Literature DB >> 25348067

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

Peilin Jia1, Quan Wang, Qingxia Chen, Katherine E Hutchinson, William Pao, Zhongming Zhao.   

Abstract

Many cancer genes form mutation hotspots that disrupt their functional domains or active sites, leading to gain- or loss-of-function. We propose a mutation set enrichment analysis (MSEA) implemented by two novel methods,MSEA-clust and MSEA-domain, to predict cancer genes based on mutation hotspot patterns. MSEA methods are evaluated by both simulated and real cancer data. We find approximately 51% of the eligible known cancer genes form detectable mutation hotspots. Application of MSEA in eight cancers reveals a total of 82 genes with mutation hotspots,including well-studied cancer genes, known cancer genes re-found in new cancer types, and novel cancer genes.

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Year:  2014        PMID: 25348067      PMCID: PMC4226881          DOI: 10.1186/s13059-014-0489-9

Source DB:  PubMed          Journal:  Genome Biol        ISSN: 1474-7596            Impact factor:   13.583


  52 in total

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3.  Principles and strategies for developing network models in cancer.

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4.  ATP11B mediates platinum resistance in ovarian cancer.

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Journal:  J Clin Invest       Date:  2013-04-15       Impact factor: 14.808

5.  Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing.

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Journal:  Cell       Date:  2012-09-14       Impact factor: 41.582

6.  dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations.

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7.  A comprehensive survey of Ras mutations in cancer.

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9.  Functional impact bias reveals cancer drivers.

Authors:  Abel Gonzalez-Perez; Nuria Lopez-Bigas
Journal:  Nucleic Acids Res       Date:  2012-08-16       Impact factor: 16.971

10.  Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers.

Authors:  Jüri Reimand; Gary D Bader
Journal:  Mol Syst Biol       Date:  2013       Impact factor: 11.429

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  18 in total

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

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2.  Metabolomics technology and bioinformatics for precision medicine.

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Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

Review 3.  Aberrant RNA splicing in cancer; expression changes and driver mutations of splicing factor genes.

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Journal:  Oncogene       Date:  2015-08-24       Impact factor: 9.867

Review 4.  Functional variomics and network perturbation: connecting genotype to phenotype in cancer.

Authors:  Song Yi; Shengda Lin; Yongsheng Li; Wei Zhao; Gordon B Mills; Nidhi Sahni
Journal:  Nat Rev Genet       Date:  2017-03-27       Impact factor: 53.242

5.  DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies.

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Journal:  Nucleic Acids Res       Date:  2019-05-07       Impact factor: 16.971

6.  Systematic Prioritization of Druggable Mutations in ∼5000 Genomes Across 16 Cancer Types Using a Structural Genomics-based Approach.

Authors:  Junfei Zhao; Feixiong Cheng; Yuanyuan Wang; Carlos L Arteaga; Zhongming Zhao
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Review 7.  Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes.

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8.  Cancer-Associated circRNA-miRNA-mRNA Regulatory Networks: A Meta-Analysis.

Authors:  Shaheerah Khan; Atimukta Jha; Amaresh C Panda; Anshuman Dixit
Journal:  Front Mol Biosci       Date:  2021-05-12

9.  A Gene Gravity Model for the Evolution of Cancer Genomes: A Study of 3,000 Cancer Genomes across 9 Cancer Types.

Authors:  Feixiong Cheng; Chuang Liu; Chen-Ching Lin; Junfei Zhao; Peilin Jia; Wen-Hsiung Li; Zhongming Zhao
Journal:  PLoS Comput Biol       Date:  2015-09-09       Impact factor: 4.475

10.  A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly mutated genes in the human cancer genomes.

Authors:  Feixiong Cheng; Junfei Zhao; Michaela Fooksa; Zhongming Zhao
Journal:  J Am Med Inform Assoc       Date:  2016-03-28       Impact factor: 7.942

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