Interpreting the non-coding human genome using chromatin and regulator dynamics in multiple cell types

Wednesday, October 20, 2010 - 7:00pm
Manolis Kellis, MIT
Manolis Kellis

IEEE Computer and Engineering in Medicine and Biology Societies, MIT biological engineering and biomedical engineering student group (BE-BMES), and GBC/ACM

Our group at MIT aims to further our understanding of the human genome by computational integration of large-scale functional and comparative genomics datasets. (1) Using alignments of multiple closely related species, we have defined evolutionary signatures for the systematic discovery and characterization of diverse classes of functional elements, including protein-coding genes, RNA structures, microRNAs, developmental enhancers, regulatory motifs, and biological networks. (2) Using epigenomics datasets of multiple chromatin marks across the complete genome, we have defined chromatin signatures that reveal numerous classes of promoter, enhancer, transcribed, and repressed regions, each with distinct functional properties. (3) Using diverse functional datasets across many cell types, we have defined multi-cell activity signatures for chromatin states, regulator expression, motif enrichment, and target gene expression, and have used their correlations to link candidate enhancers to their putative target genes, infer cell type-specific activators and repressors, and to predict and validate functional regulator binding in specific chromatin states.

We have used these evolutionary, chromatin, and activity signatures to elucidate the function and regulatory circuitry of the human and fly genomes, to reveal many new insights on animal gene regulation and development, including abundant translational read-through in neuronal proteins, functionality of anti-sense microRNA transcripts, and thousands of novel large intergenic non-coding RNAs. We have also used these signatures to revisit previously uncharacterized disease-associated single-nucleotide polymorphism (SNP) variants linked to several diseases and phenotypes from genome-wide association studies, which has enabled us to provide mechanistic insights into their likely molecular roles.

Manolis Kellis is an Associate Professor of Computer Science at MIT, a member of the Computer Science and Artificial Intelligence Laboratory and of the Broad Institute of MIT and Harvard, where he directs the MIT Computational Biology Group ( His group has recently been funded to lead the integrative analysis efforts of the modENCODE project for Drosophila melanogaster, and also for integrative analysis of the NIH Epigenome Roadmap Project. He has received the NSF CAREER award, an NIH R01 for work in Computational Genomics, the Alfred P. Sloan Fellowship, the Karl Van Tassel chair in EECS, the Distinguished Alumnus 1964 chair, and the Ruth and Joel Spira Teaching Award in EECS. He was recognized for his research in genomics as one of the top young innovators under the age of 35 by Technology Review Magazine, one of the principal investigators of the future by Genome Technology magazine, and one of three young scientists representing the next generation in biotechnology by the Boston Museum of Science. He obtained his Ph.D. from MIT, where he received the Sprowls award for the best doctorate thesis in computer science, and the first Paris Kanellakis graduate fellowship. Prior to computational biology, he worked on artificial intelligence, sketch and image recognition, robotics, and computational geometry, at MIT and at the Xerox Palo Alto Research Center. He lived in Greece and France before moving to the US.