ArticleNature communications2016
Challenges in identifying cancer genes by analysis of exome sequencing data.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 45 citations in OpenAlex.
- D'or: deep orienter of protein-protein interaction networks.Bioinformatics (Oxford, England) · 2024Article
- Exploring the Role of Non-synonymous and Deleterious Variants Identified in Colorectal Cancer: A Multi-dimensional Computational Scrutiny of Exomes.Current genomics · 2024Article
- DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods.Computational and structural biotechnology journal · 2023Article
- Widespread redundancy in -omics profiles of cancer mutation states.Genome biology · 2022Article
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- Effects of Multi-Omics Characteristics on Identification of Driver Genes Using Machine Learning Algorithms.Genes · 2022Article
- Comprehensive evaluation of computational methods for predicting cancer driver genes.Briefings in bioinformatics · 2022Article
- SuperDendrix algorithm integrates genetic dependencies and genomic alterations across pathways and cancer types.Cell genomics · 2022Article
- Novel ratio-metric features enable the identification of new driver genes across cancer types.Scientific reports · 2022Article
- Broad genic repression domains signify enhanced silencing of oncogenes.Nature communications · 2020Article
- DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features.Science advances · 2020Article
- Identification of cancer driver genes based on nucleotide context.Nature genetics · 2020Article
- Interpreting pathways to discover cancer driver genes with Moonlight.Nature communications · 2020Article
- A systematic approach to orient the human protein-protein interaction network.Nature communications · 2019Article
- CGenomics, proteomics & bioinformatics · 2019Article
- Article
- Article
- In silico cancer research towards 3R.BMC cancer · 2018Review
- A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.Nature genetics · 2018Article
- Role of variant allele fraction and rare SNP filtering to improve cellular DNA repair endpoint association.PloS one · 2018Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
Abstract
Massively parallel sequencing has permitted an unprecedented examination of the cancer exome, leading to predictions that all genes important to cancer will soon be identified by genetic analysis of tumours. To examine this potential, here we evaluate the ability of state-of-the-art sequence analysis methods to specifically recover known cancer genes. While some cancer genes are identified by analysis of recurrence, spatial clustering or predicted impact of somatic mutations, many remain undetected due to lack of power to discriminate driver mutations from the background mutational load (13-60% recall of cancer genes impacted by somatic single-nucleotide variants, depending on the method). Cancer genes not detected by mutation recurrence also tend to be missed by all types of exome analysis. Nonetheless, these genes are implicated by other experiments such as functional genetic screens and expression profiling. These challenges are only partially addressed by increasing sample size and will likely hold even as greater numbers of tumours are analysed.
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