MSEA: detection and quantification of mutation hotspots through mutation set enrichment analysis

Jia, Peilin; Wang, Quan; Chen, Qingxia; Hutchinson, Katherine E.; Pao, William; Zhao, Zhongming

doi:10.1186/preaccept-1822061407140231

Cited by 17 publications

(23 citation statements)

References 41 publications

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“…The main difference between individual methods from this group is the specific background model they use. While there are methods that rely solely on statistical models 15,23 , most of them try to integrate other biological signals such as the distribution of silent mutations 14,24 , the ratio between the different types of mutations occurring in a specific gene 25 , the probability of each mutation given the nucleotide before and after the mutated position 26,27 or by kernel density estimates across multiple biologically relevant scales 28 .…”

Section: Resultsmentioning

confidence: 99%

Comparison of algorithms for the detection of cancer drivers at subgene resolution

et al. 2017

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Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally most algorithms for cancer driver identification look for genes that have more mutations than expected from the average background mutation rate. However, there is now a wide variety of methods that look for non-random distribution of mutations within proteins as a signal they have a driving role in cancer. Here we classify and review the progress of such sub-gene resolution algorithms, compare their findings on four distinct cancer datasets from The Cancer Genome Atlas and discuss how predictions from these algorithms can be interpreted in the emerging paradigms that challenge the simple dichotomy between driver and passenger genes.

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Section: Resultsmentioning

confidence: 99%

Comparison of algorithms for the detection of cancer drivers at subgene resolution

et al. 2017

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“…Some algorithms also incorporate sequence-based data with clinical data for inferring the relationships among mutations and the affected genes 44 . In addition, drivers can be identified by finding genes that harbor significantly more mutations than expected by chance 59,60 , such as MutSig 45 and MSEA 61 . Together, although these methods are informative in driver prioritization, their accuracy may be influenced by empirically observed local mutation frequencies.…”

Section: A Cancer Network Rewiring Perspectivementioning

confidence: 99%

Functional variomics and network perturbation: connecting genotype to phenotype in cancer

Lin

et al. 2017

Nat Rev Genet

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Proteins interact with other macromolecules in complex cellular networks for signal transduction and biological function. In cancer, genetic aberrations have been traditionally thought to disrupt the entire gene function. It has been increasingly appreciated that each mutation of a gene could have a subtle but unique effect on protein function or network rewiring, contributing to diverse phenotypic consequences across cancer patient populations. In this Review, we discuss the current understanding of cancer genetic variants including the broad spectrum of mutation classes and the wide range of mechanistic effects on gene function in the context of signaling networks. We highlight recent advances in computational and experimental strategies to study the diverse functional and phenotypic consequences of mutations at the base-pair resolution. Such information is critical for understanding the complex pleiotropic effect of cancer genes, and provides a possible link between genotype and phenotype in cancer.

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“…To test the capability of identifying the driver genes in genetic interaction level, we constructed FunCoup (functional Coupling) database to explore the functional relationship between genes and their functions [ 79 ]. Genetic network is most significant method to derive genetic as well as functionally associated genes using Genemania web server and it predicts gene functions by integrating several functionally associated networks [ 81 ]. The consequent level of network analysis is performed using MUFFINN (MUtations For Functional Impact on Network Neighbors).…”

Section: Computational Approaches For Distinguishing Breast Cancer Drmentioning

confidence: 99%

Characterization of potential driver mutations involved in human breast cancer by computational approaches

Rajendran¹,

Deng²

2017

Oncotarget

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Breast cancer is the second most frequently occurring form of cancer and is also the second most lethal cancer in women worldwide. A genetic mutation is one of the key factors that alter multiple cellular regulatory pathways and drive breast cancer initiation and progression yet nature of these cancer drivers remains elusive. In this article, we have reviewed various computational perspectives and algorithms for exploring breast cancer driver mutation genes. Using both frequency based and mutational exclusivity based approaches, we identified 195 driver genes and shortlisted 63 of them as candidate drivers for breast cancer using various computational approaches. Finally, we conducted network and pathway analysis to explore their functions in breast tumorigenesis including tumor initiation, progression, and metastasis.

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MSEA: detection and quantification of mutation hotspots through mutation set enrichment analysis

Cited by 17 publications

References 41 publications

Comparison of algorithms for the detection of cancer drivers at subgene resolution

Comparison of algorithms for the detection of cancer drivers at subgene resolution

Functional variomics and network perturbation: connecting genotype to phenotype in cancer

Characterization of potential driver mutations involved in human breast cancer by computational approaches

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