ArticleGenome research2004
Elucidation of gene interaction networks through time-lagged correlation analysis of transcriptional data.
Article in Genome research, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks.Annual review of plant biology · 2021Review
- Inferring functional connectivity through graphical directed information.Journal of neural engineering · 2021Article
- Episodic Decrease in Temperature IncreasesFrontiers in microbiology · 2020Article
- An integrative approach for building personalized gene regulatory networks for precision medicine.Genome medicine · 2018Review
- Aquatic Bacterial Communities Associated With Land Use and Environmental Factors in Agricultural Landscapes Using a Metabarcoding Approach.Frontiers in microbiology · 2018Article
- Resolving host-pathogen interactions by dual RNA-seq.PLoS pathogens · 2017Review
- Algorithms for modeling global and context-specific functional relationship networks.Briefings in bioinformatics · 2016Article
- Regulatory network inferred using expression data of small sample size: application and validation in erythroid system.Bioinformatics (Oxford, England) · 2015Article
- Article
- Inference of gene interaction networks using conserved subsequential patterns from multiple time course gene expression datasets.BMC genomics · 2015Article
- A novel, dynamic pattern-based analysis of NF-κB binding during the priming phase of liver regeneration reveals switch-like functional regulation of target genes.Frontiers in physiology · 2015Article
- Article
- Article
- Linking the signaling cascades and dynamic regulatory networks controlling stress responses.Genome research · 2013Article
- Comparison of co-expression measures: mutual information, correlation, and model based indices.BMC bioinformatics · 2012Article
- Molecular ecological network analyses.BMC bioinformatics · 2012Article
- Integrated inference and analysis of regulatory networks from multi-level measurements.Methods in cell biology · 2012Review
- A parallel algorithm for reverse engineering of biological networks.Integrative biology : quantitative biosciences from nano to macro · 2011Article
- Bioinformatics and systems biology of the lipidome.Chemical reviews · 2011Review
- Angiogenesis gene expression in murine endothelial cells during post-pneumonectomy lung growth.Respiratory research · 2011Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The photosynthetic cyanobacterium Synechocystis sp. strain PCC 6803 uses a complex genetic program to control its physiological response to alternating light conditions. To study this regulatory program time-series experiments were conducted by exposing Synechocystis sp. to serial perturbations in light intensity. In each experiment whole-genome DNA microarrays were used to monitor gene transcription in 20-min intervals over 8- and 16-h periods. The data was analyzed using time-lagged correlation analysis, which identifies genetic interaction networks by constructing correlations between time-shifted transcription profiles with different levels of statistical confidence. These networks allow inference of putative cause-effect relationships among the organism's genes. Using light intensity as our initial input signal, we identified six groups of genes whose time-lagged profiles possessed significant correlation, or anti-correlation, with the light intensity. We expanded this network by using the average profile from each group of genes as a seed, and searching for other genes whose time-lagged profiles possessed significant correlation, or anti-correlation, with the group's average profile. The final network comprised 50 different groups containing 259 genes. Several of these gene groups possess known light-stimulated gene clusters, such as Synechocystis sp. photosystems I and II and carbon dioxide fixation pathways, while others represent novel findings in this work.
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