PathExpand – Extending Cellular Pathways in Interaction Networks

PathExpand

:: DESCRIPTION

PathExpand is a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and expanding them to include densely interconnected interaction partners.

::DEVELOPER

PathExpand team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

E. Glaab, A. Baudot, N. Krasnogor, A. Valencia
Extending pathways and processes using molecular interaction networks to analyse cancer genome data
in BMC Bioinformatics, 11(1):597, 2010

DynaSIN – The Dynamic Structure Interaction Network

DynaSIN

:: DESCRIPTION

DynaSIN is a resource for studying protein-protein interaction networks in the context of conformational changes.

::DEVELOPER

Gerstein Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

NO

:: MORE INFORMATION

Citation:

Protein Sci. 2011 Oct;20(10):1745-54. doi: 10.1002/pro.710.
Integration of protein motions with molecular networks reveals different mechanisms for permanent and transient interactions.
Bhardwaj N1, Abyzov A, Clarke D, Shou C, Gerstein MB.

GASOLINE 2.0 – Optimal Local multiple Alignment of Interaction NEtworks

GASOLINE 2.0

:: DESCRIPTION

GASOLINE (Greedy and Stochastic Algorithm for Optimal Local alignment of Interaction Networks) is an algorithm for multiple local network alignment based on statistical iterative sampling in connection to a greedy strategy. GASOLINE can produce biologically significant alignments in reasonable running time, even for very large input instances.

::DEVELOPER

Giovanni Micale

:: SCREENSHOTS

GASOLINE

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • CytoScape
  • JRE

:: DOWNLOAD

 GASOLINE

:: MORE INFORMATION

Citation:

Micale G, Pulvirenti A, Giugno R, Ferro A (2014)
GASOLINE: a Greedy And Stochastic algorithm for Optimal Local multiple alignment of Interaction NEtworks.
PLoS ONE 9(6): e98750. doi: 10.1371/journal.pone.0098750

MATISSE 1.1 – Detection of Functional Modules using Interaction Networks and Expression data

MATISSE 1.1

:: DESCRIPTION

MATISSE (Module Analysis via Topology of Interactions and Similarity SEts) is a program for detection of functional modules using interaction networks and expression data. A functioncal module is a group of cellular components and their interactions that can be attributed a specific biological function.

::DEVELOPER

Ron Shamir’s lab

:: SCREENSHOTS

::REQUIREMENTS

  • Windows/Linux
  • Java

:: DOWNLOAD

 MATISSE

:: MORE INFORMATION

Citation

Identification of functional modules using network topology and high-throughput data
I. Ulitsky and R. Shamir
BMC Systems Biology, Vol. 1, No. 8 (2007)

TRRUST v2 – Curated TF-target Interaction Networks in Human and Mouse

TRRUST v2

:: DESCRIPTION

TRRUST (Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining) is a manually curated database of human and mouse transcriptional regulatory networks.

::DEVELOPER

Network Biomedicine Laboratory at Yonsei University, Korea

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions.
Han H, et al.
Nucleic Acids Res. 2018 Jan 4;46(D1):D380-D386. doi: 10.1093/nar/gkx1013.

CellWhere 2019.10 – Graphical Display of Interaction Networks organized on Subcellular Localizations

CellWhere 2019.10

:: DESCRIPTION

CellWhere is a data combining and visualization tool that enables bench researchers to quickly explore the reported subcellular locations of a list of genes/proteins, and to put these subcellular locations into the context of previously identified physical interactions that could be occurring between these proteins and others within the cell.

::DEVELOPER

CellWhere team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • WEb browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

CellWhere: graphical display of interaction networks organized on subcellular localizations.
Zhu L, Malatras A, Thorley M, Aghoghogbe I, Mer A, Duguez S, Butler-Browne G, Voit T, Duddy W.
Nucleic Acids Res. 2015 Apr 16. pii: gkv354.

CIPHER – Correlating Protein Interaction Network and Phenotype Network to predict Disease Genes

CIPHER

:: DESCRIPTION

CIPHER, which stands for Correlating interactome and phenome networks to predict disease genes, is a computational framework we proposed to prioritize human disease genes. It was one of the first studies to explore interactome-phenome wide gene-disease relationships, and generated the first comprehensive genetic landscape of human disease,connecting 5080 human disease phenotypes with 14433 human genes.

::DEVELOPER

Wu Lab @ Columbia

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • MatLab

:: DOWNLOAD

NO

 :: MORE INFORMATION

Citation:

Mol Syst Biol. 2008;4:189. doi: 10.1038/msb.2008.27. Epub 2008 May 6.
Network-based global inference of human disease genes.
Wu X1, Jiang R, Zhang MQ, Li S.

PyInteraph 1.0 – Analysis of Interaction Networks in Structural Ensembles of Proteins

PyInteraph 1.0

:: DESCRIPTION

The PyInteraph software suite is a package designed to analyze intra- or inter-molecular interactions in structural ensembles derived both from molecular simulations or experiments (i.e. NMR-based structural ensembles)

::DEVELOPER

PyInteraph team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

 PyInteraph

 :: MORE INFORMATION

Citation

J Chem Inf Model. 2014 Apr 17. [Epub ahead of print]
PyInteraph: A Framework for the Analysis of Interaction Networks in Structural Ensembles of Proteins.
Tiberti M1, Invernizzi G, Lambrughi M, Inbar Y, Schreiber G, Papaleo E.

GAIN 0.2.0 / webGAIN – Genetic Association Interaction Network tool

GAIN 0.2.0 / webGAIN

:: DESCRIPTION

GAIN is based on interaction information between three attributes; in this case, between two single nucleotide polymporphisms (SNPs) and a class or phenotype attribute. Interaction information is the gain in phenotype information obtained by considering SNP A and SNP B jointly beyond the phenotype information that would be gained by considering SNPs A and B independently.

GAIN can be combined with SNPrank for a powerful analysis engine.

webGAIN is a web-based version of the Genetic Association Interaction Network (GAIN) tool

::DEVELOPER

Insilico Research Group (McKinney Laboratory for Bioinformatics and In Silico Modeling)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Python

:: DOWNLOAD

 GAIN

:: MORE INFORMATION

Citation

PLoS Genet. 2009 Mar;5(3):e1000432. doi: 10.1371/journal.pgen.1000432. Epub 2009 Mar 20.
Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis.
McKinney BA1, Crowe JE, Guo J, Tian D.

iBIG 1.0 – Building and Visualizing Interaction Network of Gene

iBIG 1.0

:: DESCRIPTION

iBIG is a web-based tool for building and visualizing interaction network of gene and gene set.

::DEVELOPER

Lei Hongxing Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Genomics Proteomics Bioinformatics. 2013 Jun;11(3):166-71. doi: 10.1016/j.gpb.2012.08.007. Epub 2013 Jan 4.
iBIG: an integrative network tool for supporting human disease mechanism studies.
Sun J1, Pan Y, Feng X, Zhang H, Duan Y, Lei H.