Nucleic Acids Research Advance Access originally published online on May 30, 2007
Nucleic Acids Research 2007 35(Web Server issue):W595-W598; doi:10.1093/nar/gkm405
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Nucleic Acids Research, 2007, Vol. 35, No. suppl_2 W595-W598
© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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CanPredict: a computational tool for predicting cancer-associated missense mutations
Department of Bioinformatics, Genentech, Inc., South San Francisco, CA 94080, USA
*To whom correspondence should be addressed. Tel: 650-225-4293; Fax: 650-225-5389; Email: zemin{at}gene.com
Received January 29, 2007. Revised April 17, 2007. Accepted May 3, 2007.
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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