Manolis Kellis

Biography

Manolis Kellis obtained his PhD from MIT where he received the Sprowls award for the best doctorate thesis in computer science and the first Paris Kanellakis graduate fellowship. Kellis is also an Associate Professor of Computer Science at MIT, a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and of the Broad Institute of MIT and Harvard, where he directs the MIT Computational Biology Group. He has received a number of awards including: the US Presidential Early Career Award in Science and Engineering (PECASE), the NSF CAREER award, the Alfred P. Sloan Fellowship, and the Karl Van Tassel chair in EECS. Prior to computational biology, Kellis worked on artificial intelligence, sketch and image recognition, robotics, and computational geometry at MIT and at the Xerox Palo Alto Research Center.

Industry Impact
Manolis Kellis research interests are in the area of computational biology, genomics, epigenomics, gene regulation, and genome evolution.

  • In the area of genome interpretation, we seek to develop comparative genomics methods to identify genes and regulatory elements systematically in the human genome.
  • In the area of gene regulation, we seek to understand the regulatory motifs involved in cell types specification during development, understand their combinatorial relationships, and how these establish expression domains in the developing embryo.
  • In the area of epigenomics, we seek to understand the chromatin signatures associated with distinct activity states, the changing chromatin states across different cell types and during differentiation, and the sequencing signals responsible for the establishment and maintenance of chromatin marks.
  • In the area of evolutionary genomics, understanding the dynamics of gene phylogenies across complete genes, the emergence of new gene functions by duplication and mutation, and the algorithmic principles behind phylogenomic.
Research/Thesis Topic

Recent Works

Variation and Disease
To understand the effects of genetic variation on molecular phenotypes and human disease. This includes methods for integrating diverse functional genomic datasets of transcription, chromatin modifications, regulator binding, and the changes across multiple conditions to interpret genetic associations, identify causal variants, and predict the effects of genetic perturbations.

Genome Interpretation
To recognize the molecular basis of human biology and disease. This requires computational methods for genome interpretation which can systematically interpret the functional elements encoded in the 4-letter DNA code. Hence, methods have been developed for the comprehensive annotation of proteins, RNAs and regulatory control elements encoded in the human genome. Exploiting genome-wide comparative genomics datasets can help recognize specific patterns of evolutionary change, or ‘evolutionary signatures’, associated with each class of functional elements and dictate the specific constraints to each type of function.

Long non-coding RNAs
Many long transcripts in the human genome do not encode proteins and open up a whole new field for the study of long non-coding RNAs (lncRNAs). Two of the genomic signatures have facilitated the discovery and characterized the chromatin signatures associated with promoters and transcribe regions, and the evolutionary signatures associates with protein-coding selection. This enables the participation in numerous collaborations that seek the discovery, annotation, and functional characterization of long non-coding RNAs. The development of computational methods enables the study of structural properties of non-coding RNAs, based on evolutionary signatures, biophysical folding properties, and recent types of experimental evidence that distinguish paired vs. unpaired positions that constrain the folding algorithms.