Hardware Acceleration of Genomics Data Analysis: Challenges and Opportunities

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Abstract

The significant decline in the cost of genome sequencing has dramatically changed the typical bioinformatics pipeline for analysing sequencing data. Where traditionally, the computational challenge of sequencing is now secondary to genomic data analysis. Short read alignment (SRA) is a ubiquitous process within every modern bioinformatics pipeline in the field of genomics and is often regarded as the principal computational bottleneck. Many hardware and software approaches have been provided to solve the challenge of acceleration. However, previous attempts to increase throughput using many-core processing strategies have enjoyed limited success, mainly due to a dependence on global memory for each computational block. The limited scalability and high energy costs of many-core SRA implementations pose a significant constraint in maintaining acceleration. The Networks-On-Chip (NoC) hardware interconnect mechanism has advanced the scalability of many-core computing systems and, more recently, has demonstrated potential in SRA implementations by integrating multiple computational blocks such as pre-alignment filtering and sequence alignment efficiently, while minimizing memory latency and global memory access. This article provides a state of the art review on current hardware acceleration strategies for genomic data analysis, and it establishes the challenges and opportunities of utilizing NoCs as a critical building block in next generation sequencing (NGS) technologies for advancing the speed of analysis.
Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalBioinformatics
Early online date25 May 2021
DOIs
Publication statusE-pub ahead of print - 25 May 2021

Keywords

  • Genomic data analysis
  • Short read alignment
  • Hardware acceleration
  • networks-on-chip

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