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.
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).
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.
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.
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.
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.