BayesDiallel 0.982 – MCMC Sampler for Analyzing Diallel Crosses of Inbred Strains

BayesDiallel 0.982

:: DESCRIPTION

BayesDiallel is a Gibbs-sampler program designed to fit, image, and give confidence for intricately modeled inheritance in Diallel F1 cross data.

::DEVELOPER

William Valdar Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 BayesDiallel

:: MORE INFORMATION

Citation

Lenaric A, Svenson K, Churchill G, Valdar W, (2012)
A General Bayesian Approach to analyzing Diallel Crosses of Infred Strains
Genetics, Vol. 190, 413-435, February 2012.

MIMAR 20101217 – MCMC Estimation of the Isolation-Migration model Allowing for Recombination

MIMAR 20101217

:: DESCRIPTION

MIMAR (MCMC estimation of the Isolation-Migration model Allowing for Recombination) is a Markov chain Monte Carlo method to estimate parameters of an isolation-migration model. It uses summaries of polymorphism data at multiple loci surveyed in a pair of diverging populations or closely related species and in contrast to previous methods, allows for intralocus recombination. Note that you need to know the ancestral allele at each polymorphic site in order to calculate the summary statistics.

::DEVELOPER

Przeworski lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C Compiler

:: DOWNLOAD

 MIMAR

:: MORE INFORMATION

Citation:

Becquet and Przewroski (2007)
A new approach to estimate parameters of speciation models with application to apes
Genome Res. 2007. 17: 000

SwiftLink 2.0 – Parallel MCMC Linkage Analysis

SwiftLink 2.0

:: DESCRIPTION

SwiftLink performs multipoint parametric linkage analysis on large consanguineous pedigrees and is primarily targeted at pedigrees that cannot be analysed by a Lander-Green algorithm based program, i.e. many markers, but larger pedigrees.

::DEVELOPER

SwiftLink team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 SwiftLink

:: MORE INFORMATION

Citation:

Bioinformatics. 2013 Feb 15;29(4):413-9. doi: 10.1093/bioinformatics/bts704. Epub 2012 Dec 13.
SwiftLink: parallel MCMC linkage analysis using multicore CPU and GPU.
Medlar A1, Głowacka D, Stanescu H, Bryson K, Kleta R.

SGD-RJ – Stochastic Gradient Descent based on Reversible jump MCMC

SGD-RJ

:: DESCRIPTION

SGD is a software for parameter inference in discretely observed stochastic kinetic models

::DEVELOPER

CBCL Lab (Computational Biology and Computational Learning) @ UCI

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • MatLab

:: DOWNLOAD

 SGD-RJ

:: MORE INFORMATION

Citation:

Yuanfeng Wang, Scott Christley, Eric Mjolsness and Xiaohui Xie,
Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent
BMC Systems Biology 2010, 4:99 doi:10.1186/1752-0509-4-99.

RPMCMC 0.2 – Repulsive Parallel MCMC Algorithm

RPMCMC 0.2

:: DESCRIPTION

RPMCMC is a parallel MCMC algorithm for discovering diverse motifs from large sequence sets

::DEVELOPER

Yoshida lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  RPMCMC

:: MORE INFORMATION

Citation:

Repulsive parallel MCMC algorithm for discovering diverse motifs from large sequence sets.
Ikebata H, Yoshida R.
Bioinformatics. 2015 Jan 11. pii: btv017

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.

BayesPhylogenies 1.1 – Inferring Phylogenetic Trees using MCMC or MCMCMC

BayesPhylogenies 1.1

:: DESCRIPTION

BayesPhylogenies is a general package for inferring phylogenetic trees using Bayesian Markov Chain Monte Carlo (MCMC) or Metropolis-coupled Markov chain Monte Carlo (MCMCMC) methods. The program allows a range of models of gene sequence evolution, models for morphological traits, models for rooted trees, gamma and beta distributed rate-heterogeneity, and implements a ‘mixture model’ (Pagel and Meade, 2004) that allows the user to fit more than one model of sequence evolution, without partitioning the data.

::DEVELOPER

Reading Evolutionary Biology Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOsX / Linux

:: DOWNLOAD

  BayesPhylogenies

:: MORE INFORMATION

Citation

A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data.
Pagel M, Meade A.
Syst Biol. 2004 Aug;53(4):571-81.

iMCMC – Illustrate Markov chain Monte Carlo (MCMC) for a Simple Landscape

iMCMC

:: DESCRIPTION

iMCMC is a Macintosh application that illustrates Markov chain Monte Carlo (MCMC) for a simple landscape.

::DEVELOPER

John Huelsenbeck

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Macintosh

:: DOWNLOAD

 iMCMC

:: MORE INFORMATION

MCMC++ 1.2 – C++ class library for Monte Carlo Markov Chain Analysis of Bayesian models

MCMC++ 1.2

:: DESCRIPTION

MCMC++ is a class library intended to speed and simplify development of Markov Chain Monte Carlo models for analysis of data in a Bayesian context.

::DEVELOPER

Kent Holsinger

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • C++ Compiler

:: DOWNLOAD

 MCMC++

:: MORE INFORMATION

BigFoot – Bayesian Alignment and Phylogenetic Footprinting with MCMC

BigFoot

:: DESCRIPTION

BigFoot: Bayesian alignment and phylogenetic footprinting with MCMC. Annotates the locations of conserved elements in multiple sequence while correcting for alignment uncertainty and error.

::DEVELOPER

Rahul Satija

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/MacOsX/Windows
  • Java

:: DOWNLOAD

 BigFoot

:: MORE INFORMATION

Citation

BMC Evol Biol. 2009 Aug 28;9:217.
BigFoot: Bayesian alignment and phylogenetic footprinting with MCMC.
Satija R, Novák A, Miklós I, Lyngs? R, Hein J.