PTree 1.2 – Pattern-based, Stochastic Search method for Maximum Parsimony Phylogenies

PTree 1.2

:: DESCRIPTION

PTree is a pattern-based, stochastic search method for maximum parsimony phylogenies.

::DEVELOPER

Algorithmic Bioinformatics, Heinrich-Heine-Universität Düsseldorf

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Java

:: DOWNLOAD

 PTree

:: MORE INFORMATION

Citation

PeerJ. 2013 Jun 25;1:e89. doi: 10.7717/peerj.89. Print 2013.
PTree: pattern-based, stochastic search for maximum parsimony phylogenies.
Gregor I, Steinbrück L, McHardy AC.

PRIMUS 1.8.0 – Pedigree Reconstruction and Identification of a Maximum Unrelated Set

PRIMUS 1.8.0

:: DESCRIPTION

PRIMUS is a pedigree reconstruction algorithm that uses estimates of genome-wide identity by descent to reconstruct pedigrees consistent with observed genetic data.

::DEVELOPER

PRIMUS team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl

:: DOWNLOAD

 PRIMUS

:: MORE INFORMATION

Citation

PRIMUS: Improving Pedigree Reconstruction using Mitochondrial and Y Haplotypes.
Staples J, Ekunwe L, Lange E, Wilson JG, Nickerson DA, Below JE.
Bioinformatics. 2015 Oct 29. pii: btv618

SPIMAP 1.1 – Species Informed Maximum A Posteriori Gene Tree Reconstruction

SPIMAP 1.1

:: DESCRIPTION

SPIMAP is a phylogenetic reconstruction method specifically designed for reconstructing gene trees in the case of a known species tree.

::DEVELOPER

Matt Rasmussen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ compiler
  • Python

:: DOWNLOAD

 SPIMAP 

:: MORE INFORMATION

Citation

A Bayesian Approach for Fast and Accurate Gene Tree Reconstruction
Matthew D. Rasmussen and Manolis Kellis.
Mol Biol Evol (2011) 28 (1): 273-290.

MECPM – Maximum Entropy Conditional Probability Moldeling

MECPM

:: DESCRIPTION

MECPM (Maximum Entropy Conditional Probability Moldeling) makes explicit and is determined by the interactions that confer phenotype-predictive power.MECPM achieved both improved sensitivity and specificity for identifying ground-truth markers and interactions.

::DEVELOPER

Computational Bioinformatics & Bio-imaging Laboratory (CBIL)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • C Compiler

:: DOWNLOAD

 MECPM

:: MORE INFORMATION

Citation:

Miller, Zhang, Yu, Liu, Chen, Langefeld, Herrington, Wang (2009),
An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions
Bioinformatics (2009) 25 (19): 2478-2485.

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