Journal Article

Bi-correlation clustering algorithm for determining a set of co-regulated genes

Anindya Bhattacharya and Rajat K. De

in Bioinformatics

Volume 25, issue 21, pages 2795-2801
Published in print November 2009 | ISSN: 1367-4803
Published online September 2009 | e-ISSN: 1460-2059 | DOI:
Bi-correlation clustering algorithm for determining a set of co-regulated genes

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Motivation: 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.

Availability: 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∼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.


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5410 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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