T-BAPS 1.0 – T-RFLP Bayesian Analysis of Population Structures

T-BAPS 1.0

:: DESCRIPTION

T-BAPS (T-RFLP Bayesian Analysis of Population Structures) is a free Windows package for performing clustering analysis using T-RFLP data. T-RFLP is a newly developed molecular fingerprinting technique mainly used to investigate population structures in microbial communities. Details of the method can be found in: Tang, J., Tao J., Urakawa, H. and Corander, J. T-BAPS: a Bayesian statistical tool for comparison of microbial communities using terminal-restriction fragment length polymorphism (T-RFLP) data.

::DEVELOPER

Bayesian Statistics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 T-BAPS

:: MORE INFORMATION

Citation:

Stat Appl Genet Mol Biol. 2007;6:Article30. Epub 2007 Nov 6.
T-BAPS: a Bayesian statistical tool for comparison of microbial communities using terminal-restriction fragment length polymorphism (T-RFLP) data.
Tang J, Tao J, Urakawa H, Corander J.

BAGEL 0.91 – Bayesian Analysis of Gene EssentiaLity

BAGEL 0.91

:: DESCRIPTION

BAGEL is a software for Bayesian analysis of gene knockout screens using pooled library CRISPR or RNAi.

::DEVELOPER

BAGEL  team

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Windows/Linux
  • Python

:: DOWNLOAD

 BAGEL

:: MORE INFORMATION

Citation

BAGEL: a computational framework for identifying essential genes from pooled library screens.
Hart T, Moffat J.
BMC Bioinformatics. 2016 Apr 16;17(1):164. doi: 10.1186/s12859-016-1015-8.

BASIS v1 – Bayesian Analysis of Splicing IsoformS

BASIS v1

:: DESCRIPTION

BASIS is a software tool to identify differentially expressed transcript isoforms from high-throughput RNA-seq data or high-density tiling arrays.

:: DEVELOPER

Liang Chen’s Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 BASIS

:: MORE INFORMATION

Citation:

Sika Zheng and Liang Chen
A hierarchical Bayesian model for comparing transcriptomes at the individual transcript isoform level
Nucleic Acids Research 37(10):e75 (2009)

EBprot 1.1.0 – Bayesian Analysis of labeling-based Quantitative Proteomics Data

EBprot 1.1.0

:: DESCRIPTION

EBprot is a novel probabilistic framework that directly models the peptide-protein hierarchy and rewards the proteins with reproducible evidence of DE over multiple peptides.

::DEVELOPER

EBprot team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Mac OsX / Windows
  • R

:: DOWNLOAD

 EBprot

:: MORE INFORMATION

Citation

EBprot: Statistical analysis of labeling-based quantitative proteomics data.
Koh HW, Swa HL, Fermin D, Ler SG, Gunaratne J, Choi H.
Proteomics. 2015 Aug;15(15):2580-91. doi: 10.1002/pmic.201400620.

StatAlign 3.2 – Bayesian Analysis of Protein, DNA and RNA Sequences

StatAlign 3.2

:: DESCRIPTION

StatAlign is an extendable software package for Bayesian analysis of Protein, DNA and RNA sequences. Multiple alignments, phylogenetic trees and evolutionary parameters are co-estimated in a Markov Chain Monte Carlo framework, allowing for reliable measurement of the accuracy of the results.

::DEVELOPER

StatAlign team

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/Windows/MacOsx
  • Java

 StatAlign

:: MORE INFORMATION

Citation

Aádám Novák, István Miklós, Rune Lyngsø and Jotun Hein
StatAlign: an extendable software package for joint Bayesian estimation of alignments and evolutionary trees
Bioinformatics (2008) 24 (20): 2403-2404.

Arunapuram P, Edvardsson I, Golden M, Anderson JW, Novák A, Sükösd Z, Hein J (2013)
StatAlign 2.0: Combining statistical alignment with RNA secondary structure prediction. 
Bioinformatics,2013 Mar 1;29(5):654-5. doi:10.1093/bioinformatics/btt025.

BAGEL 4.1.1 – Bayesian Analysis of Gene Expression Levels

BAGEL 4.1.1

:: DESCRIPTION

Bayesian Analysis of Gene Expression Levels (BAGEL) is a program that allows statistical inferences to be made regarding differential gene expression between two or more samples measured on spotted (two-channel) microarrays. BAGEL makes these inferences from normalized ratio data, on a gene-by-gene basis. The advantages of BAGEL include ease of use, straightforward interpretation of results, statistical robustness, flexibility in accepting different experimental designs, and that it is free.

::DEVELOPER

the Townsend Lab

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / Mac OsX

:: DOWNLOAD

BAGEL

:: MORE INFORMATION

Please cite:

Townsend, J.P., and D.L. Hartl. 2002.
Bayesian analysis of gene expression levels: statistical quantification of relative mRNA level across multiple strains or treatments.
Genome Biology 3 (12): research0071.1-0071.16.

BAMBE 4.01a – Bayesian Analysis of Phylogenies

BAMBE 4.01a

:: DESCRIPTION

BAMBE (Bayesian Analysis in Molecular Biology and Evolution) is a free software package for the Bayesian analysis of phylogenies. The package includes programs for analyzing aligned DNA or RNA sequence data and allows data sets with gaps or indeterminate sites. The main program uses a variety of Metropolis-Hastings algorithms to sample from the joint posterior distribution of phylogenetic trees and likelihood model parameters. Other programs in the distribution help in analysis of the sampled trees and parameter values.

::DEVELOPER

Donald Simon & Bret Larget

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux

:: DOWNLOAD

BAMBE

:: MORE INFORMATION

Citation

Simon, D. and B. Larget. 2000.
Bayesian analysis in molecular biology and evolution (BAMBE), version 2.03 beta.
Department of Mathematics and Computer Science, Duquesne University.

BALD – Bayesian Analysis of Linkage Disequilibrium Differences

BALD

:: DESCRIPTION

BALD is a Bayesian approach using covariance of single nucleotide polymorphism data to detect differences in linkage disequilibrium patterns between groups of individuals.

::DEVELOPER

BALD team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 BALD

:: MORE INFORMATION

Citation

A Bayesian approach using covariance of single nucleotide polymorphism data to detect differences in linkage disequilibrium patterns between groups of individuals.
Clark TG, Campino SG, Anastasi E, Auburn S, Teo YY, Small K, Rockett KA, Kwiatkowski DP, Holmes CC.
Bioinformatics. 2010 Aug 15;26(16):1999-2003. Epub 2010 Jun 16.

MH-ESS 1.21 – Bayesian Analysis of Transposon Mutagenesis Data

MH-ESS 1.21

:: DESCRIPTION

MH-ESS is a python implementation of the Bayesian analysis method. It utilizes read information obtained from sequencing libraries of transposon mutants, to determine the essentiality of genes. Using a Bayesian framework, essentiality is modeled through the Extreme Value (Gumbel) distribution, which characterizes the maximum run of non-insertions (i.e. number of consecutive TA sites lacking insertion in a row). Genes with significantly larger runs of non-insertion thant statistically expected have a higher likelihood of essentiality. A Metropolis-Hastings sampling procedure is utilized to sample from conditional densities of essentiality for all genes, and posterior estimates of the probability of being essential are estimated for all genes.

::DEVELOPER

Sacchettini Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • Python
  • Scipy
  • Numpy

:: DOWNLOAD

 MH-ESS

:: MORE INFORMATION

Citation

Michael DeJesus; Yanjia J. Zhang; Christopher M. Sassetti; Eric J. Rubin; James C. Sacchettini; Thomas R. Ioerger
Bayesian Analysis of Gene Essentiality based on Sequencing of Transposon Insertion Libraries
Bioinformatics (2013) 29 (6): 695-703.

BATS 20080710 – Bayesian Analysis of Time Series Microarray experiments

BATS 20080710

:: DESCRIPTION

BATS is a new user friendly GUI software for Bayesian Analysis of Time Series microarray experiments. It implements a truly functional fully Bayesian approach which allows an user to automatically identify and estimate differentially expressed genes.

::DEVELOPER

Computational & Biology Open laboratory

:: SCREENSHOTS

BATS

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • MatLab

:: DOWNLOAD

 BATS

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2008 Oct 6;9:415. doi: 10.1186/1471-2105-9-415.
BATS: a Bayesian user-friendly software for analyzing time series microarray experiments.
Angelini C1, Cutillo L, De Canditiis D, Mutarelli M, Pensky M.