OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations
By L. Mularoni, R. Sabarinathan, Jordi Deu-Pons, Abel Gonzalez-Perez, Núria López-Bigas. Genome Biology, 2016
309 citations recorded by the source corpus.
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.
Subfields: Cancer Research, Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/3538a6f75256e0c5629f6e4e004517903d00458f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cancer Research
- Elaine R. Mardis
- Gad Getz
- Bert Vogelstein
- Matthew Meyerson
- Kenneth W. Kinzler
- M. Lawrence
- Abel Gonzalez-Perez
- Núria López-Bigas
In Genetics
- P. Park
- Hui Shen
- Gad Getz
- M. Wiznerowicz
- Feixiong Cheng
- Jill S. Barnholtz-Sloan
- Christopher Sander
- Peter W. Laird
In Molecular Biology
- Elaine R. Mardis
- Wanding Zhou
- P. Park
- Hui Shen
- Peter W. Laird
- Ilya Shmulevich
- Niranjan Nagarajan
- Katherine A. Hoadley
Citations in the corpus
- Evaluating the evaluation of cancer driver genes (2016, methodology): “In the critical range typically used to assess statistical significance ( P ≤ 0.05), OncodriveClust, OncodriveFM, OncodriveFML, ActiveDriver, and MuSiC substantially underestimated P values, whereas MutsigCV substantially overestimated them (Fig.”
- Comprehensive Characterization of Cancer Driver Genes and Mutations (2018, background): “The analysis was performed usingOncodriveFML version 1.0.2-alpha with the coding indels option specified.”
- Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas (2018, unknown): “, 2014), OncoDriveFML (Mularoni et al., 2016), MuSiC2 (Dees et al.”
- Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations (2017, background): “In particular, the possibility to identify non-coding cancer drivers [28] from currently generated whole-genome sequencing data will open up the opportunity to explore the actionability of non-coding genomic alterations (https:// dcc.”