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

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 (termedf5c) to efficiently run on heterogeneous CPU-GPU architectures. Results: By optimising memory, computations and load balancing between CPU and GPU, we demonstrate howf5ccan perform similar to 3-5 x faster than an optimised version of the original CPU-only implementation of ABEA in theNanopolishsoftware package. We also show thatf5cenables 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 forf5calong with GPU optimised ABEA is available at https://github.com/hasindu2008/f5c.