Sean Eddy

Sean Eddy is the Ellmore C. Patterson Professor of Molecular and Cellular Biology and of Applied Mathematics at Harvard University. He leads a Howard Hughes Medical Institute (HHMI) laboratory within the Department of Molecular and Cellular Biology at Harvard's Cambridge campus. His lab is affiliated with the Harvard Data Science Initiative and the Center for Brain Science, reflecting the interdisciplinary nature of his work.

Dr. Eddy's research focuses on deciphering evolutionary history through comparative analysis of genome sequences. His team develops computational methods for RNA, protein, and genome sequence analysis using probabilistic modeling approaches to build statistical models of biological features. They specialize in identifying remote evolutionary relationships between distantly related protein and RNA sequences. Notable contributions include the development of Codetta for predicting genetic codes, and software tools like HMMER, Infernal, Pfam, Rfam, and Dfam that have become standard resources in the field.

Eddy's research spans computational biology, bioinformatics, evolutionary genomics, and RNA biology. His work combines theoretical advances in sequence analysis algorithms with practical applications to understanding genome evolution, non-coding RNA function, and genetic code variation. Recent publications show a continued focus on developing novel computational methods while applying them to diverse biological questions from phage genomics to mammalian brain evolution.

His laboratory has produced numerous influential software tools and databases that are widely used in genomics research. The lab maintains strong connections with multiple Harvard graduate programs including Systems, Synthetic, and Quantitative Biology, Molecules, Cells, and Organisms (MCO), and Biophysics.

• Howard Hughes Medical Institute Investigator

Dr. Eddy has mentored numerous PhD students who have gone on to make significant contributions in computational biology and genomics. His lab develops and maintains several widely used bioinformatics resources including HMMER (profile hidden Markov models), Infernal (RNA sequence/structure analysis), Pfam (protein family database), Rfam (RNA family database), and Dfam (repetitive DNA database). The lab has been instrumental in advancing methods for sequence homology search, RNA structure prediction, and genome annotation.

The Eddy laboratory operates at the intersection of computer science, statistics, and molecular biology, developing novel algorithms while applying them to pressing biological questions. Current work continues to push the boundaries of what can be learned from comparative genomic analysis, with particular emphasis on non-coding RNA discovery, genetic code evolution, and the development of increasingly sophisticated probabilistic models for biological sequence analysis.