REDUCE 1.0 – Optimal Design of Gene Knock-out (KO) for the purpose of Gene Regulatory Network (GRN) Inference

REDUCE 1.0

:: DESCRIPTION

REDUCE (REDuction of UnCertain Edges) is an algorithm for finding the optimal gene KO experiment for inferring directed graphs (digraphs) of gene regulatory network (GRN).

:: DEVELOPER

Chemical and Biological Systems Engineering Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / windows/ MacOsX
  • MatLab

:: DOWNLOAD

 REDUCE

:: MORE INFORMATION

Citation

Optimal design of gene knock-out experiments for gene regulatory network inference.
Ud-Dean SM, Gunawan R.
Bioinformatics. 2015 Nov 14. pii: btv672

CN – Inferring Gene Regulatory Networks using SORDER algorithm

CN

:: DESCRIPTION

CN (Consensus Network) is a network inference method based on the SORDER algorithm and a suitable interval threshold for Conditional Mutual Information (CMI) tests

::DEVELOPER

School of Biological Sciences, Iran

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux / MacOsX
  • MatLab

:: DOWNLOAD

 CN

:: MORE INFORMATION

Citation

CN: a consensus algorithm for inferring gene regulatory networks using the SORDER algorithm and conditional mutual information test.
Aghdam R, Ganjali M, Zhang X, Eslahchi C.
Mol Biosyst. 2015 Mar;11(3):942-9. doi: 10.1039/c4mb00413b

IPCA-CMI – Inferring Gene Regulatory Networks based on Combination of PCA-CMI and MIT score

IPCA-CMI

:: DESCRIPTION

IPCA-CMI is an algorithm for inferring gene regulatory networks based on a combination of PCA-CMI and MIT score.

::DEVELOPER

School of Biological Sciences, Iran

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux / MacOsX
  • MatLab

:: DOWNLOAD

 IPCA-CMI

:: MORE INFORMATION

Citation

PLoS One. 2014 Apr 11;9(4):e92600. doi: 10.1371/journal.pone.0092600. eCollection 2014.
IPCA-CMI: an algorithm for inferring gene regulatory networks based on a combination of PCA-CMI and MIT score.
Aghdam R1, Ganjali M1, Eslahchi C

LDGM – Identifying Gene Regulatory Network Rewiring using Latent Differential Graphical Models

LDGM

:: DESCRIPTION

LDGM estimates differential network between two tissue types directly without inferring the network for individual tissues.

::DEVELOPER

Ma Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Matlab

:: DOWNLOAD

LDGM

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2016 Sep 30;44(17):e140. doi: 10.1093/nar/gkw581.
Identifying gene regulatory network rewiring using latent differential graphical models.
Tian D, Gu Q, Ma J.

ModEnt – Reconstructing Gene Regulatory Networks

ModEnt

:: DESCRIPTION

ModEnt is a computational tool that reconstructs gene regulatory networks from high throughput experimental data.

::DEVELOPER

Ron Shamir’s lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Windows/Linux

:: DOWNLOAD

 ModEnt

:: MORE INFORMATION

Citation

Karlebach, G. and Shamir, R.,
Constructing logical models of gene regulatory networks by integrating transcription factor-DNA interactions with expression data: an entropy based approach.
Journal of Computational Biology.2012 Jan;19(1):30-41.

ChIP-Array 2 – Integrating multiple Omics data to construct Gene Regulatory Networks

ChIP-Array 2

:: DESCRIPTION

ChIP-Array integrates additional types of omics data including long-range chromatin interaction, open chromatin region and histone modification data to dissect more comprehensive GRNs involving diverse regulatory components.

::DEVELOPER

JJWang Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web  browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

ChIP-Array 2: integrating multiple omics data to construct gene regulatory networks.
Wang P, Qin J, Qin Y, Zhu Y, Wang LY, Li MJ, Zhang MQ, Wang J.
Nucleic Acids Res. 2015 Apr 27. pii: gkv398.

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

the Bimib Lab  – University of Milano – 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.

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.

GeNOSA 1.2.13 – Integrated Platform for Gene Regulatory Networks

GeNOSA 1.2.13

:: DESCRIPTION

GeNOSA included several tools to perform reconstruction of gene regulatory networks from microarray data and connectivity information.

::DEVELOPER

Intelligent Computing Lab. 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux/ MacOsX

:: DOWNLOAD

 GeNOSA

:: MORE INFORMATION

Citation

GeNOSA: inferring and experimentally supporting quantitative gene regulatory networks in prokaryotes.
Chen YH, Yang CD, Tseng CP, Huang HD, Ho SY.
Bioinformatics. 2015 Feb 24. pii: btv075.

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

ZhaoGroup 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.