DeepVariant-on-Spark: Small-Scale Genome Analysis Using a Cloud-Based Computing Framework.

Journal: Computational and mathematical methods in medicine
Published Date: Sep 1, 2020

Abstract

Although sequencing a human genome has become affordable, identifying genetic variants from whole-genome sequence data is still a hurdle for researchers without adequate computing equipment or bioinformatics support. GATK is a gold standard method for the identification of genetic variants and has been widely used in genome projects and population genetic studies for many years. This was until the Google Brain team developed a new method, DeepVariant, which utilizes deep neural networks to construct an image classification model to identify genetic variants. However, the superior accuracy of DeepVariant comes at the cost of computational intensity, largely constraining its applications. Accordingly, we present DeepVariant-on-Spark to optimize resource allocation, enable multi-GPU support, and accelerate the processing of the DeepVariant pipeline. To make DeepVariant-on-Spark more accessible to everyone, we have deployed the DeepVariant-on-Spark to the Google Cloud Platform (GCP). Users can deploy DeepVariant-on-Spark on the GCP following our instruction within 20 minutes and start to analyze at least ten whole-genome sequencing datasets using free credits provided by the GCP. DeepVaraint-on-Spark is freely available for small-scale genome analysis using a cloud-based computing framework, which is suitable for pilot testing or preliminary study, while reserving the flexibility and scalability for large-scale sequencing projects.

Authors

  • Po-Jung Huang
    Department of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan.
  • Jui-Huan Chang
    Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
  • Hou-Hsien Lin
    Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.
  • Yu-Xuan Li
    Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
  • Chi-Ching Lee
    Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.
  • Chung-Tsai Su
    Department of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan.
  • Yun-Lung Li
    Department of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan.
  • Ming-Tai Chang
    Department of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan.
  • Sid Weng
    Department of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan.
  • Wei-Hung Cheng
    Department of Parasitology, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
  • Cheng-Hsun Chiu
    Genomic Medicine Core Laboratory, Chang Gung Memorial Hospital, Linkou, Taiwan.
  • Petrus Tang
    Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.

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