ReneGENE-Novo: Co-designed algorithm-architecture for accelerated preprocessing and assembly of genomic short reads

Abstract

Sufficiently long genome strings, permitting adequate overlaps, is key to producing a quality genome assembly with minimal error rates and high coverage. Next Generation Sequencing (NGS) platforms produce large volumes (tera bytes) of short-sized raw genomic strings or reads (150�600 genomic alphabets or bases) with minimal error rates. If we are able to increase the read lengths of raw short reads computationally before assembly, then the full potential of short reads from NGS and de novo assembly can be harvested. The large data redundancy offered by billions of such raw reads, compounded by the target genome length of billions of bases, requires a complex big data engineering solution. This paper presents a co-designed algorithm-architecture model for ReneGENE de novo assembly (part of a larger ReneGENE-GI Genome Informatics pipeline). This module takes randomly presented short reads from NGS platforms and extends them iteratively to an appropriate length by identifying overlaps among them, aiding high-coverage assembly with minimal error rates. This task is parallelized across multiple processes, to allow parallel read assembly with performance scalability. Supported by parallel algorithms, multi-dimensional data structures and fine-grain synchronization, the module realises irregular computing for de novo assembly. A single FPGA realization of this model with 128 de novo compute elements, shows a 48.69x improvement in performance when compared to an 8-core implementation on a standard workstation based on Intel Core i7-4770 processors. � Springer International Publishing AG, part of Springer Nature 2018.