BMC Research Notes

Search for
Advanced search
BMC Research Notes
Tools
Open Access Technical Note

BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data

Joana P Gonçalves3,1,2*, Sara C Madeira3,1,2 and Arlindo L Oliveira1,2

Author Affiliations

1 Knowledge Discovery and Bioinformatics (KDBIO) group, INESC-ID, Rua Alves Redol, Apartado 13069, 1000-029 Lisboa, Portugal

2 Instituto Superior Técnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisboa, Portugal

3 University of Beira Interior, Rua Marquês d'Ávila e Bolama, 6201-001 Covilhã, Portugal

For all author emails, please log on.

BMC Research Notes 2009, 2:124 doi:10.1186/1756-0500-2-124

Published: 7 July 2009

Abstract

Background

The ability to monitor changes in expression patterns over time, and to observe the emergence of coherent temporal responses using expression time series, is critical to advance our understanding of complex biological processes. Biclustering has been recognized as an effective method for discovering local temporal expression patterns and unraveling potential regulatory mechanisms. The general biclustering problem is NP-hard. In the case of time series this problem is tractable, and efficient algorithms can be used. However, there is still a need for specialized applications able to take advantage of the temporal properties inherent to expression time series, both from a computational and a biological perspective.

Findings

BiGGEsTS makes available state-of-the-art biclustering algorithms for analyzing expression time series. Gene Ontology (GO) annotations are used to assess the biological relevance of the biclusters. Methods for preprocessing expression time series and post-processing results are also included. The analysis is additionally supported by a visualization module capable of displaying informative representations of the data, including heatmaps, dendrograms, expression charts and graphs of enriched GO terms.

Conclusion

BiGGEsTS is a free open source graphical software tool for revealing local coexpression of genes in specific intervals of time, while integrating meaningful information on gene annotations. It is freely available at: http://kdbio.inesc-id.pt/software/biggests webcite. We present a case study on the discovery of transcriptional regulatory modules in the response of Saccharomyces cerevisiae to heat stress.