ArticleBioinformatics (Oxford, England)2017
Accelerating Bayesian inference for evolutionary biology models.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Simultaneous Bayesian inference of phylogeny and molecular coevolution.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Characterization of tree shrew (Tupaia belangeri) interleukin-6 and its expression pattern in response to exogenous challenge.International journal of molecular medicine · 2017Article
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Authors and funding
3 authors.
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Abstract
Motivation: Bayesian inference is widely used nowadays and relies largely on Markov chain Monte Carlo (MCMC) methods. Evolutionary biology has greatly benefited from the developments of MCMC methods, but the design of more complex and realistic models and the ever growing availability of novel data is pushing the limits of the current use of these methods. Results: We present a parallel Metropolis-Hastings (M-H) framework built with a novel combination of enhancements aimed towards parameter-rich and complex models. We show on a parameter-rich macroevolutionary model increases of the sampling speed up to 35 times with 32 processors when compared to a sequential M-H process. More importantly, our framework achieves up to a twentyfold faster convergence to estimate the posterior probability of phylogenetic trees using 32 processors when compared to the well-known software MrBayes for Bayesian inference of phylogenetic trees. Availability and Implementation: https://bitbucket.org/XavMeyer/hogan. Contact: nicolas.salamin@unil.ch. Supplementary information: Supplementary data are available at Bioinformatics online.
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