Tag: Regulatory Network

ResponseNet v.3 – Revealing Signaling and Regulatory Networks linking Genetic and Transcriptomic Screening data

ResponseNet v.3

:: DESCRIPTION

ResponseNet is a computational framework that identifies high-probability signaling and regulatory paths that connect input data sets.

::DEVELOPER

Yeger-Lotem Lab

:: SCREENSHOTS

ResponseNet

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2011 Jul;39(Web Server issue):W424-9. doi: 10.1093/nar/gkr359.
ResponseNet: revealing signaling and regulatory networks linking genetic and transcriptomic screening data.
Lan A, Smoly IY, Rapaport G, Lindquist S, Fraenkel E, Yeger-Lotem E.

Nucleic Acids Res. 2013 Jul;41(Web Server issue):W198-203. doi: 10.1093/nar/gkt532.
ResponseNet2.0: Revealing signaling and regulatory pathways connecting your proteins and genes–now with human data.
Basha O, Tirman S, Eluk A, Yeger-Lotem E.

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

rNAV 2.0 – Visual Exploration of sRNA mediated Regulatory Network

rNAV 2.0

:: DESCRIPTION

rNAV (for rna NAVigator), is a major evolution of rNAV, a tool for the visual exploration and analysis of bacterial sRNA-mediated regulatory networks. rNAV has been designed to help bioinformaticians and biologists to identify, from lists of thousands of predictions, pertinent and reasonable sRNA target candidates for carrying out experimental validations.

::DEVELOPER

Romain Bourqui

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

rNAV

:: MORE INFORMATION

Citation

Bourqui R, Dutour I, Dubois J, Benchimol W, Thébault P.
rNAV 2.0: a visualization tool for bacterial sRNA-mediated regulatory networks mining.
BMC Bioinformatics. 2017 Mar 23;18(1):188. doi: 10.1186/s12859-017-1598-8. PMID: 28335718; PMCID: PMC5364647.

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.

Inferelator 2015.08.05 – Genetic Regulatory Networks Inference algorithm

Inferelator 2015.08.05

:: DESCRIPTION

Inferelator learns parsimonious regulatory networks from systems biology datasets.

::DEVELOPER

Christoph Hafemeister

:: 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

ReNE 1.95 – A Cytoscape Plugin for Regulatory Network Enhancement

ReNE 1.95

:: 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

:: 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

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.

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

SEREND 1.1 – SEmi-supervised REgulatory Network Discoverer

SEREND 1.1

:: DESCRIPTION

SEREND is a semi-supervised learning method that uses a curated database of verified transcriptional factor-gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor-gene interactions, including whether the transcription factor activates or represses the gene.

::DEVELOPER

Jason Ernst Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/MacOsX/ Windows
  • Java

:: DOWNLOAD

 SEREND

:: MORE INFORMATION

Citation

Ernst J, Beg QK, Kay KA, Balazsi G, Oltvai ZN, Bar-Joseph Z.
A Semi-Supervised Method for Predicting Transcription Factor-Gene Interactions in Escherichia coli.
PLoS Computational Biology 4: e1000044, 2008.

Exit mobile version