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Activity Number: 287 - Advanced Stochastic Models and Inference Methods for Large-Scale Phylogenetics
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #305273 Presentation
Title: Fast and Robust Evolutionary Rate and Selection Pressure Inference Using Variational Bayes Techniques
Author(s): Sergei Pond*
Companies: Temple University
Keywords: comparative genomics; viral evolution; natural selection; LDA; variational bayes; scalable methods

Many popular methods in comparative genomics are based on fitting and interpreting continuous time discrete state Markov models to large sets of homologous sequences in a variety of statistical frameworks, all of which involve the evaluation of complex and expensive phylogenetic likelihood functions. When applied to coding sequence data, parameter estimates from these models can be interpreted to glean important biological insights, e.g., genomic locations and evolutionary times during which natural selection (conservation and adaptation) acted upon sequences. These methods can gain significant power when applied to large datasets, but they become computationally intractable in current implementations.

We describe an adaptation of the ideas popularized in latent Dirichlet allocation literature to this domain. In particular, our implementation reduces the number of required likelihood calculations to an a priori fixed number (independent of the size of the data), obtains relevant parameter estimates via VB inference, and readily scales to datasets 100x larger than can be tackled with current methods. We demonstrate applications of these methods to selection inference.

Authors who are presenting talks have a * after their name.

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