Predicting the Functional Effects of Human Short Variations

A suite of programmings, HMMvar, HMMvar-multi, and HMMvar-func, is developed for predicting the functional effects of short variations.

HMMvar: Small indels account for the second largest portion of human variants, however, available methods for indel functional predictions, no matter in coding or noncoding regions, are many fewer compared to those for SNPs. We developed HMMvar for predicting the functional effects of both SNPs and indels in coding regions of sequences.

HMMvar-multi: Complex diseases are likely to be caused by multiple genes and/or multiple mutations on individual genes, so quantitively measuring the effect of multiple variants together should be helpful for detecting causal genes/mutations for diseases. HMMvar-multi predicts the functional effects of multiple variants in the same gene based on HMMvar.

HMMvar-func: Prediction in terms of whether a variant causes the variant bearing protein to lose the original function or gain new function is needed for better understanding of how the variant contributes to disease/cancer. Based on HMMvar, HMMvar-func classifies variants into four types of functional outcome: gain, loss, switch, and conservation of function.

What we have here?

Please download HMMvar or HMMvar-func from the download page. You can also download or search for the pre-computed single or set scores for the 1000 Genomes project and pre-computed functional outcome for disease/cancer variants. The pre-computed hidden Markov models are also available for you to download to get started for your customered datasets.

If you are planning to use HMMvar, please cite the papers,

Liu, M, Watson, LT, and Zhang, L: Quantitative prediction of the effect of genetic variation using hidden Markov models. BMC Bioinformatics 2014, 15:5

Liu, M, Watson, LT, and Zhang, L: Predicting the Combined Effect of Multiple Genetic Variants. Nucleic Acid Research (submitted).