ArticleNucleic acids research2019
WITER: a powerful method for estimation of cancer-driver genes using a weighted iterative regression modelling background mutation counts.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Widespread Epistasis between Cancer Driver Mutations and Allele-Specific Copy Number Variations.bioRxiv : the preprint server for biology · 2025Article
- Environment-Associated Variations in Blood Metabolism of Mongolian Cattle Grazing in the Alxa Desert of China.Veterinary sciences · 2025Article
- IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification.PLoS computational biology · 2024Article
- Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact.BMC bioinformatics · 2024Article
- Network analysis of driver genes in human cancers.Frontiers in bioinformatics · 2024Article
- DriverMP enables improved identification of cancer driver genes.GigaScience · 2022Article
- A new machine learning method for cancer mutation analysis.PLoS computational biology · 2022Article
- Deviation from baseline mutation burden provides powerful and robust rare-variants association test for complex diseases.Nucleic acids research · 2022Article
- Comprehensive evaluation of computational methods for predicting cancer driver genes.Briefings in bioinformatics · 2022Article
- Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions.Nature communications · 2022Article
- RNA-SSNV: A Reliable Somatic Single Nucleotide Variant Identification Framework for Bulk RNA-Seq Data.Frontiers in genetics · 2022Article
- KLF3 and PAX6 are candidate driver genes in late-stage, MSI-hypermutated endometrioid endometrial carcinomas.PloS one · 2022Article
- Article
- The mutational landscape of early- and typical-onset oral tongue squamous cell carcinoma.Cancer · 2021Article
- Modeling and analysis of site-specific mutations in cancer identifies known plus putative novel hotspots and bias due to contextual sequences.Computational and structural biotechnology journal · 2020Article
- FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.Frontiers in genetics · 2020Article
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11 authors.
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Abstract
Genomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modelling distribution of background mutation counts, existing statistical methods are often underpowered to discriminate cancer-driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER, http://grass.cgs.hku.hk/limx/witer or KGGSeq,http://grass.cgs.hku.hk/limx/kggseq/), to detect cancer-driver genes showing an excess of somatic mutations. By fitting the distribution of background mutation counts properly, this approach works well even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in most tested cancers. Applying this approach, we estimated 229 driver genes in 26 different types of cancers. In silico validation confirmed 78% of predicted genes as likely known drivers and many other genes as very likely new drivers for corresponding cancers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects and rescue of more genes missed by alternative tools in moderate or small samples.
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