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ClusterList.py
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ClusterList.py
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from collections import deque
from Cluster import *
from ClusterPair import *
#import pp
# from pp import *
import pysam
#from pysam import csamtools
import gc
#from memory_profiler import profile
from multiprocessing import Pool
import cPickle as pickle
import y_serial_v060 as y_serial
class ClusterList:
##@profile
def __init__(self, read_pair_list):
#list of AlignedReadPair objects
self.read_pair_list = read_pair_list
#list of Cluster Objects
self.cluster_list = []
#list of ClusterPair objects
#cluster the read pairs according to the interval defined by the non-TE mapped read
#@profile
def generate_clusters_parallel(self, verbose, num_CPUs, bin_size, psorted_bamfile_name, bed_file_handle, streaming, min_cluster_size,output_prefix):
###################### BEGIN PARALLEL VERSION ########################################
################ CLUSTER BY CHR #######################
#cluster fwd intervals
# fwd_read_pairs = [read_pair for read_pair in self.read_pair_list if read_pair.interval_direction == "fwd"]
# fwd_clusters_by_chr = cluster_read_pairs_by_chr(fwd_read_pairs)
# print "********************* total fwd non-overlapping clusters found by chr: %d" % sum([len(chr_list) for chr_list in fwd_clusters_by_chr.values()])
#
#
#
# #cluster rev intervals
# rev_read_pairs = [read_pair for read_pair in self.read_pair_list if read_pair.interval_direction == "rev"]
# rev_clusters_by_chr = cluster_read_pairs_by_chr(rev_read_pairs)
# print "********************* total rev non-overlapping clusters found by chr: %d" % sum([len(chr_list) for chr_list in rev_clusters_by_chr.values()])
############################ END CLUSTER BY CHR ###########################
################ CLUSTER BY BIN #######################
#cluster fwd intervals
fwd_read_pairs = [read_pair for read_pair in self.read_pair_list if read_pair.interval_direction == "fwd"]
fwd_clusters_by_bin = cluster_read_pairs_by_chr_and_bin(fwd_read_pairs, bin_size)
print "********************* total fwd non-overlapping clusters found by bin: %d" % sum([len(chr_list) for chr_list in fwd_clusters_by_bin.values()])
####DEBUG
print "size of fwd_read_pairs list %s"%(sys.getsizeof(fwd_read_pairs))
del(fwd_read_pairs)
gc.collect()
#cluster rev intervals
rev_read_pairs = [read_pair for read_pair in self.read_pair_list if read_pair.interval_direction == "rev"]
rev_clusters_by_bin = cluster_read_pairs_by_chr_and_bin(rev_read_pairs, bin_size)
print "********************* total rev non-overlapping clusters found by bin: %d" % sum([len(chr_list) for chr_list in rev_clusters_by_bin.values()])
###DEBUG
print "size of rev_read_pairs list %s"%(sys.getsizeof(rev_read_pairs))
print "size of read pair list initial %s"%(sys.getsizeof(self.read_pair_list))
del(rev_read_pairs)
del(self.read_pair_list)
gc.collect()
############################ END CLUSTER BY BIN ###########################
print "Debug : len FWD = %d"%(len(fwd_clusters_by_bin))
print "Debug : len REV = %d"%(len(rev_clusters_by_bin))
print "size of fwd list %s"%(sys.getsizeof(fwd_clusters_by_bin))
print "size of rev list %s"%(sys.getsizeof(rev_clusters_by_bin))
##########TODO THIS IS DEBUG
# #cluster fwd intervals
# fwd_read_pairs = [read_pair for read_pair in self.read_pair_list if read_pair.interval_direction == "fwd"]
# fwd_clusters = cluster_read_pairs_all(fwd_read_pairs)
#
# print "******************total fwd clusters found: %d" % len(fwd_clusters)
# non_overlapping_fwd_clusters = remove_overlapping_clusters(fwd_clusters)
# print "******************total fwd non-overlapping clusters found: %d" % len(non_overlapping_fwd_clusters)
#
#
# #cluster rev intervals
# rev_read_pairs = [read_pair for read_pair in self.read_pair_list if read_pair.interval_direction == "rev"]
# rev_clusters = cluster_read_pairs_all(rev_read_pairs)
#
# print "******************total rev clusters found: %d" % len(rev_clusters)
# non_overlapping_rev_clusters = remove_overlapping_clusters(rev_clusters)
# print "******************total rev non-overlapping clusters found: %d" % len(non_overlapping_rev_clusters)
# print fwd_clusters_by_chr.keys()
# print rev_clusters_by_chr.keys()
############################## END THIS IS DEBUG
############## START NEW PARALLEL VERSION #################################
input_arg_list = []
#print fwd_clusters_by_bin.keys()
#print rev_clusters_by_bin.keys()
for key in fwd_clusters_by_bin.keys():
if key in rev_clusters_by_bin.keys():
input_arg_list.append((key, fwd_clusters_by_bin[key], rev_clusters_by_bin[key], psorted_bamfile_name, verbose, bed_file_handle, streaming, min_cluster_size))
#print input_arg_list[0][0][0].readpair_list[0].read1
###DEBUG
del(fwd_clusters_by_bin)
del(rev_clusters_by_bin)
gc.collect()
print "sending %d jobs to %d processes" % (len(input_arg_list), num_CPUs)
print "#DEBUG : "
print "size of input_arg_list %s"%(sys.getsizeof(input_arg_list))
#
#pool = Pool(num_CPUs)
#
#all_clusters_by_bin = pool.map(pair_clusters_by_bin, input_arg_list)
#
#pool.close()
#pool.join()
#####FOR PROFILING######
all_clusters_by_bin = list()
for i in input_arg_list:
tmp = pair_clusters_by_bin(i)
all_clusters_by_bin.append(tmp)
#break
################ END NEW PARALLEL VERSION #################################
# (paired, fwd, rev, bed_strings) = all_clusters_by_bin[0:3]
# print all_clusters_by_bin
if streaming:
cluster_counts = [(len(p), len(f), len(r)) for (p,f,r,s) in all_clusters_by_bin]
print "******************total fwd single clusters found: %d" % sum([f for (p,f,r) in cluster_counts])
print "******************total rev single clusters found: %d" % sum([r for (p,f,r) in cluster_counts])
print "******************total cluster pairs found: %d" % sum([p for (p,f,r) in cluster_counts])
bed_string = "\n".join([s for (p,f,r,s) in all_clusters_by_bin if s != ""])
# print bed_string
bed_file_handle.write(bed_string)
bed_file_handle.close()
with open(output_prefix+'all_clusters.pkl', 'wb') as output:
### saving the biggest object in a text file to avoid os.fork later
pickle.dump(all_clusters_by_bin, output, pickle.HIGHEST_PROTOCOL)
return '' #all_clusters_by_bin
else:
cluster_counts = [(len(p), len(f), len(r)) for (p,f,r) in all_clusters_by_bin]
print "******************total fwd single clusters found: %d" % sum([f for (p,f,r) in cluster_counts])
print "******************total rev single clusters found: %d" % sum([r for (p,f,r) in cluster_counts])
print "******************total cluster pairs found: %d" % sum([p for (p,f,r) in cluster_counts])
return all_clusters_by_bin
##################### END PARALLEL VERSION #############################################
##@profile
def generate_clusters(self, verbose, psorted_bamfile_name, bed_file_handle, streaming, min_cluster_size):
##################### BEGIN NON PARALLEL VERSION ######################################
#cluster fwd intervals
fwd_read_pairs = [read_pair for read_pair in self.read_pair_list if read_pair.interval_direction == "fwd"]
fwd_clusters = cluster_read_pairs_all(fwd_read_pairs)
print "******************total fwd clusters found: %d" % len(fwd_clusters)
non_overlapping_fwd_clusters = remove_overlapping_clusters(fwd_clusters,min_cluster_size)
print "******************total fwd non-overlapping clusters found: %d" % len(non_overlapping_fwd_clusters)
#cluster rev intervals
rev_read_pairs = [read_pair for read_pair in self.read_pair_list if read_pair.interval_direction == "rev"]
rev_clusters = cluster_read_pairs_all(rev_read_pairs)
print "******************total rev clusters found: %d" % len(rev_clusters)
non_overlapping_rev_clusters = remove_overlapping_clusters(rev_clusters,min_cluster_size)
print "******************total rev non-overlapping clusters found: %d" % len(non_overlapping_rev_clusters)
#bam_file_name = output_prefix + ".proper_pair.sorted.bam"
psorted_bamfile = pysam.Samfile(psorted_bamfile_name, "rb")
#pair clusters by genomic location, keeping track of which indices in the array have been paired, so that you can pick out the unpaired ones after
cluster_pairs = []
paired_fwd_clusters_indices = []
paired_rev_clusters_indices = []
bed_string = ""
last_intersect=0
# iterate over combinations of fwd and rev clusters, skipping if clusters dont meet min size requirements
for fwd_index in range(0,len(non_overlapping_fwd_clusters)):
#if fwd_cluster.num_reads < min_cluster_size:
# continue
fwd_cluster=non_overlapping_fwd_clusters[fwd_index]
for rev_index in range(last_intersect, len(non_overlapping_rev_clusters)):
#if rev_cluster.num_reads < min_cluster_size:
# continue
rev_cluster=non_overlapping_rev_clusters[rev_index]
if fwd_cluster.is_overlapping_strict(rev_cluster):
last_intersect=rev_index
new_cluster_pair = ClusterPair(fwd_cluster, rev_cluster)
if not streaming:
reads = proper_pair_bam.fetch(new_cluster_pair.get_chr(), new_cluster_pair.get_insertion_int_start(), new_cluster_pair.get_insertion_int_end())
new_cluster_pair.calc_zygosity(reads)
else:
bed_line = new_cluster_pair.to_bed()
if bed_string == "":
bed_string = bed_line
else:
bed_string = bed_string + "\n" + bed_line
if new_cluster_pair.insertion_int_end < new_cluster_pair.insertion_int_start:
if True:
print "cluster pair not paired!"
else:
cluster_pairs.append(new_cluster_pair)
paired_fwd_clusters_indices.append(fwd_index)
paired_rev_clusters_indices.append(rev_index)
elif fwd_cluster.intersection_end < rev_cluster.intersection_start:
break
#make lists of unpaired clusters
unpaired_fwd_clusters = []
unpaired_rev_clusters = []
for fwd_index in range(len(non_overlapping_fwd_clusters)):
if fwd_index not in paired_fwd_clusters_indices:
unpaired_fwd_clusters.append(non_overlapping_fwd_clusters[fwd_index])
for rev_index in range(len(non_overlapping_rev_clusters)):
if rev_index not in paired_rev_clusters_indices:
unpaired_rev_clusters.append(non_overlapping_rev_clusters[rev_index])
print "******************total cluster pairs found: %d" % len(cluster_pairs)
if verbose:
for (fwd_cluster, rev_cluster) in cluster_pairs:
print "*************************cluster_pair:**************************************"
print "fwd cluster:"
print "cluster coordinates: %s %d %d" % (fwd_cluster[0].interval_chr, fwd_cluster[0].interval_start, fwd_cluster[-1].interval_end )
print " ".join(read.str_int() for read in fwd_cluster)
print " ".join(read.str_TE_annot_list() for read in fwd_cluster)
print "rev cluster:"
print "cluster coordinates: %s %d %d" % (rev_cluster[0].interval_chr, rev_cluster[0].interval_start, rev_cluster[-1].interval_end )
print " ".join(read.str_int() for read in rev_cluster)
print " ".join(read.str_TE_annot_list() for read in rev_cluster)
return (cluster_pairs, unpaired_fwd_clusters, unpaired_rev_clusters, bed_string)
############################### END NON PARALLEL VERSION ########################################################
def generate_clusters_db(self,db,binsize,output_prefix,bam_file_name, verbose, bed_file_handle, streaming, min_cluster_size):
#"Reads the database with the valid read pairs"
#"Recovers the tables and extracts the ID fwd and rev"
#" Table name format: chr_start_end_direction"
print "Generating clusters for each bin"
output=list()
bed_string=""
total_fwd_clusters=0
total_rev_clusters=0
total_pairs=0
#import sqlite3 as ysql
#con = ysql.connect( db.db, timeout = db.TIMEOUT,isolation_level = db.TRANSACT )
#cur = con.cursor()
#cur.execute("SELECT name FROM sqlite_master WHERE type='table';")
#table_list = cur.fetchall()
read_pair_database = y_serial.Main(db)
table_list=read_pair_database.select(0,'bin_list')
fwd_bins=list()
rev_bins=list()
#con.close()
for tablename in table_list:
#print tablename
#tablename=str(element[0])
if 'fwd' in tablename:
fwd_bins.append(tablename.split('_fwd')[0])
elif 'rev' in tablename:
rev_bins.append(tablename.split('_rev')[0])
else:
print tablename
raise
#"Generate dictionaries with all bins in the two directions"
#read_pair_database = y_serial.Main(db)
common_bins = list(set(fwd_bins) & set(rev_bins))
tmp_list= list()
#For all common keys
with open(output_prefix +'all_clusters.pkl' ,'wb') as of:
for bin_key in common_bins:
#print "Processing %s Bin"%bin_key
# retrieve all read pairs for FWD and REV
# generate cluster in this Bin
fwd_read_pairs = list()
rev_read_pairs = list()
iterable = read_pair_database.selectdic(bin_key.replace('[','')+'_fwd','read_pairs')
for k,v in iterable.items():
fwd_read_pairs.append(v[2])
fwd_clusters=cluster_read_pairs_all(fwd_read_pairs)
del fwd_read_pairs
iterable = read_pair_database.selectdic(bin_key.replace('[','')+'_rev','read_pairs')
for k,v in iterable.items():
rev_read_pairs.append(v[2])
rev_clusters=cluster_read_pairs_all(rev_read_pairs)
del rev_read_pairs
# pair the clusters in this bin
# save iteratively the value in a pickled file
tmp = pair_clusters_by_bin((bin_key, fwd_clusters, rev_clusters, bam_file_name, verbose, bed_file_handle, streaming, min_cluster_size))
#print tmp
#
#tmp_list.append(tmp)
pickle.dump(tmp,of,pickle.HIGHEST_PROTOCOL)
bed_string += tmp[3]
bed_string+='\n'
total_fwd_clusters+=len(tmp[1])
total_rev_clusters+=len(tmp[2])
total_pairs+=len(tmp[0])
#pickle.dump(tmp_list,of,pickle.HIGHEST_PROTOCOL)
#Get back to Run_TE_xxx with the all_cluster file already saved
#cluster_counts = [(len(p), len(f), len(r)) for (p,f,r,s) in tmp]
print "******************total fwd single clusters found: %d"%(total_fwd_clusters)
print "******************total rev single clusters found: %d"%(total_rev_clusters)
print "******************total cluster pairs found: %d"%(total_pairs)
# print bed_string
bed_file_handle.write(bed_string)
bed_file_handle.close()
return
#@profile
def pair_clusters_by_bin((key, fwd_clusters, rev_clusters, bam_file_name, verbose, bed_file_handle, streaming, min_cluster_size)):
print "processing cluster pairs on %s" % (key)
#print "pairing clusters in parallel for chr %s" % fwd_clusters[0].chr
non_overlapping_fwd_clusters = remove_overlapping_clusters(fwd_clusters,min_cluster_size)
if verbose:
print "non overlapping fwd clusters\t%d" % (len(non_overlapping_fwd_clusters))
non_overlapping_rev_clusters = remove_overlapping_clusters(rev_clusters,min_cluster_size)
if verbose:
print "non overlapping rev clusters\t%d" % (len(non_overlapping_rev_clusters))
if not streaming:
proper_pair_bam = pysam.Samfile(bam_file_name, "rb")
#print "haha"
#print "ok1"
#pair clusters by genomic location, keeping track of which indices in the array have been paired, so that you can pick out the unpaired ones after
cluster_pairs = []
paired_fwd_clusters_indices = []
paired_rev_clusters_indices = []
last_intersect=0
bed_string = ""
for fwd_index in range(0,len(non_overlapping_fwd_clusters)):
#if fwd_cluster.num_reads < min_cluster_size:
# continue
fwd_cluster=non_overlapping_fwd_clusters[fwd_index]
for rev_index in range(last_intersect, len(non_overlapping_rev_clusters)):
#if rev_cluster.num_reads < min_cluster_size:
# continue
rev_cluster=non_overlapping_rev_clusters[rev_index]
if fwd_cluster.is_overlapping_strict(rev_cluster):
new_cluster_pair = ClusterPair(fwd_cluster, rev_cluster)
last_intersect=rev_index
#print new_cluster_pair.get_chr()
if not streaming:
reads = proper_pair_bam.fetch(new_cluster_pair.get_chr(), new_cluster_pair.get_insertion_int_start(), new_cluster_pair.get_insertion_int_end())
new_cluster_pair.calc_zygosity(reads)
else:
bed_line = new_cluster_pair.to_bed()
bed_string = bed_string + "\n" + bed_line
#print "poop"
if new_cluster_pair.get_insertion_int_end() < new_cluster_pair.get_insertion_int_start():
if True:
print "cluster pair not paired!"
else:
cluster_pairs.append(new_cluster_pair)
paired_fwd_clusters_indices.append(fwd_index)
paired_rev_clusters_indices.append(rev_index)
elif fwd_cluster.intersection_end < rev_cluster.intersection_start:
break
#make lists of unpaired clusters
unpaired_fwd_clusters = []
unpaired_rev_clusters = []
for fwd_index in range(len(non_overlapping_fwd_clusters)):
if fwd_index not in paired_fwd_clusters_indices:
unpaired_fwd_clusters.append(non_overlapping_fwd_clusters[fwd_index])
for rev_index in range(len(non_overlapping_rev_clusters)):
if rev_index not in paired_rev_clusters_indices:
unpaired_rev_clusters.append(non_overlapping_rev_clusters[rev_index])
if streaming:
return (cluster_pairs, unpaired_fwd_clusters, unpaired_rev_clusters, bed_string)
else:
return (cluster_pairs, unpaired_fwd_clusters, unpaired_rev_clusters)
#helper functions
##@profile
def cluster_read_pairs_by_chr(read_pair_list):
"""this generates a list of maximal clusters, ie sets of overlapping read pairs. note: these clusters can be themselves overlapping.
returns a disctionary of lists of Cluster objects, one entry per chromosome"""
#sort according to end position then chromosome. sort is stable so the second sort will not unsort the positions
read_pair_list.sort(key=lambda read_pair: read_pair.interval_end)
read_pair_list.sort(key=lambda read_pair: read_pair.interval_chr)
#store each a list of current Cluster objects, which contains a list of AlignedReadPair objects
cluster_list = []
#store a seperate cluster_list for each chromosome
chr_cluster_lists = {}
#read_pair_Q stores a list of currently overlapping read pair intervals
read_pair_Q = deque([read_pair_list[0]])
for read_pair in read_pair_list:
#print read_pair_Q
#if you can add the next interval to the current list of overlapping intervals, do so
if read_pair.interval_chr == read_pair_Q[0].interval_chr and read_pair.interval_start <= read_pair_Q[0].interval_end:
read_pair_Q.append(read_pair)
#if the current read is from another chromosome, save the list of currently overlapping intervals as a cluster and empty it
elif read_pair.interval_chr != read_pair_Q[0].interval_chr:
new_cluster = Cluster(list(read_pair_Q))
cluster_list.append(new_cluster)
chr_cluster_lists[read_pair_Q[0].interval_chr] = cluster_list
#empty queue and current cluster list since we are starting with a new chromosome
cluster_list = []
read_pair_Q.clear()
read_pair_Q.append(read_pair)
#otherwise, save the list of currently overlapping intervals as a cluster
else:
new_cluster = Cluster(list(read_pair_Q))
cluster_list.append(new_cluster)
# and pop off intervals in the Q as long as they do not overlap with your current interval -- these cannot constitute another maximal cluster
while len(read_pair_Q) != 0 and read_pair.interval_start > read_pair_Q[0].interval_end:
read_pair_Q.popleft()
#then add your current read to the Q
read_pair_Q.append(read_pair)
#for cluster in cluster_list:
# print " ".join(read.str_int() for read in cluster)
#print " ".join(read.str_TE_annot_list() for read in cluster)
last_cluster = Cluster(list(read_pair_Q))
cluster_list.append(last_cluster)
chr_cluster_lists[read_pair_Q[0].interval_chr] = cluster_list
return chr_cluster_lists
#@profile
def cluster_read_pairs_by_chr_and_bin(read_pair_list, bin_size):
"""this generates a list of maximal clusters, ie sets of overlapping read pairs. note: these clusters can be themselves overlapping.
returns a disctionary of lists of Cluster objects, one entry per bin"""
#sort according to end position then chromosome. sort is stable so the second sort will not unsort the positions
read_pair_list.sort(key=lambda read_pair: read_pair.interval_end)
read_pair_list.sort(key=lambda read_pair: read_pair.interval_chr)
current_bin_start = 0
current_bin_end = bin_size - 1
current_chr = read_pair_list[0].interval_chr
current_bin_key = "%s %d-%d" % (current_chr, current_bin_start, current_bin_end)
#store each a list of current Cluster objects, which contains a list of AlignedReadPair objects
current_bin_cluster_list = []
#store a seperate cluster_list for each bin
bin_cluster_lists = {}
#read_pair_Q stores a list of currently overlapping read pair intervals
read_pair_Q = deque([read_pair_list[0]])
for read_pair in read_pair_list:
#print read_pair_Q
#if you can add the next interval to the current list of overlapping intervals, do so
if read_pair.interval_chr == read_pair_Q[0].interval_chr and read_pair.interval_start <= read_pair_Q[0].interval_end:
read_pair_Q.append(read_pair)
#if the current read is from another chromosome, or from another bin, save the list of currently overlapping intervals as a cluster and empty it
elif read_pair.interval_chr != current_chr or read_pair.interval_start > current_bin_end:
new_cluster = Cluster(list(read_pair_Q))
current_bin_cluster_list.append(new_cluster)
bin_cluster_lists[current_bin_key] = current_bin_cluster_list
#empty queue and current cluster list since we are starting with a new bin
current_bin_cluster_list = []
read_pair_Q.clear()
read_pair_Q.append(read_pair)
#update the current chromosome and bins
if read_pair.interval_chr != current_chr:
current_bin_start = 0
current_bin_end = bin_size - 1
current_chr = read_pair.interval_chr
else:
current_bin_start = current_bin_end + 1
current_bin_end = current_bin_start + bin_size - 1
current_bin_key = "%s %d-%d" % (current_chr, current_bin_start, current_bin_end)
#otherwise, save the list of currently overlapping intervals as a cluster
else:
new_cluster = Cluster(list(read_pair_Q))
current_bin_cluster_list.append(new_cluster)
# and pop off intervals in the Q as long as they do not overlap with your current interval -- these cannot constitute another maximal cluster
while len(read_pair_Q) != 0 and read_pair.interval_start > read_pair_Q[0].interval_end:
read_pair_Q.popleft()
#then add your current read to the Q
read_pair_Q.append(read_pair)
#for cluster in cluster_list:
# print " ".join(read.str_int() for read in cluster)
#print " ".join(read.str_TE_annot_list() for read in cluster)
last_cluster = Cluster(list(read_pair_Q))
current_bin_cluster_list.append(last_cluster)
bin_cluster_lists[current_bin_key] = current_bin_cluster_list
return bin_cluster_lists
#@profile
def cluster_read_pairs_all(read_pair_list):
"""this generates a list of maximal clusters, ie sets of overlapping read pairs. note: these clusters can be themselves overlapping.
returns a disctionary of lists of Cluster objects, one entry per chromosome"""
#sort according to end position then chromosome. sort is stable so the second sort will not unsort the positions
read_pair_list.sort(key=lambda read_pair: read_pair.interval_end)
read_pair_list.sort(key=lambda read_pair: read_pair.interval_chr)
#store each cluster as a list of AlignedReadPair objects
cluster_list = []
#read_pair_Q stores a list of currently overlapping read pair intervals
read_pair_Q = deque([read_pair_list[0]])
for read_pair in read_pair_list:
#print read_pair_Q
#if you can add the next interval to the current list of overlapping intervals, do so
if read_pair.interval_chr == read_pair_Q[0].interval_chr and read_pair.interval_start <= read_pair_Q[0].interval_end:
read_pair_Q.append(read_pair)
#if the current read is from another chromosome, save the list of currently overlapping intervals as a cluster and empty it
elif read_pair.interval_chr != read_pair_Q[0].interval_chr:
new_cluster = Cluster(list(read_pair_Q))
cluster_list.append(new_cluster)
read_pair_Q.clear()
read_pair_Q.append(read_pair)
#otherwise, save the list of currently overlapping intervals as a cluster
else:
new_cluster = Cluster(list(read_pair_Q))
cluster_list.append(new_cluster)
# and pop off intervals in the Q as long as they do not overlap with your current interval -- these cannot constitute another maximal cluster
while len(read_pair_Q) != 0 and read_pair.interval_start > read_pair_Q[0].interval_end:
read_pair_Q.popleft()
#then add your current read to the Q
read_pair_Q.append(read_pair)
last_cluster = Cluster(list(read_pair_Q))
cluster_list.append(last_cluster)
#for cluster in cluster_list:
# print " ".join(read.str_int() for read in cluster)
#print " ".join(read.str_TE_annot_list() for read in cluster)
return cluster_list
#@profile
def remove_overlapping_clusters(cluster_list,min_size=0):
"""returns a list of clusters that do not overlap with any other. input is a list of lists of AlignedReadPair objects, sorted by end position.
thus the start coordinate of the cluster will be the start coordinate of its first element: cluster[0]
and the end coordinate of the cluster will be the end coordinate of its last element: cluster[-1]"""
non_overlapping_clusters = []
current_cluster = cluster_list[0]
current_cluster_is_overlapped = False
next_cluster_is_overlapped = False
for next_cluster in cluster_list[1:]:
#if the current cluster does not overlap teh next one,
if current_cluster.cluster_end < next_cluster.cluster_start:
next_cluster_is_overlapped = False
#and is not overlapped itself
if not current_cluster_is_overlapped and current_cluster.num_reads >= min_size:
#add it to the list
non_overlapping_clusters.append(current_cluster)
#otherwise, flag the next cluster as overlapped
else:
next_cluster_is_overlapped = True
#update current to next
current_cluster = next_cluster
current_cluster_is_overlapped = next_cluster_is_overlapped
## MATTIA comment:
# The last cluster is never printed right?
# maybe an escape line like
# if currennt_cluster_is_overlapped == F:
# non_overlapping_clusters.append(current_cluster)
# should be added when the for ends.
#for cluster in non_overlapping_clusters:
# print " ".join(read.str_int() for read in cluster)
return non_overlapping_clusters
def table_header(library_name, bam_file_name, te_annot):
param_string = "#this table describes the read clusters identified in the bam file %s and corresponding to the transposon annotations in %s\n" % (bam_file_name, te_annot)
title_string = "#this table contains three types of lines:\
#** insertion lines: one per predicted insertion site, corresponding to a pair of overlapping clusters, one fwd, one rev\n\
I\tcluster_pair_ID\tlib\tchrom\tstart\tend\tnum_fwd_reads\tnum_rev_reads\tfwd_span\trev_span\tbest_sc_pos_st\tbest_sc_pos_end\tsc_pos_support\n\
#here the start and end are defined as the intersection of the intervals predicted by the leftmost forward read and the rightmost reverse read.\n\n\
#** cluster lines (two per insertion, one fwd and one rev):\n\
C\tcluster_pair_ID\tlib\tdirection\tstart\tend\tchrom\tnum_reads\tspan\n\
#span is defined as the range of start positions in the cluster. A span of 0 means that all the reads originate at the same start site, and are probably an artifact. \
#a span the size of the fragment length indicates good coverage. \n\n\
#**read lines (fwd reads consitute the fwd clusters, rev reads the rev clusters)\n\
#the reads that are \"anchor\" are those that consitute the cluster, the reads that are \"mate\" are the anchors' mates, which map to a TE\n\
R\tcluster_pair_ID\tlib\tdirection\tinterval_start\tinterval_end\tchrom\tstatus\tbam_line\n\n\
#this file is meant to be easily manipulated with tools like grep and sed, for example\n\
#grep ^C table_file > cluster_pairs.out\n\
#will give you a list of all the clusters pairs\n\
#the R lines sharing the same ID all come from the same cluster pair, with itself the same ID, corresponding to the the insertion of that same ID, thus \n\
#grep -w cluster_pair_ID_X table_file > predicted_insertion_X.table.out\n\
#will give you the definition line of the the predicted insertion site, the fwd and rev clusters comprising insertion X and the description of the reads that constitute them. \n"
return param_string + title_string