|Stochastic Systems Group|
Evolutionary signatures, chromatin signatures, and general ways for understanding genomes and their regulation
Prof. Manolis Kellis
Our group is focused on the computational underpinning of genomics, developing new algorithms and machine learning techniques for studying complete genomes, understanding their regulatory constructs, and their evolutionary dynamics. We have defined evolutionary signatures in the nucleotide alignments of multiple related species, enabling 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. We have also defined distinct chromatin signatures, or combinations of chromatin marks, in genome-wide epigenomic datasets, revealing numerous classes of promoter, enhancer, transcribed, and repressed regions, each with distinct functional properties. These techniques have enabled us to discover many new insights into animal gene regulation, including abundant translational read-through in neuronal proteins, functionality of anti-sense microRNA transcripts, and thousands of novel large intergenic non-coding RNAs. Going forward, we are applying such techniques to understand the logic of global gene regulation during development and differentiation in human and fly, in the context of the ENCODE and modENCODE projects.
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