PhyloBayes 4.1c – Bayesian Phylogenetic software based on Mixture Models

PhyloBayes 4.1c

:: DESCRIPTION

PhyloBayes is a Bayesian Monte Carlo Markov Chain (MCMC) sampler for phylogenetic reconstruction using protein alignments. Compared to other phylogenetic MCMC samplers (e.g. MrBayes), the main distinguishing feature of PhyloBayes is the underlying probabilistic model, CAT. It is particularly well suited for large multigene alignments, such as those used in phylogenomics.

::DEVELOPER

Nicolas Lartillot (nicolas.lartillot@umontreal.ca)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / Mac OsX

:: DOWNLOAD

 PhyloBayes

:: MORE INFORMATION

Citation

Nicolas Lartillot, Thomas Lepage and Samuel Blanquart
PhyloBayes 3: a Bayesian software package for phylogenetic reconstruction and molecular dating
Bioinformatics (2009) 25 (17): 2286-2288.

MixNet 1.1.2 / MixeR 1.9 – Analyzes Biological Networks using Mixture Models

MixNet 1.1.2 / MixeR 1.9

:: DESCRIPTION

MixNet (Erdös-Renyi Mixture for Networks) is the first publicly available computer software that analyzes biological networks using mixture models.This model is based on the hypothesis that real networks are made of classes which show specific connectivity patterns.

The MixeR allows the use of the basical options of MixNet software and the post-treatment of the results.

::DEVELOPER

SSB group.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/MacOsX
  • C Complier / R package

:: DOWNLOAD

 MixNet / MixeR

:: MORE INFORMATION

Citation

Picard, F., Miele, V., Daudin,J-J., Cottret,L., Robin, S.,
Deciphering the connectivity structure of biological networks using MixNet,
MC Bioinformatics. 2009 Jun 16;10 Suppl 6:S17. doi: 10.1186/1471-2105-10-S6-S17.

HTTMM – Hierarchical Taxonomy Tree based Mixture Model

HTTMM

:: DESCRIPTION

HTTMM is a package designed for estimating the abundance of taxon within a microbial community by incorporating the structure of the taxonomy tree.

::DEVELOPER

Shihua Zhang’s Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux
  • MatLab

:: DOWNLOAD

 HTTMM

:: MORE INFORMATION

Citation

IEEE/ACM Trans Comput Biol Bioinform. 2015 Sep-Oct;12(5):1112-22. doi: 10.1109/TCBB.2015.2415814.
Infer Metagenomic Abundance and Reveal Homologous Genomes Based on the Structure of Taxonomy Tree.
Qiu YQ, Tian X, Zhang S.

MixTreEM – Species Tree Inference Using a Mixture Model

MixTreEM

:: DESCRIPTION

MixTreEM (Mixture of Trees using Expectation Maximization) is a species tree reconstruction method.

::DEVELOPER

MixTreEM team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 MixTreEM

:: MORE INFORMATION

Citation:

Species Tree Inference Using a Mixture Model.
Ullah I, Parviainen P, Lagergren J.
Mol Biol Evol. 2015 Sep;32(9):2469-82. doi: 10.1093/molbev/msv115.

MixClone 1.1.6 – Mixture Model for Inferring Tumor Subclonal Populations

MixClone 1.1.6

:: DESCRIPTION

MixClone is a comprehensive software package to study the subclonal structures of tumor genomes, including subclonal cellular prevalences estimation, allelic configuration estimation, absolute copy number estimation and a few visualization tools

::DEVELOPER

CBCL Lab (Computational Biology and Computational Learning) @ UCI

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/MacOsX
  • Python

:: DOWNLOAD

 MixClone

:: MORE INFORMATION

Citation

BMC Genomics. 2015;16 Suppl 2:S1. doi: 10.1186/1471-2164-16-S2-S1. Epub 2015 Jan 21.
MixClone: a mixture model for inferring tumor subclonal populations.
Li Y, Xie X.

VarMixt 0.2-4 – Differential Analysis of Microarray data whose Variances are Modelled by a Mixture model

VarMixt 0.2-4

:: DESCRIPTION

VarMixt is an efficient variance modelling for the differential analysis of replicated gene expression data.

::DEVELOPER

SSB group.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • R pacakge

:: DOWNLOAD

 VarMixt

:: MORE INFORMATION

Citation

Bioinformatics. 2005 Feb 15;21(4):502-8.
VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data.
Delmar P, Robin S, Daudin JJ.

DIME 1.2 – Identifying Differential ChIP-seq Based on an Ensemble of Mixture Models

DIME 1.2

:: DESCRIPTION

DIME (Differential Identification using Mixtures Ensemble) is an ensemble of methods for differential analysis. Specifically, it considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) for analyzing ChIP-seq data comparing two samples. This package can also be used to identify differential in other high throughput data such as microarray and DNA methylation.

::DEVELOPER

Statistical Genetics and Bioinformatics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 DIME

:: MORE INFORMATION

Citation

Taslim, C., Huang, T., and Lin, S. (2011).
DIME: R-package for Identifying Differential ChIP-seq Based on an Ensemble of Mixture Models.
Bioinformatics, 27, 1569-1570.

Mixer 1.03 – ChIP-chip Analysis by Mixture Model approach

Mixer 1.03

:: DESCRIPTION

Mixer is a mixture model approach to analyze ChIP-chip or ChIP-seq data, also with some utility functions to process DNA sequence data. It includes statistical methods for both data normalization and peak detection. The peak detection and quantification relies on a mixer model approach that dissects the distribution of background signals and the Immunoprecipitated signals. In contrast to many existing methods, mixer is more flexible by imposing less restrictive assumptions and allowing a relatively large proportion of peak regions. Robust performance on data sets predicted to contain numerous peaks is very important for the studies of the transcription factors with abundant binding sites, and common chromatin features or epigenetic marks.

::DEVELOPER

Wei Sun

:: SCREENSHOTS

N/A

::REQUIREMENTS

:: DOWNLOAD

  Mixer

:: MORE INFORMATION

Citation

Wei Sun, Michael J Buck, Mukund Patel and Ian J Davis (2009),
Improved ChIP-chip analysis by mixture model approach.
BMC Bioinformatics 2009, 10:173

MixSIH 1.0.0 – Haplotype Assembly with Mixture Model

MixSIH 1.0.0

:: DESCRIPTION

MixSIH is a probabilistic model for solving the single individual haplotyping (SIH) or haplotype assembly problem.

::DEVELOPER

hirotaka MATSUMOTOKiryu Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 MixSIH

:: MORE INFORMATION

Citation

BMC Genomics. 2013;14 Suppl 2:S5. doi: 10.1186/1471-2164-14-S2-S5.
MixSIH: a mixture model for single individual haplotyping.
Matsumoto H, Kiryu H.

fitGCP – Fitting Genome Coverage Distributions with Mixture Models

fitGCP

:: DESCRIPTION

fitGCP is a framework for fitting mixtures of probability distributions to genome coverage profiles. Besides commonly used distributions, fitGCP uses distributions tailored to account for common artifacts. The mixture models are iteratively fitted based on the Expectation-Maximization algorithm.

::DEVELOPER

JRG 4: Bioinformatics Research Group Bioinformatics (NG4), Robert Koch-Institut

: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Python

:: DOWNLOAD

 fitGCP

:: MORE INFORMATION

Citation

Analyzing genome coverage profiles with applications to quality control in metagenomics
Martin S. Lindner, Maximilian Kollock, Franziska Zickmann and Bernhard Y. Renard,
Bioinformatics (2013) 29 (10): 1260-1267.