CUDA-Enabled Applications for Nextgeneration. Bertil Schmidt

Transcription

1 CUDA-Enabled Applications for Nextgeneration Sequencing Bertil Schmidt

2 Next-Generation Sequencing (NGS) DNA Read-sequences May contain errors! DNA-sequence 1x drop from -1 Illumina HiSeq Read length (typical) 1 bps Reads per run 1. billion Run Time (paired end) hours

3 Short Read Mapping Find alignments taking many copies of a book, passing them all through a shredder find the location of each piece in a given very similar book Problem definition Given a huge set of reads and a reference genome, identify where each read is from in the reference genome Computational challenges Throughput (make it fast) BWA..9 with human reference genome.m reads/hour on quad-core CPU ( threads, x1bps reads) 1 CPU week for aligning a human genome at x coverage Sensitivity (make it accurate) Sequencing errors Genomic variation

4 Common Algorithmic Patterns in NGS Data Analysis Indexing and lookup Hash tables BWT and FM-Index Bloom filter Dynamic programming Smith-Waterman, Needleman-Wunsch NGS Bioinformatics Challenges Scalability to deal with huge amounts of reads Algorithm design to deal with short reads Parallelisation Many Bioinformatics algorithms are irregular and therefore challenging to map to parallel architectures

5 Local Pairwise Sequence Alignment Align S1=ATCTCGTATGATG S=GTCTATCAC G T C T A T C A C A T C T C G T A T G A T G else ) ( if ), ( y x y x Sbt =1, =1 A T C T C G T A T G A T G G T C T A T C A C ) 1, ( 1) 1, ( 1 1), ( 1 ) 1, ( max ), ( j i S S Sbt j i H j i H j i H j i H

6 Background on Short Read Mapping Reference Genome and reads are too large for direct DP approach Seed-and-Extend : Use an index data structure to rapidly find short exact matches to seed longer in-exact alignments, e.g. Build a hash table of all k-mers of genome Segment query sequence into k-mer seeds Ref Genome: Read Seed: CAAACCAGCTCTTATGGTCAGAACTCTGAAAGACAACTGAGCTGCTG TGGTCAGAAC k-mer positions

7 CUSHAW: CUDA-enabled short-read aligner to human genome based on BWT Indexing approaches (memory sizes for human genome) Suffix tree (> 3 GB) Suffix array (> 1 GB) Hash tables (> 1 GB) CUSHAW: GPU-Approach Index Reference Genome using BWT (Burrows Wheeler Transform) Needs. GB memory for Human Genome fits on Fermi C optimized for Fermi architecture using CUDA CUSHAW currently only supports a restrictive alignment models exhaustive search with certain constraints allows few mismatches (default = ) in seed no indels

8 BWT(cattattagga$) Cyclic Rotations c a t t a t t a g g a $ a t t a t t a g g a $ c t t a t t a g g a $ c a t a t t a g g a $ c a t a t t a g g a $ c a t t t t a g g a $ c a t t a t a g g a $ c a t t a t a g g a $ c a t t a t t g g a $ c a t t a t t a g a $ c a t t a t t a g a $ c a t t a t t a g g $ c a t t a t t a g g a Sorting $ c a t t a t t a g g a a $ c a t t a t t a g g a g g a $ c a t t a t t a t t a g g a $ c a t t a t t a t t a g g a $ c c a t t a t t a g g a $ g a $ c a t t a t t a g g g a $ c a t t a t t a t a g g a $ c a t t a t t a t t a g g a $ c a t t t a g g a $ c a t t a t t a t t a g g a $ c a BWT a g t t c $ g a t t a a

9 [1,11] [8,9] [1,] Backward search to calculate SA interval for tta in BWT(cattattagga$) t t a $ c a t t a t t a g g a a $ c a t t a t t a g g a g g a $ c a t t a t t a t t a g g a $ c a t t a t t a t t a g g a $ c c a t t a t t a g g a $ g a $ c a t t a t t a g g g a $ c a t t a t t a t a g g a $ c a t t a t t a t t a g g a $ c a t t t a g g a $ c a t t a t t a t t a g g a $ c a t t a $ c a t t a t t a g g a a $ c a t t a t t a g g a g g a $ c a t t a t t a t t a g g a $ c a t t a t t a t t a g g a $ c c a t t a t t a g g a $ g a $ c a t t a t t a g g g a $ c a t t a t t a t a g g a $ c a t t a t t a t t a g g a $ c a t t t a g g a $ c a t t a t t a t t a g g a $ c a t t a $ c a t t a t t a g g a a $ c a t t a t t a g g a g g a $ c a t t a t t a t t a g g a $ c a t t a t t a t t a g g a $ c c a t t a t t a g g a $ g a $ c a t t a t t a g g g a $ c a t t a t t a t a g g a $ c a t t a t t a t t a g g a $ c a t t t a g g a $ c a t t a t t a t t a g g a $ c a Ia( i) C( S[ i]) Occ( S[ i], Ia( i 1) 1) 1, i S Ib( i) C( S[ i]) Occ( S[ i], Ib( i 1)), i S

10 CUSHAW: short-read aligner to human genome based on BWT SRR3 8.M Reads, 36bp ERR89.3M Reads, 1bp ERR3.M Reads, bps CUSHAW (Tesla M9) 1.1 mins. mins 33.3 mins BWA..9 (AMD quadcore CPU, threads) 1.9 mins 6.6 mins 91.9 mins Speedup CUSHAW becomes less efficient with growing read length

11 Background: Seeding CUSHAW: fixed-length seed from high-quality read-end allowing some mismatches 31 1 seed extension 3 mismatches allowed mismatches allowed 31 seed extension 3 mismatches allowed mismatches allowed CUSHAW: variable-length seed based on maximal exact match m n 99 extension seed extension 3 no mismatches allowed optimal local alignment

12 CUSHAW: Program Workflow Comprised of three stages for SE alignments: Generating MEM seeds Selecting the best mapping regions on the genome Producing the final alignments Two additional stages for PE alignments Seed pairing heuristic Read rescuing Read S 1 Read S MEM seed generation Selection of best mapping regions SE PE Seed pairing Read mate rescuing (conditionally) MEM seed generation Selection of best mapping regions PE SE Produce and report final alignments

13 Program Outline of CUSHAW-GPU Read batch Seed generation Top seeds selection Alignment generation Compute suffix array intervals of seeds on GPU Perform scoreonly Smith- Waterman on GPU Compute alignments of top seeds on GPU Locate each seed on the reference on GPU Sort all seeds on GPU Read pairing and rescuing (for PE only)

14 Results: Simulated Reads BWA-SW (v.6.-r16), Bowtie (v..6) Simulated reads: 1-bp, 1-bp, -bp datasets different uniform base error rates: %, %, 6% wgsim utility in SAMtools v.1.18 (Illumina-like) Alignment results on simulated 1-bp reads % % 6% Aligner Recall Prec. Recall Prec. Recall Prec. Single-End (SE) CUSHAW-GPU BWA-SW Bowtie Paired-End (PE) CUSHAW-GPU BWA-SW Bowtie

15 Results: Simulated Reads Simulated reads: -like reads Mason simulator, default error mode settings 1bps 1bps bps Aligner Recall Prec. Recall Prec. Recall Prec. SE CUSHAW-GPU BWA-SW Bowtie

16 Effectiveness of mapping quality scores 1-bp dataset with % error rate Cumulative recall and precision calculated from the cumulative number of reported alignments and the cumulative number of correct alignments from high to low mapping quality scores

17 Runtime Evaluation 6-core Intel Xeon E-6. GHz CPU (using 1 threads), 16 GB RAM K GPU Runtimes in minutes SRX6 (, M Reads, 3bp) SRX88 (Ion Torrent, M Reads, 183bp) SRX619 (Illumina, 1M Reads, 1bp) ERX968 (Illumina, 3M Reads, 11bp) CUSHAW-GPU CUSHAW-CPU BWA-SW Bowtie

18 Other CUDA-enabled HPC Bioinformatics Software developed by my group Sequence database searching CUDASW++ (Smith-Waterman) CUDA-BLASTP Multiple sequence alignment MSA-CUDA Next-Generation Sequencing (NGS) DecGPU (short-read error correction) CUSHAW (short-read mapping) CRiSPy-CUDA and CRiSPy-Embed (short-read clustering) Motif finding CUDA-MEME Accessible via:

19 Conclusion CUSHAW is a CUDA-based short read aligner Open-source: Speedup of one order-of-magnitude on a Tesla M9 compared to BWA on a Quad-core CPU for short reads Less efficient for longer read length CUSHAW is a fast gapped NGS read aligner, employing MEMs as seeds Open-source: Consistently among the highest-ranked aligners for both SE and PE in terms of recall, precision, and speed Partial Funding: NVIDIA Foundation (OpenGE) Publications Y. Liu, B.Schmidt, D. Maskell: CUSHAW: a CUDA compatible short read aligner to large genomes based on the Burrows-Wheeler transform, Bioinformatics 8(1), , 1 Y. Liu, B. Schmidt: Long read alignment based on maximal exact match seeds, Bioinformatics 8(18), i318-3, 1

20 Conclusion NGS technologies establish the need for scalable Bioinformatics tools that can process massive amounts of short reads CUDA is a highly suitable technology to address this need NGS algorithms need to be adapted since throughput and read length continues to increase Website