DiGeST: Distributed Computing for Scalable Gene and Variant Ranking with Hadoop/Spark

Yann-Aël Le Borgne, Raphaël Helaers, Tom Lenaerts, Marc Abramowicz, Guillaume Smits, Gianluca Bontempi

Research output: Contribution to journalArticle

Abstract

Background: The advent of next-generation sequencing technologies has opened new avenues for clinical genomics research. In particular, as sequencing costs continue to decrease, an ever-growing number of clinical genomics insti-tutes now rely on DNA sequencing studies at varying scales -genome, exome, mendeliome -for uncovering disease-associated variants or genes, in both rare and non-rare diseases. A common methodology for identifying such variants or genes is to rely on genetic association studies (GAS), that test whether allele or genotype fre-quencies differ between two groups of individuals, usually diseased subjects and healthy controls. Current bioinformatics tools for performing GAS are designed to run on standalone machines, and do not scale well with the increasing size of study designs and the search for multi-locus genetic associations. More effi-cient distributed and scalable data analysis solutions are needed to address this challenge. Results: We developed a Big Data solution stack for distributing computa-tions in genetic association studies, that address both single and multi-locus as-sociations. The proposed stack, called DiGeST (Distributed Gene/variant Scor-ing Tool) is divided in two main components: a Hadoop/Spark high-performance computing back-end for efficient data storage and distributed computing, and a Web front-end providing users with a rich set of options to filter, compare and explore exome data from different sample populations. Using exome data 1 from the 1000 Genomes Project, we show that our distributed implementation smoothly scales with computing resources. We make the resulting software stack Open-Source, and provide virtualisation scripts to run the complete environment both on standalone machine or Hadoop-based cluster. Conclusions: Hadoop/Spark provides a powerful and well-suited distributed computing framework for genetic association studies. Our work illustrates the flexibility, ease of use and scalability of the framework, and more generally advocates for its wider adoption in bioinformatics pipelines. Background
Original languageEnglish
Pages (from-to)1-28
Number of pages28
JournalBIORXIV
Volume2017
DOIs
Publication statusPublished - 2017

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