Tag: Gene Regulatory Network

CABERNET 1.1 – A Cytoscape APP for Augmented Boolean Models of Gene Regulatory Networks

CABERNET 1.1

:: DESCRIPTION

CABERNET is a Cytoscape 3.2.0 app for the generation, the simulation, the analysis and the visualization of Boolean models of gene regulatory networks, particularly focused on the investigation of their robustness.

::DEVELOPER

Data and Computational Biology @ University of Milan – Bicocca.

:: SCREENSHOTS

CABERNET

::REQUIREMENTS

  • Linux/windows/MacOsX
  • Java
  • Cytoscape

:: DOWNLOAD

 CABERNET

:: MORE INFORMATION

Citation

CABeRNET: a Cytoscape app for augmented Boolean models of gene regulatory NETworks.
Paroni A, Graudenzi A, Caravagna G, Damiani C, Mauri G, Antoniotti M.
BMC Bioinformatics. 2016 Feb 4;17(1):64. doi: 10.1186/s12859-016-0914-z.

Netview – Explore the Human and Mouse Gene Regulatory Networks

Netview

:: DESCRIPTION

Netview is a free web tool that allow end users to explore the human and mouse gene regulatory networks. Users can query the system by providing a gene identifier and the boundaries of the subnetwork being explored. The system provides the list of interactions, the enriched Gene Ontology Terms together with a graphical representation of the subnetwork.

::DEVELOPER

di Bernardo Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Methods Mol Biol. 2014;1101:179-96. doi: 10.1007/978-1-62703-721-1_10.
Reverse engineering transcriptional gene networks.
Belcastro V, di Bernardo D.

PTHGRN – Post-translational Hierarchal Gene Regulatory Network

PTHGRN

:: DESCRIPTION

PTHGRN is able to unravel interrelationships among PTMs, TFs, epigenetic modifications and gene expression and to reconstruct hierarchical gene regulatory networks underlying biological functions.

::DEVELOPER

Systems bioinformatics lab , Hong Kong Baptist University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

PTHGRN: unraveling post-translational hierarchical gene regulatory networks using PPI, ChIP-seq and gene expression data.
Guan D, Shao J, Zhao Z, Wang P, Qin J, Deng Y, Boheler KR, Wang J, Yan B.
Nucleic Acids Res. 2014 Jul;42(Web Server issue):W130-6. doi: 10.1093/nar/gku471. Epub 2014 May 29

NIMEFI – Gene Regulatory Network inference using Multiple Ensemble Feature Importance Algorithms

NIMEFI

:: DESCRIPTION

NIMEFI is an R package of gene regulatory network inference using multiple ensemble feature importance algorithms.

::DEVELOPER

Joeri Ruyssinck Email: joeri.ruyssinck@intec.ugent.be

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • R package

:: DOWNLOAD

 NIMEFI

:: MORE INFORMATION

Citation:

PLoS One. 2014 Mar 25;9(3):e92709. doi: 10.1371/journal.pone.0092709. eCollection 2014.
NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.
Ruyssinck J, Huynh-Thu VA, Geurts P, Dhaene T, Demeester P, Saeys Y

GENIE3 – Inference of Gene Regulatory Networks from Expression data

GENIE3

:: DESCRIPTION

GENIE3 is an algorithm for the inference of gene regulatory networks from expression data. It decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link.

dynGENIE3 : Extension of GENIE3 for time series data

::DEVELOPER

vân anh huynh-thu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • MatLab / R Package

:: DOWNLOAD

 GENIE3 / dynGENIE3

:: MORE INFORMATION

Citation

Huynh-Thu VA, Geurts P.
dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data.
Sci Rep. 2018 Feb 21;8(1):3384. doi: 10.1038/s41598-018-21715-0. PMID: 29467401; PMCID: PMC5821733.

Inferring regulatory networks from expression data using tree-based methods
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., and Geurts, P.
PLoS ONE, 5(9):e12776, 2010.

NARROMI – Inferring Gene Regulatory Networks from Gene Expression data

NARROMI

:: DESCRIPTION

NARROMI is a MATLAB program for inferring gene regulatory networks from gene expression data. It is a novel method combining ordinary differential equation based recursive optimization (RO) and information-theory based mutual information (MI).

::DEVELOPER

Zhao Group at the Tongji University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux / Mac OsX
  • MatLab

:: DOWNLOAD

 NARROMI

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Jan 1;29(1):106-13. doi: 10.1093/bioinformatics/bts619. Epub 2012 Oct 18.
NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference.
Zhang X1, Liu K, Liu ZP, Duval B, Richer JM, Zhao XM, Hao JK, Chen L.

CMI2NI – Inferring Gene Regulatory Networks from Gene Expression data

CMI2NI

:: DESCRIPTION

CMI2NI (CMI2-based network inference) is a software for inferring gene regulatory networks from gene expression data. It is a novel method using a new proposed concept of Conditional Mutual Inclusive Information (CMI2) which can accurately measure direct dependences between genes. Given the small size samples of gene expression data, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the dependence or regulation strength between genes.

::DEVELOPER

ZhaoGroup at the Tongji University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux / Mac OsX
  • MatLab

:: DOWNLOAD

  CMI2NI

:: MORE INFORMATION

Citation

Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks.
Zhang X, Zhao J, Hao JK, Zhao XM, Chen L.
Nucleic Acids Res. 2014 Dec 24. pii: gku1315.

BNArray 1.0 – Constructing Gene Regulatory Networks from Microarray data by using Bayesian network

BNArray 1.0

:: DESCRIPTION

BNArray is a systemized tool developed in R. It facilitates the construction of gene regulatory networks from DNA microarray data by using Bayesian network. Significant submodules of regulatory networks with high confidence are reconstructed using our extended sub-network mining algorithm for directed graphs.

::DEVELOPER

Ming Chen’s Bioinformatics Group, Zhejiang University.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux/MacOsX
  • R package

:: DOWNLOAD

 BNArray

:: MORE INFORMATION

Citation

Bioinformatics. 2006 Dec 1;22(23):2952-4.
BNArray: an R package for constructing gene regulatory networks from microarray data by using Bayesian network.
Chen X, Chen M, Ning K.

PathRNet – Robust Inference of the Context Specific Structure and Temporal Dynamics of Gene Regulatory Network

PathRNet

:: DESCRIPTION

A novel network model PathRNet and a reconstruction approach PATTERN are proposed for reconstructing the context specific time varying regulatory networks by integrating microarray gene expression profiles and existing knowledge of pathways and transcription factors.

::DEVELOPER

PathRNet Team

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux/ Windows/MacOsX
  • MatLab

:: DOWNLOAD

 PathRNet

:: MORE INFORMATION

Citation

BMC Genomics. 2010 Dec 1;11 Suppl 3:S11. doi: 10.1186/1471-2164-11-S3-S11.
Robust inference of the context specific structure and temporal dynamics of gene regulatory network.
Meng J1, Lu M, Chen Y, Gao SJ, Huang Y.

mirConnX – Analysis of mRNA and microRNA (miRNA) Gene Regulatory Networks

mirConnX

:: DESCRIPTION

mirConnX is a user-friendly web interface for inferring, displaying and parsing mRNA and microRNA (miRNA) gene regulatory networks. mirConnX combines sequence information, and computational predictions with gene expression data analysis to create a disease-specific, genome-wide regulatory network.

::DEVELOPER

Benos Lab

 SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2011 Jul;39(Web Server issue):W416-23. doi: 10.1093/nar/gkr276. Epub 2011 May 10.
mirConnX: condition-specific mRNA-microRNA network integrator.
Huang GT, Athanassiou C, Benos PV.

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