CRAM-Seq: Accelerating RNA-Seq Abundance Quantification Using Computational RAM
Zamshed I. Chowdhury, S. Karen Khatamifard, Salonik Resch, Hüsrev Cılasun, Zhengyang Zhao, Masoud Zabihi, Meisam Razaviyayn, Jian‐Ping Wang, Sachin S. Sapatnekar, Ulya R. Karpuzcu
Abstract
RNA Sequence (RNA-Seq) abundance quantification is an important application in different fields of genomic studies, e.g., analysis offunctionally 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> 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.