Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs
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1 Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs Anas Abu-Doleh 1,2, Erik Saule 1, Kamer Kaya 1 and Ümit V. Çatalyürek 1,2 1 Department of Biomedical Informatics 2 Department of Electrical and Computer Engineering The Ohio State University
2 Outline I. Introduction Motivation Contribution Related Work II. Masher Workflow Index Construction Mapping III. Experiments and Results IV. Conclusion and Future Work A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 2
3 Motivation The read length of next generation sequencing (NGS) devices is continuously increasing so there is a wide interest in efficient and accurate mapping of long(er) reads. Utilizing the powerful capabilities of GPUs to improve the mapping of NGS reads. A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 3
4 Related Work and Contributions Contribution A novel hash-based indexing technique by which: For large genomes, the memory footprint small enough to be stored in a restricted-memory device such as a GPU. The index data structure is more suitable for GPU parallelization Related Work Burrows-Wheeler Transform (BWT) o Bowtie2 o CUSHAW2 o Soap3-dp Hash Indexing o SeqAlto o BFAST ) A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 4
5 Masher workflow A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 5
6 Index Construction Processing genome file Base pairs to 2 bit format. Replacing each N with A. A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 6
7 Index Construction Processing genome file Base pairs to 2 bit format. Replacing each N with A. Indexing Seed length L S Indexing step size G A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 7
8 Index Construction Index arrays - Locations array Genome length, N Stores the indexed locations in order for each seed Location array size = log 2 (N) x N/ G Size 2.9 GB, hg19, G = 4 A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 8
9 Index Construction Index arrays - Count array Stores the number of occurrences for each seed Size = 4 Ls x log 2 N/ G Store at most 255 locations. Appear more than 255, do uniform selection. Size = 1 GB, L S = 15. A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 9
10 Index Construction Index arrays - Ptrs array Stores the starting index at locs array for a group of seeds Seed group size, δ. Group id = seed/δ Size = 4 L / δ x log 2 ( N/ G Size = 0.5 GB, δ = 8, G = 4. A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 10
11 Index Construction Index arrays L S = 15, G = 4, δ = 8, hg19 Total indexing arrays size = = 4.4 GB. Space time tradeoff A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 11
12 Index Construction Accessing the Index Count array Assume seed = i + 4 Belongs to seed group (i, i + δ 1 ), δ = 8, i mod δ = 0. Seed index in group, k = (i +4) mod δ C k=4 = count[i + 4 ] Ptrs array j = seed /δ, Locs group index (Lgi) = ptrs[ j ] Locs seed index (Lsi) = Lgi + n=0 k 1 C n Locs array Extract locations from (Lsi, Lsi + C k - 1 ) A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 12
13 Pr(count <= x) Index Construction Seeds count A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 13
14 Mapping Seed & hash Read step size, R Read length, L R N seeds = G x (L R L S )/ R Locate candidate alignment locations (CALs) Each thread is assigned to a specific seed. A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 14
15 Mapping Merge CALs and weights In merging CALs, if two CALs are within a threshold distance, the second weight will be added to the first weight. For efficiency purpose, Masher consists of two main loops. A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 15
16 Mapping Sorting and Batching CALs Sorting and setting the CALs in batches with respect to their weights. At this stage, a filter operation for CALs with low weight could be applied. A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 16
17 Mapping Sorting and Batching CALs Sorting and setting the CALs in batches with respect to their weights. At this stage, a filter operation for CALs with low weight could be applied. Bounded local Alignment A parameterized variant of Smith-Waterman (SW) algorithm supporting affinity gap scoring. Bounded alignment, only the matrix cells (i, j) where i - j <= w are visited and scored. Masher does two passes and sets w to 4 and 16 respectively GPU block performs multiple SWs in parallel. A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 17
18 Experiments and Results Platform Intel core i7-960 CPU clocked at 3.2 Ghz. 4 Hyper-Threading cores, 24GB of DDR3 memory. Tesla K20c GPU, 4.8GB of global memory. CUDA 5.0 and GCC Human genome and Simulated Reads Human genome hg19 Wgsim simulator, 100K reads of length 100, 300, 500, and 1000 with error rates 2%, 4%, 6%, and 8%. A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 18
19 Experiments and Results Metrics for comparison Sensitivity, is the percentage of the aligned reads. Accuracy, is the percentage of the reads correctly aligned to simulator locations among all aligned reads. Execution time: Only alignment time was measured. The lower bound for a valid alignment score is set to score LB = L R x ( x Error Rate) Two modes of Masher Normal mode, R = 0.7 L R Fast mode, R = L R Comparison with Bowtie2 (sensitive and fast), 8 threads SOAP3-dp CUSHAW2-GPU. A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 19
20 Accuracy % Sensitivity % Experiments and Results L R = 100 bps. Masher Masher-fast Bowtie2 Bowtie2-fast SOAP3-dp CUSHAW2-GPU % 4% 6% 8% Error rate A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 20
21 Accuracy % Sensitivity % Experiments and Results L R = 500 bps. Masher Masher-fast Bowtie2 Bowtie2-fast SOAP3-dp % 4% 6% 8% Error rate A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 21
22 Accuracy % Sensitivity % Experiments and Results L R = 1000 bps. Masher Masher-fast Bowtie2 Bowtie2-fast SOAP3-dp % 4% 6% 8% Error rate A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 22
23 Execution time (sec.) in log scale Experiments and Results L R = 100 bps. Masher Masher-fast Bowtie2 Bowtie2-fast SOAP3-dp CUSHAW2-GPU % 4% 6% 8% Error rate A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 23
24 Execution time (sec.) in log scale Experiments and Results L R = 500 bps. Masher Masher-fast Bowtie2 Bowtie2-fast SOAP3-dp % 4% 6% 8% Error rate A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 24
25 Execution time (sec.) in log scale Experiments and Results L R = 1000 bps. Masher Masher-fast Bowtie2 Bowtie2-fast SOAP3-dp % 4% 6% 8% Error rate A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 25
26 Execution time (sec.) in log scale Experiments and Results Masher Masher-fast Bowtie2 Bowtie2-fast Sensitivity % Accuracy % SOAP3-dp L R = 1000 bps, Error rate 2% A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 26
27 Conclusion and future work Conclusion Masher, a fast and accurate short/long read mapper, which uses memory efficient indexing scheme to reduce the size of a human genome index and to make it fit to the memory of a GPU. The results show that Masher produces accurate alignments. Its speed is competitive with the tested state-of-the-art tools for reads of length less than 500 and an order of magnitude faster when the reads are longer than 500. Future work Making the software publicly available. Improving Masher s performance further by using GPU-specific optimizations and with a better CPU/GPU pipelining. Adding new features such as a support for paired-end sequences or fastq format. A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 27
28 Thanks For more information Visit Acknowledgement of Support A Abu-Doleh Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs" 28
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