Affiliation:
1. Department of Electronic Systems Engineering, School of Engineering, University of São Paulo, São Paulo 05508-010, Brazil
2. Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo 05508-000, Brazil
Abstract
In genomic analysis, long reads are an emerging type of data processed by assembly algorithms to recover the complete genome sample. They are, on average, one or two orders of magnitude longer than short reads from the previous generation, which provides important advantages in information quality. However, longer sequences bring new challenges to computer processing, undermining the performance of assembly algorithms developed for short reads. This issue is amplified by the exponential growth of genetic data generation and by the slowdown of transistor technology progress, illustrated by Moore’s Law. Minimap2 is the current state-of-the-art long-read assembler and takes dozens of CPU hours to assemble a human genome with clinical standard coverage. One of its bottlenecks, the alignment stage, has not been successfully accelerated on FPGAs in the literature. GACT-X is an alignment algorithm developed for FPGA implementation, suitable for any size input sequence. In this work, GACT-X was adapted to work as the aligner of Minimap2, and these are integrated and implemented in an FPGA cloud platform. The measurements for accuracy and speed-up are presented for three different datasets in different combinations of numbers of kernels and threads. The integrated solution’s performance limitations due to data transfer are also analyzed and discussed.
Funder
National Council for Scientific and Technological Development
Coordination of Superior Level Staff Improvement and by the University of São Paulo
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
5 articles.
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