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

BMC Bioinformatics. 2020 Aug 5;21(1):343. doi: 10.1186/s12859-020-03697-x.

Abstract

Background: 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.

Results: By optimising memory, computations and load balancing between CPU and GPU, we demonstrate how f5c can perform ∼3-5 × faster than an optimised version of the original CPU-only implementation of ABEA in the Nanopolish software package. We also show that f5c enables DNA methylation detection on-the-fly using an embedded System on Chip (SoC) equipped with GPUs.

Conclusions: Our work not only demonstrates that complex genomics analyses can be performed on lightweight computing systems, but also benefits High-Performance Computing (HPC). The associated source code for f5c along with GPU optimised ABEA is available at https://github.com/hasindu2008/f5c .

Keywords: Event alignment; GPU; GPU acceleration; Methylation; Nanopolish; Nanopore; Optimisation; Signal alignment; SoC; f5c.

Publication types

  • Comparative Study

MeSH terms

  • Algorithms
  • Computational Biology
  • Computer Graphics*
  • Databases as Topic
  • Genome, Human
  • Humans
  • Nanopores*
  • Sequence Analysis
  • Signal Processing, Computer-Assisted*