ArticleGenome biology2016
OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations.
Article in Genome biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 214 papers, 1 of them a synthesis that pooled it.
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Who cites it
214 citing papers in PubMed, 1 synthesis or guideline pooled it, 366 citations in OpenAlex.
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- MutAnt: mutation annotation tool predicts deleteriousness of missense mutations and improves mutation calling from transcriptomics.Human genetics · 2025Article
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- Mutation of CMTR2 in Lung Adenocarcinoma Alters RNA Alternative Splicing and Reveals Therapeutic Vulnerabilities.Nature communications · 2025Article
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- Analysis of IDH1 and IDH2 mutations as causes of the hypermethylator phenotype in colorectal cancer.The Journal of pathology · 2025Article
- Whole-miRNome sequencing: a panel for the targeted sequencing of all human miRNA genes.Nucleic acids research · 2025Article
154 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Distinguishing the driver mutations from somatic mutations in a tumor genome is one of the major challenges of cancer research. This challenge is more acute and far from solved for non-coding mutations. Here we present OncodriveFML, a method designed to analyze the pattern of somatic mutations across tumors in both coding and non-coding genomic regions to identify signals of positive selection, and therefore, their involvement in tumorigenesis. We describe the method and illustrate its usefulness to identify protein-coding genes, promoters, untranslated regions, intronic splice regions, and lncRNAs-containing driver mutations in several malignancies.
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