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Systematic benchmarking of omics computational tools

Author

Listed:
  • Serghei Mangul

    (University of California Los Angeles
    University of California Los Angeles)

  • Lana S. Martin

    (University of California Los Angeles)

  • Brian L. Hill

    (University of California Los Angeles)

  • Angela Ka-Mei Lam

    (University of California Los Angeles)

  • Margaret G. Distler

    (University of California Los Angeles)

  • Alex Zelikovsky

    (Georgia State University
    I.M. Sechenov First Moscow State Medical University)

  • Eleazar Eskin

    (University of California Los Angeles
    University of California Los Angeles)

  • Jonathan Flint

    (University of California Los Angeles)

Abstract

Computational omics methods packaged as software have become essential to modern biological research. The increasing dependence of scientists on these powerful software tools creates a need for systematic assessment of these methods, known as benchmarking. Adopting a standardized benchmarking practice could help researchers who use omics data to better leverage recent technological innovations. Our review summarizes benchmarking practices from 25 recent studies and discusses the challenges, advantages, and limitations of benchmarking across various domains of biology. We also propose principles that can make computational biology benchmarking studies more sustainable and reproducible, ultimately increasing the transparency of biomedical data and results.

Suggested Citation

  • Serghei Mangul & Lana S. Martin & Brian L. Hill & Angela Ka-Mei Lam & Margaret G. Distler & Alex Zelikovsky & Eleazar Eskin & Jonathan Flint, 2019. "Systematic benchmarking of omics computational tools," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09406-4
    DOI: 10.1038/s41467-019-09406-4
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