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OMEN : network-based driver gene identification using mutual exclusivity

Dries Van Daele, Bram Weytjens (UGent) , Luc De Raedt and Kathleen Marchal (UGent)
(2022) BIOINFORMATICS. 38(12). p.3245-3251
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Abstract
Motivation: Network-based driver identification methods that can exploit mutual exclusivity typically fail to detect rare drivers because of their statistical rigor. Propagation-based methods in contrast allow recovering rare driver genes, but the interplay between network topology and high-scoring nodes often results in spurious predictions. The specificity of driver gene detection can be improved by taking into account both gene-specific and gene-set properties. Combining these requires a formalism that can adjust gene-set properties depending on the exact network context within which a gene is analyzed. Results: We developed OMEN: a logic programming framework based on random walk semantics. OMEN presents a number of novel concepts. In particular, its design is unique in that it presents an effective approach to combine both gene-specific driver properties and gene-set properties, and includes a novel method to avoid restrictive, a priori filtering of genes by exploiting the gene-set property of mutual exclusivity, expressed in terms of the functional impact scores of mutations, rather than in terms of simple binary mutation calls. Applying OMEN to a benchmark dataset derived from TCGA illustrates how OMEN is able to robustly identify driver genes and modules of driver genes as proxies of driver pathways.
Keywords
SOMATIC MUTATIONS, CANCER, PATTERNS, FRAMEWORK, PATHWAYS

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Citation

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MLA
Van Daele, Dries, et al. “OMEN : Network-Based Driver Gene Identification Using Mutual Exclusivity.” BIOINFORMATICS, vol. 38, no. 12, 2022, pp. 3245–51, doi:10.1093/bioinformatics/btac312.
APA
Van Daele, D., Weytjens, B., De Raedt, L., & Marchal, K. (2022). OMEN : network-based driver gene identification using mutual exclusivity. BIOINFORMATICS, 38(12), 3245–3251. https://doi.org/10.1093/bioinformatics/btac312
Chicago author-date
Van Daele, Dries, Bram Weytjens, Luc De Raedt, and Kathleen Marchal. 2022. “OMEN : Network-Based Driver Gene Identification Using Mutual Exclusivity.” BIOINFORMATICS 38 (12): 3245–51. https://doi.org/10.1093/bioinformatics/btac312.
Chicago author-date (all authors)
Van Daele, Dries, Bram Weytjens, Luc De Raedt, and Kathleen Marchal. 2022. “OMEN : Network-Based Driver Gene Identification Using Mutual Exclusivity.” BIOINFORMATICS 38 (12): 3245–3251. doi:10.1093/bioinformatics/btac312.
Vancouver
1.
Van Daele D, Weytjens B, De Raedt L, Marchal K. OMEN : network-based driver gene identification using mutual exclusivity. BIOINFORMATICS. 2022;38(12):3245–51.
IEEE
[1]
D. Van Daele, B. Weytjens, L. De Raedt, and K. Marchal, “OMEN : network-based driver gene identification using mutual exclusivity,” BIOINFORMATICS, vol. 38, no. 12, pp. 3245–3251, 2022.
@article{8754594,
  abstract     = {{Motivation: Network-based driver identification methods that can exploit mutual exclusivity typically fail to detect rare drivers because of their statistical rigor. Propagation-based methods in contrast allow recovering rare driver genes, but the interplay between network topology and high-scoring nodes often results in spurious predictions. The specificity of driver gene detection can be improved by taking into account both gene-specific and gene-set properties. Combining these requires a formalism that can adjust gene-set properties depending on the exact network context within which a gene is analyzed. Results: We developed OMEN: a logic programming framework based on random walk semantics. OMEN presents a number of novel concepts. In particular, its design is unique in that it presents an effective approach to combine both gene-specific driver properties and gene-set properties, and includes a novel method to avoid restrictive, a priori filtering of genes by exploiting the gene-set property of mutual exclusivity, expressed in terms of the functional impact scores of mutations, rather than in terms of simple binary mutation calls. Applying OMEN to a benchmark dataset derived from TCGA illustrates how OMEN is able to robustly identify driver genes and modules of driver genes as proxies of driver pathways.}},
  author       = {{Van Daele, Dries and Weytjens, Bram and De Raedt, Luc and Marchal, Kathleen}},
  issn         = {{1367-4803}},
  journal      = {{BIOINFORMATICS}},
  keywords     = {{SOMATIC MUTATIONS,CANCER,PATTERNS,FRAMEWORK,PATHWAYS}},
  language     = {{eng}},
  number       = {{12}},
  pages        = {{3245--3251}},
  title        = {{OMEN : network-based driver gene identification using mutual exclusivity}},
  url          = {{http://doi.org/10.1093/bioinformatics/btac312}},
  volume       = {{38}},
  year         = {{2022}},
}
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