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 |
|---|---|
| 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 |