NNN 1.01 – Nearest Neighbor Networks Clustering

NNN 1.01

:: DESCRIPTION

NNN (Nearest Neighbor Networks) is a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods.

::DEVELOPER

NNN team

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • java

:: DOWNLOAD

 NNN

:: MORE INFORMATION

Citation

Huttenhower, C., Flamholz, A., Landis, J., Sahi, S., Myers, C., Olszewski, K., Hibbs, M., Siemers, N., Troyanskaya, O., Coller, H.,
Nearest Neighbor Networks: Clustering Expression Data Based on Gene Neighborhoods“,
BMC Bioinformatics 8:250, 2007

JustClust – Analysing Biological Data with Cluster Analysis

JustClust

:: DESCRIPTION

JustClust is a tool for analysing biological data with cluster analysis. JustClust can handle many formats of data and cluster the data with many state-of-the-art techniques. The aim of JustClust is to provide an easy-to-use application which can perform any analysis on any data.

::DEVELOPER

Paccanaro Lab

:: SCREENSHOTS

::REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Java

:: DOWNLOAD

 JustClust

:: MORE INFORMATION

SCPS 0.9.8 – Spectral Clustering of Protein Sequences

SCPS 0.9.8

:: DESCRIPTION

SCPS is an efficient, user-friendly, scalable and multi-platform implementation of a spectral clustering method for clustering homologous proteins. SCPS also implements connected component analysis and hierarchical clustering, integrates TribeMCL and interfaces with external tools such as Cytoscape and NCBI BLAST.

::DEVELOPER

Paccanaro Lab

:: SCREENSHOTS

::REQUIREMENTS

  • Windows/ Linux / MacOsX

:: DOWNLOAD

 SCPS

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2010 Mar 9;11:120. doi: 10.1186/1471-2105-11-120.
SCPS: a fast implementation of a spectral method for detecting protein families on a genome-wide scale.
Nepusz T1, Sasidharan R, Paccanaro A.

SiLiX 1.2.11 – Ultra-fast Sequence Clustering from Similarity Networks

SiLiX 1.2.11

:: DESCRIPTION

The software package SiLiX (SIngle LInkage Clustering of Sequences) implements a new algorithm for the clustering of homologous sequences, based on single transitive links (single linkage) with alignment coverage constraints.

::DEVELOPER

Laboratoire de Biométrie et Biologie évolutive

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Mac / Linux
  • C++ Compiler

:: DOWNLOAD

 SiLiX

:: MORE INFORMATION

Citation:

Ultra-fast sequence clustering from similarity networks with SiLiX.
Miele V, Penel S, Duret L.
BMC Bioinformatics. 2011 Apr 22;12:116. doi: 10.1186/1471-2105-12-116.

RPMM 1.25 – Recursively Partitioned Mixture Model for Beta and Gaussian Mixtures

RPMM 1.25

:: DESCRIPTION

RPMM is a model-based clustering algorithm that returns a hierarchy of classes, similar to hierarchical clustering, but also similar to finite mixture models

::DEVELOPER

E. Andres Houseman

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /Windows/ MacOsX
  • R package

:: DOWNLOAD

 RPMM

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2008 Sep 9;9:365. doi: 10.1186/1471-2105-9-365.
Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions.
Houseman EA, Christensen BC, Yeh RF, Marsit CJ, Karagas MR, Wrensch M, Nelson HH, Wiemels J, Zheng S, Wiencke JK, Kelsey KT.

CrunchClust V43 – Clustering software for 454 Sequence

CrunchClust V43

:: DESCRIPTION

Crunchclust is an efficient clustering algorithm that is capable of handling the most common Roche’s 454 sequencing error ( Homopolymers ). It uses Levenshtein distance for sequence comparison during clustering. It is also used successfully for the clustering of Illumina Miseq sequences.

::DEVELOPER

Martin Hartmann

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • C++ Compiler

:: DOWNLOAD

  CrunchClust 

:: MORE INFORMATION

Citation:

Hartmann M, Howes CG, VanInsberghe D, Yu H, Bachar D, Christen R, Nilsson RH, Hallam SJ, Mohn WW (2012).
Significant and persistent impact of timber Harvesting on soil microbial communities in Northern coniferous forests.
The ISME Journal 6: 2199-2218.

CLUSTOM-CLOUD 1.0.0 – CLUSTering 16S NGS Sequences by Overlap Minimization

CLUSTOM-CLOUD 1.0.0

:: DESCRIPTION

CLUSTOM-CLOUD that is categorized into hierarchical clustering approach is a program for clustering high-throughput 16S sequences with user-defined thresholds.

::DEVELOPER

CLUSTOM team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 CLUSTOM

:: MORE INFORMATION

Citation

Oh J, Choi CH, Park MK, Kim BK, Hwang K, Lee SH, Hong SG, Nasir A, Cho WS, Kim KM.
CLUSTOM-CLOUD: In-Memory Data Grid-Based Software for Clustering 16S rRNA Sequence Data in the Cloud Environment.
PLoS One. 2016 Mar 8;11(3):e0151064. doi: 10.1371/journal.pone.0151064. PMID: 26954507; PMCID: PMC4783016.

CLUSTOM: a novel method for clustering 16S rRNA next generation sequences by overlap minimization.
Hwang K, Oh J, Kim TK, Kim BK, Yu DS, Hou BK, Caetano-Anollés G, Hong SG, Kim KM.
PLoS One. 2013 May 1;8(5):e62623. doi: 10.1371/journal.pone.0062623

Mfuzz 2.51.0 – Soft Clustering of Microarray data

Mfuzz 2.51.0

:: DESCRIPTION

Mfuzz implementing soft clustering tools for microarray data analysis.

::DEVELOPER

SysBioLab

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • R package
  • BioConductor
  • R-TclTk

:: DOWNLOAD

 Mfuzz

:: MORE INFORMATION

Citation

Mfuzz: a software package for soft clustering of microarray data.
Kumar L, E Futschik M.
Bioinformation. 2007 May 20;2(1):5-7.

Spark 1.3.0 – Interactive Cluster Visualization Tool

Spark 1.3.0

:: DESCRIPTION

Spark is a discovery tool intended to help you explore the patterns in your genome-wide data. While genome browsers offer a powerful means to integrate diverse data types, their view is inherently limited to individual genomic loci and it can be difficult to obtain a global overview of the predominant data patterns. To address this need, we developed Spark, which enables interactive data clustering and visualization, and serves as a complement to genome browsing.

::DEVELOPER

Canada’s Michael Smith Genome Sciences Centre

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • Java

:: DOWNLOAD

 Spark

:: MORE INFORMATION

Citation

Cydney B. Nielsen, Hamid Younesy, Henriette O’Geen, Xiaoqin Xu, Andrew R. Jackson, Aleksandar Milosavljevic, Ting Wang, Joseph F. Costello, Martin Hirst, Peggy J. Farnham, Steven J.M. Jones.
Spark: A navigational paradigm for genomic data exploration.
Genome Research. 2012 Nov;22(11):2262-9.

KAUSTNMF – Non-negative Matrix Factorization by Maximizing Correntropy for Cancer Clustering

KAUSTNMF

:: DESCRIPTION

KAUSTNMF is a maximum correntropy criterion-based non-negative matrix factorization package.

::DEVELOPER

Structural and Functional Bioinformatics Group, King Abdullah University of Science and Technology

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • MatLab

:: DOWNLOAD

 KAUSTNMF 

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2013 Mar 24;14:107. doi: 10.1186/1471-2105-14-107.
Non-negative matrix factorization by maximizing correntropy for cancer clustering.
Wang JJ1, Wang X, Gao X.