SIREN 1.0 – Signing of Regulatory Networks

SIREN 1.0

:: DESCRIPTION

The SIREN algorithm can infer the regulatory type (positive or negative regulation) of interactions in a known gene regulatory network given corresponding genome-wide gene expression data.

::DEVELOPER

Bader Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 WordCloud

:: MORE INFORMATION

Citation

Algorithms Mol Biol. 2015 Jul 8;10:23. doi: 10.1186/s13015-015-0054-4. eCollection 2015.
Inferring interaction type in gene regulatory networks using co-expression data.
Khosravi P#, Gazestani VH#, Pirhaji L, Law B, Sadeghi M, Goliaei B, Bader GD.

GRAM 0.6 – Discovery of Gene Modules and Regulatory Networks

GRAM 0.6

:: DESCRIPTION

GRAM (Genetic RegulAtory Modules) identifies modules, collections of genes that share common regulators as well as expression profiles.

::DEVELOPER

the Gifford Laboratory

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/ Windows/MacOsX
  • Java

:: DOWNLOAD

GRAM

:: MORE INFORMATION

Citation

Nat Biotechnol. 2003 Nov;21(11):1337-42. Epub 2003 Oct 12.
Computational discovery of gene modules and regulatory networks.
Bar-Joseph Z, Gerber GK, Lee TI, Rinaldi NJ, Yoo JY, Robert F, Gordon DB, Fraenkel E, Jaakkola TS, Young RA, Gifford DK.

CoMoFinder – Identify Composite Network Motifs in Genome-scale Co-regulatory Networks

CoMoFinder

:: DESCRIPTION

CoMoFinder strives to discover reliable composite network motifs in co-regulatory networks which consist of microRNAs, transcriptional regulators and genes.

::DEVELOPER

Yue Li

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux
  • JDK

:: DOWNLOAD

 CoMoFinder

:: MORE INFORMATION

Citation:

A novel motif-discovery algorithm to identify co-regulatory motifs in large transcription factor and microRNA co-regulatory networks in human.
Liang C, Li Y, Luo J, Zhang Z.
Bioinformatics. 2015 Mar 18. pii: btv159

EpiRegNet – Epigenetic Regulatory Network from High Throughput Gene Expression

EpiRegNet

:: DESCRIPTION

EpiRegNet aims to build a transcriptional regulatory network composing of histone modification and transcription factor binding in promoters and interactions between factors in these two fields.

::DEVELOPER

JJWang Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Epigenetics. 2011 Dec;6(12):1505-12. doi: 10.4161/epi.6.12.18176.
EpiRegNet: constructing epigenetic regulatory network from high throughput gene expression data for humans.
Wang LY1, Wang P, Li MJ, Qin J, Wang X, Zhang MQ, Wang J.

ReNE 1.9 – A Cytoscape Plugin for Regulatory Network Enhancement

ReNE 1.9

:: DESCRIPTION

ReNE plugin, is a new Cytoscape 3.x plugin, which enables integration, merging, enhancement, visualization, and exporting of pathways from multiple repositories.

::DEVELOPER

The SysBIO research group

:: SCREENSHOTS

ReNE

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • JRE
  • Cytoscape

:: DOWNLOAD

 ReNE

:: MORE INFORMATION

Citation

PLoS One. 2014 Dec 26;9(12):e115585. doi: 10.1371/journal.pone.0115585. eCollection 2014.
ReNE: a cytoscape plugin for regulatory network enhancement.
Politano G, Benso A, Savino A, Di Carlo S.

idFBA – Dynamic analysis of integrated Signaling, Metabolic, and Regulatory Networks

idFBA

:: DESCRIPTION

idFBA (integrated dynamic FBA) is a flux balance analysis (FBA)-based strategy  that dynamically simulates cellular phenotypes arising from integrated networks.

::DEVELOPER

the Computational Systems Biology Laboratory, Department of Biomedical Engineering, University of Virginia.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • MatLab

:: DOWNLOAD

idFBA

:: MORE INFORMATION

Citation:

Lee, J.M., E.P. Gianchandani, J.A. Eddy, and J.A. Papin. 2008.
Dynamic analysis of integrated signaling, metabolic, and regulatory networks.
PLoS Computational Biology, 4(5): e1000086

Inferelator 2013.3.RC3 – Genetic Regulatory Networks Inference algorithm

Inferelator 2013.3.RC3

:: DESCRIPTION

Inferelator learns parsimonious regulatory networks from systems biology datasets.

::DEVELOPER

the Bonneau Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • R package

:: DOWNLOAD

  Inferelator

:: MORE INFORMATION

Citation

Alex Greenfield, Christoph Hafemeister, and Richard Bonneau
Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks
Bioinformatics (2013) 29 (8): 1060-1067. doi:10.1093/bioinformatics/btt099

RENATO – REgulatory Network Analysis TOol

RENATO

:: DESCRIPTION

RENATO is a network-based analysis web tool for the interpretation and visualization of transcriptional and post-transcriptional regulatory information, designed to identify common regulatory elements in a list of genes. RENATO maps these genes to the regulatory network, extracts the corresponding regulatory connections and evaluate each regulator for significant over-representation in the list.

::DEVELOPER

RENATO Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • HTML5

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2012 Jul;40(Web Server issue):W168-72. doi: 10.1093/nar/gks573. Epub 2012 Jun 11.
Inferring the regulatory network behind a gene expression experiment.
Bleda M1, Medina I, Alonso R, De Maria A, Salavert F, Dopazo J.

TIDAL 1.0.1 – Generates Transcription Factor Regulatory Network from Time-series Gene Expression data

TIDAL 1.0.1

:: DESCRIPTION

The TIDAL (TIme-Dependent Activity Linker) generates a transcription factor regulatory network from time-series gene expression data. It will identify transcription factors that are active at each time-point in your data, and link these factors in a coherent cascade which can be visualized.

::DEVELOPER

Kleinstein Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2013;14 Suppl 6:S1. doi: 10.1186/1471-2105-14-S6-S1. Epub 2013 Apr 17.
Reconstruction of regulatory networks through temporal enrichment profiling and its application to H1N1 influenza viral infection.
Zaslavsky E1, Nudelman G, Marquez S, Hershberg U, Hartmann BM, Thakar J, Sealfon SC, Kleinstein SH.

RIPE 1.1 – Regulatory Network Inference

RIPE 1.1

:: DESCRIPTION

RIPE (Regulatory network Inference from joint Perturbation and Expression data) is a novel three-step method that integrates both perturbation data and steady state gene expression data in order to estimate a regulatory network.

::DEVELOPER

Alexandra Jauhiainen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • R

:: DOWNLOAD

 RIPE

:: MORE INFORMATION

Citation

PLoS One. 2014 Feb 28;9(2):e82393. doi: 10.1371/journal.pone.0082393. eCollection 2014.
Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles.
Shojaie A1, Jauhiainen A2, Kallitsis M3, Michailidis G