GPU accelerated adaptive banded event alignment for rapid comparative nanopore signal analysis
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.
|Authors||Gamaarachchi, Hasindu; Lam, Chun Wai; Jayatilaka, Gihan; Samarakoon, Hiruna; Simpson, Jared T.; Smith, Martin A.; Parameswaran, Sri|
|Responsible Garvan Author|
|Publisher Name||BMC BIOINFORMATICS|
|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|