iBPP v2.1.3 – Bayesian Species Delimitation Integrating Genes and Traits data

iBPP v2.1.3

:: DESCRIPTION

iBPP is a program of integration of genes and traits for Bayesian Phylogenetics and Phylogeography.

::DEVELOPER

Solis-Lemus lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

iBPP

:: MORE INFORMATION

Citation

Solís-Lemus C, Knowles LL, Ané C.
Bayesian species delimitation combining multiple genes and traits in a unified framework.
Evolution. 2015 Feb;69(2):492-507. doi: 10.1111/evo.12582. Epub 2015 Jan 16. PMID: 25495061.

Phylo-SMC r0 – Bayesian Phylogenetic Inference tool based on Sequential Monte Carlo

Phylo-SMC r0

:: DESCRIPTION

Phylo-SMC is a Bayesian phylogenetic inference tool based on Sequential Monte Carlo (SMC), an alternative to the standard Markov Chain Monte Carlo approach (MCMC).

::DEVELOPER

Alexandre BouchardSriram Sankararaman, and Michael I. Jordan

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /MacOsX / Windows
  • Java
  • Phylip
  • Mrbayes
  • R package

:: DOWNLOAD

 Phylo-SMC

:: MORE INFORMATION

Citation

Syst Biol. 2012 Jul;61(4):579-93. doi: 10.1093/sysbio/syr131.
Phylogenetic inference via sequential Monte Carlo.
Bouchard, A., Sankararaman, S., Jordan, M.I

BPrimm 1.12 – Bayesian and Penalized Regression in Multiple Loci Mapping

BPrimm 1.12

:: DESCRIPTION

BPrimm (Bayesian and Penalized regression in multiple loci mapping) includes a set of tools for simultaneously multiple loci mapping, and two novel methods named the Bayesian adaptive Lasso and the Iterative Adaptive Lasso

::DEVELOPER

Wei Sun

:: SCREENSHOTS

N/A

::REQUIREMENTS

:: DOWNLOAD

 BPrimm

:: MORE INFORMATION

Citation

Wei Sun, Joseph G. Ibrahim and Fei Zou
Genome-wide Multiple Loci Mapping in Experimental Crosses by the Iterative Adaptive Penalized Regression
Genetics May 2010 vol. 185 no. 1 349-359

BnpC – Bayesian non-parametric Clustering of Single-cell Mutation Profiles

BnpC

:: DESCRIPTION

BnpC is a novel non-parametric method to cluster individual cells into clones and infer their genotypes based on their noisy mutation profiles.

::DEVELOPER

Computational Biology Group (CBG)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  •  Linux
  • Python

:: DOWNLOAD

BnpC

:: MORE INFORMATION

Citation:

Borgsmüller N, Bonet J, Marass F, Gonzalez-Perez A, Lopez-Bigas N, Beerenwinkel N.
BnpC: Bayesian non-parametric clustering of single-cell mutation profiles.
Bioinformatics. 2020 Dec 8;36(19):4854-4859. doi: 10.1093/bioinformatics/btaa599. PMID: 32592465; PMCID: PMC7750970.

PSTk-Classifier – Classify DNA using a Bayesian approach

PSTk-Classifier

:: DESCRIPTION

PSTk-Classifier is a software written in C++ for classifying DNA using a Bayesian approach. Different underlying models can be selected — Naive (Nk), Markov (Mk) and Variable Length Markov (VLMK). The classifier works by first constructing profiles for all groups using fasta-files directly. The profiles are kept in a directory. Then sample sequences (in a multifasta file) can be scored against the profiles and a high-score list will be presented.

::DEVELOPER

Daniel Dalevi (daniel.dalevi@gmail.com)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  PSTk-Classifier

:: MORE INFORMATION

Citation

Dalevi D, Dubhashi D, Hermansson M (2006)
Bayesian classifiers for detecting HGT using fixed and variable order markov models of genomic signatures.
Bioinformatics. 2006 Mar 1;22(5):517-22.

BIMBAM 1.0 – Bayesian IMputation-Based Association Mapping

BIMBAM 1.0

:: DESCRIPTION

BIMBAM (Bayesian IMputation-Based Association Mapping)implements methods for assocation mapping. BIMBAM can handle both large association studies (e.g., genome scans) and smaller studies of candidate genes/regions.

::DEVELOPER

Yongtao Guan

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Mac

:: DOWNLOAD

BIMBAM

:: MORE INFORMATION

Citation

PLoS Genet. 2008 Dec;4(12):e1000279. doi: 10.1371/journal.pgen.1000279. Epub 2008 Dec 5.
Practical issues in imputation-based association mapping.
Guan Y1, Stephens M.

Servin, B and Stephens, M (2007).
Imputation-based analysis of association studies: candidate genes and quantitative traits.
PLoS Genet. 2007 Jul;3(7):e114. Epub 2007 May 30.

GControl – Bayesian Genomic Control Software

GControl

:: DESCRIPTION

GControl is a computer program for Bayesian analysis of case-control data that controls for population stratification and cryptic relatedness.  GControl performs these analyses using Markov chain Monte Carlo algorithms.

::DEVELOPER

The Devlin lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • C Compiler

:: DOWNLOAD

 GControl 

:: MORE INFORMATION

Citation:

Devlin, B. and Roeder, K (1999)
Genomic Control for Association Studies.
Biometrics 55, 997-1004.

HaMMLET – Fast Bayesian Hidden Markov Model with Wavelet Compression

HaMMLET

:: DESCRIPTION

HaMMLET is a fast Forward-Backward Gibbs sampler for Bayesian inference on Hidden Markov Models (HMM). It uses the Haar wavelet transform to dynamically compress the data based on the current variance sample in each iteration.

::DEVELOPER

Schliep lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • GCC

:: DOWNLOAD

 HaMMLET

:: MORE INFORMATION

Citation

Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression.
Wiedenhoeft J, Brugel E, Schliep A.
PLoS Comput Biol. 2016 May 13;12(5):e1004871. doi: 10.1371/journal.pcbi.1004871.

LAMARC 2.1.10 – Maximum Likelihood & Bayesian Estimation of Population Parameters

LAMARC 2.1.10

:: DESCRIPTION

LAMARC (Likelihood Analysis with Metropolis Algorithm using Random Coalescence) is a program which estimates population-genetic parameters such as population size, population growth rate, recombination rate, and migration rates. It approximates a summation over all possible genealogies that could explain the observed sample, which may be sequence, SNP, microsatellite, or electrophoretic data.

::DEVELOPER

Felsenstein/Kuhner lab

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / MacOSX

:: DOWNLOAD

LAMARC

:: MORE INFORMATION

Citation:

Mary K. Kuhner
LAMARC 2.0: maximum likelihood and Bayesian estimation of population parameters
Bioinformatics (2006) 22 (6): 768-770.

Triodenovo 0.06 – A Bayesian framework for de novo Mutation Calling in Parents-offspring Trios

Triodenovo 0.06

:: DESCRIPTION

The program triodenovo implemented a Bayesian framework for calling de novo mutations in trios for next-generation sequencing data.

::DEVELOPER

Triodenovo team

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux

:: DOWNLOAD

 Triodenovo

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Dec 21. pii: btu839.
A Bayesian framework for de novo mutation calling in parents-offspring trios.
Wei Q, Zhan X, Zhong X, Liu Y, Han Y, Chen W, Li B

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