4.7 Article

Alignment-free Genomic Analysis via a Big Data Spark Platform

Journal

BIOINFORMATICS
Volume 37, Issue 12, Pages 1658-1665

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btab014

Keywords

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Funding

  1. GNCS Project 2019 'Innovative methods for the solution of medical and biological big data'
  2. MIUR-PRIN project 'Multicriteria Data Structures and Algorithms: from compressed to learned indexes, and beyond' [2017WR7SHH]
  3. Universita di Roma-La Sapienza Research Project 2018'Analisi, sviluppo e sperimentazione di algoritmipraticamenteefficienti'

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The study introduces a new big data platform, FADE, for alignment-free genomic analysis, supporting 18 best-performing AF functions, with faster execution time and user-friendly software design. Additionally, it provides a novel analysis of the informativeness and robustness of AF functions, finding that only a handful of functions out of the 18 included in FADE can actually be used.
Motivation: Alignment-free distance and similarity functions (AF functions, for short) are a well-established alternative to pairwise and multiple sequence alignments for many genomic, metagenomic and epigenomic tasks. Due to data-intensive applications, the computation of AF functions is a Big Data problem, with the recent literature indicating that the development of fast and scalable algorithms computing AF functions is a high-priority task. Somewhat surprisingly, despite the increasing popularity of Big Data technologies in computational biology, the development of a Big Data platform for those tasks has not been pursued, possibly due to its complexity. Results: We fill this important gap by introducing FADE, the first extensible, efficient and scalable Spark platform for alignment-free genomic analysis. It supports natively eighteen of the best performing AF functions coming out of a recent hallmark benchmarking study. FADE development and potential impact comprises novel aspects of interest. Namely, (i) a considerable effort of distributed algorithms, the most tangible result being a much faster execution time of reference methods like MASH and FSWM; (ii) a software design that makes FADE user-friendly and easily extendable by Spark non-specialists; (iii) its ability to support data- and compute-intensive tasks. About this, we provide a novel and much needed analysis of how informative and robust AF functions are, in terms of the statistical significance of their output. Our findings naturally extend the ones of the highly regarded benchmarking study, since the functions that can really be used are reduced to a handful of the eighteen included in FADE.

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