Overview:

The Functional Analysis through Hidden Markov Models (fathmm) software and server is is capable of predicting the functional effects of protein missense mutations by combining sequence conservation within hidden Markov models (HMMs), representing the alignment of homologous (orthologous and/or paralogous) sequences and conserved protein domains, with "pathogenicity weights", representing the overall tolerance of the corresponding model to mutations.

For more information, please refer to the following publications:

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

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.

Input Format:

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

• <protein> <substitution>
• dbSNP rs identifiers

P43026 L441P

rs137854462

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 

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