Chapter title |
Detection of Combinatorial Mutational Patterns in Human Cancer Genomes by Exclusivity Analysis
|
---|---|
Chapter number | 1 |
Book title |
Cancer Systems Biology
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7493-1_1 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7492-4, 978-1-4939-7493-1
|
Authors |
Hua Tan, Xiaobo Zhou |
Abstract |
Cancer genes may tend to mutate in a co-mutational or mutually exclusive manner in a tumor sample of a specific cancer, which constitute two known combinatorial mutational patterns for a given gene set. Previous studies have established that genes functioning in different signaling pathways can mutate in the same sample, i.e., a tumor from one patient, while genes operating in the same pathway are rarely mutated in the same cancer genome. Therefore, reliable identification of combinatorial mutational patterns of candidate cancer genes has important ramifications in inferring signaling network modules in a particular cancer type. While algorithms for discovering mutated driver pathways based on mutual exclusivity of mutations in cancer genes have been proposed, a systematic pipeline for identifying both co-mutational and mutually exclusive patterns with rational significance estimation is still lacking. Here, we describe a reliable framework with detailed procedures to simultaneously explore both combinatorial mutational patterns from public cross-sectional gene mutation data. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 12 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 4 | 33% |
Researcher | 2 | 17% |
Professor | 1 | 8% |
Student > Bachelor | 1 | 8% |
Student > Postgraduate | 1 | 8% |
Other | 0 | 0% |
Unknown | 3 | 25% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 2 | 17% |
Nursing and Health Professions | 1 | 8% |
Computer Science | 1 | 8% |
Physics and Astronomy | 1 | 8% |
Other | 1 | 8% |
Unknown | 3 | 25% |