Exomiser is a Java program that functionally annotates variants from whole-exome sequencing data starting from a VCF file (version 4). The functional annotation code is based on Jannovar and uses UCSC KnownGene transcript definitions and hg19 genomic coordinates.
Variants are prioritized according to user-defined criteria on variant frequency, pathogenicity, quality, inheritance pattern, and model organism phenotype data. Predicted pathogenicity data was extracted from the dbNSFP resource. Cross-species phenotype comparisons are powered by the OWLSim algorithm.
Exomiser is currently using mouse phenotypes and will soon be leveraging the zebrafish phenotype data. Worm and fly phenotype data will be available later this year.
Exomiser is available from the Sanger Institute. A paper describing Exomiser is available here.

Phenotypic Interpretation of eXomes, is a pipeline for prioritizing candidate genes in exomes or NGS panels with comprehensive coverage of human Mendelian disease genes. It ranks genes based on predicted variant pathogenicity as well as phenotypic similarity of diseases associated with the genes harboring these variants to the phenotypic profile of the individual being investigated. PhenIX requires a VCF file mapped to hg19/Gchr37, as well as a list of HPO terms representing the phenotype observed in the patient. PhenIX is available here from Peter Robinson's group at Charité.

ExomeWalker is a computational method to prioritize a set of candidates in exome sequencing projects that aim to identify novel Mendelian disease genes. This approach involves filtering a Variant Call Format (VCF) file according to a number of user-definable criteria.
Genes are prioritized according to a variant score (predicted pathogenicity, rarity, pattern of variants compatible with the assumed mode of inheritance) and to their vicinity to other genes that belong to the same phenotypic disease family within a protein protein interaction (PPI) network, using the Random-Walk method as described in Köhler et al. (2008) to determine similarity within the PPI network on the basis of the global characteristics of the network.