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. 2007 Jul;35(Web Server issue):W595-8.
doi: 10.1093/nar/gkm405. Epub 2007 May 30.

CanPredict: a computational tool for predicting cancer-associated missense mutations

Joshua S Kaminker  1 , Yan Zhang, Colin Watanabe, Zemin Zhang
Affiliations

CanPredict: a computational tool for predicting cancer-associated missense mutations

Joshua S Kaminker et al. Nucleic Acids Res. 2007 Jul.

Abstract

Various cancer genome projects are underway to identify novel mutations that drive tumorigenesis. While these screens will generate large data sets, the majority of identified missense changes are likely to be innocuous passenger mutations or polymorphisms. As a result, it has become increasingly important to develop computational methods for distinguishing functionally relevant mutations from other variations. We previously developed an algorithm, and now present the web application, CanPredict (http://www.canpredict.org/ or http://www.cgl.ucsf.edu/Research/genentech/canpredict/), to allow users to determine if particular changes are likely to be cancer-associated. The impact of each change is measured using two known methods: Sorting Intolerant From Tolerant (SIFT) and the Pfam-based LogR.E-value metric. A third method, the Gene Ontology Similarity Score (GOSS), provides an indication of how closely the gene in which the variant resides resembles other known cancer-causing genes. Scores from these three algorithms are analyzed by a random forest classifier which then predicts whether a change is likely to be cancer-associated. CanPredict fills an important need in cancer biology and will enable a large audience of biologists to determine which mutations are the most relevant for further study.

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Figures

Figure 1.
Figure 1.
The home page of the CanPredict application.
Figure 2.
Figure 2.
The results summary page of the CanPredict application.
See this image and copyright information in PMC

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