Bioinformatics -- Sean Eddy

Sean Eddy's most significant contribution is that he suggests and developed many tools based on probabilistic models of biological sequence and structure. Hidden Markov models (stochastic regular grammars) are useful for primary structure analysis of proteins and DNA. Stochastic context-free grammars are ideal for analysis of RNA secondary structure.

His work include include the HMMER profile HMM search software and the Pfam protein domain database; the Infernal structural profile SCFG search software for RNAs and the Rfam RNA domain database; and the QRNA genefinding program for ncRNA genes.

He also apply probabilistic modeling and other computational algorithms to identify interesting genetic features in large-scale DNA sequence.

His work in this area is summerized his book

Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
by Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison


Now, he focus on identifying novel structural and catalytic RNAs. He proposed the "modern RNA hypothesis" stating that: far from being a few scattered relics, RNAs are in fact in widespread use in modern organisms in a variety of roles. We now argue for a “modern RNA world” hypothesis: many of the RNAs we see today are modern inventions, highly adapted to regulatory roles in complex organisms.