ArticleAmerican journal of human genetics2013
DrGaP: a powerful tool for identifying driver genes and pathways in cancer sequencing studies.
Article in American journal of human genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed, 75 citations in OpenAlex.
- Computational Approaches: A New Frontier in Cancer Research.Combinatorial chemistry & high throughput screening · 2024Review
- Alterations in the Epigenetic Machinery Associated with Prostate Cancer Health Disparities.Cancers · 2023Article
- Noninvasive genomic profiling of somatic mutations in oral cavity cancers.Oral oncology · 2023Article
- WMDS.net: a network control framework for identifying key players in transcriptome programs.Bioinformatics (Oxford, England) · 2023Article
- DrGA: cancer driver gene analysis in a simpler manner.BMC bioinformatics · 2022Article
- OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers.Nucleic acids research · 2021Article
- Multiple microarray analyses identify key genes associated with the development of Non-Small Cell Lung Cancer from Chronic Obstructive Pulmonary Disease.Journal of Cancer · 2021Article
- Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data.Scientific reports · 2020Article
- Comprehensive characterization of driver genes in diffuse large B cell lymphoma.Oncology letters · 2020Article
- ecTMB: a robust method to estimate and classify tumor mutational burden.Scientific reports · 2020Article
- FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network.Frontiers in genetics · 2020Article
- An Effective Graph Clustering Method to Identify Cancer Driver Modules.Frontiers in bioengineering and biotechnology · 2020Article
- CGenomics, proteomics & bioinformatics · 2019Article
- DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies.Nucleic acids research · 2019Article
- Integrative analysis of cancer driver genes in prostate adenocarcinoma.Molecular medicine reports · 2019Article
- Article
- ZSCAN10 promotes cell proliferation, upregulates OCT4 expression, and activates Wnt/β-catenin signaling in glioma.International journal of clinical and experimental pathology · 2019Article
- Identification of Common and Subtype-Specific Mutated Sub-Pathways for a Cancer.Frontiers in genetics · 2019Article
- Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network.BMC bioinformatics · 2018Article
- Applying Expression Profile Similarity for Discovery of Patient-Specific Functional Mutations.High-throughput · 2018Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Cancers are caused by the accumulation of genomic alterations. Driver mutations are required for the cancer phenotype, whereas passenger mutations are irrelevant to tumor development and accumulate through DNA replication. A major challenge facing the field of cancer genome sequencing is to identify cancer-associated genes with mutations that drive the cancer phenotype. Here, we describe a powerful and flexible statistical framework for identifying driver genes and driver signaling pathways in cancer genome-sequencing studies. Biological knowledge of the mutational process in tumors is fully integrated into our statistical models and includes such variables as the length of protein-coding regions, transcript isoforms, variation in mutation types, differences in background mutation rates, the redundancy of genetic code, and multiple mutations in one gene. This framework provides several significant features that are not addressed or naively obtained by previous methods. In particular, on the observation of low prevalence of somatic mutations in individual tumors, we propose a heuristic strategy to estimate the mixture proportion of chi-square distribution of likelihood ratio test (LRT) statistics. This provides significantly increased statistical power compared to regular LRT. Through a combination of simulation and analysis of TCGA cancer sequencing study data, we demonstrate high accuracy and sensitivity in our methods. Our statistical methods and several auxiliary bioinformatics tools have been incorporated into a computational tool, DrGaP. The newly developed tool is immediately applicable to cancer genome-sequencing studies and will lead to a more complete identification of altered driver genes and driver signaling pathways in cancer.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.