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.
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.
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.
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.
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.
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.
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.