ICSearcher – Identifying Protein Complexes in PPI networks

ICSearcher

:: DESCRIPTION

ICSearcher is a tool which is based on BMM(the algorithm Based on the new Modularity function for Merging modules) for identifying protein complexes.

::DEVELOPER

NClab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java

:: DOWNLOAD

ICSearcher

:: MORE INFORMATION

 

CPL – An approach to identify Protein Complexes

CPL

:: DESCRIPTION

CPL is a graph clustering software, which is designed to detect protein complexes in protein-protein network (PPI). It detects the complexes by propagating labels, which is to simulate the interacting activities of proteins.

::DEVELOPER

NClab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java

:: DOWNLOAD

CPL

:: MORE INFORMATION

Citation

Journal of Computer Science and Technology November 2014, Volume 29, Issue 6, pp 1083–1093
CPL: Detecting Protein Complexes by Propagating Labels on Protein-Protein Interaction Network
Qi-Guo DaiMao-Zu GuoEmail authorXiao-Yan LiuZhi-Xia TengChun-Yu Wang

PLSMC – Identifying Protein Complexes in PPI Network

PLSMC

:: DESCRIPTION

PLSMC is a novel algorithm based on a penalized least square method to detect complex in PPI network. PLSMC is to minimize the distances between the interaction and co-complex of protein pairs.

::DEVELOPER

NClab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java

:: DOWNLOAD

PLSMC

:: MORE INFORMATION

Citation

A least square method based model for identifying protein complexes in protein-protein interaction network.
Dai Q, Guo M, Guo Y, Liu X, Liu Y, Teng Z.
Biomed Res Int. 2014;2014:720960. doi: 10.1155/2014/720960.

PCIA – Identify Protein Complexes in Protein-protein Interaction Networks

PCIA

:: DESCRIPTION

PCIA is an algorithm proposed to identify protein complexes in protein-protein interaction networks. It can make use of both topological and attribute information to identify protein complexes more efficiently.

::DEVELOPER

Allen, Lun Hu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux
  • JRE

:: DOWNLOAD

 PCIA

:: MORE INFORMATION

Citation

Allen L. Hu and Keith C. C. Chan,
Ultizing both topological and attribute informatin for protein complex identification in PPI Networks,
IEEE TCBB, vol. 10(3), pp. 780-792, 2013

CoAch – COre-AttaCHment based Complex Mining

CoAch

:: DESCRIPTION

CoAch: A Core-Attachment based Method to Detect Protein Complexes in PPI Networks

::DEVELOPER

Xiaoli Li, PhD

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  •  Windows

:: DOWNLOAD

 CoAch

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2009 Jun 2;10:169. doi: 10.1186/1471-2105-10-169.
A core-attachment based method to detect protein complexes in PPI networks.
Wu M1, Li X, Kwoh CK, Ng SK.

ComplexCorr – Predict the Connectivity of Subunits within large Protein Complexes

ComplexCorr

:: DESCRIPTION

With ComplexCorr you can predict the connectivity of subunits within large protein complexes.

::DEVELOPER

Department of Genome Oriented Bioinformatics, Technische Universität München

:: SCREENSHOTS

N/a

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

J Mol Biol. 2010 Nov 19;404(1):158-71. doi: 10.1016/j.jmb.2010.09.029. Epub 2010 Sep 22.
Coevolution predicts direct interactions between mtDNA-encoded and nDNA-encoded subunits of oxidative phosphorylation complex i.
Gershoni M1, Fuchs A, Shani N, Fridman Y, Corral-Debrinski M, Aharoni A, Frishman D, Mishmar D.

TSN-PCD / DFM-CIN – Identify Protein Complexes / Functional Modules from these TSNs

TSN-PCD / DFM-CIN

:: DESCRIPTION

The algorithm TSN-PCD was developed to identify protein complexes from these TSNs (time-sequenced subnetworks).

DFM-CIN is proposed to discover functional modules based on the identified complexes.

::DEVELOPER

Min Li (limin@mail.csu.edu.cn)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Java

:: DOWNLOAD

 TSN-PCD / DFM-CIN

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2012 May 23;13:109. doi: 10.1186/1471-2105-13-109.
Towards the identification of protein complexes and functional modules by integrating PPI network and gene expression data.
Li M1, Wu X, Wang J, Pan Y.

PLW – Detecting Protein Complexes from Protein Interaction Networks

PLW

:: DESCRIPTION

PLW (Probabilistic Local Walks) is a graph clustering algorithm, and was designed to detect protein complexes in protein-protein interaction (PPI) networks with high accuracy and efficiency.

::DEVELOPER

PLW team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX

:: DOWNLOAD

 PLW

:: MORE INFORMATION

Citation

BMC Genomics. 2013;14 Suppl 5:S15. doi: 10.1186/1471-2164-14-S5-S15. Epub 2013 Oct 16.
PLW: Probabilistic Local Walks for detecting protein complexes from protein interaction networks.
Wong D, Li XL, Wu M, Zheng J, Ng SK.

ClusterONE 1.0 – Detecting Overlapping Protein Complexes in Protein-protein Interaction Networks

ClusterONE 1.0

:: DESCRIPTION

ClusterONE (Clustering with Overlapping Neighborhood Expansion) is a graph clustering algorithm that is able to handle weighted graphs and readily generates overlapping clusters. Owing to these properties, it is especially useful for detecting protein complexes in protein-protein interaction networks with associated confidence values.

::DEVELOPER

Paccanaro Lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Windows/ Linux / MacOsX
  • Java

:: DOWNLOAD

 ClusterONE

:: MORE INFORMATION

Citation

Nat Methods. 2012 Mar 18;9(5):471-2. doi: 10.1038/nmeth.1938.
Detecting overlapping protein complexes in protein-protein interaction networks.
Nepusz T1, Yu H, Paccanaro A.

PEWCC – Identify Protein Complexes from PPI

PEWCC

:: DESCRIPTION

PEWCC is a novel graph mining algorithm to identify protein complexes from protein-protein interaction data.

::DEVELOPER

Nazar Zaki , Bioinformatics Laboratory, UAE University.

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Windows / Linux/ MacOsX
  • Python

:: DOWNLOAD

 PEWCC

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2013 May 20;14:163. doi: 10.1186/1471-2105-14-163.
Protein complex detection using interaction reliability assessment and weighted clustering coefficient.
Zaki N, Efimov D, Berengueres J.