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GPU-accelerated and pipelined methylation calling.

Yilin Feng1, Gulsum Gudukbay Akbulut1, Xulong Tang2

  • 1Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA.

Bioinformatics Advances
|January 26, 2023
PubMed
Summary

Galaxy-methyl accelerates DNA methylation calling for Nanopore sequencing data by optimizing the Hidden Markov Model (HMM) step on GPUs. This new tool significantly speeds up analysis compared to existing methods like Nanopolish and f5c.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Third-generation DNA sequencing, including Nanopore Sequencing, generates massive datasets requiring efficient computational analysis.
  • The Nanopolish software package analyzes Oxford Nanopore sequencing data, utilizing Hidden Markov Models (HMM) for methylation detection.
  • Existing tools like Nanopolish face performance bottlenecks in computationally intensive steps, such as Adaptive Banded Event Alignment (ABEA), which has been optimized by f5c on GPUs, shifting the bottleneck to methylation score calculation.

Purpose of the Study:

  • To address the performance bottleneck in the methylation score calculation step of Nanopolish.
  • To develop an optimized GPU-accelerated solution for DNA methylation calling from Nanopore sequencing data.
  • To improve the overall efficiency and hardware resource utilization for Nanopore data analysis.

Main Methods:

  • Development of Galaxy-methyl, a software package that parallelizes and optimizes the methylation score calculation using GPUs.
  • Implementation of a pipeline that integrates the four steps of the Nanopolish call-methylation module.
  • Enhancement of execution concurrency across CPUs and GPUs to maximize hardware utilization.

Main Results:

  • Galaxy-methyl achieves a 3x-5x speedup compared to Nanopolish for methylation calling.
  • The proposed method reduces the total execution time by an average of 35% compared to f5c.
  • Improved hardware resource utilization on both CPUs and GPUs was observed.

Conclusions:

  • Galaxy-methyl offers a significant performance improvement for DNA methylation analysis of Nanopore sequencing data.
  • The GPU-accelerated approach effectively overcomes the computational bottlenecks in existing methylation calling tools.
  • The optimized pipeline enhances the efficiency of processing large-scale sequencing data.