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.