IMHRC V1 – Inter-Module Hub Removal Clustering

IMHRC V1

:: DESCRIPTION

IMHRC is a graph clustering algorithm that is developed based on inter-module hub removal in the weighted graphs which can detect overlapped clusters. Due to these properties, it is especially useful for detecting protein complexes in protein-protein interaction (PPI) networks with associated confidence values.

::DEVELOPER

School of Biological Sciences, Iran

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux
  • Java

:: DOWNLOAD

IMHRC

:: MORE INFORMATION

Citation

Sci Rep. 2017 Jun 12;7(1):3247. doi: 10.1038/s41598-017-03268-w.
Discovering overlapped protein complexes from weighted PPI networks by removing inter-module hubs.
Maddi AMA, Eslahchi C

LRC – Logistic Regression Model and Clustering Approach

LRC

:: DESCRIPTION

LRC is a more appropriate epitope prediction method that is based on a combination of physicochemical and structural properties

:: DESCRIPTION

School of Biological Sciences, Iran

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux
  • MatLab

:: DOWNLOAD

 LRC

:: MORE INFORMATION

Citation

LRC: A new algorithm for prediction of conformational B-cell epitopes using statistical approach and clustering method.
Habibimy M, Bakhshi PK, Aghdam R.
J Immunol Methods. 2015 Oct 8. pii: S0022-1759(15)30048-X. doi: 10.1016/j.jim.2015.09.006.

OrthoClust – Orthology-based Network Framework for Clustering data across multiple Species

OrthoClust

:: DESCRIPTION

OrthoClust is a clustering algorithm built on a multilayer network framework. It concatenates networks from individual species by their orthology relationships, arriving at a multiplex network. By optimizing the a generalized modularity function, OrthoClust returns a set of modules that could be either conserved or species-specific.

::DEVELOPER

Gerstein Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

OrthoClust

:: MORE INFORMATION

Citation:

Genome Biol. 2014 Aug 28;15(8):R100. doi: 10.1186/gb-2014-15-8-r100.
OrthoClust: an orthology-based network framework for clustering data across multiple species.
Yan KK, Wang D, Rozowsky J, Zheng H, Cheng C, Gerstein M.

MGclus – Network Clustering Employing shared Neighbors

MGclus

:: DESCRIPTION

MGclus is a new algorithm designed to detect modules with a strongly interconnected neighborhood in large scale biological interaction networks.

:: DEVELOPER

Sonnhammer Bioinformatics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Java

 MGclus

:: MORE INFORMATION

Citation

Mol Biosyst. 2013 Jul;9(7):1670-5. doi: 10.1039/c3mb25473a. Epub 2013 Feb 11.
MGclus: network clustering employing shared neighbors.
Frings O, Alexeyenko A, Sonnhammer EL.

SPICi – Fast Biological Network Clustering Algorithm

SPICi

:: DESCRIPTION

SPICi (Speed and Performance In Clustering) is a fast local network clustering algorithm. SPICi runs in time O(Vlog V +E) and space O(E), where V and E are the number of vertices and edges in the network. It also has state-of the-art performance with respect to the quality of the clusters it uncovers.

::DEVELOPER

Mona Singh

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 SPICi

:: MORE INFORMATION

Citation

Bioinformatics. 2010 Apr 15;26(8):1105-11. doi: 10.1093/bioinformatics/btq078. Epub 2010 Feb 24.
SPICi: a fast clustering algorithm for large biological networks.
Jiang P1, Singh M.

SCSC – Cross-species Soft Clustering

 

SCSC

:: DESCRIPTION

SCSC (Soft Cross Species Clustering) clustering two species gene expression data, and identifies shared as well as species-specific co-expression modules.

::DEVELOPER

Zhong Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

SCSC

:: MORE INFORMATION

Citation

PLoS Comput Biol. 2010 Mar 12;6(3):e1000707. doi: 10.1371/journal.pcbi.1000707.
Modeling co-expression across species for complex traits: insights to the difference of human and mouse embryonic stem cells.
Cai J, Xie D, Fan Z, Chipperfield H, Marden J, Wong WH, Zhong S.

C3 – Correlation Clustering method for Cancer Mutation analysis

C3

:: DESCRIPTION

C3 (Cancer Correlation Clustering) identifies cancer mutation patterns from patient cohort by leveraging mutual exclusivity of mutations, patient coverage and driver network concentration principles.

::DEVELOPER

Ma Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Python

:: DOWNLOAD

C3

:: MORE INFORMATION

Citation

A new correlation clustering method for cancer mutation analysis.
Hou JP, Emad A, Puleo GJ, Ma J, Milenkovic O.
Bioinformatics. 2016 Dec 15;32(24):3717-3728.

MaxCluster – Protein Structure Comparison and Clustering

MaxCluster

:: DESCRIPTION

MaxCluster (MaxSub and Clustering) is a command-line tool for the comparison of protein structures. It provides a simple interface for a large number of common structure comparison tasks. A key feature of the program is the ability to process thousands of structures, either against a single reference protein or in an all-verses-all comparison.
::DEVELOPER

Alex Herbert , Structural Bioinformatics Group, Imperial College

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • GCC

:: DOWNLOAD

 MaxCluster

:: MORE INFORMATION

geneRxCluster 1.22.0 – Detect Differential Clustering of Genomic Sites

geneRxCluster 1.22.0

:: DESCRIPTION

geneRxCluster detects differential clustering of genomic sites such as gene therapy integrations.

::DEVELOPER

Charles Berry <ccberry at ucsd.edu>

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows / MacOsX
  • R
  • BioConductor

:: DOWNLOAD

 geneRxCluster

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Jun 1;30(11):1493-500. doi: 10.1093/bioinformatics/btu035. Epub 2014 Jan 30.
Comparing DNA integration site clusters with scan statistics.
Berry CC, Ocwieja KE, Malani N, Bushman FD.

ClustEval 1.6 – Integrative Clustering Evaluation Framework

ClustEval 1.6

:: DESCRIPTION

ClustEval is a free and extendable opensource platform for objective performance comparison of arbitrary Clustering Methods on different datasets.

::DEVELOPER

Baumbach lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • JRE

:: DOWNLOAD

 ClustEval

:: MORE INFORMATION

Citation

Guiding biomedical clustering with ClustEval.
Wiwie C, Baumbach J, Röttger R.
Nat Protoc. 2018 Jun;13(6):1429-1444. doi: 10.1038/nprot.2018.038.

Comparing the performance of biomedical clustering methods.
Wiwie C, Baumbach J, Röttger R.
Nat Methods. 2015 Nov;12(11):1033-8. doi: 10.1038/nmeth.3583.