7 @@ import sys
 import os
 import tiddit.DBSCAN as DBSCAN
 import numpy
-import statistics 
-from statistics import mode
+from collections import Counter
 
 def find_discordant_pos(fragment,is_mp):
 	if is_mp:
@@ -267,16 +266,16 @@ def main(prefix,chromosomes,contig_length,samples,is_mp,epsilon,m,max_ins_len,mi
 
 
 				if candidates[chrA][chrB][candidate]["N_splits"] and min_reads <= candidates[chrA][chrB][candidate]["N_splits"]:
-					candidates[chrA][chrB][candidate]["posA"]=mode(candidates[chrA][chrB][candidate]["positions_A"]["splits"])
-					candidates[chrA][chrB][candidate]["posB"]=mode(candidates[chrA][chrB][candidate]["positions_B"]["splits"])
+					candidates[chrA][chrB][candidate]["posA"]=Counter(candidates[chrA][chrB][candidate]["positions_A"]["splits"]).most_common(1)[0][0]
+					candidates[chrA][chrB][candidate]["posB"]=Counter(candidates[chrA][chrB][candidate]["positions_B"]["splits"]).most_common(1)[0][0]
 
 				elif candidates[chrA][chrB][candidate]["N_contigs"]:
-					candidates[chrA][chrB][candidate]["posA"]=mode(candidates[chrA][chrB][candidate]["positions_A"]["contigs"])
-					candidates[chrA][chrB][candidate]["posB"]=mode(candidates[chrA][chrB][candidate]["positions_B"]["contigs"])
+					candidates[chrA][chrB][candidate]["posA"]=Counter(candidates[chrA][chrB][candidate]["positions_A"]["contigs"]).most_common(1)[0][0]
+					candidates[chrA][chrB][candidate]["posB"]=Counter(candidates[chrA][chrB][candidate]["positions_B"]["contigs"]).most_common(1)[0][0]
 
 				elif candidates[chrA][chrB][candidate]["N_splits"]:
-					candidates[chrA][chrB][candidate]["posA"]=mode(candidates[chrA][chrB][candidate]["positions_A"]["splits"])
-					candidates[chrA][chrB][candidate]["posB"]=mode(candidates[chrA][chrB][candidate]["positions_B"]["splits"])				
+					candidates[chrA][chrB][candidate]["posA"]=Counter(candidates[chrA][chrB][candidate]["positions_A"]["splits"]).most_common(1)[0][0]
+					candidates[chrA][chrB][candidate]["posB"]=Counter(candidates[chrA][chrB][candidate]["positions_B"]["splits"]).most_common(1)[0][0]		
 
 				else:
 					reverse_A = candidates[chrA][chrB][candidate]["positions_A"]["orientation_discordants"].count("True")
@@ -329,9 +328,8 @@ def main(prefix,chromosomes,contig_length,samples,is_mp,epsilon,m,max_ins_len,mi
 								candidates[chrA][chrB][candidate]["posB"]=min(candidates[chrA][chrB][candidate]["positions_B"]["discordants"])
 
 					else:
-						candidates[chrA][chrB][candidate]["posA"]=mode(candidates[chrA][chrB][candidate]["positions_A"]["discordants"])
-						candidates[chrA][chrB][candidate]["posB"]=mode(candidates[chrA][chrB][candidate]["positions_B"]["discordants"])
-
+						candidates[chrA][chrB][candidate]["posA"]=Counter(candidates[chrA][chrB][candidate]["positions_A"]["discordants"]).most_common(1)[0][0]
+						candidates[chrA][chrB][candidate]["posB"]=Counter(candidates[chrA][chrB][candidate]["positions_B"]["discordants"]).most_common(1)[0][0]
 
 				candidates[chrA][chrB][candidate]["startB"]=min(candidates[chrA][chrB][candidate]["positions_B"]["start"])
 				candidates[chrA][chrB][candidate]["endB"]=max(candidates[chrA][chrB][candidate]["positions_B"]["end"])

tiddit/tiddit_signal.pyx

@@ -4,11 +4,12 @@ import os
 import itertools
 import time
 from joblib import Parallel, delayed
+from pysam.libcalignmentfile cimport AlignmentFile, AlignedSegment
 
 import tiddit.tiddit_coverage as tiddit_coverage
 
 def find_SA_query_range(SA):
-	a =pysam.AlignedSegment()
+	cdef a =pysam.AlignedSegment()
 	a.reference_start=int( SA[1] )
 
 	if SA[2] == "+":
@@ -143,21 +144,27 @@ def SA_analysis(read,min_q,tag,reference_name):
 
 	return(split)
 
-def worker(str chromosome, str bam_file_name,str ref,str prefix,int min_q,int max_ins,str sample_id, int bin_size):
+def worker(str chromosome, str bam_file_name,str ref,str prefix,int min_q,int max_ins,str sample_id, int bin_size,skip_index):
 	print("Collecting signals on contig: {}".format(chromosome))
-	samfile = pysam.AlignmentFile(bam_file_name, "r",reference_filename=ref,index_filename="{}_tiddit/{}.csi".format(prefix,sample_id))
+
+	bam_index="{}_tiddit/{}.csi".format(prefix,sample_id)
+	if skip_index:
+		bam_index=False
+
+	cdef AlignmentFile samfile = pysam.AlignmentFile(bam_file_name, "r",reference_filename=ref,index_filename=bam_index)
 	bam_header=samfile.header
 	coverage_data,end_bin_size=tiddit_coverage.create_coverage(bam_header,bin_size,chromosome)	
 
-	clips=[]
-	data=[]
-	splits=[]
+	cdef list clips=[]
+	cdef list data=[]
+	cdef list splits=[]
 
-	clip_dist=100
+	cdef int clip_dist=100
 
 	cdef long read_position
 	cdef long read_end
 	cdef int mapq
+	cdef AlignedSegment read
 
 	for read in samfile.fetch(chromosome,until_eof=True):
 
@@ -219,16 +226,16 @@ def worker(str chromosome, str bam_file_name,str ref,str prefix,int min_q,int ma
 
 	return(chromosome,data,splits,coverage_data, "{}_tiddit/clips/{}.fa".format(prefix,chromosome) )
 
-def main(str bam_file_name,str ref,str prefix,int min_q,int max_ins,str sample_id, int threads, int min_contig):
+def main(str bam_file_name,str ref,str prefix,int min_q,int max_ins,str sample_id, int threads, int min_contig,skip_index):
 
-	samfile = pysam.AlignmentFile(bam_file_name, "r",reference_filename=ref,index_filename="{}_tiddit/{}.csi".format(prefix,sample_id))
+	cdef AlignmentFile samfile = pysam.AlignmentFile(bam_file_name, "r",reference_filename=ref)
 	bam_header=samfile.header
 	samfile.close()
 	cdef int bin_size=50
-	cdef str file_type="wig"
+	cdef str file_type=u"wig"
 	cdef str outfile=prefix+".tiddit_coverage.wig"
 
-	t_tot=0
+	cdef long t_tot=0
 
 	cdef dict data={}
 	cdef dict splits={}
@@ -248,7 +255,7 @@ def main(str bam_file_name,str ref,str prefix,int min_q,int max_ins,str sample_i
 			splits[chrA["SN"]][chrB["SN"]]={}
 
 	t=time.time()
-	res=Parallel(n_jobs=threads)( delayed(worker)(chromosome,bam_file_name,ref,prefix,min_q,max_ins,sample_id,bin_size) for chromosome in chromosomes )
+	res=Parallel(n_jobs=threads,timeout=99999)( delayed(worker)(chromosome,bam_file_name,ref,prefix,min_q,max_ins,sample_id,bin_size,skip_index) for chromosome in chromosomes )
 
 	chromosomes=set(chromosomes)
 	for i in range(0,len(res)):

tiddit/tiddit_stats.py

@@ -1,5 +1,7 @@
 import pysam
 import numpy
+import time
+
 def statistics(bam_file_name,ref,min_mapq,max_ins_len,n_reads):
 	library={}
 	samfile = pysam.AlignmentFile(bam_file_name, "r",reference_filename=ref)
@@ -10,8 +12,16 @@ def statistics(bam_file_name,ref,min_mapq,max_ins_len,n_reads):
 	is_outtie=0
 
 	n_sampled=0
+	t=time.time()
+
 	for read in samfile.fetch():
-	
+
+		read_length.append( read.query_length )	
+		n_sampled+=1
+
+		if n_sampled > n_reads:
+			break
+
 		if read.mate_is_unmapped:
 			continue
 
@@ -27,25 +37,27 @@ def statistics(bam_file_name,ref,min_mapq,max_ins_len,n_reads):
 		if read.is_supplementary or read.is_secondary or read.is_duplicate or read.mapq < min_mapq:
 			continue
 
-		n_sampled+=1
-
 		insert_size.append( read.template_length )
-		read_length.append( read.query_length )
 
 		if read.is_reverse and not read.mate_is_reverse:
 			is_outtie+=1
 		else:
 			is_innie+=1
 
-		if n_sampled > n_reads:
-			break
 
 	samfile.close()
 
 	library["avg_read_length"]=numpy.average(read_length)
-	library["avg_insert_size"]=numpy.average(insert_size)
-	library["std_insert_size"]=numpy.std(insert_size)
-	library["percentile_insert_size"]=numpy.percentile(insert_size, 99.9)
+	if len(insert_size):
+		library["avg_insert_size"]=numpy.average(insert_size)
+		library["std_insert_size"]=numpy.std(insert_size)
+		library["percentile_insert_size"]=numpy.percentile(insert_size, 99.9)
+	else:
+		library["avg_insert_size"]=0
+		library["std_insert_size"]=0
+		library["percentile_insert_size"]=0
+
+		
 
 	print("LIBRARY STATISTICS")
 	if is_innie > is_outtie:
@@ -60,6 +72,7 @@ def statistics(bam_file_name,ref,min_mapq,max_ins_len,n_reads):
 	print("\tAverage insert size = {}".format(library["avg_insert_size"]) )
 	print("\tStdev insert size = {}".format(library["std_insert_size"] ) )
 	print("\t99.95 percentile insert size = {}".format( library["percentile_insert_size"]) )
+	print("Calculated statistics in: " + str( t-time.time() ))
 	print("")
 
 	return(library)

tiddit/tiddit_variant.pyx

@@ -3,10 +3,8 @@ import math
 import numpy
 from joblib import Parallel, delayed
 
-#from pysam.libcalignmentfile cimport AlignmentFile, AlignedSegment
-from pysam import AlignmentFile, AlignedSegment
-
-
+import pysam
+from pysam.libcalignmentfile cimport AlignmentFile, AlignedSegment
 
 def percentile(a, q):
 	size = len(a)
@@ -53,15 +51,14 @@ def scoring(scoring_dict,percentiles):
 
 	return(max(score))
 
-def get_region(samfile,str chr,int start,int end,int bp,int min_q,int max_ins, contig_number):
+def get_region(AlignmentFile samfile,str chr,int start,int end,int bp,int min_q,int max_ins, contig_number):
 
 	cdef int low_q=0
 	cdef int n_reads=0
 	cdef long bases=0
 	cdef int n_discs=0
 	cdef int n_splits=0
 
-
 	cdef int crossing_r=0
 	cdef int crossing_f=0
 
@@ -83,6 +80,8 @@ def get_region(samfile,str chr,int start,int end,int bp,int min_q,int max_ins, c
 	cdef long r_start
 	cdef long r_end
 
+	cdef AlignedSegment read
+
 	for read in samfile.fetch(chr, q_start, q_end):
 		if read.is_unmapped:
 			continue
@@ -233,13 +232,14 @@ def sv_filter(sample_data,args,chrA,chrB,posA,posB,max_ins_len,n_discordants,n_s
 	return(filt)
 
 def define_variant(str chrA, str bam_file_name,dict sv_clusters,args,dict library,int min_mapq,samples,dict coverage_data,contig_number,max_ins_len,contig_seqs):
-
-	samfile  = AlignmentFile(bam_file_name, "r",reference_filename=args.ref,index_filename="{}_tiddit/{}.csi".format(args.o,samples[0]))
+	cdef AlignmentFile samfile  = AlignmentFile(bam_file_name, "r",reference_filename=args.ref,index_filename="{}_tiddit/{}.csi".format(args.o,samples[0]))
 	variants=[]
 
 	var_n=0
 	for chrB in sv_clusters[chrA]:
+
 		for cluster in sv_clusters[chrA][chrB]:
+
 			n_discordants=sv_clusters[chrA][chrB][cluster]["N_discordants"]
 			n_splits=sv_clusters[chrA][chrB][cluster]["N_splits"]
 			n_contigs=sv_clusters[chrA][chrB][cluster]["N_contigs"]
@@ -261,21 +261,24 @@ def define_variant(str chrA, str bam_file_name,dict sv_clusters,args,dict librar
 			s=int(math.floor(sv_clusters[chrA][chrB][cluster]["startA"]/50.0))
 			e=int(math.floor(sv_clusters[chrA][chrB][cluster]["endA"]/50.0))+1
 			avg_a=numpy.average(coverage_data[chrA][s:e])
+			#print(f"{chrA}-{posA}-{chrB}")
 
 			if avg_a > args.max_coverage*library[ "avg_coverage_{}".format(chrA) ]:
 				continue
 			elif (args.max_coverage*n_discordants/avg_a < args.p_ratio/2 and args.max_coverage*n_splits/avg_a < args.r_ratio/2) and not n_contigs:
 				continue
 
-			avg_b=numpy.average(coverage_data[chrA][s:e])
+			s=int(math.floor(sv_clusters[chrA][chrB][cluster]["startB"]/50.0))
+			e=int(math.floor(sv_clusters[chrA][chrB][cluster]["endB"]/50.0))+1
+			avg_b=numpy.average(coverage_data[chrB][s:e])
+
 			if avg_b == 0:
 				continue
 			elif avg_b > args.max_coverage*library[ "avg_coverage_{}".format(chrB) ]:
 				continue
 			elif (args.max_coverage*n_discordants/avg_b < args.p_ratio/2 and args.max_coverage*n_splits/avg_b < args.r_ratio/2) and not n_contigs:
 				continue
 
-
 			var_n+=1
 			sample_data={}
 			for sample in samples:
@@ -544,7 +547,7 @@ def main(str bam_file_name,dict sv_clusters,args,dict library,int min_mapq,sampl
 		for chrB in sv_clusters[chrA]:
 			variants[chrB]=[]
 
-	variants_list=Parallel(n_jobs=args.threads)( delayed(define_variant)(chrA,bam_file_name,sv_clusters,args,library,min_mapq,samples,coverage_data,contig_number,max_ins_len,contig_seqs) for chrA in sv_clusters)
+	variants_list=Parallel(n_jobs=args.threads,prefer="threads",timeout=99999)( delayed(define_variant)(chrA,bam_file_name,sv_clusters,args,library,min_mapq,samples,coverage_data,contig_number,max_ins_len,contig_seqs) for chrA in sv_clusters)
 
 	ratios={"fragments_A":[],"fragments_B":[],"reads_A":[],"reads_B":[]}
 	for v in variants_list: