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.

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

:: 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.

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

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

ResponseNet 2.0 – Revealing Signaling and Regulatory Networks linking Genetic and Transcriptomic Screening data

ResponseNet 2.0

:: DESCRIPTION

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

::DEVELOPER

Yeger-Lotem Lab

:: SCREENSHOTS

:: 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.

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.