Publications
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 |
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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 |
OpenAccess link to author's accepted manuscript version | https://publications.gimr.garvan.org.au/open-access/15388 |