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FUN-L: gene prioritization for RNAi screens.

Jonathan G Lees1, Jean-Karim Hériché1, Ian Morilla1

  • 1Research Department of Structural & Molecular Biology, University College London, London, UK, Cell Biology/Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany, Inflamex-Laboratoire Analyse Géométrie et Applications, Université Paris Nord-Sorbonne, France, Structural Bioinformatics Group, Spanish National Cancer Research Centre (CNIO) and Spanish National Bioinformatics Institute (INB), Madrid, Spain, Institute of Molecular and Cell Biology, and Institute of Computer Science, University of Tartu, Tartu, Estonia and Department of Molecular Biology and Biochemistry-CIBER de Enfermedades Raras, University of Malaga, Malaga, Spain.

Bioinformatics (Oxford, England)
|February 11, 2015
PubMed
Summary

Identifying key genes for biological research is crucial. The Functional Lists (FUN-L) method prioritizes candidate genes using data integration, improving experimental efficiency and discovery of novel biological functions.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Many biological processes are incompletely understood, with numerous components yet to be identified.
  • Functional genomic screens are resource-intensive, necessitating methods to prioritize candidate genes for investigation.

Purpose of the Study:

  • To present a novel computational method, Functional Lists (FUN-L), for prioritizing candidate genes.
  • To introduce a user-friendly website interface for the FUN-L method.

Main Methods:

  • FUN-L utilizes state-of-the-art data integration and mining techniques.
  • The method analyzes known functionally related gene sets to generate ranked candidate gene lists.
  • A web-based front end facilitates access to the FUN-L tool.

Main Results:

  • FUN-L successfully generates ranked lists of candidate genes for functional testing.
  • Validation using independent RNA interference (RNAi) screens confirmed enrichment of relevant genes in FUN-L predictions.
  • The FUN-L website provides a practical platform for researchers.

Conclusions:

  • The FUN-L method enhances the efficiency of biological discovery by prioritizing candidate genes.
  • The web tool empowers researchers to identify high-priority genes for functional assays.
  • This approach aids in navigating complex biological systems and accelerating research progress.