In RNA-Informatics Group we engage in a number of research projects in bioinformatics. They aim at solving various problems in RNA bioinformatics, protein tertiary structure prediction, and SNP and disease association, based on novel, fundamental algorithmic research.
ncRNA Analysis and Prediction:
We are interested in efficient and effective computational approaches for non-coding RNA gene finding. Our research includes algorithms, techniques and tools for RNA pseudoknot modeling, search, analysis, and prediction. We have developed a conformatioal graph model and tree decomposition based RNA pseudoknot profiling and search tool RNATOPS and online server RNATOPS-W, RNA analysis tool RNApasta, and specialized tool TRFOLDER for telomerase RNA analysis and prediction. We are developing RNA multiple alignment algorithms and extending the developed tools to a pipeline for systematic development of RNA families databases including pseudoknots. We are also investigating syntactic rules for RNA description and prediction, novel methods for RNA evolution, effective techniques for ncRNA gene finding.
Protein Structure Prediction:
Our work in protein tertiary structure prediction is conducted with two different approaches: threading based and ab initio predictions. Both methods are built upon conformational modeling of tertiary structure. Tree decomposition based algorithmic techniques are developed for very efficient threading alignment so that sophisticated energy functions can be incorporated to achieve accurate threaing results. Such techniques also make it possible to accomplish simultaneous backbone fold recognition and sidechain packing. Our ab initio prediction work is based on syntactic rules defining the space of tertiary structures which also make it possible to yield efficient algorithms for computing the optimal fold.
SNP and Disease Association:
We are investigating novel graph-theoretic approaches and information theory techniques for disease modeling on genomes, especially mapping of genes, single nucleotide polymorphism (SNP), and their association with diseases.
This research focuses on developing practically feasible algorithms for solving intractable bioinformatics problems, based on advanced algorithmic graph theories and probabilistic methods. In particular, we are interested in non-conventional frameworks (e.g., parameterized algorithms), topological graph theoretics such as tree decomposition, and randomization techniques like color coding.