Ben Li is a Ph.D. candidate in the Department of Biostatistics and Bioinformatics at Emory University.
His research mainly focuses on the development of statistical and computational methods for high-throughput genetics and genomics data. During his Ph.D. studies, he has published methodological papers on hierarchical models incorporating historical data to detect differentially expressed genes on microarray data (IPBT, Bioinformatics, 2016), detecting differentially methylated regions on 450K methylation array and bisulfite sequencing data (adaptiveHM, Statistics in Biosciences, 2016), novel supervised learning approach to predict TF–DNA interaction using bisulfite sequencing methylation data (Methylphet, Nucleic acids research, 2015). He is currently working on developing new statistical methods for detecting differentially expressed genes without replicates for both microarray and RNA-seq data.
In addition, he has worked together with collaborators of different backgrounds and created computational tools to analyze different types of high-throughput data more efficiently. For example, he developed Binstrain for Chlamydia trachomatis Strains in Single and Mixed Infection Clinical Samples (PLoS one, 2014) and for detecting Staphylococcus aureus subtypes from metagenome shotgun sequence data (PeerJ, 2016). His other collaborative projects include analyses of bisulfite sequencing data (Nucleic acids research, 2016), Hi-C data (Statistics in Biosciences, 2016), single cell RNA-seq data, and ChIP-seq data.