these mutations disrupt genes that regulate normal cellular processes , thereby providing growth advantages and metastatic capabilities to tumor cells . understanding how such changes lead to an oncogenic phenotype can provide a deeper understanding of the molecular nature of different cancers while also revealing novel therapeutic targets . there are a number of well - known somatic mutations ( 1 ) and germline mutations ( 2,3 ) that have been implicated in cancer progression . however , there are likely many more mutations that have not yet been found ( 4 ) . the identification and study of these additional mutations presents an important opportunity for further understanding of the biological processes and pathways underlying cancer . many large - scale screens have been initiated to identify novel cancer - causing mutations ( 47 ) ( http://cancergenome.nih.gov ) . these efforts have relied on sequence analysis of a few hundred to several thousand genes across multiple tumor and cell line samples . while these screens are extremely important for further understanding of tumorigenesis , the results are difficult to interpret because the majority of identified changes are not cancer - causing . in fact , a recent large - scale survey of mutations in breast and colon cancers indicates that causal mutations likely account for less than 1% of all observed non - synonymous changes ( 4 ) . the high level of background signal can be attributed in part to single nucleotide polymorphisms ( snps ) and passenger mutations . snps can be distinguished from true cancer mutation data by a variety of methods including identifying the same change in a matched normal tissue sample , or identifying the same , change in a database of known snps such as dbsnp . however , such approaches can be complicated by many factors including a lack of matched normal samples for re - sequencing putative cancer mutations . additionally , known snp databases are largely incomplete ( 8) and can contain unreliable records , making it difficult to positively identify a particular change as an snp . it is even more difficult to distinguish passenger mutations from true cancer mutations as this usually requires laboratory experimentation . recently , a method was developed by sjoblom and colleagues ( 4 ) to identify passenger mutations by uncovering those changes that occur at a higher than expected frequency in a set of tumor samples . but , since this method is highly dependant on large numbers of representative tumor samples , well - known oncogenes such as braf were not identified due to their low observed frequency in the sjoblom data . thus , without methods specifically designed to analyze the mutations generated from these genome - scale screens , it is likely that a large number of true causal mutations will be overlooked . different algorithms have been developed to measure the effect a particular mutation might have on protein function . these approaches include sorting intolerant from tolerant ( sift ) ( 9 ) , the pfam - based logr.e - value metric ( 10 ) , polyphen ( 11 ) , ls - snp ( 12 ) , statistical geometry methods ( 13 ) , support vector machine methods ( 14 ) , decision trees ( 15 ) and random forest classifiers ( 16 ) . additionally , methods based on the gene ontology such as the gene ontology similarity score ( goss ) ( 17 ) can also provide a measure as to how similar a gene of interest is to other known cancer - causing genes . while these algorithms may provide some indication about the nature of a particular mutation , it remains unclear whether by themselves such methods could be directly applicable in cancer mutation analysis . recently , using algorithms described earlier , we found that relevant somatic missense mutations behave differently from snps , and based on this distinction we developed a computational method to predict whether a variant is likely to be cancer - causing or not ( 17 ) . logr.e - value and goss metrics to generate a prediction to distinguish relevant mutations from other missense changes . we demonstrated that this approach could be potentially useful in distinguishing causal from passenger mutations ( 17 ) . while this method was described in detail , its implementation requires a thorough understanding of random forest algorithms and the r programming language , likely impeding a large number of experimental biologists from attempting to classify their mutations . here , we present a web application , canpredict , that provides a clean and straightforward interface to our algorithm . changes identified on a refseq protein sequence can be submitted and a prediction is generated as to whether the changes are cancer - associated or not . this application provides the first public interface to an important algorithm that can provide insight into the large amount of mutation data being generated from cancer re - sequencing projects . the algorithm supporting the canpredict application uses a random forest ( rf ) classifier to predict whether an amino acid change is likely to be cancer - causing or not . rf classifiers divide a large pool of data into smaller subsets based on characteristics of each datum ( 18 ) . for the canpredict application , the three characteristics used to describe each mutation are scores from sift , the pfam - based logr.e - value and the goss metrics . the sift algorithm uses similarity between closely related proteins to identify potentially deleterious changes ( 9 ) . sift scores < 0.05 are predicted to be deleterious ( 9 ) and only sift scores with a median information content score < 3.25 are included for predictions since higher values likely indicate unreliable sift scores ( 9 ) . also , because the computation time to generate alignments used by the sift algorithm is lengthy , the alignments for all refseq protein sequences have been pre - computed and are stored on the server . the pfam - based logr.e - value score predicts whether a change will alter protein function by determining the difference in fit of a wild - type version of the protein to a particular pfam model ( 10 ) . these scores were derived from values provided by the hmmer 2.3.2 software and the ls mode was used to search against the pfam protein family database . the logr.e - value score was calculated as : log10(e - valuevariant / e - valuecanonical ) . lastly , the goss metric uses the gene ontology to measure the similarity of the submitted refseq gene to other known cancer - causing genes ( 17 ) . the training data set used to construct the classifier is composed of 200 randomly selected known somatic cancer mutations and 800 non - cancer , non - synonymous variants . the cancer mutations were downloaded from data stored in the cosmic database ( 1 ) and the non - cancer variants were selected randomly from snps stored in dbsnp with a minor allele frequency > 20% . for each mutation in the training data , a score from the sift , these values were used to build the classifier using the package randomforest 4.5 - 16 ( http://stat-www.berkeley.edu/users/breiman/randomforests ) for the r statistical environment ( http://www.r-project.org ) . the out - of - bag error , an internal measure of the rate of misclassification of the classifier , was determined to be 3.19% suggesting that the classifier is very effective . 17 ) , data from three different experiments suggest that the predictor can function very well to highlight putative cancer mutations . first , in a cross - validation experiment , the classifier consistently revealed a very low false - positive rate of 1.7% for distinguishing relevant mutations from common snps ( 17 ) . second , an experiment was performed to distinguish recurrently identified mutations from mutations occurring only one time ; causal mutations are more likely than passenger changes to be seen in multiple different tumor samples because they are under positive selection in tumor samples . in this analysis , 58% of variants observed more than 10 times were predicted to be cancer - associated while only 43% of variants occurring only one time were predicted cancer - associated ( p - value 0.018 , two - tailed fisher exact test ) ( 17 ) . third , the classifier was used to analyze recent data from a large - scale screen for cancer mutations performed by sjoblom and colleagues ( 4 ) . in the paper by sjoblom , mutations were grouped into those genes likely to cause cancer and those genes unlikely to cause cancer , can genes and non - can genes , respectively . the canpredict classifier revealed that mutations in can genes were more likely to be predicted as cancer - associated than mutations in non - can genes ( 26.3% to 13.3% , respectively ; p - value 8.8e-6 ; two - tailed fisher exact test ) ( 17 ) . the user - supplied mutations and protein sequence data are validated via a server process , and the analysis status is instantly updated without the user leaving the input page . the results summary page is automatically loaded when the ajax call detects that the analysis is complete . the dojo library ( www.dojotoolkit.org ) implements ajax calls by providing support for the back and forward buttons , changing the url in the address bar to allow for bookmarking , and gracefully degrading when ajax or javascript are not fully supported on the client . the canpredict application can be used to submit a single full - length refseq protein sequence or accession and multiple associated changes ( figure 1 ) . additionally , from the batch submission page , the application will accept multiple refseq protein accessions and associated changes . there is no limit to the number of changes that can be analyzed from the batch submission page . changes are validated by the server to ensure that the amino acid specified in the change string occurs in the indicated sequence . for testing the application , users can either enter their own mutations or use the test - it link to submit example mutations . included in these examples results of the analysis are returned to the user in a summary page where they can also access all other submitted changes using links at the top of the summary ( figure 2 ) . there is also a link directing users to a detailed description of the scores produced from each metric . within the submission summary is a prediction from the classifier indicating likely cancer , likely non - cancer or not determined . the sequence flanking the change is included to allow the user to confirm the precise sequence used in the analysis . below the submission summary are data from the sift , logr.e - value and goss analyses . as alignment files used by the sift algorithm are time - consuming to produce , they are available for download using the provided link . sift scores and median information content are also presented and only scores with a median information content of < 3.25 are considered reliable ( 9 ) and will be used to generate a prediction from the classifier . if there are multiple domains covering the same mutation , the domain with the most deleterious ( largest ) logr.e - value score will be selected for display and will be used by the classifier . the goss score is indicated last , and will be present only if the submitted change resides in a gene with a gene ontology description . the result pages can be bookmarked , and the associated data are saved in the server for a week . finally , a link presented on the results summary page allows users to download their results in a tab - delimited format . results from the batch submission page will be returned in a similar tab - delimited format . the canpredict application provides an easily accessible interface for users to determine if an amino acid change is likely to be cancer - causing .
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 .