BaMFA – Bayesian Metabolic Flux Analysis

BaMFA

:: DESCRIPTION

BaMFA is a matlab package of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions.

::DEVELOPER

Computational systems biology group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Matlab

:: DOWNLOAD

BaMFA

:: MORE INFORMATION

Citation

Bioinformatics, 35 (14), i548-i557 2019 Jul 15
Bayesian Metabolic Flux Analysis Reveals Intracellular Flux Couplings
Markus Heinonen et al.

BEGFE 1.1 – Bayesian Estimation of Gene Family Evolution

BEGFE 1.1

:: DESCRIPTION

BEGFE implements a Markov Chain Monte Carlo algorithm to estimate the posterior probability distribution of the birth and death rate parameters and the numbers of gene copies at the internodes of the phylogenetic tree. In addition, BEGFE can simulate gene family data under the birth and death model.

::DEVELOPER

Phylogenetics Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • MacOsX/ Windows

:: DOWNLOAD

BEGFE

:: MORE INFORMATION

Citation

Liu, L., L. Yu, V. Kalavacharla, Z. Liu.
A Bayesian model for gene family evolution.
BMC Bioinformatics. 2011, 12:426

BHC 1.1.0 – Bayesian Hierarchical Clustering for R

BHC 1.1.0

:: DESCRIPTION

BHC is an R/Bioconductor port of the fast novel algorithm for Bayesian agglomerative hierarchical clustering.

::DEVELOPER

Warwick Systems Biology Centre

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Windows/Linux/ MacOsX
  • R package

:: DOWNLOAD

 BHC

:: MORE INFORMATION

Citation

BMC Bioinformatics 2009, 10:242
R/BHC: fast Bayesian hierarchical clustering for microarray data
Richard S Savage et al.

BNW – Bayesian Network Web Server

BNW

:: DESCRIPTION

BNW is a comprehensive web server for Bayesian network modeling of biological data sets. It is designed so that users can quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data, and use the model to understand and make predictions about relationships between the variables in the model.

::DEVELOPER

Yan Cui’s Lab at University of Tennessee Health Science Center

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 BNW

:: MORE INFORMATION

Citation:

Bioinformatics. 2013 Nov 1;29(21):2801-3. doi: 10.1093/bioinformatics/btt472. Epub 2013 Aug 21.
Bayesian Network Webserver: a comprehensive tool for biological network modeling.
Ziebarth JD1, Bhattacharya A, Cui Y.

MSBayesPro – Bayesian Protein Inference for LC-MS/MS Proteomics experiment

MSBayesPro

:: DESCRIPTION

MSBayesPro is a software package and web tool for Bayesian protein inference from tandem mass spectrometry peptide identifications. It uses a set of identified peptides (or peptides with scores in a MS/MS search), peptide detectability, and a protein database to provide probabilities of protein identifications.

::DEVELOPER

Yong Fuga Li

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows

:: DOWNLOAD

 MSBayesPro

:: MORE INFORMATION

Citation

J Comput Biol. 2009 Aug;16(8):1183-93. doi: 10.1089/cmb.2009.0018.
A bayesian approach to protein inference problem in shotgun proteomics.
Li YF1, Arnold RJ, Li Y, Radivojac P, Sheng Q, Tang H.

FreeBayes 1.3.1 – Bayesian Genetic Variant Detector

FreeBayes 1.3.1

:: DESCRIPTION

FreeBayes is a high-performance, flexible, and open-source Bayesian genetic variant detector. It operates on BAM alignment files, which are produced by most contemporary short-read aligners.

::DEVELOPER

The MarthLab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  FreeBayes

:: MORE INFORMATION

Citation

Haplotype-based variant detection from short-read sequencing
Erik Garrison, Gabor Marth

BEAST 1.10.4 / BEAST2 2.6.1 – Bayesian Evolutionary Analysis of Molecular Sequences

BEAST 1.10.4 / BEAST2 2.6.1

:: DESCRIPTION

BEAST (Bayesian Evolutionary Analysis Samling Trees) is a cross-platform program for Bayesian MCMC analysis of molecular sequences. It is entirely orientated towards rooted, time-measured phylogenies inferred using strict or relaxed molecular clock models. It can be used as a method of reconstructing phylogenies but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology. BEAST uses MCMC to average over tree space, so that each tree is weighted proportional to its posterior probability. We include a simple to use user-interface program for setting up standard analyses and a suit of programs for analysing the results.

BEAST 2 is an open source cross-platform program for Bayesian MCMC phylogenetic analysis of molecular sequences.

::DEVELOPER

The University of Auckland Computational Evolution Group

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / MacOS
  • Java

:: DOWNLOAD

BEAST /BEAST2

:: MORE INFORMATION

Citation:

BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis.
Bouckaert R, Vaughan TG, Barido-Sottani J, Duchêne S, Fourment M, Gavryushkina A, Heled J, Jones G, Kühnert D, De Maio N, Matschiner M, Mendes FK, Müller NF, Ogilvie HA, du Plessis L, Popinga A, Rambaut A, Rasmussen D, Siveroni I, Suchard MA, Wu CH, Xie D, Zhang C, Stadler T, Drummond AJ.
PLoS Comput Biol. 2019 Apr 8;15(4):e1006650. doi: 10.1371/journal.pcbi.1006650.

Bayesian inference of sampled ancestor trees for epidemiology and fossil calibration.
Gavryushkina A, Welch D, Stadler T, Drummond AJ.
PLoS Comput Biol. 2014 Dec 4;10(12):e1003919. doi: 10.1371/journal.pcbi.1003919.

Alexei J Drummond and Andrew Rambaut
BEAST: Bayesian evolutionary analysis by sampling trees
BMC Evolutionary Biology 2007, 7:214doi:10.1186/1471-2148-7-214

Tracer 1.7.1 – Analyse Results from Bayesian MCMC programs such as BEAST & MrBayes

Tracer 1.7.1

:: DESCRIPTION

Tracer is a program for analysing the trace files generated by Bayesian MCMC runs (that is, the continuous parameter values sampled from the chain). It can be used to analyse runs of BEAST, MrBayes, LAMARC and possibly other MCMC programs.

::DEVELOPER

Andrew Rambaut Group

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / MacOS
  • Java

:: DOWNLOAD

Tracer

:: MORE INFORMATION

Citation

Syst Biol. 2018 Sep 1;67(5):901-904. doi: 10.1093/sysbio/syy032.
Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7.
Rambaut A, Drummond AJ, Xie D, Baele G, Suchard MA.

MrBayes 3.2.7a – Bayesian Inference of Phylogeny

MrBayes 3.2.7a

:: DESCRIPTION

MrBayes is a program for the Bayesian estimation of phylogeny. Bayesian inference of phylogeny is based upon a quantity called the posterior probability distribution of trees, which is the probability of a tree conditioned on the observations. The conditioning is accomplished using Bayes’s theorem. The posterior probability distribution of trees is impossible to calculate analytically; instead, MrBayes uses a simulation technique called Markov chain Monte Carlo (or MCMC) to approximate the posterior probabilities of trees.

::DEVELOPER

MrBayes Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOsX / Linux

:: DOWNLOAD

MrBayes

:: MORE INFORMATION

Citation:

MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space.
Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Höhna S, Larget B, Liu L, Suchard MA, Huelsenbeck JP.
Syst Biol. 2012 May;61(3):539-42. doi: 10.1093/sysbio/sys029.

Ronquist F, Huelsenbeck JP.
MrBayes 3: Bayesian phylogenetic inference under mixed models.
Bioinformatics. 2003 Aug 12;19(12):1572-4.

ExaBayes 1.5 – Parallelized Bayesian Tree Inference for large-scale datasets

ExaBayes 1.5

:: DESCRIPTION

ExaBayes is a software package for Bayesian tree inference. It is particularly suitable for large-scale analyses on computer clusters.

::DEVELOPER

the Exelixis Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOs

:: DOWNLOAD

ExaBayes

:: MORE INFORMATION

Citation

ExaBayes: massively parallel bayesian tree inference for the whole-genome era.
Aberer AJ, Kobert K, Stamatakis A.
Mol Biol Evol. 2014 Oct;31(10):2553-6. doi: 10.1093/molbev/msu236