Seminar talk: Sensitive and fast DNA homology search with profile HMMs in HMMER
- Monday, December 5, 2016 from 4:00pm to 5:00pm
- Barnard Hall - view map
Sequence database homology searches are an essential part of molecular biology, providing information about the function and evolutionary history of proteins, RNA molecules and DNA sequence elements. I will describe a tool for DNA/DNA sequence comparison that is built on the HMMER framework, which applies probabilistic inference methods based on hidden Markov models to the problem of homology search. This tool, called nhmmer, enables improved detection of remote DNA homologs, and has been used in combination with Dfam and RepeatMasker to improve annotation of transposable elements in the human genome. I will then describe an algorithm, based on the Burrows Wheeler Transform, that speeds one simple but time-consuming part of nhmmer, yielding more than an order of magnitude acceleration over a highly optimized implementation.
Bio: Travis Wheeler is an Assistant Professor at the University of Montana Computer Science Department, where his group develops methods in computational biology, with an emphasis on genomic sequence analysis. For the most part, that involves development of algorithms that increase the speed, power, and accuracy of sequence database homology search using profile hidden Markov models, and application of these methods topics motivated by biology, especially those involving transposable elements and regulatory elements. Travis earned his Bachelors in Evolutionary Biology from the University of Arizona in 1995. He spent several years in industry and academia as a telecom and web software developer, then earned a PhD in Computer Science in 2009, under the guidance of John Kececioglu and Mike Sanderson at the University of Arizona. He worked in Sean Eddy's group (HHMI Janelia Research Campus) as a postdoc and software engineer until 2014, when moved to his current position.
Host: Dr. Brendan Mumey, Gianforte School of Computing