##################### Cluster discordant read pairs that match TE #####################
# Cluster the list of AlignedReadPair objects that have one read overlapping a TE
# according to the uniquely mapping non-TE genomic location
# then pair a fwd and rev cluster if they are overlapping
# for the clusters that can be paired, calculate the softclipped support and the core reads, which will indicate heterozygosity of predicted TE insertion
# bed file to store the insertion regions, to use to query the bam file later to calculate the zygosity
bed_file_name = output_prefix + ".insertion_regions.bed"
bed_file_handle = open(bed_file_name, "w")
print "generating clusters..."
if len(read_pair_one_overlap_TE_list) == 0:
print "there might be an error, no discordant reads mapped to a TE location. please check the gff file... are the chromosome names the same as in the reference?"
sys.exit(2)
cluster_list = ClusterList(read_pair_one_overlap_TE_list)
# if not parallel:
# (cluster_pairs, unpaired_fwd_clusters, unpaired_rev_clusters) = cluster_list.generate_clusters(verbose, psorted_bamfile_name, bed_file_handle, True, min_cluster_size)
# all_clusters = [(cluster_pairs, unpaired_fwd_clusters, unpaired_rev_clusters)]
##parallel version:
# else:
# # empty string is psorted bed file name, and True is streaming
# all_clusters = cluster_list.generate_clusters_parallel(verbose, num_CPUs, bin_size, "", bed_file_handle, True, min_cluster_size,output_prefix)
# # bed_file_handle.close()
##### Alternative with low RAM because it prints out all the results at once and use the database file sqlite from before.
all_clusters = cluster_list.generate_clusters_db(database_file, bin_size,
output_prefix, "",
verbose, bed_file_handle,
True, min_cluster_size)
def run_jitterbug(psorted_cramfile_name, already_calc_discordant_reads, valid_discordant_reads_file_name, verbose,
te_annot, te_seqs, library_name, num_sdev, output_prefix, TE_name_tag, parallel, num_CPUs, bin_size,
min_mapq, generate_test_bam, print_extra_output, conf_lib_stats, mem, min_cluster_size,
reference_genome):
mem_debug = False
# print te_annot
# print min_mapq
# NOTE: comment this later !!!!!!!!!!!!!!!!
# sorted_bam_reader = BamReader(output_prefix + ".proper_pair.sorted.bam", output_prefix)
if mem_debug:
reportResource("1")
print "processing " + psorted_cramfile_name
if not output_prefix:
output_prefix = psorted_cramfile_name
# Make BamReader object with bam file of mapped and sorted reads
psorted_cram_reader = CramReader(psorted_cramfile_name, output_prefix, reference_genome)
if generate_test_bam:
print "generating test bam"
psorted_cram_reader.output_one_chr_reads()
return None
start_time = datetime.datetime.now()
print "starting at %s" % (str(start_time))
if conf_lib_stats:
# Get the mean and sdev of insert size from supplied config file
stats = {}
for line in open(conf_lib_stats):
line = line.strip()
(tag, val) = line.split("\t")
stats[tag] = (float(val))
isize_mean = stats["fragment_length"]
isize_sdev = stats["fragment_length_SD"]
rlen_mean = stats["read_length"]
rlen_sdev = stats["read_length_SD"]
print "mean fragment length taken from config file: %.2f" % (isize_mean)
print "standard deviation of fragment_length: %.2f" % (isize_sdev)
print "mean read length: %.2f" % (rlen_mean)
print "standard deviation of read length: %.2f" % (rlen_sdev)
else:
# Get the mean and sdev of insert size from the original bam file
print "calculating mean insert size..."
iterations = 1000000
(isize_mean, isize_sdev, rlen_mean, rlen_sdev) = psorted_cram_reader.calculate_mean_sdev_isize(iterations)
print "mean fragment length over %d reads: %.2f" % (iterations, isize_mean)
print "standard deviation of fragment_length: %.2f" % (isize_sdev)
print "mean read length: %.2f" % (rlen_mean)
print "standard deviation of read length: %.2f" % (rlen_sdev)
stats_file = open(output_prefix + ".read_stats.txt", "w")
stats_file.write("fragment_length\t%.2f\n" % (isize_mean))
stats_file.write("fragment_length_SD\t%.2f\n" % (isize_sdev))
stats_file.write("read_length\t%.2f\n" % (rlen_mean))
stats_file.write("read_length_SD\t%.2f" % (rlen_sdev))
stats_file.close()
# if fragment sdev is much larger than expected, there might be a problem with the reads of the mapping. Set as default 0.1*fragment length as a reasonable guess.
# This is necessary because aberrant values for sdev will mess up the interval overlap calculation and the filtering
if isize_sdev > 0.2 * isize_mean:
isize_sdev = 0.1 * isize_mean
print "WARNING: fragment length standard deviation seems way too large to be realistic.\\n\
There is maybe something weird with the flags in your bam mapping, or a very large number of large SV \\n\
that are messing up the count.\\n\
Setting the stdev to 0.1*fragment_length = %.2f for downstream calculations" % isize_sdev
time = datetime.datetime.now()
print "elapsed time: " + str(time - start_time)
################# Find valid discordant reads in given sorted bam file ################
# This will print bam file(s) with the set(s) of valid discordant reads meaning
# that the reads in apair are mapped to distances greater than expected or to
# two different chromosomes, and with at least one read that is mapped uniquely
# if strict_repetitive is TRUE, will print out two bam files:
# <bam_file_name>.valid_discordant_pairs_strict_rep.bam which is all valid discordant
# read pairs with exactly one uniquely mapping and one repetitively mapping read pair
#
# <bam_file_name>.valid_discordant_pairs.bam which are all discordant valid discordant
# read pairs with two uniquely mapping reads
# if strict_repetitive is TRUE, will output both sets to a file named
# <bam_file_name>.valid_discordant_pairs.bam
# if have already run the prgm and calculated the discordant reads, dont do it again and look for a file called
# <bam_file_name>.valid_discordant_pairs or <bam_file_name>.valid_discordant_pairs_strict_rep depending on the value of -s
if not already_calc_discordant_reads:
if mem_debug:
reportResource("2")
valid_discordant_reads_file_name = output_prefix + ".valid_discordant_pairs.cram"
database_file = output_prefix + "dbfile.sqlite"
print "selecting discordant reads..."
# this writes the bam file of discordant reads to disc to be used later, and return the counts of different types of reads
(bam_stats, ref_lengths, ref_names) = psorted_cram_reader.select_discordant_reads_psorted(verbose, isize_mean,
valid_discordant_reads_file_name)
# print ref_names, ref_lengths
coverage = (bam_stats["total_reads"] * rlen_mean) / sum(ref_lengths)
filter_conf_file = open(output_prefix + ".filter_config.txt", "w")
filter_conf_file.write("cluster_size\t2\t%d\n" % (5 * coverage))
filter_conf_file.write("span\t2\t%d\n" % isize_mean)
filter_conf_file.write(
"int_size\t%d\t%d\n" % (rlen_mean, 2 * (isize_mean + 2 * isize_sdev - (rlen_mean - rlen_sdev))))
filter_conf_file.write("softclipped\t2\t%d\n" % (5 * coverage))
filter_conf_file.write("pick_consistent\t0\t-1")
filter_conf_file.close()
cram_stats_file = open(output_prefix + ".cram_stats.txt", "w")
for key, value in bam_stats.items():
cram_stats_file.write("%s\t%d\n" % (key, value))
if mem_debug:
reportResource("3")
cram_stats_file.close()
time = datetime.datetime.now()
if mem_debug:
reportResource("4")
print "elapsed time: " + str(time - start_time)
# print "sorting proper pair bam file in the background... "
# args = ["samtools", "sort", output_prefix + ".proper_pair.bam", output_prefix + ".proper_pair.sorted"]
# proper_pair_sort = subprocess.Popen(args)
else:
print "using already selected discordant reads in %s" % (valid_discordant_reads_file_name)
################### Select valid discordant reads that match a TE #####################
# Of the valid discordant read pairs, select those that have exactly one side that
# overlaps a TE. This will
# return a list of AlignedReadPair objects
# the interval size in calculated as the INSIDE interval between the two reads ?? is this true? , plus numsdev * the sd of the insert size
print "selecting discordant read pairs where exactly one maps to a TE..."
interval_size = isize_mean + num_sdev * isize_sdev
if te_annot:
discordant_bam_reader = CramReader(valid_discordant_reads_file_name, output_prefix, reference_genome)
read_pair_one_overlap_TE_list = discordant_bam_reader.select_read_pair_one_overlap_TE_annot(te_annot,
interval_size,
min_mapq,
database_file,
bin_size)
read_pair_one_overlap_TE_list = [1, 1]
if not (print_extra_output or already_calc_discordant_reads):
os.remove(valid_discordant_reads_file_name)
else:
# here you would map mate reads to TE sequences and whatnot.
pass
if mem_debug:
reportResource("5")
time = datetime.datetime.now()
print "elapsed time: " + str(time - start_time)
######################## wait till the proper pair bam file is sorted, and index it ###########################
##################### Cluster discordant read pairs that match TE #####################
# Cluster the list of AlignedReadPair objects that have one read overlapping a TE
# according to the uniquely mapping non-TE genomic location
# then pair a fwd and rev cluster if they are overlapping
# for the clusters that can be paired, calculate the softclipped support and the core reads, which will indicate heterozygosity of predicted TE insertion
print "generating clusters..."
if len(read_pair_one_overlap_TE_list) == 0:
print "there might be an error, no discordant reads mapped to a TE location. please check the gff file... are the chromosome names the same as in the reference?"
sys.exit(2)
cluster_list = ClusterList(read_pair_one_overlap_TE_list)
#### COMMENTED TO TAKE ADVANTAGE OF THE GENERATED DATABASE FILE###
# if not parallel:
# (cluster_pairs, unpaired_fwd_clusters, unpaired_rev_clusters, bed_string) = cluster_list.generate_clusters(verbose, psorted_bamfile_name, "", False, min_cluster_size)
# all_clusters = [(cluster_pairs, unpaired_fwd_clusters, unpaired_rev_clusters)]
##parallel version:
# else:
# # last tow args are bed file handle and streaming, unnecessary and False, in this version
# all_clusters = cluster_list.generate_clusters_parallel(verbose, num_CPUs, bin_size, psorted_bamfile_name, "", False, min_cluster_size)
bed_file_name = output_prefix + ".insertion_regions.bed"
bed_file_handle = open(bed_file_name, "w")
all_clusters = cluster_list.generate_clusters_db(database_file, bin_size, output_prefix, "", verbose,
bed_file_handle, True, min_cluster_size)
all_clusters = list(load_pickle(output_prefix + 'all_clusters.pkl'))
ins_regions_cram_name = output_prefix + ".insertion_regions.reads.cram"
args = ["samtools", "view", "-h", "-C", "-T", reference_genome, "-L", bed_file_name, "-o", ins_regions_cram_name,
psorted_cramfile_name]
# open subprocess
int_bed_reads_select = subprocess.Popen(args)
# wait till it finishes
outcode = int_bed_reads_select.wait()
if outcode == 0:
print "retrieving reads overlapping bed annots successful"
# construct list of args
args = ["samtools", "index", ins_regions_cram_name]
# open subprocess
int_bed_reads_index = subprocess.Popen(args)
# wait till it finishes
outcode = int_bed_reads_index.wait()
if outcode == 0:
print "indexing successful"
else:
print "indexing failed"
else:
command = "\t".join(args)
print "retrieving reads overlapping bed annots failed! command: %s " % (command)
sys.exit(1)
insertion_regions_reads_cram = pysam.AlignmentFile(ins_regions_cram_name, mode="rc",
reference_filename=reference_genome)
for (cluster_pairs, fwd, rev, string) in all_clusters:
for cluster_pair in cluster_pairs:
try:
reads = insertion_regions_reads_cram.fetch(cluster_pair.get_chr(),
cluster_pair.get_insertion_int_start(),
cluster_pair.get_insertion_int_end())
cluster_pair.calc_zygosity(reads)
except:
print "error calculating zygosity of: "
print cluster_pair
raise
print "Done calculating zygosity of each cluster pair"
del read_pair_one_overlap_TE_list
gc.collect()
if mem_debug:
reportResource("5")
time = datetime.datetime.now()
print "elapsed time: " + str(time - start_time)
###################### print reads that were clustered to bam, output to gff and table ########################################
print "writing clustered reads to bam file, writing to gff and tables... "
pair_gff_output_file = open(output_prefix + ".TE_insertions_paired_clusters.gff3", "w")
pair_table_output_file = open(output_prefix + ".TE_insertions_paired_clusters.supporting_clusters.table", "w")
pair_table_output_file.write(table_header(library_name, library_name, te_annot))
# if print_extra_output:
# single_gff_output_file = open(output_prefix + ".TE_insertions_single_cluster.gff3", "w")
# single_table_output_file = open(output_prefix + ".TE_insertions_single_cluster.supporting_clusters.table", "w")
# single_table_output_file.write(table_header(library_name, library_name, te_annot))
print len(all_clusters)
cluster_ID = 0
for (cluster_pairs, unpaired_fwd_clusters, unpaired_rev_clusters, strings) in all_clusters:
# unpaired clusters are no longer reported
# if print_extra_output:
# for fwd_cluster in unpaired_fwd_clusters:
# single_gff_output_file.write(fwd_cluster.to_gff(cluster_ID, library_name, TE_name_tag) + "\n")
# single_table_output_file.write(fwd_cluster.to_table(cluster_ID, library_name))
# cluster_ID += 1
# for rev_cluster in unpaired_rev_clusters:
# single_gff_output_file.write(rev_cluster.to_gff(cluster_ID, library_name, TE_name_tag) + "\n")
# single_table_output_file.write(rev_cluster.to_table(cluster_ID, library_name))
# cluster_ID += 1
# print cluster_ID
for cluster_pair in cluster_pairs:
pair_gff_output_file.write(cluster_pair.to_gff(cluster_ID, library_name, TE_name_tag) + "\n")
pair_table_output_file.write(cluster_pair.to_table(cluster_ID, library_name))
cluster_ID += 1
# clustered_reads_bam_file.close()
time = datetime.datetime.now()
print "elapsed time: " + str(time - start_time)
pair_gff_output_file.close()
pair_table_output_file.close()
# if print_extra_output:
# single_gff_output_file.close()
# single_table_output_file.close()
end_time = str(datetime.datetime.now())
print "done! at " + end_time
run_stats = open(output_prefix + ".run_stats.txt", "w")
run_stats.write("lib\t%s\n" % (library_name))
if not already_calc_discordant_reads:
run_stats.write("coverage\t%s\n" % (coverage))
run_stats.write("runtime\t%s\n" % (datetime.datetime.now() - start_time))
run_stats.write("numCPUs\t%s\n" % (num_CPUs))
run_stats.close()
def run_jitterbug(psorted_bamfile_name, already_calc_discordant_reads, valid_discordant_reads_file_name, verbose, te_annot, \
te_seqs, library_name, num_sdev, output_prefix, TE_name_tag, parallel, num_CPUs, bin_size, min_mapq, generate_test_bam, print_extra_output, conf_lib_stats, mem, min_cluster_size):
mem_debug = False
# print te_annot
# print min_mapq
#NOTE: comment this later !!!!!!!!!!!!!!!!
#sorted_bam_reader = BamReader(output_prefix + ".proper_pair.sorted.bam", output_prefix)
if mem_debug:
reportResource("1")
print "processing " + psorted_bamfile_name
if not output_prefix:
output_prefix = psorted_bamfile_name
# Make BamReader object with bam file of mapped and sorted reads
# NOTE: uncomment this later !!!!!!!!!!!!!
psorted_bam_reader = BamReader(psorted_bamfile_name, output_prefix)
if generate_test_bam:
print "generating test bam"
psorted_bam_reader.output_one_chr_reads()
return None
start_time = datetime.datetime.now()
print "starting at %s" % (str(start_time))
if conf_lib_stats:
# Get the mean and sdev of insert size from supplied config file
stats = {}
for line in open(conf_lib_stats):
line = line.strip()
(tag, val) = line.split("\t")
stats[tag] = (float(val))
isize_mean = stats["fragment_length"]
isize_sdev = stats["fragment_length_SD"]
rlen_mean = stats["read_length"]
rlen_sdev = stats["read_length_SD"]
print "mean fragment length taken from config file: %.2f" % (isize_mean)
print "standard deviation of fragment_length: %.2f" % (isize_sdev)
print "mean read length: %.2f" % (rlen_mean)
print "standard deviation of read length: %.2f" % (rlen_sdev)
else:
# Get the mean and sdev of insert size from the original bam file
print "calculating mean insert size..."
iterations = 1000000
(isize_mean, isize_sdev, rlen_mean, rlen_sdev) = psorted_bam_reader.calculate_mean_sdev_isize(iterations)
print "mean fragment length over %d reads: %.2f" % (iterations, isize_mean)
print "standard deviation of fragment_length: %.2f" % (isize_sdev)
print "mean read length: %.2f" % (rlen_mean)
print "standard deviation of read length: %.2f" % (rlen_sdev)
stats_file = open(output_prefix + ".read_stats.txt", "w")
stats_file.write("fragment_length\t%.2f\n" % (isize_mean))
stats_file.write("fragment_length_SD\t%.2f\n" % (isize_sdev))
stats_file.write("read_length\t%.2f\n" % (rlen_mean))
stats_file.write("read_length_SD\t%.2f" % (rlen_sdev))
stats_file.close()
#if fragment sdev is much larger than expected, there might be a problem with the reads of the mapping. Set as default 0.1*fragment length as a reasonable guess.
# This is necessary because aberrant values for sdev will mess up the interval overlap calculation and the filtering
if isize_sdev > 0.2*isize_mean:
isize_sdev = 0.1*isize_mean
print "WARNING: fragment length standard deviation seems way too large to be realistic.\\n\
There is maybe something weird with the flags in your bam mapping, or a very large number of large SV \\n\
that are messing up the count.\\n\
Setting the stdev to 0.1*fragment_length = %.2f for downstream calculations" % isize_sdev
time = datetime.datetime.now()
print "elapsed time: " + str(time - start_time)
################# Find valid discordant reads in given sorted bam file ################
# This will print bam file(s) with the set(s) of valid discordant reads meaning
# that the reads in apair are mapped to distances greater than expected or to
# two different chromosomes, and with at least one read that is mapped uniquely
# if strict_repetitive is TRUE, will print out two bam files:
# <bam_file_name>.valid_discordant_pairs_strict_rep.bam which is all valid discordant
# read pairs with exactly one uniquely mapping and one repetitively mapping read pair
#
# <bam_file_name>.valid_discordant_pairs.bam which are all discordant valid discordant
# read pairs with two uniquely mapping reads
# if strict_repetitive is TRUE, will output both sets to a file named
# <bam_file_name>.valid_discordant_pairs.bam
#if have already run the prgm and calculated the discordant reads, dont do it again and look for a file called
#<bam_file_name>.valid_discordant_pairs or <bam_file_name>.valid_discordant_pairs_strict_rep depending on the value of -s
if not already_calc_discordant_reads:
if mem_debug:
reportResource("2")
valid_discordant_reads_file_name = output_prefix + ".valid_discordant_pairs.bam"
database_file=output_prefix+"dbfile.sqlite"
print "selecting discordant reads..."
#this writes the bam file of discordant reads to disc to be used later, and return the counts of different types of reads
(bam_stats, ref_lengths, ref_names) = psorted_bam_reader.select_discordant_reads_psorted( verbose, isize_mean, valid_discordant_reads_file_name)
#print ref_names, ref_lengths
coverage = (bam_stats["total_reads"] * rlen_mean )/ sum(ref_lengths)
filter_conf_file = open(output_prefix + ".filter_config.txt", "w")
filter_conf_file.write("cluster_size\t2\t%d\n" % (5*coverage))
filter_conf_file.write("span\t2\t%d\n" % isize_mean)
filter_conf_file.write("int_size\t%d\t%d\n" % (rlen_mean, 2*(isize_mean + 2*isize_sdev - (rlen_mean - rlen_sdev))) )
filter_conf_file.write("softclipped\t2\t%d\n" % (5*coverage))
filter_conf_file.write("pick_consistent\t0\t-1")
filter_conf_file.close()
bam_stats_file = open(output_prefix + ".bam_stats.txt", "w")
for key, value in bam_stats.items():
bam_stats_file.write("%s\t%d\n" % (key, value))
if mem_debug:
reportResource("3")
bam_stats_file.close()
time = datetime.datetime.now()
if mem_debug:
reportResource("4")
print "elapsed time: " + str(time - start_time)
# print "sorting proper pair bam file in the background... "
# args = ["samtools", "sort", output_prefix + ".proper_pair.bam", output_prefix + ".proper_pair.sorted"]
# proper_pair_sort = subprocess.Popen(args)
else:
print "using already selected discordant reads in %s" % (valid_discordant_reads_file_name)
################### Select valid discordant reads that match a TE #####################
# Of the valid discordant read pairs, select those that have exactly one side that
# overlaps a TE. This will
# return a list of AlignedReadPair objects
# the interval size in calculated as the INSIDE interval between the two reads ?? is this true? , plus numsdev * the sd of the insert size
print "selecting discordant read pairs where exactly one maps to a TE..."
interval_size = isize_mean + num_sdev * isize_sdev
if te_annot:
discordant_bam_reader = BamReader(valid_discordant_reads_file_name, output_prefix)
read_pair_one_overlap_TE_list = discordant_bam_reader.select_read_pair_one_overlap_TE_annot(te_annot, interval_size, min_mapq,database_file,bin_size)
read_pair_one_overlap_TE_list=[1,1]
if not (print_extra_output or already_calc_discordant_reads):
os.remove(valid_discordant_reads_file_name)
else:
#here you would map mate reads to TE sequences and whatnot.
pass
if mem_debug:
reportResource("5")
time = datetime.datetime.now()
print "elapsed time: " + str(time - start_time)
######################## wait till the proper pair bam file is sorted, and index it ###########################
##################### Cluster discordant read pairs that match TE #####################
# Cluster the list of AlignedReadPair objects that have one read overlapping a TE
# according to the uniquely mapping non-TE genomic location
# then pair a fwd and rev cluster if they are overlapping
# for the clusters that can be paired, calculate the softclipped support and the core reads, which will indicate heterozygosity of predicted TE insertion
print "generating clusters..."
if len(read_pair_one_overlap_TE_list) == 0:
print "there might be an error, no discordant reads mapped to a TE location. please check the gff file... are the chromosome names the same as in the reference?"
sys.exit(2)
cluster_list = ClusterList(read_pair_one_overlap_TE_list)
#### COMMENTED TO TAKE ADVANTAGE OF THE GENERATED DATABASE FILE###
#if not parallel:
# (cluster_pairs, unpaired_fwd_clusters, unpaired_rev_clusters, bed_string) = cluster_list.generate_clusters(verbose, psorted_bamfile_name, "", False, min_cluster_size)
# all_clusters = [(cluster_pairs, unpaired_fwd_clusters, unpaired_rev_clusters)]
##parallel version:
#else:
# # last tow args are bed file handle and streaming, unnecessary and False, in this version
# all_clusters = cluster_list.generate_clusters_parallel(verbose, num_CPUs, bin_size, psorted_bamfile_name, "", False, min_cluster_size)
bed_file_name = output_prefix + ".insertion_regions.bed"
bed_file_handle = open(bed_file_name, "w")
all_clusters = cluster_list.generate_clusters_db(database_file,bin_size,output_prefix,"", verbose, bed_file_handle, True, min_cluster_size)
all_clusters=list(load_pickle(output_prefix+'all_clusters.pkl'))
del read_pair_one_overlap_TE_list
gc.collect()
if mem_debug:
reportResource("5")
time = datetime.datetime.now()
print "elapsed time: " + str(time - start_time)
###################### print reads that were clustered to bam, output to gff and table ########################################
print "writing clustered reads to bam file, writing to gff and tables... "
pair_gff_output_file = open(output_prefix + ".TE_insertions_paired_clusters.gff3", "w")
pair_table_output_file = open(output_prefix + ".TE_insertions_paired_clusters.supporting_clusters.table", "w")
pair_table_output_file.write(table_header(library_name, library_name, te_annot))
# if print_extra_output:
# single_gff_output_file = open(output_prefix + ".TE_insertions_single_cluster.gff3", "w")
# single_table_output_file = open(output_prefix + ".TE_insertions_single_cluster.supporting_clusters.table", "w")
# single_table_output_file.write(table_header(library_name, library_name, te_annot))
print len(all_clusters)
cluster_ID = 0
for (cluster_pairs, unpaired_fwd_clusters, unpaired_rev_clusters,strings) in all_clusters:
# unpaired clusters are no longer reported
# if print_extra_output:
# for fwd_cluster in unpaired_fwd_clusters:
# single_gff_output_file.write(fwd_cluster.to_gff(cluster_ID, library_name, TE_name_tag) + "\n")
# single_table_output_file.write(fwd_cluster.to_table(cluster_ID, library_name))
# cluster_ID += 1
# for rev_cluster in unpaired_rev_clusters:
# single_gff_output_file.write(rev_cluster.to_gff(cluster_ID, library_name, TE_name_tag) + "\n")
# single_table_output_file.write(rev_cluster.to_table(cluster_ID, library_name))
# cluster_ID += 1
#print cluster_ID
for cluster_pair in cluster_pairs:
pair_gff_output_file.write(cluster_pair.to_gff(cluster_ID, library_name, TE_name_tag) + "\n")
pair_table_output_file.write(cluster_pair.to_table(cluster_ID, library_name))
cluster_ID += 1
#clustered_reads_bam_file.close()
time = datetime.datetime.now()
print "elapsed time: " + str(time - start_time)
pair_gff_output_file.close()
pair_table_output_file.close()
# if print_extra_output:
# single_gff_output_file.close()
# single_table_output_file.close()
end_time = str(datetime.datetime.now())
print "done! at " + end_time
run_stats = open(output_prefix + ".run_stats.txt", "w")
run_stats.write("lib\t%s\n" % (library_name))
if not already_calc_discordant_reads:
run_stats.write("coverage\t%s\n" % (coverage))
run_stats.write("runtime\t%s\n" % ( datetime.datetime.now() - start_time))
run_stats.write("numCPUs\t%s\n" % (num_CPUs))
run_stats.close()
##################### Cluster discordant read pairs that match TE #####################
# Cluster the list of AlignedReadPair objects that have one read overlapping a TE
# according to the uniquely mapping non-TE genomic location
# then pair a fwd and rev cluster if they are overlapping
# for the clusters that can be paired, calculate the softclipped support and the core reads, which will indicate heterozygosity of predicted TE insertion
# bed file to store the insertion regions, to use to query the bam file later to calculate the zygosity
bed_file_name = output_prefix + ".insertion_regions.bed"
bed_file_handle = open(bed_file_name, "w")
print "generating clusters..."
if len(read_pair_one_overlap_TE_list) == 0:
print "there might be an error, no discordant reads mapped to a TE location. please check the gff file... are the chromosome names the same as in the reference?"
sys.exit(2)
cluster_list = ClusterList(read_pair_one_overlap_TE_list)
#if not parallel:
# (cluster_pairs, unpaired_fwd_clusters, unpaired_rev_clusters) = cluster_list.generate_clusters(verbose, psorted_bamfile_name, bed_file_handle, True, min_cluster_size)
# all_clusters = [(cluster_pairs, unpaired_fwd_clusters, unpaired_rev_clusters)]
##parallel version:
#else:
# # empty string is psorted bed file name, and True is streaming
# all_clusters = cluster_list.generate_clusters_parallel(verbose, num_CPUs, bin_size, "", bed_file_handle, True, min_cluster_size,output_prefix)
# # bed_file_handle.close()
##### Alternative with low RAM because it prints out all the results at once and use the database file sqlite from before.
all_clusters = cluster_list.generate_clusters_db(database_file,bin_size,output_prefix,"", verbose, bed_file_handle, True, min_cluster_size)
### retrieve reads in the intervals where insertions were predicted and use them to calculate allelic frequency (zygosity) of the predictions
ins_regions_bam_name = output_prefix + ".insertion_regions.reads.bam"
