Genomics
Genomics uses a combination of recombinant DNA, DNA sequencing methods, and bioinformatics
to sequence, assemble, and analyse the structure and function of genomes. With the
development of various array based and sequencing based technologies, recent genomic
studies are capable of measuring gene expression levels, methylation pattern, chromatin
accessibility, transcription factor binding sites, all at the genome-wide scale, and
often at the single cell level or in an allele specific fashion. Investigators in
the Department of Biostatistics are involved in developing novel statistical methods
and computational tools for the analysis of various different types of genomic data
including bulk RNA sequencing data, single cell RNA sequencing data, bisulfite sequencing
data, ChIP sequencing data, ATAC sequencing data etc. These statistical methods need
to account for various technical issues encountered in the collection of these genomic
data, including sample non-independence due to batch effects or individual relatedness.
Methods are developed for a wide variety of genomics applications such as single cell
data imputation, data normalization, removal of batch effects, clustering, spatial
analysis, differential expression analysis, expression quantitative trait locus mapping,
allelic specific expression, differential methylation analysis, methylation quantitative
trait locus mapping, allelic specific methylation, gene set enrichment analysis, as
well as integrating with genome wide association studies.
Faculty: V. Baladandayuthapani, M. Boehnke, H. Jiang, J. Kang, G. Li, Y. Li, J. Morrison, L. Scott, X. Zhou