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

BACKGROUND: The increasing availability of resequencing data has led to a better understanding of the most important genes in cancer development. Nevertheless, the mutational landscape of many tumor types is heterogeneous and encompasses a long tail of potential driver genes that are systematically...

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Detalles Bibliográficos
Autores principales: Melloni, Giorgio E. M., de Pretis, Stefano, Riva, Laura, Pelizzola, Mattia, Céol, Arnaud, Costanza, Jole, Müller, Heiko, Zammataro, Luca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Software
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4748640/
https://www.ncbi.nlm.nih.gov/pubmed/26860319
http://dx.doi.org/10.1186/s12859-016-0935-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4748640/
https://www.ncbi.nlm.nih.gov/pubmed/26860319
http://dx.doi.org/10.1186/s12859-016-0935-7

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