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

PhysioFit 1.0.2 – Estimation of Extracellular Fluxes and Growth Rate

PhysioFit 1.0.2

:: DESCRIPTION

PhysioFit is a scientific tool designed to i) quantify exchange (production and consumption) fluxes and ii) cell growth rate during (batch) cultivations of microorganisms. Fluxes are estimated from time-course measurements of extracellular metabolites and biomass concentrations.

::DEVELOPER

MetaSys

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsx
  • R

:: DOWNLOAD

PhysioFit

:: MORE INFORMATION

GVCBLUP 3.9 / GVCeasy 1.3 – Genomic Prediction and Variance Component Estimation

GVCBLUP 3.9 / GVCeasy 1.3

:: DESCRIPTION

GVCBLUP is a computer package for genomic prediction and variance component estimation for additive and dominance effects using SNP markers.

GVCeasy is the graphical user interface for the GVCBLUP package.

::DEVELOPER

The authors of GVCBLUP:
Chunkao Wang, Dzianis Prakapenka, Shengwen Wang,
H. Birali Runesha and Yang Da

The authors of GVCeasy:
Sujata Pulugurta, Chunkao Wang, Dzianis Prakapenka,
H. Birali Runesha, Yang Da

, Department of Animal Science, University of Minnesota

:: SCREENSHOTS

GVCeasy

:: REQUIREMENTS

  • Linux / MacOsX / Windows
  • Java

:: DOWNLOAD

GVCBLUP  , GVCeasy

:: MORE INFORMATION

Citation

Da Y, Wang C, Wang S, Hu G (2014)
Mixed Model Methods for Genomic Prediction and Variance Component Estimation of Additive and Dominance Effects Using SNP Markers.
PLoS ONE 9(1): e87666. doi:10.1371/journal.pone.0087666.

RidgeRace – Ridge Regression for Continuous Ancestral Character Estimation on Phylogenetic Trees

RidgeRace

:: DESCRIPTION

RidgeRace is a tool to reconstruct ancestral character states and phenotypic rates.

::DEVELOPER

Algorithmic Bioinformatics, Heinrich-Heine-Universität Düsseldorf

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 RidgeRace

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Sep 1;30(17):i527-i533. doi: 10.1093/bioinformatics/btu477.
RidgeRace: ridge regression for continuous ancestral character estimation on phylogenetic trees.
Kratsch C, McHardy AC.

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.

SiGN-SSM 1.3.0 / SiGN-L1 1.1.0 – Gene Network Estimation Software

SiGN-SSM 1.3.0 / SiGN-L1 1.1.0

:: DESCRIPTION

SiGN-SSM  is open source gene network estimation software able to run in parallel on PCs and massively parallel supercomputers. The software estimates a state space model (SSM), that is a statistical dynamic model suitable for analyzing short time and/or replicated time series gene expression profiles. SiGN-SSM implements a novel parameter constraint effective to stabilize the estimated models. Also, by using a supercomputer, it is able to determine the gene network structure by the statistical permutation test in a practical time. SiGN-SSM is applicable not only to analyzing temporal regulatory dependencies between genes, but also to extracting the differentially regulated genes from time series expression profiles.

SiGN-L1 is network estimation software using sparse learning. It uses L1-regularization for simultaneous parameter estimation and model selection of statistical graphical models such as graphical Gaussian models and vector autoregressive models.

::DEVELOPER

SiGN-SSM TEam

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / Mac OsX

:: DOWNLOAD

 SiGN-SSM , SiGN-L1

:: MORE INFORMATION

Citation

Tamada, Y., Yamaguchi, R., Imoto, S., Hirose, O., Yoshida, R., Nagasaki, M., and Miyano, S. (2011).
SiGN-SSM: open source parallel software for estimating gene networks with state space models.
Bioinformatics. 2011 Apr 15;27(8):1172-3. doi: 10.1093/bioinformatics/btr078.

Genome Inform. 2011;25(1):40-52.
Sign: large-scale gene network estimation environment for high performance computing.
Tamada Y, Shimamura T, Yamaguchi R, Imoto S, Nagasaki M, Miyano S.

EM-SNP – Allele Frequency Estimation, SNP Detection and Association Studies

EM-SNP

:: DESCRIPTION

EM-SNP is an unified approach for allele frequency estimation, SNP detection and association studies based on pooled sequencing data using EM algorithms

::DEVELOPER

Fengzhu Sun

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • R

:: DOWNLOAD

  EM-SNP

:: MORE INFORMATION

Citation

Quan Chen and Fengzhu Sun (2013):
A unified approach for allele frequency estimation, SNP detection and association studies on pooled sequencing data using EM algorithms.
BMC Genomics. 2013;14 Suppl 1:S1. doi: 10.1186/1471-2164-14-S1-S1.

micropower – Power Estimation for Microbiome Studies

micropower

:: DESCRIPTION

The micropower package is designed to facilitate power estimation for microbiome studies that will be analyzed with pairwise distances (beta diversity) and PERMANOVA (a non-parametric extension of multivariate analysis of variance to a matrix of pairwise distances).

::DEVELOPER

Bushman Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R

:: DOWNLOAD

 micropower

:: MORE INFORMATION

Citation

Power and Sample-Size Estimation for Microbiome Studies Using Pairwise Distances and PERMANOVA.
Kelly BJ, Gross R, Bittinger K, Sherrill-Mix S, Lewis JD, Collman RG, Bushman FD, Li H.
Bioinformatics. 2015 Mar 29. pii: btv183.

Phyto-PhyloPars – Phylogenetic approach to the Estimation of Phytoplankton Traits

Phyto-PhyloPars

:: DESCRIPTION

Phyto-PhyloPars web server takes an evolutionary perspective to the variability across phytoplankton taxa in order to estimate the size, maximum growth rate, phosphate affinity and susceptibility to predation of any phytoplankton taxon, based on over one thousand observations on freshwater species.

::DEVELOPER

The Centre for Integrative Bioinformatics VU

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bruggeman, J. (2011)
A phylogenetic approach to the estimation of phytoplankton traits.
Journal of Phycology 47(1): 52-65.

PhyloPars – Estimation of Missing Parameter Values using Phylogeny

PhyloPars

:: DESCRIPTION

PhyloPars provides an efficient and statistically consistent method of combining any number of empirical observations with the phylogenetic tree to arrive at complete set of estimates for missing parameter values.

::DEVELOPER

The Centre for Integrative Bioinformatics VU

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2009 Jul;37(Web Server issue):W179-84. doi: 10.1093/nar/gkp370.
PhyloPars: estimation of missing parameter values using phylogeny.
Bruggeman J1, Heringa J, Brandt BW.