Bi-correlation clustering algorithm for determining a set of co-regulated genes.
BACKGROUND
Biclustering has been emerged as a powerful tool for identification of a group of co-expressed genes under a subset of experimental conditions (measurements) present in a gene expression dataset. Several biclustering algorithms have been proposed till date. In this article, we address some of the important shortcomings of these existing biclustering algorithms and propose a new correlation-based biclustering algorithm called bi-correlation clustering algorithm (BCCA).


RESULTS
BCCA has been able to produce a diverse set of biclusters of co-regulated genes over a subset of samples where all the genes in a bicluster have a similar change of expression pattern over the subset of samples. Moreover, the genes in a bicluster have common transcription factor binding sites in the corresponding promoter sequences. The presence of common transcription factors binding sites, in the corresponding promoter sequences, is an evidence that a group of genes in a bicluster are co-regulated. Biclusters determined by BCCA also show highly enriched functional categories. Using different gene expression datasets, we demonstrate strength and superiority of BCCA over some existing biclustering algorithms.


BACKGROUND
The software for BCCA has been developed using C and Visual Basic languages, and can be executed on the Microsoft Windows platforms. The software may be downloaded as a zip file from http://www.isical.ac.in/ approximately rajat. Then it needs to be installed. Two word files (included in the zip file) need to be consulted before installation and execution of the software.


BACKGROUND
rajat@isical.ac.in


BACKGROUND
Supplementary data are available at Bioinformatics online.
Bhattacharya
Anindya
A
Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata 700152, India.
De
Rajat K
RK
D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans