Асосий контентга ўтиш
AkademIndex

Маҳсулотлар

Ишлаб чиқувчилар учун

AkademBaseтез орадаЭкотизим учун очиқ API
Лотин
Ўзбек
Мақола

MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

Fredrik RonquistDepartment of Biodiversity Informatics, Swedish Museum of Natural History, SE-10405 Stockholm, SwedenMaxim TeslenkoDepartment of Biodiversity Informatics, Swedish Museum of Natural History, SE-10405 Stockholm, SwedenPaul van der MarkDepartment of Scientific Computing, Florida State University, FL 32306, USADaniel L. AyresCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USAAaron E. DarlingGenome Center, University of California, Davis, CA 95616, USASebastian HöhnaDepartment of Mathematics, Stockholm University, SE-10691 Stockholm, SwedenBret LargetDepartments of Statistics and Botany, University of Wisconsin, Madison, WI 53706, USALiang LiuDepartments of Agriculture and Natural Resources, Delaware State University, Dover, DE 19901, USAMarc A. SuchardDepartments of Biomathematics, Biostatistics and Human Genetics, University of California, Los Angeles, CA 90095, USAJohn P. HuelsenbeckDepartment of Integrative Biology, University of California, Berkeley, CA 94720, USA
2012en
ABI

Аннотация

Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d(N)/d(S) rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.

Идентификаторлар

Иқтибослар ва манбалар

74 та иқтибос0 та фойдаланилган манба
Кўрсаткичлар — AkademScholar · Тез орада