LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer-Reference-Cited by-同舟云学术

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LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer

Published:2016-02-09 Issue:1 Volume:17 Page:
ISSN:1471-2105
Container-title:BMC Bioinformatics
language:en
Short-container-title:BMC Bioinformatics

Author:

Melloni Giorgio E. M.,de Pretis Stefano,Riva Laura,Pelizzola Mattia,Céol Arnaud,Costanza Jole,Müller Heiko,Zammataro Luca

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology

Link

http://link.springer.com/content/pdf/10.1186/s12859-016-0935-7

Reference53 articles.

1. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Kinzler KW. Cancer genome landscapes. Science. 2013;339:1546–58.

2. Tamborero D, Gonzalez-Perez A, Lopez-Bigas N. OncodriveCLUST: Exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics. 2013;29:2238–44.

3. Davoli T, Xu AW, Mengwasser KE, Sack LM, Yoon JC, Park PJ, et al. Cumulative Haploinsufficiency and Triplosensitivity Drive Aneuploidy Patterns and Shape the Cancer Genome. Cell. 2013;155:948–62.

4. Melloni GE, Ogier AG, de Pretis S, Mazzarella L, Pelizzola M, Pelicci P, et al. DOTS-Finder: a comprehensive tool for assessing driver genes in cancer genomes. Genome Med. 2014;6:44.

5. Creixell P, Reimand J, Haider S, Wu G, Shibata T, Vazquez M, et al. Pathway and network analysis of cancer genomes. Nat Methods. 2015;12:615–21.

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