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VMCMC: A graphical and statistical analysis tool for Markov chain Monte Carlo traces
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, Centres, SeRC - Swedish e-Science Research Centre.
KTH, School of Information and Communication Technology (ICT).
KTH, School of Information and Communication Technology (ICT).
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, Centres, SeRC - Swedish e-Science Research Centre.ORCID iD: 0000-0002-6664-1607
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2017 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 18, no 1, 97Article in journal (Refereed) Published
Abstract [en]

Background: MCMC-based methods are important for Bayesian inference of phylogeny and related parameters. Although being computationally expensive, MCMC yields estimates of posterior distributions that are useful for estimating parameter values and are easy to use in subsequent analysis. There are, however, sometimes practical difficulties with MCMC, relating to convergence assessment and determining burn-in, especially in large-scale analyses. Currently, multiple software are required to perform, e.g., convergence, mixing and interactive exploration of both continuous and tree parameters. Results: We have written a software called VMCMC to simplify post-processing of MCMC traces with, for example, automatic burn-in estimation. VMCMC can also be used both as a GUI-based application, supporting interactive exploration, and as a command-line tool suitable for automated pipelines. Conclusions: VMCMC is a free software available under the New BSD License. Executable jar files, tutorial manual and source code can be downloaded from https://bitbucket.org/rhali/visualmcmc/.

Place, publisher, year, edition, pages
BioMed Central, 2017. Vol. 18, no 1, 97
Keyword [en]
Convergence, Markov chain Monte Carlo, Metropolis-Hastings, Phylogenetics, Software, Visualization
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-208116DOI: 10.1186/s12859-017-1505-3ISI: 000397489700003PubMedID: 28187712Scopus ID: 2-s2.0-85012066451OAI: oai:DiVA.org:kth-208116DiVA: diva2:1106300
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceSwedish e‐Science Research Center
Note

QC 20170607

Available from: 2017-06-07 Created: 2017-06-07 Last updated: 2017-06-07Bibliographically approved

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Ali, Raja HashimBark, MikaelMiró, JorgeMuhammad, Sayyed AuwnZubair, Syed M.
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