GMD 0.3.3 – Generalized Minimum Distance of Distributions

GMD 0.3.3

:: DESCRIPTION

GMD is an R package to assess the similarity between spatial distributions of read-based sequencing data such as ChIP-seq and RNA-seq. GMD calculates the optimal distance between pairs of normalized signal distributions, optionally sliding one distribution over the other to ‘align’ the distributions. GMD also provides graphical and downstream clustering tools.

::DEVELOPER

Xiaobei Zhao <xiaobei at binf.ku.dk>, Albin Sandelin@The Bioinformatics Centre , University of Copenhagen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX/ Windows
  • R package

:: DOWNLOAD

 GMD

:: MORE INFORMATION

Citation:

Xiaobei Zhao and Albin Sandelin
GMD: measuring the distance between histograms with applications on high-throughput sequencing reads
Bioinformatics (2012) 28 (8): 1164-1165.

CellOrganizer 2.8.1 – Image-derived Models of Subcellular Organization and Protein Distribution

CellOrganizer 2.8.1

:: DESCRIPTION

The CellOrganizer project provides tools for :learning generative models of cell organization directly from images/ storing and retrieving those models in XML files/ synthesizing cell images (or other representations) from one or more models

::DEVELOPER

CellOrganizer TEam

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Mac /  Linux
  • MatLab

:: DOWNLOAD

  CellOrganizer

:: MORE INFORMATION

Citation

Methods Cell Biol. 2012;110:179-93.
CellOrganizer: Image-derived models of subcellular organization and protein distribution.
Murphy RF.

DNC-MIX – Model Distribution of Gene Expression Profile of Test Sample as Mixture of Distributions

DNC-MIX

:: DESCRIPTION

DNC-MIX models the distribution of the gene expression profile of a test sample as a mixture, with each component characterizing the expression levels in a class, and assigns a class label to each test sample

::DEVELOPER

Statistical Genetics and Bioinformatics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 DNC-MIX

:: MORE INFORMATION

Citation

Alexandridis, R., Lin, S., Irwin, M. (2004)
Class discovery and classification of tumor samples using mixture modeling of gene expression data.
Bioinformatics, 20, 2545-2552.

 

CENTDIST – Discovery of Co-associated Factors by Motif Distribution

CENTDIST

:: DESCRIPTION

CENTDIST is a novel web-application for identifying co-localized transcription factors around ChIP-seq peaks. Unlike traditional motif scanning program, CENTDIST does not require any user-specific parameters and the background. It automatically learns the best set of parameters for different motifs and ranks them based on the skewness of their distribution around ChIP-seq peaks.

::DEVELOPER

Sung Wing Kin, Ken

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web  Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2011 Jul;39(Web Server issue):W391-9. doi: 10.1093/nar/gkr387. Epub 2011 May 20.
CENTDIST: discovery of co-associated factors by motif distribution.
Zhang Z1, Chang CW, Goh WL, Sung WK, Cheung E.

Sampbias – Sampling Bias in Species Distribution Records

Sampbias

:: DESCRIPTION

Sampbias is a method and tool to 1) visualize the distribution of occurrence records and species in any user-provided dataset, 2) quantify the biasing effect of geographic features related to human accessibility, such as proximity to cities, rivers or roads, and 3) create publication-level graphs of these biasing effects in space.

::DEVELOPER

Antonelli Lab

:: SCREENSHOTS

N/a

:: REQUIREMENTS

  • Linux /  MacOsX
  • R

:: DOWNLOAD

Sampbias

:: MORE INFORMATION

MPFE 1.8.0 – Estimation of Methylation Pattern Distribution from Deep Sequencing data

MPFE 1.8.0

:: DESCRIPTION

MPFE is an R Bioconductor package for calculation of the estimated distribution over methylation patterns.

::DEVELOPER

Peijie Lin, Sylvain Foret, Conrad Burden <conrad.burden at anu.edu.au>

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux/ MacOsX
  • R
  • BioConductor

:: DOWNLOAD

 MPFE

:: MORE INFORMATION

Citation

Estimation of the methylation pattern distribution from deep sequencing data.
Lin P, Forêt S, Wilson SR, Burden CJ.
BMC Bioinformatics. 2015 May 6;16(1):145.

FuseNet – Gene Network Inference by Fusing data from Diverse Distributions

FuseNet

:: DESCRIPTION

FuseNet is a Markov network formulation that infers networks from a collection of nonidentically distributed datasets.

::DEVELOPER

FuseNet team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

 FuseNet

:: MORE INFORMATION

Citation

Gene network inference by fusing data from diverse distributions.
Žitnik M, Zupan B.
Bioinformatics. 2015 Jun 15;31(12):i230-i239. doi: 10.1093/bioinformatics/btv258.

Words Viewer – Distribution of Motifs in a group of Nucleotide Sequences

Words Viewer

:: DESCRIPTION

WORDS VIEWER is a free software which allows to see the distribution of motifs in a group of nucleotide sequences.

::DEVELOPER

Gruppo di Biologia Computazionale

:: SCREENSHOTS

WORDSVIEWER

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 WORDS VIEWER

:: MORE INFORMATION

DistG – Distribution Graph

DistG

:: DESCRIPTION

DistG counts the length of fasta sequences and make a graphical representation about. There are two king of graphs. The first is the distribution graph, giving an overview of all read fasta files (sequences) so far. The second graph displays a specific file’s counting as a set of rectangles.

::DEVELOPER

Institute of Bioinformatics WWU Muenster

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Java

:: DOWNLOAD

 DistG

:: MORE INFORMATION

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.