Abstract

Mutations in cancer are heterogeneous. As it is challenging to identify cancer relevant mutations, some previous studies focus on identifying significant mutation from background mutation distribution, others focus on mutant residues clustered near known functional sites by criteria of Euclidean distance. However, these methods are inflexible and cannot examine biophysical mechanism caused by mutation. We have developed a novel method that identifies deleterious regions of mutation by integrating evolutionary signals and structural analysis of proteins.

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