EhecRegNet v2 – Database of Pathogenic E. coli Transcriptional Gene Regulatory Networks

EhecRegNet v2

:: DESCRIPTION

EhecRegNet is a database of gene regulations conserved between E. coli K12 and human pathogenic EHEC strains.

::DEVELOPER

Baumbach lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Integr Biol (Camb). 2012 Jul;4(7):728-33. doi: 10.1039/c2ib00132b.
On the trail of EHEC/EAEC–unraveling the gene regulatory networks of human pathogenic Escherichia coli bacteria.
Pauling J1, Röttger R, Neuner A, Salgado H, Collado-Vides J, Kalaghatgi P, Azevedo V, Tauch A, Pühler A, Baumbach J.

jump3 – Inference of Gene Regulatory Networks

jump3

:: DESCRIPTION

Jump3 is based on a formal on/off model of gene expression, but uses a non-parametric procedure based on decision trees (called “jump trees”) to reconstruct the GRN topology, allowing the inference of networks of hundreds of genes.

::DEVELOPER

vân anh huynh-thu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • MatLab / R Package

:: DOWNLOAD

 jump3

:: MORE INFORMATION

Citation

Combining tree-based and dynamical systems for the inference of gene regulatory networks.
Huynh-Thu VA, Sanguinetti G.
Bioinformatics. 2015 Jan 7. pii: btu863.

cGRNB 1.0 – combinatorial Gene Regulatory Networks Builder

cGRNB 1.0

:: DESCRIPTION

cGRNB is a web server for constructing combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets.

::DEVELOPER

Shanghai Center for Biofinformation Technology 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • R
  • Perl

:: DOWNLOAD

 cGRNB

:: MORE INFORMATION

Citation

BMC Syst Biol. 2013;7 Suppl 2:S7. doi: 10.1186/1752-0509-7-S2-S7. Epub 2013 Oct 14.
cGRNB: a web server for building combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets.
Xu H, Yu H, Tu K, Shi Q, Wei C, Li YY, Li YX.

OKVAR-Boost 1.0 – Infer Nonlinear Dynamics and Interactions in Gene Regulatory Networks

OKVAR-Boost 1.0

:: DESCRIPTION

OKVAR-Boost is a flexible boosting algorithm that shares features from L2-boosting and randomization-based algorithms is developed to perform the tasks of parameter learning and network inference for the proposed model.

::DEVELOPER

AROBAS group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • MatLab

:: DOWNLOAD

 OKVAR-Boost

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Jun 1;29(11):1416-23. doi: 10.1093/bioinformatics/btt167. Epub 2013 Apr 10.
OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks.
Lim N, Senbabaoglu Y, Michailidis G, d’Alché-Buc F.

CMGRN – Constructing Multilevel Gene Regulatory Networks

CMGRN

:: DESCRIPTION

CMGRN is a web server for constructing multilevel gene regulatory networks using ChIP-seq and gene expression data.

::DEVELOPER

Systems bioinformatics lab , Hong Kong Baptist University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Jan 21.
CMGRN: a web server for constructing multilevel gene regulatory networks using ChIP-seq and gene expression data.
Guan D1, Shao J, Deng Y, Wang P, Zhao Z, Liang Y, Wang J, Yan B.

GESTODifferent 1.0 – Cytoscape plugin for the Identification of Boolean Gene Regulatory Networks describing the stochastic differentiation process

GESTODifferent 1.0

:: DESCRIPTION

GESTODifferent is a plugin for Cytoscape for the identification of Boolean gene regulatory networks describing the stochastic differentiation process.

::DEVELOPER

Silvia Crippa, under the supervision of Giulio CaravagnaAlex Graudenzi and Marco Antoniotti

:: SCREENSHOTS

::REQUIREMENTS

  • Linux/ Windows/MacOsX
  • Java
  • Cytoscape

:: DOWNLOAD

  GESTODifferent

:: MORE INFORMATION

Citation

Villani M, Barbieri A, Serra R (2011)
A Dynamical Model of Genetic Networks for Cell Differentiation.
PLoS ONE 6(3): e17703. doi:10.1371/journal.pone.0017703″.