Research Interests
We are interested in developing and applying computational tools for the study of functional effects for mutations in human genomes. One current focus is to develop reliable and efficient ways of analyzing synonymous mutations and indels (insertion/deletions). Another focus is to develop computational methods to predict the effects of mutations on protein-protein interactions and protein-nucleic acids interactions. To make our predictions we rely on a number of sequence-based features (including physicochemical features, predicted structural features, evolutionary information, and available functional annotations) and utilize a variety of computational methodologies (including machine learning methods). We are also actively developing bioinformatics databases and predictors.
Databases
dbDSM
database of Deleterious Synonymous Mutation
dbCRSR
database of Cancer Radiosensitivity Regulation Factors
dbCPM
database of Cancer Passenger Mutations
dbCID
database of Cancer Driver InDels
Predictors
PrPDH
Prediction of Protein-DNA binding Hot spot
PDHOT
Computational Prediction of Hot Spots in Protein-DNA Complexes
SPHot
Sequence-based Prediction of RNA-binding Hot Spots
PredCID
Predictor for Cancer Driver Frameshift InDels
PrDSM
Prediction of Deleterious Symonymous Mutations
BBPpred
A Blood-brain Barrier Peptides predictor