degPrune 1.0 – Density based Pruning for Identification of Differentially Expressed Genes

degPrune 1.0

:: DESCRIPTION

degPrune is a program for pruning most non-differentially expressed genes from microarray data in PCL or NCBI GEO format

::DEVELOPER

Machine Learning and Evolution Laboratory (MLEG)

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Linux
  • Java
:: DOWNLOAD

 degPrune

:: MORE INFORMATION

Citation:

BMC Genomics. 2010 Nov 2;11 Suppl 2:S3. doi: 10.1186/1471-2164-11-S2-S3.
Density based pruning for identification of differentially expressed genes from microarray data.
Hu J1, Xu J.

SpectralTDF – Spectral Representation of Transition Density Functions

SpectralTDF

:: DESCRIPTION

SpectralTDF is a program for computing the transition density function (TDF) of the diffusion approximation of the Wright-Fisher process with general diploid selection and recurrent mutation.

::DEVELOPER

Yun S. Song

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /MacOsX / WIndows
  • Java

:: DOWNLOAD

 SpectralTDF

:: MORE INFORMATION

Citation

SpectralTDF: transition densities of diffusion processes with time-varying selection parameters, mutation rates, and effective population sizes.
Steinrücken M, Jewett EM, Song YS.
Bioinformatics. 2015 Nov 9. pii: btv627.

Twilight 1.13 – A tool for Ligand Density Validation

Twilight 1.13

:: DESCRIPTION

Twilight is a standalone script for analysis, visualization, and annotation of a pre-filtered set of protein/ligand complexes deposited with the PDB with ligand RSCC values that are below a threshold of 0.6.

::DEVELOPER

Twilight team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Python

:: DOWNLOAD

 Twilight

:: MORE INFORMATION

Citation:

Acta Crystallogr Sect F Struct Biol Cryst Commun. 2013 Feb 1;69(Pt 2):195-200. doi: 10.1107/S1744309112044387.
Visualizing ligand molecules in Twilight electron density.
Weichenberger CX1, Pozharski E, Rupp B.

DEEGEP – Density Estimates by Expansions of GEgenbauer Polynomials

DEEGEP

:: DESCRIPTION

DEEGEP is a simple statistical method for testing for natural selection from a high dimensional (i.e. multiple population) allele frequency spectrum. The method uses a neutral control defined by the user (e.g. intergenic sites) to learn a background neutral distribution. This is accomplished by approximating the density with a finite expansion of Gegenbauer polynomials, inspired by Kimura’s classic result in theoretical population genetics. One can then test for natural selection on a second set of putatively functional sites (e.g. microRNAs) by comparing the multi-population allele frequency spectrum to the neutral control.

::DEVELOPER

Kevin Chen’s group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Mac OsX / Windows
  • Python

:: DOWNLOAD

 DEEGEP

:: MORE INFORMATION