Toggle Main Menu Toggle Search

Open Access padlockePrints

An FPGA based Energy-Efficient Read Mapper with Parallel Filtering and in-situ Verification

Lookup NU author(s): Venkateshwarlu Gudur, Dr Sidharth Maheshwari, Dr Rishad Shafik

Downloads


Licence

This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2021.

For re-use rights please refer to the publisher's terms and conditions.


Abstract

In the assembly pipeline of Whole Genome Sequencing (WGS), read mapping is a widely used method to re-assemble the genome. It employs approximate string matching and dynamic programming-based algorithms on a large volume of data and associated structures, making it a computationally intensive process. Currently, the state-of-the-art data centers for genome sequencing incur substantial setup and energy costs for maintaining hardware, data storage and cooling systems. To enable low-cost genomics, we propose an energy-efficient architectural methodology for read mapping using a single system-on-chip (SoC) platform. The proposed methodology is based on the q-gram lemma and designed using a novel architecture for filtering and verification. The filtering algorithm is designed using a parallel sorted q-gram lemma based method for the first time, and it is complemented by an in-situ verification routine using parallel Myers bit-vector algorithm. We have implemented our design on the Zynq Ultrascale+ XCZU9EG MPSoC platform. It is then extensively validated using real genomic data to demonstrate up to 7.8 energy reduction and up to 13.3 less resource utilization when compared with the state-of-the-art software and hardware approaches.


Publication metadata

Author(s): Gudur VY, Maheshwari S, Acharyya A, Shafik R

Publication type: Article

Publication status: Published

Journal: IEEE/ACM Transactions on Computational Biology and Bioinformatics

Year: 2021

Pages: epub ahead of print

Online publication date: 20/08/2021

Acceptance date: 17/08/2021

Date deposited: 24/09/2021

ISSN (print): 1545-5963

ISSN (electronic): 1557-9964

Publisher: IEEE

URL: https://doi.org/10.1109/TCBB.2021.3106311

DOI: 10.1109/TCBB.2021.3106311


Altmetrics

Altmetrics provided by Altmetric


Funding

Funder referenceFunder name
EPSRC
IE161183Royal Society

Share