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.

SparseDC 0.1.17 – Sparse Differential Clustering

SparseDC 0.1.17

:: DESCRIPTION

SparseDC is an algorithm, which identifies cell types, traces their changes across conditions and identifies genes which are marker genes for these changes.

::DEVELOPER

Jun Li

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • MacOsX/  Linux / WIndows
  • R Package

:: DOWNLOAD

SparseDC

:: MORE INFORMATION

Citation

Nucleic Acids Res, 46 (3), e14 2018 Feb 16
A Sparse Differential Clustering Algorithm for Tracing Cell Type Changes via Single-Cell RNA-sequencing Data
Martin Barron , Siyuan Zhang , Jun Li

HiFiX 1.0.6 – High-quality Sequence Clustering

HiFiX 1.0.6

:: DESCRIPTION

The software package HiFiX implements the novel algorithm for HIgh FIdelity Clustering of Sequences.

::DEVELOPER

PRABI-Doua

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Mac / Linux
  • Python
  • BioPython

:: DOWNLOAD

 HiFiX

:: MORE INFORMATION

Citation:

Bioinformatics. 2012 Apr 15;28(8):1078-85. doi: 10.1093/bioinformatics/bts098.
High-quality sequence clustering guided by network topology and multiple alignment likelihood.
Miele V, Penel S, Daubin V, Picard F, Kahn D, Duret L.

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

Treebic 1.11 – Hierarchical Generative Biclustering for MicroRNA Expression Analysis

Treebic 1.11

:: DESCRIPTION

Treebic is a Software package for hierarchical biclustering.

::DEVELOPER

Probabilistic Machine Learning

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows /MacOsX
  • R package

:: DOWNLOAD

 Treebic

:: MORE INFORMATION

Citation

J. Caldas and S. Kaski.
Hierarchical generative biclustering for microRNA expression analysis.
J. Caldas and S. Kaski. Journal of Computational Biology, 18(3):251-261, 2011

ClusterLustre 1.2 – Consensus Clustering

ClusterLustre 1.2

:: DESCRIPTION

ClusterLustre is a Matlab program package for consensus clustering that is combining multiple clustering runs into a single more robust clustering

::DEVELOPER

Ole Winther

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Matlab

:: DOWNLOAD

 ClusterLustre

:: MORE INFORMATION

Citation

Bioinformatics. 2006 Jan 1;22(1):58-67. Epub 2005 Oct 27.
Robust multi-scale clustering of large DNA microarray datasets with the consensus algorithm.
Grotkjaer T, Winther O, Regenberg B, Nielsen J, Hansen LK.

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.