tuning tophat2 belinda giardine. tophat2 aligns reads from rna to the genome ribonucleic acid (rna)...
TRANSCRIPT
Tophat2
Aligns reads from RNA to the genomeRibonucleic acid (RNA) is a ubiquitous family of large
biological molecules that perform multiple vital roles in the coding, decoding, regulation, and expression of genes.
Adds on dealing with gaps in the alignments by breaking the reads into small pieces ~20 bases and reassembling the reads after mapping.
Though the new version is more parallel still slow (more than 4 days for recent runs)
It uses Bowtie to do the actual mapping
RNA-seq
image from wikipedia
fastq file, a single read:@DGM97JN1:330:C3EW0ACXX:1:1101:2723:1993 1:N:0:NAAGGCGAATGCCCCCGGCCGTCCCTCTTAATCATGGCCTCAGTTCCGAAAACCANCAAAATAGAACCGCGGTCCTATTNN+CCCFFFFFHHHHGIIFGIIIIJJIIJIFGIJEHIIJIGHIJHAGHHFEE#,;;BACEEDDDDDD@B>BBDCDC##
Tophat2Pipeline written in C++ (34,351 lines of code in 63
files)Wrapper written in Python
3 of the programs use Boost pthreads long_spanning_reads.cpp segment_juncs.cpp tophat_reports.cpp
Programs are compiled as one unit under autoconfig and automake, communication between programs with temporary files.
Many prerequisites: zlib, Boost, samtools, Bowtie, this and the amount of file IO makes running on MIC only not feasible.
Data files
Reads in fastq format, 20–200 million reads (2 x 20gb for my test)
Reference sequence and indexes used for mapping 6gb for mouse
Final output 14gb for my test
Work from last time
Compiling start with gcc then icc then add –mmic (this failed in trying to get all the
prerequisites)
Test run on host, using Tophat’s log of run for time.Run on biostar(Xeon) using 8 threads took 26 hoursRun on stampede (host) using 16 threads took 19
hours, 40 minsRun on stampede (host) using 32 threads took 24
hours
New workPython wrapper and long run times makes gprof and
vtune difficult to profile code with.
Going from my experience in Biostar, I am starting with segment_juncs executable.
Keeping the temporary files that are used for passing data between programs, I ran just segment_juncs.
Time for segment_junctions run alone:8 threads 2 hours 13 minutes16 threads 1 hour 15 minutes (2 ½ out of 19 ½ hours
total) of this 76% is spent in the parallel section
32 threads 2 hours 12 minutes
Failed attemptsRun vtune on segment_juncs
times out of full data license errors
Check loops in par_report that are assumed dependencies. lines of code indicated not loops or in loops? contradictory lines
Offloading threaded section of code in segment_juncs.cpp. Will it actually improve speed or too much file IO? Lots of variables to copyFile IO
vec_report3
segment_juncs.cpp(135): (col. 32) remark: loop was not vectorized: existence of vector dependence.
segment_juncs.cpp(135): (col. 32) remark: vector dependence: assumed ANTI dependence between r.92068 line 135 and r.92068 line 135.
segment_juncs.cpp(135): (col. 32) remark: vector dependence: assumed FLOW dependence between r.92068 line 135 and r.92068 line 135.
Line 135:
left_seg.left = max(0, T.right() - 2);
opt_report
REMOVED VAR left_mismatches.201433.0_V$78b
REMOVED PACK left_mismatches.201433.0
REMOVED VAR right_mismatches.201433.0_V$78d
REMOVED PACK right_mismatches.201433.0
gprof output for segment_juncs
Each sample counts as 0.01 seconds.
% cumulative self self total
time seconds seconds calls Ts/call Ts/call name
100.01 0.01 0.01 extend_from_seeds(std::vector<SeedExtension, std::allocator<SeedExtension> >&, PackedSplice const&, std::vector<std::vector<ReadHit, std::allocator<ReadHit> >, std::allocator<std::vector<ReadHit, std::allocator<ReadHit> > > > const&, std::string const&, std::string const&, unsigned long, unsigned long, int)
0.00 0.01 0.00 89528 0.00 0.00 pack_splice(std::string const&, int, int, unsigned int)
0.00 0.01 0.00 3 0.00 0.00 __do_global_dtors_aux
0.00 0.01 0.00 2 0.00 0.00 pack_right_splice_half(std::string const&, unsigned int, unsigned int)
Parallel section of code: vector<boost::thread*> threads; for (int i = 0; i < num_threads; ++i) {
SegmentSearchWorker worker; worker.rt = &rt; worker.reads_fname = left_reads_fname; worker.segmap_fnames = &left_segmap_fnames; worker.partner_reads_map_fname = right_reads_map_fname; worker.seg_partner_reads_map_fname = right_seg_fname_for_segment_search; worker.juncs = &vseg_juncs[i]; worker.deletions = &vdeletions[i]; worker.insertions = &vinsertions[i]; worker.fusions = &vfusions[i]; worker.read = READ_LEFT; worker.partner_hit_offset = 0; worker.seg_partner_hit_offset = 0;
if (i == 0) { worker.begin_id = 0; worker.seg_offsets = vector<int64_t>(left_segmap_fnames.size(), 0); worker.read_offset = 0; } else { worker.begin_id = read_ids[i-1]; worker.seg_offsets.insert(worker.seg_offsets.end(), offsets[i-1].begin()+1, offsets[i-1].end()); worker.read_offset = offsets[i-1][0]; if (partner_offsets.size() > 0) worker.partner_hit_offset = partner_offsets[i-1]; if (seg_partner_offsets.size() > 0) worker.seg_partner_hit_offset = seg_partner_offsets[i-1]; } worker.end_id = (i+1 < num_threads) ? read_ids[i] : std::numeric_limits<uint64_t>::max(); //Geo debug: //fprintf(stderr, "Worker %d: begin_id=%lu, end_id=%lu\n", i, worker.begin_id, worker.end_id);
if (num_threads > 1 && i + 1 < num_threads) threads.push_back(new boost::thread(worker)); else worker(); }