Computational methods for the detection of positive and lineage-specific selection from genomic sequence data


March 3, 2008


Adam Siepel


Cornell University


In recent years, abundant DNA sequence data has led to widespread interest in computational methods for detecting sequences that are evolving faster, slower, or by different patterns of evolution than would be expected under neutral drift, and hence are likely to have evolutionarily important biological functions. These methods are providing new insights into the evolutionary dynamics that have shaped present-day genomes, and they are beginning to reveal the genetic basis of key differences between species, including some of the specific differences that differentiate humans from other mammals. In this talk, I will review established methods for detecting positive and lineage-specific selection, focusing on likelihood ratio tests based on continuous-time Markov models of nucleotide and codon substitution. In addition, I will discuss new methods for detecting lineage-specific selection, and for evaluating the statistical significance of apparent lineage-specific changes in evolutionary rate. I will also discuss a comprehensive study of positively selected protein-coding genes in mammals that my group has recently completed. The talk will be geared for an audience of computational and statistical scientists, and no background in biology is required.


Adam Siepel

Adam Siepel is an Assistant Professor in the Department of Biological Statistics and Computational Biology at Cornell University. His research focuses on comparative genomics, particularly of mammals, and includes a mixture of statistical modeling, algorithms development, software implementation, and scientific discovery. Siepel received a B.S. in Agricultural and Biological Engineering from Cornell in 1994, then worked in software development for bioinformatics for several years in the late 1990s, first at Los Alamos National Laboratory and then at the National Center for Genome Resources in Santa Fe. In 2001, he received an M.S. in Computer Science from the University of New Mexico, and, in 2005, a Ph.D. in Computer Science from UC Santa Cruz. Siepel is a winner of a Microsoft Research New Faculty Fellowship, a Packard Fellowship, and an National Science Foundation Career Award. He serves on the Editorial Board of the journal Genome Research, on advisory panels for the National Human Genome Research Institute and the National Science Foundation, and on the Program Committees of the RECOMB and WABI computational biology conferences. He teaches courses in computational genomics and machine learning at Cornell, and is a member of the graduate fields of Computational Biology, Computer Science, Biometry, and Genetics & Development.