Tuning Tophat2Belinda Giardine

Tophat2Aligns reads from RNA to the genome

Ribonucleic 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 filesReads in fastq format, 20–200 million reads (2 x

20gb for my test)Reference sequence and indexes used for

mapping 6gb for mouseFinal output 14gb for my test

Work from last timeCompiling

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

Hardison Lab

vec_report3segment_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(); }