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GPU accelerated adaptive banded event alignment for rapid comparative nanopore signal analysis

Abstract

Nanopore sequencing enables portable, real-time sequencing applications, including point-of-care diagnostics and in-the-field genotyping. Achieving these outcomes requires efficient bioinformatic algorithms for the analysis of raw nanopore signal data. However, comparing raw nanopore signals to a biological reference sequence is a computationally complex task. The dynamic programming algorithm called Adaptive Banded Event Alignment (ABEA) is a crucial step in polishing sequencing data and identifying non-standard nucleotides, such as measuring DNA methylation. Here, we parallelise and optimise an implementation of the ABEA algorithm (termed f5c) to efficiently run on heterogeneous CPU-GPU architectures.

Type Journal
ISBN 1471-2105
Authors Gamaarachchi, Hasindu; Lam, Chun Wai; Jayatilaka, Gihan; Samarakoon, Hiruna; Simpson, Jared T.; Smith, Martin A.; Parameswaran, Sri
Responsible Garvan Author (missing name)
Publisher Name BMC BIOINFORMATICS
Published Date 2020-08-05
Published Volume 21
Published Issue 1
Published Pages 343
Status Published in-print
DOI 10.1186/s12859-020-03697-x
URL link to publisher's version https://www.ncbi.nlm.nih.gov/pubmed/32758139