CRAM-Seq: Accelerating RNA-Seq Abundance Quantification using Computational RAM

Zamshed I Chowdhury, S. Karen Khatamifard, Salonik Resch, Husrev Cilasun, Zhengyang Zhao, Masoud Zabihi, Meisam Razaviyayn, Jian Ping Wang, Sachin Sapatnekar, Ulya R. Karpuzcu

Research output: Contribution to journalArticlepeer-review

Abstract

RNA Sequence (RNA-Seq) abundance quantification is an important application in different fields of genomic studies, e.g., analysis of functionally similar genes in a biological sample. This application depends on the availability of high volume of sequence data for high accuracy abundance estimation, which is made possible by next generation sequencing platforms. Large scale data processing requirements of this quantification application push conventional computing systems to their limits due to excessive data movement required between processing and memory elements. Processing-In-memory presents a viable solution to this drawback, through in-situ processing of the genomic data. In this paper, we present CRAM-Seq, an accelerator for RNA-Seq abundance quantification based on Computational RAM (CRAM) an in-memory processing substrate capable of high degree of parallel processing with very low energy consumption. Through hardware/software co-design, we demonstrate that CRAM-Seq outperforms a commonly used state-of-the-art software abundance quantification algorithm, Kallisto in terms of throughput and energy efficiency, while being highly scalable.

Original languageEnglish (US)
JournalIEEE Transactions on Emerging Topics in Computing
DOIs
StateAccepted/In press - 2022

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Biology
  • CRAM
  • Genomics
  • Magnetic tunneling
  • RNA
  • RNA-Seq
  • Random access memory
  • SHE-MTJ
  • Sequential analysis
  • Throughput
  • abundance
  • accelerator
  • quantification
  • spintronics

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