In the analysis of next-generation DNA sequencing data, Hidden Markov Models (HMMs) are used to perform variant calling between DNA sequences and a reference genome. The PairHMM model is solved by the Forward Algorithm, for which the performance and power efficiency can be increased tremendously using systolic arrays (SAs) in FPGAs. We model the performance characteristics of such SAs, and propose a novel architecture that allows the computational units to continuously perform useful work on the input data. The implementation achieves up to 90\% of the theoretical throughput for a real dataset. The implementation of the proposed architecture achieves more than 2.5x throughput over the state-of-the-art on a similar contemporary platform.
Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
EditorsTianhai Tian, Qinghua Jiang, Yunlong Liu, Kevin Burrage, Jiangning Song, Yadong Wang, Xiaohua Hu, Shinichi Morishita, Qian Zhu, Guohua Wang
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages758-762
Number of pages5
ISBN (Electronic)978-1-5090-1610-5
DOIs
Publication statusPublished - Dec 2016
EventIEEE International Conference on Bioinformatics and Biomedicine 2016 - Kylin Villa Hotel, Shenzhen, China
Duration: 15 Dec 201618 Dec 2016
https://cci.drexel.edu/ieeebibm/bibm2016/

Conference

ConferenceIEEE International Conference on Bioinformatics and Biomedicine 2016
Abbreviated titleBIBM 2016
CountryChina
CityShenzhen
Period15/12/1618/12/16
Internet address

    Research areas

  • High-Throughput Sequencing, GATK, Haplotype-Caller, PairHMM, Systolic Array, FPGA

ID: 10266383