Analyze Cancer-Associated Variants

Enter Your Mutations:

Overview:


Our cancer-specific algorithm is capable of predicting the functional effects of cancer-associated protein missense mutations by combining sequence conservation within hidden Markov models (HMMs), representing the alignment of homologous sequences and conserved protein domains, with cancer-specific "pathogenicity weights", representing the overall tolerance of the corresponding model to cancer mutations.

For more information, please refer to the following publications:

Shihab HA, Gough J, Cooper DN, Day INM, Gaunt, TR. (2013). Predicting the Functional Consequences of Cancer-Associated Amino Acid Substitutions. Bioinformatics 29:1504-1510. Cancer-Specific Paper

Shihab HA, Gough J, Cooper DN, Stenson PD, Barker GLA, Edwards KJ, Day INM, Gaunt, TR. (2013). Predicting the Functional, Molecular and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models. Hum. Mutat., 34:57-65 fathmm Paper


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Input Format:


Our software accepts one of the following formats (see here for annotating VCF files):

  • <protein> <substitution>
  • dbSNP rs identifiers

In the above, <protein> is the protein identifier and <substitution> is the amino acid substitution in the conventional one letter format. At present, our server accepts SwissProt/TrEMBL, RefSeq and Ensembl protein identifiers, e.g.:

P43026 L441P
or:

rs137854462


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Batch Submission:


It is possible to submit multiple amino acid substitutions as a 'Batch Submission' via our server. Here, all amino acid substitutions for a protein can be entered on a single line and should be separated by a comma, e.g:

P43026 L441P
ENSP00000325527 N548I,E1073K,C2307S 

Note: this option is not available when analysing dbSNP rs identifiers.


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Prediction Threshold:


As described in our paper, our server uses a default prediction threshold of -0.75. Here, predictions with scores less than this indicate the mutation is potentially associated with cancer; however, our prediction threshold this can be adjusted and tuned to cater for your individual needs. For example, if you are interested in minimising the number of false positives in your analysis, then you should opt for a more conservative threshold, e.g. -3.0; however, if you are interested in capturing a large proportion of cancer-associated mutations (regardless of the number of false positives), then a less stringent threshold should be selected, e.g. 0.0 or higher. To inform you of this choice, the specificity and sensitivity of our software at various prediction thresholds can be seen using the below interactive graph:


Prediction Threshold


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VCF Annotation:


Unfortunately, due to disk space constraints, we are unable to annotate Variant Call Format (VCF) files on your behalf. However, the consequences of all VCF variants can be derived using the Ensembl Variant Effect Predictor (VEP). Once annotated, the following script (available here) is capable of parsing these annotations and will provide you with a list of protein consequences which can then be used as input into our server/software.

Additional help on using our script is available by typing the following command:

python parseVCF.py --help


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