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A large-scale benchmark of gene prioritization methods.

Dimitri Guala1, Erik L L Sonnhammer1

  • 1Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden.

Scientific Reports
|April 22, 2017
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Summary

A new benchmark strategy using Gene Ontology (GO) and network data enables objective comparison of gene prioritization tools. This helps researchers select the best tools for identifying disease-associated genes from high-throughput studies.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput studies like Genome-Wide Association Studies (GWAS) generate vast amounts of genetic data.
  • Identifying disease-associated genes requires effective gene prioritization tools, but objective benchmarking is lacking.
  • Existing tools often rely on internal validation, limiting performance comparisons.

Purpose of the Study:

  • To develop a robust and objective retrospective benchmarking strategy for gene prioritization tools.
  • To enable researchers to select the most suitable tools for disease gene identification.
  • To guide the development of more accurate gene prioritization methods.

Main Methods:

  • Utilized the Gene Ontology (GO) structure, where genes are clustered by annotation terms, to create objective benchmarks.
  • Employed the FunCoup protein-protein interaction network for network-based gene prioritization.
  • Implemented cross-validation and performance measures to compare state-of-the-art algorithms: NetRank, Random Walk with Restart, and MaxLink.

Main Results:

  • Demonstrated a systematic approach to construct retrospective benchmarks for gene prioritization tools.
  • Provided a comparative analysis of network-based prioritization algorithms using the developed benchmark suite.
  • The benchmark suite facilitates objective evaluation and selection of gene prioritization tools.

Conclusions:

  • The proposed benchmarking strategy, leveraging GO and network data, offers a robust and objective method for evaluating gene prioritization tools.
  • This approach addresses the need for retrospective validation, overcoming limitations of prospective benchmarks.
  • The benchmark suite empowers researchers to choose optimal tools for gene discovery and aids in advancing computational biology methods.