Sets2Networks – Network Inference from Repeated Observations of Sets

Sets2Networks

:: DESCRIPTION

Sets2Networks (S2N) is general method for network inference from repeated observations of sets of related entities. Given experimental observations of sets of related entities, S2N infers the underlying network of binary interactions between these entities by generating an ensemble of networks consistent with the data; the frequency of occurrence of a given interaction throughout this ensemble is interpreted as the probability that the interaction is present in the underlying real network.

::DEVELOPER

Ma’ayan Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Syst Biol. 2012 Jul 23;6:89. doi: 10.1186/1752-0509-6-89.
Sets2Networks: network inference from repeated observations of sets.
Clark NR, Dannenfelser R, Tan CM, Komosinski ME, Ma’ayan A.

GeneNetWeaver 3.1.3 Beta – in silico benchmark Generation and performance profiling of Network Inference Methods

GeneNetWeaver 3.1.3 Beta

:: DESCRIPTION

GeneNetWeaver (GNW) is an open-source tool for in silico benchmark generation and performance profiling of network inference methods.

::DEVELOPER

GeneNetWeaver team

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Java

:: DOWNLOAD

 GeneNetWeaver

:: MORE INFORMATION

Citation:

Schaffter, T. et al. (2011).
GeneNetWeaver: In silico benchmark generation and performance profiling of network inference methods.
Bioinformatics, 27(16):2263-70

GEMINI – Gene Expression and Metabolism Integrated for Network Inference

GEMINI

:: DESCRIPTION

GEMINI produces a regulatory network that is simultaneously consistent with observed gene knockout phenotypes, gene expression data, and the corresponding metabolic network.

::DEVELOPER

The Hood-Price Lab for Systems Biomedicine

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ WIndows/ MacOsX
  • Matlab

:: DOWNLOAD

GEMINI

:: MORE INFORMATION

SpliceNet 1.0 – Splicing Isoform Specific Differential Network Inference from RNA-Seq data

SpliceNet 1.0

:: DESCRIPTION

SpliceNet is a method to infer isoform-specific co-expression networks from exon-level RNA-Seq data, using large dimensional trace.

::DEVELOPER

JJWang Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOS
  • R

:: DOWNLOAD

SpliceNet

:: MORE INFORMATION

Citation

SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples.
Yalamanchili HK, Li Z, Wang P, Wong MP, Yao J, Wang J.
Nucleic Acids Res. 2014 Nov 1;42(15):e121. doi: 10.1093/nar/gku577.

DDGni – Gapped Alignment based Network Inference

DDGni

:: DESCRIPTION

DDGni (dynamic delay gene-network inference) is a novel gene network inference algorithm based on the gapped local alignment of gene expression profiles.

::DEVELOPER

Bioinformatics Group, HKU Department of Biochemistry

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOS
  • Perl

:: DOWNLOAD

DDGni

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Feb 1;30(3):377-83. doi: 10.1093/bioinformatics/btt692. Epub 2013 Nov 27.
DDGni: dynamic delay gene-network inference from high-temporal data using gapped local alignment.
Yalamanchili HK1, Yan B, Li MJ, Qin J, Zhao Z, Chin FY, Wang J.

Cyni 1.0.0.beta6 – Cytoscape Network Inference Toolbox

Cyni 1.0.0.beta6

:: DESCRIPTION

Cyni (Cytoscape Network Inference Toolbox) is a new Cytoscape App that puts together several tools that allow infering networks from biological data. Each of the tools can be used independently or together to perform several tasks.

::DEVELOPER

Systems Biology Lab, Institut Pasteur, Paris

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Java
  • Cytoscape

:: DOWNLOAD

 Cyni

:: MORE INFORMATION

Citation

The Cyni framework for network inference in Cytoscape.
Guitart-Pla O, Kustagi M, Rügheimer F, Califano A, Schwikowski B.
Bioinformatics. 2014 Dec 18. pii: btu812.

NIMOO – Network-Inference with Multi Objective Optimization

NIMOO

:: DESCRIPTION

NIMOO: Inference of Gene Regulatory Networks By Integrating Multiple Data Sources: A Multi-Objective Optimisation Approach(NI_MOO)

::DEVELOPER

Integrative Genomics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOSX
  • MatLab

:: DOWNLOAD

 NIMOO

:: MORE INFORMATION

Citation

BMC Syst Biol. 2011 Apr 13;5:52. doi: 10.1186/1752-0509-5-52.
A computational framework for gene regulatory network inference that combines multiple methods and datasets.
Gupta R1, Stincone A, Antczak P, Durant S, Bicknell R, Bikfalvi A, Falciani F.

NIR – Network Inference by Reverse-engineering

NIR

:: DESCRIPTION

In order to estimate the coefficient of the gene interactions NIR solves a linear regression problem for each gene considering a fixed number of k regressors. The regressor set is chosen according the residual sum of square error (RSS) minimization criterion. In this version, NIR exhaustively searches the best regressors in the space of all the possible k-tuples of genes.

::DEVELOPER

di Bernardo Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • MatLab / OCTAVE

:: DOWNLOAD

 NIR

:: MORE INFORMATION

Citation

Pac Symp Biocomput. 2004:486-97.
Robust identification of large genetic networks.
Di Bernardo D, Gardner TS, Collins JJ.

ebdbNet 1.2.3 – Empirical Bayes Dynamic Bayesian Network Inference

ebdbNet 1.2.3

:: DESCRIPTION

ebdbNet is an R package for reverse-engineering directed gene regulatory networks from time-course gene expression data using Dynamic Bayesian networks (DBN) and empirical Bayes methodology.

::DEVELOPER

Andrea Rau

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX/ Windows
  • R package

:: DOWNLOAD

 ebdbNet

:: MORE INFORMATION

Citation

Andrea Rau, Florence Jaffrézic, Jean-Louis Foulley, and R. W. Doerge.
An empirical Bayesian method for estimating biological networks from temporal microarray data.
Statistical Applications in Genetics and Molecular Biology 9: 1-28.

NETI 1.2 – Network Inference

NETI 1.2

:: DESCRIPTION

NETI (Network Inference) implements the generalized additive regulation model with adjustable kernel functions for network inference from time series data.

::DEVELOPER

Eugene Novikov and Emmanuel Barillot @ Bioinformatics Laboratory of Institut Curie (Paris).

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

  NETI

:: MORE INFORMATION

Citation:

Eugene Novikov and Emmanuel Barillot
Regulatory network reconstruction using an integral additive model with flexible kernel functions
BMC Systems Biology 2008, 2:8