BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data
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- TL;DR
- BiGGEsTS is a free, open-source software tool designed to analyze time series gene expression data through biclustering and gene annotation integration.
- Problem
- While general biclustering is NP-hard, time series biclustering is tractable and allows for efficient algorithms. However, there remains a need for specialized applications that leverage the temporal properties of expression data from both computational and biological perspectives.
- Method
- BiGGEsTS provides state-of-the-art biclustering algorithms, preprocessing and post-processing methods, Gene Ontology (GO) annotations to assess biological relevance, and a visualization module featuring heatmaps, dendrograms, expression charts, and GO term graphs.
- Results
- A case study using BiGGEsTS successfully demonstrated the discovery of transcriptional regulatory modules in Saccharomyces cerevisiae's response to heat stress.
- Contributions
- The tool integrates biclustering algorithms, preprocessing/post-processing methods, GO annotation assessments, and interactive visual representations in a single open-source graphical software package.
- Limitations
- Not specified in the abstract.
- Takeaways
- BiGGEsTS is a free, open-source graphical software tool that reveals local gene coexpression over specific time intervals while incorporating meaningful gene annotation data.
- Applications
- Analyzing time series gene expression data to uncover local temporal expression patterns and potential regulatory mechanisms in biological processes.
- Topics
- Bioinformatics, Gene Expression Analysis, Biclustering, Time Series Data
- For industry
- Not specified in the abstract.
- Why it matters
- Not specified in the abstract.
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. We present a case study on the discovery of transcriptional regulatory modules in the response of Saccharomyces cerevisiae to heat stress.