Predicting the functional consequences of cancer-associated amino acid substitutions

Hashem A Shihab, Julian Gough, David N Cooper, Ian N M Day, Tom R Gaunt

Research output: Contribution to journalArticle (Academic Journal)peer-review

176 Citations (Scopus)


The number of missense mutations being identified in cancer genomes has greatly increased as a consequence of technological advances and the reduced cost of whole-genome/whole-exome sequencing methods. However, a high proportion of the amino acid substitutions detected in cancer genomes have little or no effect on tumour progression (passenger mutations). Therefore, accurate automated methods capable of discriminating between driver (cancer-promoting) and passenger mutations are becoming increasingly important. In our previous work, we developed the Functional Analysis through Hidden Markov Models (FATHMM) software and, using a model weighted for inherited disease mutations, observed improved performances over alternative computational prediction algorithms. Here, we describe an adaptation of our original algorithm that incorporates a cancer-specific model to potentiate the functional analysis of driver mutations.
Original languageEnglish
Pages (from-to)1504-10
Number of pages7
Issue number12
Early online date25 Apr 2013
Publication statusPublished - 15 Jun 2013


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