Yuandong Chan, Kai Xu, Haidong Lan, Weiguo Liu, Yongchao Liu, B. Schmidt
{"title":"新一代测序读比对的并行unapped -Alignment种子验证算法","authors":"Yuandong Chan, Kai Xu, Haidong Lan, Weiguo Liu, Yongchao Liu, B. Schmidt","doi":"10.1109/IPDPS.2017.35","DOIUrl":null,"url":null,"abstract":"The progress of next-generation sequencing has a major impact on medical and genomic research. This technology can now produce billions of short DNA fragments (reads) in a single run. One of the most demanding computational problems used by almost every sequencing pipeline is short-read alignment; i.e. determining where each fragment originated from in the original genome. Most current solutions are based on a seed-and-extend approach, where promising candidate regions (seeds) are first identified and subsequently extended in order to verify whether a full high-scoring alignment actually exists in the vicinity of each seed. Seed verification is the main bottleneck in many state-of-the-art aligners and thus finding fast solutions is of high importance. We present a parallel ungapped-alignment-featured seed verification (PUNAS) algorithm, a fast filter for effectively removing the majority of false positive seeds, thus significantly accelerating the short-read alignment process. PUNAS is based on bit-parallelism and takes advantage of SIMD vector units of modern microprocessors. Our implementation employs a vectorize-and-scale approach supporting multi-core CPUs and many-core Knights Landing (KNL)-based Xeon Phi processors. Performance evaluation reveals that PUNAS is over three orders-of-magnitude faster than seed verification with the Smith-Waterman algorithm and around one order-of-magnitude faster than seed verification with the banded version of Myers bit-vector algorithm. Using a single thread it achieves a speedup of up to 7.3, 27.1, and 11.6 compared to the shifted Hamming distance filter on a SSE, AVX2, and AVX-512 based CPU/KNL, respectively. The speed of our framework further scales almost linearly with the number of cores. PUNAS is open-source software available at https://github.com/Xu-Kai/PUNASfilter.","PeriodicalId":209524,"journal":{"name":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"PUNAS: A Parallel Ungapped-Alignment-Featured Seed Verification Algorithm for Next-Generation Sequencing Read Alignment\",\"authors\":\"Yuandong Chan, Kai Xu, Haidong Lan, Weiguo Liu, Yongchao Liu, B. Schmidt\",\"doi\":\"10.1109/IPDPS.2017.35\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The progress of next-generation sequencing has a major impact on medical and genomic research. This technology can now produce billions of short DNA fragments (reads) in a single run. One of the most demanding computational problems used by almost every sequencing pipeline is short-read alignment; i.e. determining where each fragment originated from in the original genome. Most current solutions are based on a seed-and-extend approach, where promising candidate regions (seeds) are first identified and subsequently extended in order to verify whether a full high-scoring alignment actually exists in the vicinity of each seed. Seed verification is the main bottleneck in many state-of-the-art aligners and thus finding fast solutions is of high importance. We present a parallel ungapped-alignment-featured seed verification (PUNAS) algorithm, a fast filter for effectively removing the majority of false positive seeds, thus significantly accelerating the short-read alignment process. PUNAS is based on bit-parallelism and takes advantage of SIMD vector units of modern microprocessors. Our implementation employs a vectorize-and-scale approach supporting multi-core CPUs and many-core Knights Landing (KNL)-based Xeon Phi processors. Performance evaluation reveals that PUNAS is over three orders-of-magnitude faster than seed verification with the Smith-Waterman algorithm and around one order-of-magnitude faster than seed verification with the banded version of Myers bit-vector algorithm. Using a single thread it achieves a speedup of up to 7.3, 27.1, and 11.6 compared to the shifted Hamming distance filter on a SSE, AVX2, and AVX-512 based CPU/KNL, respectively. The speed of our framework further scales almost linearly with the number of cores. PUNAS is open-source software available at https://github.com/Xu-Kai/PUNASfilter.\",\"PeriodicalId\":209524,\"journal\":{\"name\":\"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPDPS.2017.35\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPDPS.2017.35","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PUNAS: A Parallel Ungapped-Alignment-Featured Seed Verification Algorithm for Next-Generation Sequencing Read Alignment
The progress of next-generation sequencing has a major impact on medical and genomic research. This technology can now produce billions of short DNA fragments (reads) in a single run. One of the most demanding computational problems used by almost every sequencing pipeline is short-read alignment; i.e. determining where each fragment originated from in the original genome. Most current solutions are based on a seed-and-extend approach, where promising candidate regions (seeds) are first identified and subsequently extended in order to verify whether a full high-scoring alignment actually exists in the vicinity of each seed. Seed verification is the main bottleneck in many state-of-the-art aligners and thus finding fast solutions is of high importance. We present a parallel ungapped-alignment-featured seed verification (PUNAS) algorithm, a fast filter for effectively removing the majority of false positive seeds, thus significantly accelerating the short-read alignment process. PUNAS is based on bit-parallelism and takes advantage of SIMD vector units of modern microprocessors. Our implementation employs a vectorize-and-scale approach supporting multi-core CPUs and many-core Knights Landing (KNL)-based Xeon Phi processors. Performance evaluation reveals that PUNAS is over three orders-of-magnitude faster than seed verification with the Smith-Waterman algorithm and around one order-of-magnitude faster than seed verification with the banded version of Myers bit-vector algorithm. Using a single thread it achieves a speedup of up to 7.3, 27.1, and 11.6 compared to the shifted Hamming distance filter on a SSE, AVX2, and AVX-512 based CPU/KNL, respectively. The speed of our framework further scales almost linearly with the number of cores. PUNAS is open-source software available at https://github.com/Xu-Kai/PUNASfilter.