GOrilla – Gene Ontology enRIchment anaLysis and visuaLizAtion tool

GOrilla

:: DESCRIPTION

GOrilla is a web-based application that identifies enriched GO terms in ranked lists of genes, without requiring the user to provide explicit target and background sets. This is particularly useful in many typical cases where genomic data may be naturally represented as a ranked list of genes (e.g. by level of expression or of differential expression).

::DEVELOPER

Yakhini Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Eran Eden, Roy Navon, Israel Steinfeld, Doron Lipson and Zohar Yakhini.
GOrilla: A Tool For Discovery And Visualization of Enriched GO Terms in Ranked Gene Lists“,
BMC Bioinformatics 2009, 10:48.

ENViz 3.1.5 – Integrated Statistical Analysis and Visualization of Sample-Matched Data with Multiple Data Types

ENViz 3.1.5

:: DESCRIPTION

ENViz (Enrichment Analysis and Visualization) is a Cytoscape app that performs joint enrichment analysis of two types of sample matched datasets in the context of systematic annotations.

::DEVELOPER

Agilent Laboratories

:: SCREENSHOTS

ENViz

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • Java
  • Cytoscape

:: DOWNLOAD

 ENViz

:: MORE INFORMATION

Citation

ENViz: a Cytoscape App for integrated statistical analysis and visualization of sample-matched data with multiple data types.
Steinfeld I, Navon R, Creech ML, Yakhini Z, Tsalenko A.
Bioinformatics. 2015 Jan 9. pii: btu853

LocFuse – Human protein-protein Interaction Prediction

LocFuse

:: DESCRIPTION

LocFuse is a novel ensemble learning method of human protein-protein interaction prediction via classifier fusion using protein localization information.

::DEVELOPER

Laboratory of Systems Biology & Bioinformatics (LBB)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux
  • JRE

:: DOWNLOAD

 LocFuse

:: MORE INFORMATION

Citation

LocFuse: human protein-protein interaction prediction via classifier fusion using protein localization information.
Zahiri J, Mohammad-Noori M, Ebrahimpour R, Saadat S, Bozorgmehr JH, Goldberg T, Masoudi-Nejad A.
Genomics. 2014 Dec;104(6 Pt B):496-503. doi: 10.1016/j.ygeno.2014.10.006.

ScanToolBox 1.0 – Structural Cobra Add-oN for Metabolic Networks

ScanToolBox 1.0

:: DESCRIPTION

SCAN is a MATLAB Toolbox which uses SBML files in COBRA structure format as input and builds different Metabolite-centric and Enzyme-centric Networks which are needed for pre and post-process of structural analysis softwares such as Cytoscape.

::DEVELOPER

Laboratory of Systems Biology & Bioinformatics (LBB)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • MatLab
  • COBRA

:: DOWNLOAD

 ScanToolBox

:: MORE INFORMATION

CytoKavosh 1.1 / Kavosh – Finding Network Motifs in Large Biological Networks

CytoKavosh 1.1 / Kavosh

:: DESCRIPTION

CytoKavosh is a Cytoscape plug-in which allows discovering network motifs with less memory and CPU time in comparison with other existing tools. CytoKavosh plug-in uses Kavosh algorithm for finding network motifs and is based on counting all k-size sub-graphs of a given network graph (directed or undirected).

Kavosh is a new algorithm for finding network motifs.

::DEVELOPER

Laboratory of Systems Biology & Bioinformatics (LBB)

:: SCREENSHOTS

CytoKavosh

:: REQUIREMENTS

  • Linux/ Windows / MacOsX
  • Java
  • Cytoscape

:: DOWNLOAD

 CytoKavosh , Kavosh

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2009 Oct 4;10:318. doi: 10.1186/1471-2105-10-318.
Kavosh: a new algorithm for finding network motifs.
Kashani ZR1, Ahrabian H, Elahi E, Nowzari-Dalini A, Ansari ES, Asadi S, Mohammadi S, Schreiber F, Masoudi-Nejad A.

PLoS One. 2012;7(8):e43287. doi: 10.1371/journal.pone.0043287. Epub 2012 Aug 29.
CytoKavosh: a cytoscape plug-in for finding network motifs in large biological networks.
Masoudi-Nejad A1, Ansariola M, Kashani ZR, Salehzadeh-Yazdi A, Khakabimamaghani S.

MODA – Network Motif Discovery in Biological Networks

MODA

:: DESCRIPTION

MODA is an efficient algorithm for network motif discovery in biological networks.

::DEVELOPER

Laboratory of Systems Biology & Bioinformatics (LBB)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 MODA

:: MORE INFORMATION

Citation

Genes Genet Syst. 2009 Oct;84(5):385-95.
MODA: an efficient algorithm for network motif discovery in biological networks.
Omidi S1, Schreiber F, Masoudi-Nejad A.

ChIPModule 20130227 – Systematic discovery of Transcription Factors and their Cofactors from ChIP-seq data

ChIPModule 20130227

:: DESCRIPTION

ChIPModule is a software tool for systematical discoveray of transcription factors and their cofactors from ChIP-seq data. Given a ChIP-seq dataset and motifs of a large number of transcription factors, ChIPModule can efficiently identify groups of motifs,whose instances significantly co-occur in the ChIP-seq peak regions.

::DEVELOPER

Hu Lab – Data Integration and Knowledge Discovery @ UCF

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows / MacOsX
  • Python

:: DOWNLOAD

 ChIPModule

:: MORE INFORMATION

Citation:

Pac Symp Biocomput. 2013:320-31.
ChIPModule: systematic discovery of transcription factors and their cofactors from ChIP-seq data.
Ding J, Cai X, Wang Y, Hu H, Li X.

Hi-Jack – Pathway-based Inference of Host-pathogen Interactions

Hi-Jack

:: DESCRIPTION

Hi-Jack, a novel computational framework, for inferring pathway-based interactions between a host and a pathogen that relies on the idea of metabolite hijacking.

::DEVELOPER

InfoCloud Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOsX / Linux
  • C++ Compiler

:: DOWNLOAD

Hi-Jack

:: MORE INFORMATION

Citation:

Hi-Jack: A novel computational framework for pathway-based inference of host-pathogen interactions.
Kleftogiannis D, Wong L, Archer JA, Kalnis P.
Bioinformatics. 2015 Mar 9. pii: btv138

TopoGSA – Network Topological Gene Set Analysis

TopoGSA

:: DESCRIPTION

TopoGSA (Topology-based Gene Set Analysis) computes and visualise the topological properties of a set of genes/proteins mapped onto a molecular interaction network. Different topological characteristics, such as the centrality of nodes in the network or their tendency to form clusters, are computed and compared against those of known cellular pathways and processes (KEGG, BioCarta, GO, etc.).

::DEVELOPER

TopoGSA team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Bioinformatics. 2010 May 1;26(9):1271-2. doi: 10.1093/bioinformatics/btq131. Epub 2010 Mar 24.
TopoGSA: network topological gene set analysis.
Glaab E, Baudot A, Krasnogor N, Valencia A.

PathExpand – Extending Cellular Pathways in Interaction Networks

PathExpand

:: DESCRIPTION

PathExpand is a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and expanding them to include densely interconnected interaction partners.

::DEVELOPER

PathExpand team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

E. Glaab, A. Baudot, N. Krasnogor, A. Valencia
Extending pathways and processes using molecular interaction networks to analyse cancer genome data
in BMC Bioinformatics, 11(1):597, 2010