DNACLUST r3 – Cluster Millions of short DNA sequences

DNACLUST r3

:: DESCRIPTION

DNACLUST is a tool for clustering millions of short DNA sequences. The clusters are created in such a way that the “radius” of each clusters is no more than the specified threshold.

::DEVELOPER

the Center for Bioinformatics and Computational Biology

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  DNACLUST

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2011 Jun 30;12:271.
DNACLUST: accurate and efficient clustering of phylogenetic marker genes.
Ghodsi M, Liu B, Pop M.

pClust 1.0 – Parallel Identification of Dense Protein Clusters

pClust 1.0

:: DESCRIPTION

 PClust is a scalable parallel software for detecting dense subgraphs.

::DEVELOPER

Ananth Kalyanaraman

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C Compiler

:: DOWNLOAD

 pClust

:: MORE INFORMATION

Citation

C. Wu, A. Kalyanaraman.
An efficient parallel approach for identifying protein families in large-scale metagenomic data sets.
Proc. ACM/IEEE Supercomputing Conference (SC’08), Austin, TX, November 15-21. pp. 1-10. 2008

AncestralClust – Cluster Divergent Sequences

AncestralClust

:: DESCRIPTION

AncestralClust is a clustering program, which is developed for clustering divergent sequences.

::DEVELOPER

Lenore Pipes @ Nielsen Berkeley Lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux

:: DOWNLOAD

AncestralClust

:: MORE INFORMATION

Citation

Pipes L, Nielsen R.
AncestralClust: Clustering of Divergent Nucleotide Sequences by Ancestral Sequence Reconstruction using Phylogenetic Trees.
Bioinformatics. 2021 Oct 20:btab723. doi: 10.1093/bioinformatics/btab723. Epub ahead of print. PMID: 34668516.

CFinder 2.0.6 – Cluster data represented by Large Graphs

CFinder 2.0.6

:: DESCRIPTION

CFinder offers a fast and efficient method for clustering data represented by large graphs, such as genetic or social networks and microarray data. CFinder is a free software for finding overlapping dense groups of nodes in networks, based on the Clique Percolation Method, CPM, of Palla et. al. Nature (2005).

::DEVELOPER

CFinder Team

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / MacOsX /  Linux
  • Java

:: DOWNLOAD

  CFinder

:: MORE INFORMATION

Citation

CFinder: locating cliques and overlapping modules in biological networks.
Adamcsek B, Palla G, Farkas IJ, Derényi I, Vicsek T.
Bioinformatics. 2006 Apr 15;22(8):1021-3. Epub 2006 Feb 10.

rKOMICS 1.1 – Minicircle Sequence Cluster (MSC) Analyses

rKOMICS 1.1

:: DESCRIPTION

rKOMICS is an R package for processing mitochondrial minicircle assemblies in population-scale genome projects

::DEVELOPER

rKOMICS team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • R

:: DOWNLOAD

rKOMICS

:: MORE INFORMATION

Citation

Geerts M, Schnaufer A, Van den Broeck F.
rKOMICS: an R package for processing mitochondrial minicircle assemblies in population-scale genome projects.
BMC Bioinformatics. 2021 Sep 28;22(1):468. doi: 10.1186/s12859-021-04384-1. PMID: 34583651; PMCID: PMC8479924.

parastructure 0.9 – Run the Population Genetics software STRUCTURE in Parallel on a Cluster

parastructure 0.9

:: DESCRIPTION

parastructure is a perl script collection to run the population genetics software STRUCTURE in parallel on a cluster (beowulf type).

::DEVELOPER

Jacques Lagnel (lagnel@her.hcmr.gr)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 parastructure

:: MORE INFORMATION

MarVis Suite 2.0 – Clustering & Visualization of Metabolomic Markers

MarVis Suite 2.0

:: DESCRIPTION

MarVis (Marker Visualization) is designed for intensity-based clustering and visualization of large sets of metabolomic markers.The application of 1D-SOMs gives a convenient overview on relevant profiles and groups of profiles. The specialized visualization effectively supports researchers in analyzing a large number of putative clusters, even though the true number of biologically meaningful groups is unknown. Although MarVis has been developed for the analysis of metabolomic data, the tool may be applied to gene expression data as well.

::DEVELOPER

Department of Bioinformatics ,  University of Göttingen

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows

:: DOWNLOAD

MarVis

:: MORE INFORMATION

Citation

Kaever A, Lingner T, Feussner K, G?bel C, Feussner I, Meinicke P:
MarVis: a tool for clustering and visualization of metabolic biomarkers.
BMC Bioinformatics 2009, 10:92.

CAFS 1.0 – Cluster of Functional Shifts

CAFS 1.0

:: DESCRIPTION

CAFS (Clustering Analysis of Functional Shifts / Clusterfunc) is a simple and fast method for Clustering functionally divergent genes by Functional Category. The program analyses alignments and provides the user with the best putative sites under functional divergence.

::DEVELOPER

Dr Mario Fares 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

 CAFS

:: MORE INFORMATION

Citation

Caffrey BE, Williams TA, Jiang X, Toft C, Hokamp K, et al. (2012)
Proteome-wide analysis of functional divergence reveals the molecular basis of ecological adaptations in bacteria .
PLoS ONE 7(4): e35659. doi:10.1371/journal.pone.0035659

Bison 0.4.0 – Bisulfite Alignment On Nodes of a Cluster

Bison 0.4.0

:: DESCRIPTION

Bison allows users with access to a computer cluster to rapidly align whole-genome bisulfite sequencing or RRBS reads. It can align both directional and non-directional libraries and uses bowtie2.

:: DEVELOPER

Devon Ryan

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

 Bison

:: MORE INFORMATION

Citation

Bison: bisulfite alignment on nodes of a cluster.
Ryan DP, Ehninger D.
BMC Bioinformatics. 2014 Oct 18;15:337. doi: 10.1186/1471-2105-15-337.

Cluster 3.0 20190830 – Enhanced Version of Cluster

Cluster 3.0 20190830

:: DESCRIPTION

Cluster 3.0 is an enhanced version of Cluster, which was originally developed by Michael Eisen while at Stanford University.

Cluster is program that provide a computational and graphical environment for analyzing data from DNA microarray experiments, or other genomic datasets. The program Cluster can organize and analyze the data in a number of different ways.

The main improvement consists of the k-means algorithm, which now includes multiple trials to find the best clustering solution. This is crucial for the k-means algorithm to be reliable.The routine for self-organizing maps was extended to include 2D rectangular geometries. The Euclidean distance and the city-block distance were added to the available measures of similarity.

::DEVELOPER

Michiel de Hoon of the University of Tokyo

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows
  • Mac OS X
  • Linux/Unix with Motif

:: DOWNLOAD

Cluster 

:: MORE INFORMATION

Reference: M. J. L. de Hoon, S. Imoto, J. Nolan, and S. Miyano:
Open Source Clustering Software.
Bioinformatics20 (9): 1453–1454 (2004).