MACML 1.1.2 – Model Averaging Clustering by Maximum Likelihood

MACML 1.1.2

:: DESCRIPTION

MACML is a program that clusters sequences into heterogeneous regions with specific site types, without requiring any prior knowledge.

::DEVELOPER

the Townsend Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • C++Compiler

:: DOWNLOAD

 MACML

:: MORE INFORMATION

Citation

PLoS Comput Biol. 2009 Jun;5(6):e1000421. Epub 2009 Jun 26.
Maximum-likelihood model averaging to profile clustering of site types across discrete linear sequences.
Zhang Z, Townsend JP.

MBCluster.Seq 1.0 – Model-Based Clustering for RNA-seq Data

MBCluster.Seq 1.0

:: DESCRIPTION

MBCluster.Seq : Cluster genes based on Poisson or Negative-Binomial model for RNA-Seq or other digital gene expression (DGE) data

::DEVELOPER

Yaqing Si <siyaqing at gmail.com>

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • R

:: DOWNLOAD

 MBCluster.Seq

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Jan 15;30(2):197-205. doi: 10.1093/bioinformatics/btt632. Epub 2013 Nov 4.
Model-based clustering for RNA-seq data.
Si Y1, Liu P, Li P, Brutnell TP.

CAPIU 0.2 – Clustering using A Priori Information via Unsupervised decision trees

CAPIU 0.2

:: DESCRIPTION

CAPIU is a novel approach for clustering samples (treatments, patients, condition etc) by using annotational information about the genes. The algorithm searches all pre-defined gene classes for classes that exhibit a strong clustering of the samples. These are then used to split the samples in two groups until no significant splits can be found. The result is visualized as a tree with gene classes as nodes and groups of samples as leaves.

::DEVELOPER

Max Planck Institute for Molecular Plant Physiology

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • R package
  • Biobase, MASS, mclust, e1071, cluster, hu6800, ellipse, GO.

:: DOWNLOAD

 CAPIU

:: MORE INFORMATION

Citation

Biom J. 2007 Apr;49(2):214-29.
Integrating functional knowledge during sample clustering for microarray data using unsupervised decision trees.
Redestig H, Repsilber D, Sohler F, Selbig J.

AutoSOME 2.0 – Clustering method for Identifying Gene Expression Modules

AutoSOME 2.0

:: DESCRIPTION

AutoSOME (Automatic clustering of density-equalized self-organizing map ensembles) is a new unsupervised multidimensional clustering method for identifying clusters of diverse shapes and sizes from large numerical datasets without prior knowledge of cluster number. Given the general nature of data clustering, AutoSOME has utility for a wide range of applications, including whole-genome co-expression and transcriptome analysis.

::DEVELOPER

Newman Lab

:: SCREENSHOTS

AutoSOME

:: REQUIREMENTS

  • Linux /  MacOsX/ Windows
  • Java

:: DOWNLOAD

  AutoSOME

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2010 Mar 4;11:117. doi: 10.1186/1471-2105-11-117.
AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number.
Newman AM, Cooper JB.

isONclust 0.0.4 – de novo Clustering of long Transcript Reads into Genes

isONclust 0.0.4

:: DESCRIPTION

isONclust is a tool for clustering either PacBio Iso-Seq reads, or Oxford Nanopore reads into clusters, where each cluster represents all reads that came from a gene.

::DEVELOPER

Medvedev Group

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux / MacOsX
  • Python

:: DOWNLOAD

isONclust

:: MORE INFORMATION

Citation

Kristoffer Sahlin, Paul Medvedev (2019)
De Novo Clustering of Long-Read Transcriptome Data Using a Greedy, Quality-Value Based Algorithm“,
RECOMB 2019

GibbsCluster 2.0 – Simultaneous Alignment and Clustering of Peptide data

GibbsCluster 2.0

:: DESCRIPTION

GibbsCluster is a web server for simultaneous alignment and clustering of peptide data

::DEVELOPER

DTU Health Tech

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 GibbsCluster

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Jan 1;29(1):8-14. doi: 10.1093/bioinformatics/bts621. Epub 2012 Oct 24.
Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach.
Andreatta M1, Lund O, Nielsen M.

OLYMPUS – Hybrid Clustering method in Time Series Gene Expression

OLYMPUS

:: DESCRIPTION

OLYMPUS is an automated hybrid clustering method in the field of time series gene expression analysis.

::DEVELOPER

the Biosignal Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • MatLab

:: DOWNLOAD

 OLYMPUS

:: MORE INFORMATION

Citation

OLYMPUS: an automated hybrid clustering method in time series gene expression. Case study: host response after Influenza A (H1N1) infection.
Dimitrakopoulou K, Vrahatis AG, Wilk E, Tsakalidis AK, Bezerianos A.
Comput Methods Programs Biomed. 2013 Sep;111(3):650-61. doi: 10.1016/j.cmpb.2013.05.025.

SCUDO – Signature-based Clustering of Expression Profiles

SCUDO

:: DESCRIPTION

SCUDO (Signature-based ClUstering for DiagnOstic purposes) is a tool for clustering gene expression profiles for diagnostic purposes using a new type of rank-based signatures

::DEVELOPER

The Microsoft Research – University of Trento Centre for Computational and Systems Biology

:: SCREENSHOTS

N/a

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

SCUDO: a tool for signature-based clustering of expression profiles.
Lauria M, Moyseos P, Priami C.
Nucleic Acids Res. 2015 May 9. pii: gkv449.

Sparcl – Arbitrary Shape Clustering

Sparcl

:: DESCRIPTION

Sparcl finds shape-based clusters. It uses a two step approach: in the first step we select a relatively large number of candidate centroids (via ROBIN) to find seed clusters via the K-means algorithm and in the second step we use a novel similarity kernel to merge the initial seed clusters to yield the final arbitrary shaped clusters.

::DEVELOPER

Mohammed J. Zaki

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

Sparcl

:: MORE INFORMATION

Citation

Vineet Chaoji, Mohammad Al Hasan, Saeed Salem and Mohammed J. Zaki (2009)
SPARCL: An Effective and Efficient Algorithm for Mining Arbitrary Shape-based Clusters.
Knowledge and Information Systems, 21(2), Nov, pp.201-229.

TriCluster / MicroCluster – Microarray Gene Expression Clustering

TriCluster / MicroCluster

:: DESCRIPTION

Tricluster is the first tri-clustering algorithm for microarray expression clustering. It builds upon the new microCluster bi-clustering approach. Tricluster first mines all the bi-clusters across the gene-sample slices, and then it extends these into tri-clusters across time or space (depending on the third dimension). It can find both scaling and shifting patterns

MicroCluster is a deterministic biclustering algorithm that can mine arbitrarily positioned and overlapping clusters of gene expression data to find interesting patterns

::DEVELOPER

Mohammed J. Zaki

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

 TriCluster / MicroCluster

:: MORE INFORMATION

Citation

Lizhuang Zhao and Mohammed J. Zaki,
TriCluster: An Effective Algorithm for Mining Coherent Clusters in 3D Microarray Data.
In ACM SIGMOD Conference on Management of Data. Jun 2005.

Lizhuang Zhao and Mohammed J. Zaki,
MicroCluster: An Efficient Deterministic Biclustering Algorithm for Microarray Data.
IEEE Intelligent Systems, 20(6):40-49. Nov/Dec 2005