GPU Accelerated Adaptive Banded Event Alignment for Rapid Comparative Nanopore Signal Analysis

Abstract

Nanopore sequencing has the potential to revolutionise genomics by realising portable, real-time sequencing applications, including point-of-care diagnostics and in-the-field genotyping. Achieving these applications requires efficient bioinformatic algorithms for the analysis of raw nanopore signal data. For instance, comparing raw nanopore signals to a biological reference sequence is a computationally complex task despite leveraging a dynamic programming algorithm for Adaptive Banded Event Alignment (ABEA)-a commonly used approach to polish sequencing data and identify 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. By optimising memory, compute and load balancing between CPU and GPU, we demonstrate how f5c can perform~3-5× faster than the original 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. 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.

NanoMethViz: an R/Bioconductor package for visualizing long-read methylation data

“…The retained reads were haplotyped using WhatsHap (17) using mouse variant information provided by the Sanger Institute (13). Methylation calling was performed by f5c (8) and associated with the haplotype information through the read IDs. bsseq (v1.22.0) (18) was used to identify differentially methylated regions and all visualisations in NanoMethViz were created using CRAN packages ggplot2 (19) and patchwork .…”

“…The NanoMethViz package provides conversion of data formats output by popular methylation callers nanopolish (5), f5c (8), and Megalodon into formats compatible with Bioconductor packages for DMR analysis.…”

F5N: Nanopore Sequence Analysis Toolkit for Android Smartphones

“…Nanopolish [12] is the widely used polishing tool for nanopore data. We adopt a re-engineered version of Nanopolish called F5C [8], which is both memory and time efficient. F5C first indexes base called reads and raw signal data.…”

“…METHODOLOGY F5N Android Application (GUI and the framework) was developed using Java programming language. Popular longread aligner Minimap2 [6], the sequence data manipulator Samtools [7] and the methylation caller F5C [8] (optimised version of the popular tool Nanopolish [9]) were re-configured and cross-compiled to produce shared libraries (.so files) to run on Android over the ARM processor architecture. The interface between the Android Java application and the native code (compiled shared libraries) was written using the Java Native Interface (JNI).…”

Chromosome-length genome assembly and structural variations of the primal Basenji dog (Canis lupus familiaris) genome

“…Bisulphite conversion efficiency was 99.71%, estimated using unmethylated lambda phage spike-in control. Nanopore reads were aligned to the generated reference genome using Minimap2 v2.17 [28], and CpG methylation sites were called with f5c v0.6 [75], which is an accelerated version of nanopolish [76]. Methylation frequency was then collated for each CpG site.…”