def main():
if len(sys.argv) < 4:
print sys.argv[0] + " <genepred> <genome fasta> <output fasta> <(optional) 'directionless' for no RC>"
sys.exit()
genepred_filename = sys.argv[1]
genome_filename = sys.argv[2]
output_filename = sys.argv[3]
dodirectionless = 0
if len(sys.argv) == 5:
dodirectionless = 1
if dodirectionless == 1:
genepred_basics.write_genepred_to_fasta_directionless(genepred_filename,genome_filename,output_filename)
else:
genepred_basics.write_genepred_to_fasta(genepred_filename,genome_filename,output_filename)
def main():
if len(sys.argv) < 4:
print sys.argv[
0] + " <genepred> <genome fasta> <output fasta> <(optional) 'directionless' for no RC>"
sys.exit()
genepred_filename = sys.argv[1]
genome_filename = sys.argv[2]
output_filename = sys.argv[3]
dodirectionless = 0
if len(sys.argv) == 5:
dodirectionless = 1
if dodirectionless == 1:
genepred_basics.write_genepred_to_fasta_directionless(
genepred_filename, genome_filename, output_filename)
else:
genepred_basics.write_genepred_to_fasta(genepred_filename,
genome_filename,
output_filename)
def main():
if len(sys.argv) < 6:
print sys.argv[
0] + ' <genome> <uniquely named short reads file> <transcriptome file> <output file> <temp directory>'
sys.exit()
genome_filename = sys.argv[1]
sruniq_filename = sys.argv[2]
transcriptome_filename = sys.argv[3]
output_file = sys.argv[4]
temp_foldername = sys.argv[5]
genome_bowtie2_index = ''
if len(sys.argv) >= 7: genome_bowtie2_index = sys.argv[6]
transcriptome_bowtie2_index = ''
if len(sys.argv) == 8: transcriptome_bowtie2_index = sys.argv[7]
if not os.path.isdir(temp_foldername):
print "Error: Expecting a temporary folder that already exists."
print temp_foldername + " does not exist."
sys.exit()
#1. Make a sub-directory to do our work in
local_temp_foldername = temp_foldername.rstrip('/') + '/uniqueness'
if not os.path.isdir(local_temp_foldername):
print "Creating subdirectory " + local_temp_foldername
os.system("mkdir " + local_temp_foldername)
#2. map reads to the genome fasta
genome_base_name = local_temp_foldername.rstrip('/') + '/genome'
sam_filename = local_temp_foldername.rstrip('/') + '/genome.sam'
map_reads_to_fasta(genome_filename, sruniq_filename, genome_base_name,
genome_bowtie2_index)
#3. count number of times we observe reads
read_counts = read_map_count(sruniq_filename, sam_filename)
#4. get unmapped reads into a fasta
unmapped_read_names = get_unmapped_read_names(read_counts)
unmapped_sruniq_filename = make_unmapped_short_read_file(
sruniq_filename, unmapped_read_names, local_temp_foldername)
#4. Make a fasta based on a transcriptome genepred file
# first ensure the assumption that the genepred file contains only unqiuely named transcripts
transcriptome_uniquename_filename = local_temp_foldername.rstrip(
'/') + '/txn_uniq.gpd'
genepred_basics.write_uniquely_named_genepred(
transcriptome_filename, transcriptome_uniquename_filename)
transcriptome_fa = local_temp_foldername.rstrip('/') + '/txn.fa'
genepred_basics.write_genepred_to_fasta_directionless(
transcriptome_uniquename_filename, genome_filename, transcriptome_fa)
#5. Mapping previously unmapped reads to the transcriptome
txn_base_name = local_temp_foldername.rstrip('/') + '/txn'
txn_sam_filename = local_temp_foldername.rstrip('/') + '/txn.sam'
map_reads_to_fasta(transcriptome_fa, unmapped_sruniq_filename,
txn_base_name, transcriptome_bowtie2_index)
#6. Convert coordinates of the mapped reads back to reference
# Note these coordinates are zero indexed for both start and end coordiantes.
txn_map_filename = local_temp_foldername.rstrip('/') + '/txn.map'
sam_basics.convert_directionless_gpd_alignment_to_reference(
txn_sam_filename, transcriptome_uniquename_filename, txn_map_filename)
#7. Consolidate repetative read mapping due to repeats junctions among isoforms
txn_uniq_map_filename = local_temp_foldername.rstrip('/') + '/txn_uniq.map'
# we are only interested in the unique coordinate sets for each entry
os.system("cat " + txn_map_filename + " | cut -f 1,3 | sort | uniq > " +
txn_uniq_map_filename)
#8. Add transcriptome mapping counts
transcriptome_read_counts = get_transcriptome_read_counts(
txn_uniq_map_filename)
#add those transcriptome_read_counts to our previous read counts
for name in transcriptome_read_counts:
read_counts[name] += transcriptome_read_counts[name]
#9. finished! Now we can print the reads and their counts
ofile = open(output_file, 'w')
for name in read_counts:
ofile.write(name + "\t" + str(read_counts[name]) + "\n")
ofile.close()
def main():
if len(sys.argv) != 5:
print sys.argv[0]+' <long reads fasta> <reference genome> <transcriptome genepred>'
return
longreadfname = sys.argv[1]
genomefname = sys.argv[2]
usergenepredfname = sys.argv[3]
outbase = sys.argv[4]
#get read count
readcount = 0
with open(longreadfname) as f:
for line in f:
if re.match('^>',line): readcount+=1
tdir = FileBasics.make_tempdir2('weirathe','annlong')
# 1. Make sure the transcriptome is uniquely named uniquely mapped entries
genepredfname = tdir+'/txn.gpd'
make_unique_genepred(usergenepredfname,genepredfname)
print 'made unique genepred file'
# 2. Make a transcriptome to align to.
transcriptomefasta = tdir+'/txn.fa'
genepred_basics.write_genepred_to_fasta_directionless(genepredfname,genomefname,transcriptomefasta)
print 'made transcriptome fasta'
# 3. Make a bed file of junction locations in that transcriptome.
junctionbedfname = tdir+'/junction.bed'
junction_counts = make_junction_bed_file(genepredfname,junctionbedfname)
print 'made junction bed file'
# 4. Build a gmap index of the transcriptome
transcriptomeindex = tdir+'/gmap_txn'
aligner_basics.build_gmap_index(transcriptomefasta,transcriptomeindex)
print 'made gmap index of transcriptome'
# 5. Align the long reads to the transcriptome with gmap
alignmentfname = tdir+'/reads.psl'
aligner_basics.gmap_all(longreadfname,transcriptomeindex,alignmentfname)
print 'made gmap alignment of reads to transcriptome'
# 6. Generate get the genepred of the long reads on the transcriptome coordinates.
# Smooth that genepred by a smoothing factor
# And make a bed file of the best alignment. see function for specifications
bestalignmentbedfname = tdir+'/reads.bed'
make_best_continuous_alignment_bed(alignmentfname,bestalignmentbedfname)
print 'made best continuous alignment bed file'
# 10. Print per-gene count info
genenames = genepred_basics.get_transcript_to_gene_name_dictionary(genepredfname)
print 'got gene name conversions'
# 7. Make a report of all prefilter alignments
bestprefilter = tdir+'/prefilter.txt'
prefilter_alignments = make_best_alignment_summary(bestalignmentbedfname,junctionbedfname,junction_counts,bestprefilter)
print 'made best alignment prefilter summary'
report_file = tdir +'/report.txt'
orep = open(report_file,'w')
orep.write('Basename:'+"\t"+outbase+"\n")
orep.write('Temp directory:'+"\t"+tdir+"\n")
orep.write('Long Read Count:'+"\t"+str(readcount)+"\n")
# 8. Filter the full length alignments
full_length_alignments = filter_alignments(prefilter_alignments,'full')
full_length_alignment_file = tdir+'/full_length_alignment.txt'
[full_length_read_count, full_length_transcript_count] = write_alignments(full_length_alignments,full_length_alignment_file,genenames)
orep.write('Read count - full length reads mapped:'+"\t" +str(len(full_length_alignments))+"\n")
orep.write('Transcript count - full length reads mapped:'+"\t" +str(full_length_transcript_count)+"\n")
unambiguous_full_length_alignment_file = tdir+'/unambiguous_full_length_alignment.txt'
unambiguous_full_length_alignments = filter_unambiguous_alignments(full_length_alignments)
[unambiguous_full_length_read_count, unambiguous_full_length_transcript_count] = write_alignments(unambiguous_full_length_alignments,unambiguous_full_length_alignment_file,genenames)
orep.write('Read count - full length reads mapped with unambiguous matches:'+"\t"+str(len(unambiguous_full_length_alignments))+"\n")
orep.write('Transcript count - full length reads mapped with unambiguous matches:'+"\t"+str(unambiguous_full_length_transcript_count)+"\n")
# 9. Filter the full length alignments
prepartial_alignments = filter_alignments(prefilter_alignments,'partial')
prepartial_alignment_file = tdir+'/prepartial_alignment.txt'
write_alignments(prepartial_alignments,prepartial_alignment_file,genenames)
partial_alignments = filter_by_priority_alignments(prepartial_alignments)
partial_alignment_file = tdir+'/partial_alignment.txt'
[partial_read_count, partial_transcript_count] = write_alignments(partial_alignments,partial_alignment_file,genenames)
orep.write('Read count - reads mapped with partial hits best junction and length matches:'+"\t" + str(len(partial_alignments))+"\n")
orep.write('Transcript count - reads mapped with partial hits best junction and length matches:'+"\t" + str(partial_transcript_count)+"\n")
unambiguous_partial_alignments = filter_unambiguous_alignments(partial_alignments)
unambiguous_partial_alignment_file = tdir + '/unambiguous_partial_alignments.txt'
[unambiguous_partial_read_count, unambiguous_partial_transcript_count] = write_alignments(unambiguous_partial_alignments,unambiguous_partial_alignment_file,genenames)
orep.write('Read count - reads mapped with partial hits unambiguous matches:'+"\t"+str(len(unambiguous_partial_alignments))+"\n")
orep.write('Transcript count - reads mapped with partial hits unambiguous matches:'+"\t"+str(unambiguous_partial_transcript_count)+"\n")
partial_gene_counts = get_uniquely_mappable_gene_counts(partial_alignments,genenames)
partial_gene_counts_file = tdir+'/partial_match_uniquely_mappable_gene_counts.txt'
write_gene_counts(partial_gene_counts,partial_gene_counts_file)
full_gene_counts = get_uniquely_mappable_gene_counts(full_length_alignments,genenames)
full_gene_counts_file = tdir+'/full_length_match_uniquely_mappable_gene_counts.txt'
write_gene_counts(full_gene_counts,full_gene_counts_file)
orep.write('Gene count - full length matches uniquely mapped:'+"\t"+str(len(full_gene_counts))+"\n")
orep.write('Gene count - partial matches uniquely mapped:'+"\t"+str(len(partial_gene_counts))+"\n")
orep.close()
copyfile(report_file,outbase+'.Report.txt')
copyfile(full_gene_counts_file,outbase+'.FullGeneCounts.txt')
copyfile(partial_gene_counts_file,outbase+'.PartialGeneCounts.txt')
copyfile(full_length_alignment_file,outbase+'.FullAlignment.txt')
copyfile(unambiguous_full_length_alignment_file,outbase+'.UnambiguousFullAlignment.txt')
copyfile(partial_alignment_file,outbase+'.PartialAlignment.txt')
copyfile(unambiguous_partial_alignment_file,outbase+'.UnambiguousFullAlignment.txt')
rmtree(tdir)
def main():
if len(sys.argv) < 6:
print sys.argv[0] + ' <genome> <uniquely named short reads file> <transcriptome file> <output file> <temp directory>'
sys.exit()
genome_filename = sys.argv[1]
sruniq_filename = sys.argv[2]
transcriptome_filename = sys.argv[3]
output_file = sys.argv[4]
temp_foldername = sys.argv[5]
genome_bowtie2_index = ''
if len(sys.argv) >= 7: genome_bowtie2_index = sys.argv[6]
transcriptome_bowtie2_index = ''
if len(sys.argv) == 8: transcriptome_bowtie2_index = sys.argv[7]
if not os.path.isdir(temp_foldername):
print "Error: Expecting a temporary folder that already exists."
print temp_foldername + " does not exist."
sys.exit()
#1. Make a sub-directory to do our work in
local_temp_foldername = temp_foldername.rstrip('/')+'/uniqueness'
if not os.path.isdir(local_temp_foldername):
print "Creating subdirectory "+local_temp_foldername
os.system("mkdir "+local_temp_foldername)
#2. map reads to the genome fasta
genome_base_name = local_temp_foldername.rstrip('/')+'/genome'
sam_filename = local_temp_foldername.rstrip('/')+'/genome.sam'
map_reads_to_fasta(genome_filename,sruniq_filename,genome_base_name,genome_bowtie2_index)
#3. count number of times we observe reads
read_counts = read_map_count(sruniq_filename, sam_filename)
#4. get unmapped reads into a fasta
unmapped_read_names = get_unmapped_read_names(read_counts)
unmapped_sruniq_filename = make_unmapped_short_read_file(sruniq_filename,unmapped_read_names,local_temp_foldername)
#4. Make a fasta based on a transcriptome genepred file
# first ensure the assumption that the genepred file contains only unqiuely named transcripts
transcriptome_uniquename_filename = local_temp_foldername.rstrip('/')+'/txn_uniq.gpd'
genepred_basics.write_uniquely_named_genepred(transcriptome_filename,transcriptome_uniquename_filename)
transcriptome_fa = local_temp_foldername.rstrip('/')+'/txn.fa'
genepred_basics.write_genepred_to_fasta_directionless(transcriptome_uniquename_filename,genome_filename,transcriptome_fa)
#5. Mapping previously unmapped reads to the transcriptome
txn_base_name = local_temp_foldername.rstrip('/')+'/txn'
txn_sam_filename = local_temp_foldername.rstrip('/')+'/txn.sam'
map_reads_to_fasta(transcriptome_fa,unmapped_sruniq_filename,txn_base_name,transcriptome_bowtie2_index)
#6. Convert coordinates of the mapped reads back to reference
# Note these coordinates are zero indexed for both start and end coordiantes.
txn_map_filename = local_temp_foldername.rstrip('/') + '/txn.map'
sam_basics.convert_directionless_gpd_alignment_to_reference(txn_sam_filename, transcriptome_uniquename_filename,txn_map_filename)
#7. Consolidate repetative read mapping due to repeats junctions among isoforms
txn_uniq_map_filename = local_temp_foldername.rstrip('/') + '/txn_uniq.map'
# we are only interested in the unique coordinate sets for each entry
os.system("cat "+txn_map_filename+" | cut -f 1,3 | sort | uniq > "+txn_uniq_map_filename)
#8. Add transcriptome mapping counts
transcriptome_read_counts = get_transcriptome_read_counts(txn_uniq_map_filename)
#add those transcriptome_read_counts to our previous read counts
for name in transcriptome_read_counts: read_counts[name]+=transcriptome_read_counts[name]
#9. finished! Now we can print the reads and their counts
ofile = open(output_file,'w')
for name in read_counts:
ofile.write(name + "\t" + str(read_counts[name])+"\n")
ofile.close()
