VANTED 2.6.5 – Visualization and Analysis of Networks containing Experimental Data

VANTED 2.6.5

:: DESCRIPTION

VANTED (Visualisation and Analysis of Networks containing Experimental Data) is an open source software that offers the possibility to load and edit graphs, which may represent biological pathways or functional hierarchies. It allows to integrate different *omics data into the functional context and provides a variety of functions for data mapping and processing, statistical analysis, and visualisation. With the VANTED Add-on interface it is easily possible to extend the functionality of the software.

::DEVELOPER

VANTED Team

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux/  MacOSX
  • Java

:: DOWNLOAD

 VANTED

:: MORE INFORMATION

Citation

Klukas and Schreiber (2010)
Integration of -omics data and networks for biomedical research with VANTED.
Journal of Integrative Biology, 7(2)

Björn H. Junker, Christian Klukas and Falk Schreiber (2006)
VANTED: A system for advanced data analysis and visualization in the context of biological networks.
BMC Bioinformatics, 7:109

Genonets – Analysis and Visualization of Genotype Networks

Genonets

:: DESCRIPTION

Genonets Server is a tool that provides the following features: (i) the construction of genotype networks for categorical and univariate phenotypes from DNA, RNA, amino acid or binary sequences; (ii) analyses of genotype network topology and how it relates to robustness and evolvability, as well as analyses of genotype network topography and how it relates to the navigability of a genotype network via mutation and natural selection; (iii) multiple interactive visualizations that facilitate exploratory research and education.

::DEVELOPER

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Genonets server-a web server for the construction, analysis and visualization of genotype networks.
Khalid F, Aguilar-Rodríguez J, Wagner A, Payne JL.
Nucleic Acids Res. 2016 Apr 22. pii: gkw313.

NEST – Network Essentiality Scoring Tool

NEST 1.0

:: DESCRIPTION

The NEST is designed to predict the gene essentiality based on protein interaction network and gene expression or CRISPR screen results.

::DEVELOPER

X. Shirley Liu Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

 NEST

:: MORE INFORMATION

Citation

Network analysis of gene essentiality in functional genomics experiments.
Jiang P, Wang H, Li W, Zang C, Li B, Wong YJ, Meyer C, Liu JS, Aster JC, Liu XS.
Genome Biol. 2015 Oct 30;16:239. doi: 10.1186/s13059-015-0808-9.

ViSEN 1.0beta0.3 – Visualization tool for Statistical Epistasis Networks

ViSEN 1.0beta0.3

:: DESCRIPTION

ViSEN provides a graphical visualization for statistical epistasis.  Pairwise and three-way epistatic interactions are measured using information gain and are represented using networks.

::DEVELOPER

ViSEN team

:: SCREENSHOTS

ViSEN

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java

:: DOWNLOAD

 ViSEN

:: MORE INFORMATION

Citation

Genet Epidemiol. 2013 Apr;37(3):283-5. doi: 10.1002/gepi.21718. Epub 2013 Mar 6.
ViSEN: methodology and software for visualization of statistical epistasis networks.
Hu T, Chen Y, Kiralis JW, Moore JH.

MetaNet – Network Construction and Biologically Significant Module Detection

MetaNet

:: DESCRIPTION

MetaNet is a software tool in MATLAB for network construction and biologically significant module detection.

::DEVELOPER

MetaNet team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux
  • MatLab

:: DOWNLOAD

 MetaNet

:: MORE INFORMATION

Citation

Network construction and structure detection with metagenomic count data.
Liu Z, Lin S, Piantadosi S.
BioData Min. 2015 Dec 12;8:40. doi: 10.1186/s13040-015-0072-2.

linkcomm 1.0-11 – Tools for Generating, Visualizing, and Analysing Link Communities in Networks

linkcomm 1.0-11

:: DESCRIPTION

linkcomm is an R package for the generation, visualization, and analysis of link communities in networks of arbitrary size and type.

::DEVELOPER

linkcomm team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOSX
  • R

:: DOWNLOAD

  linkcomm

:: MORE INFORMATION

Citation

Bioinformatics. 2011 Jul 15;27(14):2011-2. doi: 10.1093/bioinformatics/btr311. Epub 2011 May 19.
linkcomm: an R package for the generation, visualization, and analysis of link communities in networks of arbitrary size and type.
Kalinka AT, Tomancak P.

NeEMO 1.0 – NEtwork Enthalpic MOdelling

NeEMO 1.0

:: DESCRIPTION

NeEMO is a tool for the evaluation of stability changes using an effective representation of proteins based on residue interaction networks (RINs).

::DEVELOPER

The BioComputing UP lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

BMC Genomics. 2014;15 Suppl 4:S7. doi: 10.1186/1471-2164-15-S4-S7. Epub 2014 May 20.
NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation.
Giollo M, Martin AJ, Walsh I, Ferrari C, Tosatto SC.

RIDDLE – Network-assisted Gene Set Analysis

RIDDLE

:: DESCRIPTION

RIDDLE (Reflective diffusion and local extension)is a network-based method for characterizing gene sets. It asks if an input query set is significantly “close” to a known pathway or disease set in the human functional network.

::DEVELOPER

Lee Lab at Yonsei University, Korea and the Marcotte Lab at University of Texas at Austin

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Genome Biol. 2012 Dec 26;13(12):R125. doi: 10.1186/gb-2012-13-12-r125.
RIDDLE: reflective diffusion and local extension reveal functional associations for unannotated gene sets via proximity in a gene network.
Wang PI, Hwang S, Kincaid RP, Sullivan CS, Lee I, Marcotte EM.

HAC – Hierarchical Agglomerative Clustering for a large-scale Network data

HAC 1.2.1

:: DESCRIPTION

HAC is developed for fast clustering of heterogeneous interaction networks.

::DEVELOPER

Joel Bader lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux

:: DOWNLOAD

 HAC

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2011 Feb 15;12 Suppl 1:S44. doi: 10.1186/1471-2105-12-S1-S44.
Resolving the structure of interactomes with hierarchical agglomerative clustering.
Park Y, Bader JS.