Bioinformatics and Genomics Lab

One enduring question is how a genotype contributes to a phenotype. We have seen dramatic advances in high-throughput technology, and high-throughput studies of biological systems are rapidly accumulating a wealth of 'omics'-scale data. The development of Next Generation Sequencing technology is rapidly changing the face of the genome annotation and analysis field. We are now able to use genome sequence and mRNA expression data to improve our understanding of the pathogenic phenotype of human diseases or complex traits. There is a biological mechanism to relate the genome to the transcriptome. Short-term goal is to characterize this biological mechanism between these data that connect genotype to phenotype by focusing on  alternative splicing (AS). Long-term goal is to create a molecular picture for genomics and personalized medicine. Our previous works have already developed the computational pipeline for integrating genomics with transcriptomics and providing functional annotation for intragenic SNPs involved in splicing regulation. The prominent works include incorporating these element  into i) study of genetic basis of variations that affect splicing in human populations, ii) crosstalk between epigenetics and AS for exon recognition, and iii) resource generation for scientific communities. These resources harness the power of genome variation that facilitates enhanced understanding of its contribution to health disparities for diseases

NetAS  Generation of AS-driven protein-protein interaction network

Epi AS  Genetic and epigenetic regulation of alternative splicing

F STAS  Evidence of selection on loci affecting splicing in human populations

MirAS   Regulation of microRNA biogenesis by splicing

AS-SNP  Computational method to systematically identify genetic variations affecting splicing regulatory elements and exon skipping

Turing Method into Translation

PharmAS  Database including genetic variations affecting exon skipping in Pharmacogenes. Web-site on the Open Science Data Cloud (OSDC) System



Genome-wide research has generated various data including multiple genome, transcriptome, epigenome, microRNAome, and proteome data, making it possible to conduct an integrative omics analysis. There exists clear recognition that the utilization of these multi-layered omics data is highly informative in understanding the complexity of RNA regulation. Therefore, this project develops general resources that provide mechanistic information between DNA sequences and phenotypes through RNA regulation. In addition to work in FSTAS andEpiAS, we are working towards further exploring the microRNA biogenesis regulated by AS and building the AS-driven protein-protein interaction network by incorporating alternative splicing exons affecting the protein domains and structure

Turing Method into Discovery