E-Driver: A novel method to identify protein regions driving cancer

Bioinformatics

Volumn 30, Issue 21, 2014, Pages 3109-3114

  Porta Pardo, Eduard ^a   Godzik, Adam ^a  

^a BURNHAM INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

TUMOR PROTEIN;

COMPUTER PROGRAM; GENETICS; GENOMICS; HUMAN; MISSENSE MUTATION; MUTATION RATE; NEOPLASM; PROCEDURES; TUMOR GENE;

GENES, NEOPLASM; GENOMICS; HUMANS; MUTATION RATE; MUTATION, MISSENSE; NEOPLASM PROTEINS; NEOPLASMS; SOFTWARE;

EID: 84911379962     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btu499     Document Type: Article

References (21)

1. Chang, K. et al. (2013) The cancer genome atlas pan-cancer analysis project. Nat. Genet., 45, 1113-1120.
2. Ciriello, G. et al. (2013) Emerging landscape of oncogenic signatures across human cancers. Nat. Genet., 45, 1127-1133.
3. De, S. and Michor F. (2011) DNA replication timing and long-range DNA interactions predict mutational landscapes of cancer genomes. Nat. Biotechnol., 29, 1103-1108.
4. Dees, N.D. et al. (2012) MuSiC: identifying mutational significance in cancer genomes. Genome Res., 22, 1589-1598.
5. Dunker, A.K. et al. (2005) Flexible nets. The roles of intrinsic disorder in protein interaction networks. FEBS J., 272, 5129-5148.
6. Futreal, P. et al. (2004) A census of human cancer genes. Nat. Rev. Cancer, 4, 177-183.
7. Gonzalez-Perez, A. and Lopez-Bigas, N. (2012) Functional impact bias reveals cancer drivers. Nucleic Acids Res., 40, e169.
8. Hudson, T. et al. (2010) International network of cancer genome projects. Nature, 464, 993-998.
9. Kandoth, C. et al. (2013) Mutational landscape and significance across 12 major cancer types. Nature, 502, 333-339.
10. Lawrence, M.S. et al. (2014) Discovery and saturation analysis of cancer genes across 21 tumour types. Nature, 505, 495-501.
11. Prilusky, J. and Felder, C. (2005) FoldIndex: a simple tool to predict whether a given protein sequence is intrinsically unfolded. Bioinformatics, 21, 3435-3438.
12. Punta, M. et al. (2012) The Pfam protein families database. Nucleic Acids Res., 40, D290-D301.
13. Reimand, J. et al. (2013) The mutational landscape of phosphorylation signaling in cancer. Sci. Rep., 3, 2651.
14. Reimand, J. and Bader, G.D. (2013) Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Mol. Syst. Biol., 9, 637.
15. Tamborero, D. et al. (2013a) Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci. Rep., 3, 2650.
16. Tamborero, D. et al. (2013b) OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics, 29, 2238-2244.
17. Vivanco, I. et al. (2012) Differential sensitivity of glioma- versus lung cancer-specific EGFR mutations to EGFR kinase inhibitors. Cancer Discov., 2, 458-471.
18. Wang, X. et al. (2012) Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat. Biotechnol., 30, 159-164.
19. Xu, D. et al. (2014) AIDA: ab initio domain assembly server. Nucleic Acids Res., 42, W308-W313.
20. Yan, H. et al. (2009) IDH1 and IDH2 mutations in Gliomas. N. Engl. J. Med., 360, 765-773.
21. Zhong, Q. et al. (2009) Edgetic perturbation models of human inherited disorders. Mol. Syst. Biol., 5, 321.