GPU-accelerated and pipelined methylation calling

Yilin Feng, Gulsum Gudukbay Akbulut, Xulong Tang, Jashwant Raj Gunasekaran, Amatur Rahman, Paul Medvedev, Mahmut Kandemir

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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. Nanopolish is a software package for signal-level analysis of Oxford Nanopore sequencing data. Call-methylation module of Nanopolish can detect methylation based on Hidden Markov Model (HMM). However, Nanopolish 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, f5c, 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 Nanopolish package. We propose Galaxy-methyl, which parallelizes and optimizes the methylation score calculation step on GPU and then pipelines the four steps of the call-methylation module. Galaxy-methyl increases the execution concurrency across CPUs and GPUs as well as hardware resource utilization for both. The experimental results collected indicate that Galaxy-methyl can achieve 3×-5× speedup compared with Nanopolish, and reduce the total execution time by 35% compared with f5c, on average.

Original languageEnglish (US)
Article numbervbac088
JournalBioinformatics Advances
Volume2
Issue number1
DOIs
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Genetics
  • Molecular Biology
  • Structural Biology
  • Computer Science Applications

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