GPU-accelerated and pipelined methylation calling.

MOTIVATION

The third-generation DNA sequencing technologies, such as Nanopore Sequencing, can operate at very high speeds and produce longer reads, which in turn results in a challenge for the computational analysis of such massive data. is a software package for signal-level analysis of Oxford Nanopore sequencing data. Call-methylation module of can detect methylation based on Hidden Markov Model (HMM). However, is limited by the long running time of some serial and computationally expensive processes. Among these, Adaptive Banded Event Alignment (ABEA) is the most time-consuming step, and the prior work, , has already parallelized and optimized ABEA on GPU. As a result, the remaining methylation score calculation part, which uses HMM to identify if a given base is methylated or not, has become the new performance bottleneck.

RESULTS

This article focuses on the call-methylation module that resides in the package. We propose , which parallelizes and optimizes the methylation score calculation step on GPU and then pipelines the four steps of the call-methylation module. increases the execution concurrency across CPUs and GPUs as well as hardware resource utilization for both. The experimental results collected indicate that can achieve 3×-5× speedup compared with , and reduce the total execution time by 35% compared with , on average.

AVAILABILITY AND IMPLEMENTATION

The source code of is available at https://github.com/fengyilin118/.