TY - GEN
T1 - Acceleration of Short Read Alignment with Runtime Reconfiguration
AU - Ng, Ho Cheung
AU - Liu, Shuanglong
AU - Coleman, Izaak
AU - Chu, Ringo S.W.
AU - Yue, Man Chung
AU - Luk, Wayne
N1 - Funding Information:
The support of the Lee Family Scholarship, the Great Britain-China Educational Trust, the Hong Kong Scholarship For Excellence Scheme, the UK EPSRC (EP/L016796/1, EP/N031768/1, EP/P010040/1 and EP/L00058X/1), the National Natural Science Foundation of China (62001165), Maxeler, Intel and Xilinx is gratefully acknowledged.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - Recent advancements in the throughput of next-generation sequencing machines pose a huge computational challenge in analyzing the massive quantities of sequenced data produced. A critical initial step of genomic data analysis is short read alignment, which is a bottleneck in the analysis workflow. This paper explores the use of a reconfigurable architecture to accelerate this process, based on the seed-And-extend model of Bowtie2. In the proposed approach, complete information available in sequencing data is integrated into an FPGA alignment pipeline for biologically accurate runtime acceleration. Experimental results show that our architecture achieves a similar alignment rate compared to Bowtie2, mapping reads around twice as fast. Particularly, the alignment time is reduced from 50 minutes to 26 minutes when processing 300M reads.
AB - Recent advancements in the throughput of next-generation sequencing machines pose a huge computational challenge in analyzing the massive quantities of sequenced data produced. A critical initial step of genomic data analysis is short read alignment, which is a bottleneck in the analysis workflow. This paper explores the use of a reconfigurable architecture to accelerate this process, based on the seed-And-extend model of Bowtie2. In the proposed approach, complete information available in sequencing data is integrated into an FPGA alignment pipeline for biologically accurate runtime acceleration. Experimental results show that our architecture achieves a similar alignment rate compared to Bowtie2, mapping reads around twice as fast. Particularly, the alignment time is reduced from 50 minutes to 26 minutes when processing 300M reads.
UR - http://www.scopus.com/inward/record.url?scp=85102083294&partnerID=8YFLogxK
U2 - 10.1109/ICFPT51103.2020.00044
DO - 10.1109/ICFPT51103.2020.00044
M3 - Conference article published in proceeding or book
AN - SCOPUS:85102083294
T3 - Proceedings - 2020 International Conference on Field-Programmable Technology, ICFPT 2020
SP - 256
EP - 262
BT - Proceedings - 2020 International Conference on Field-Programmable Technology, ICFPT 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Field-Programmable Technology, ICFPT 2020
Y2 - 7 December 2020 through 8 December 2020
ER -