Document Type
Article
Publication Date
10-28-2016
Department
Mathematics, Statistics, and Computer Science
Keywords
atomic regulon, clustering, gene expression analysis, transcriptomic data, Escherichia coli, hierarchical clustering, CLR, K-Means clustering
Abstract
Understanding gene function and regulation is essential for the interpretation prediction and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets Atomic Regulons ARs represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here we describe an approach for inferring ARs that leverages large-scale expression data sets gene context and functional relationships among genes.
Source Publication Title
Frontiers in Microbiology
Publisher
Frontiers
Volume
7
First Page
1819
DOI
10.3389/fmicb.2016.01819
Recommended Citation
Faria, J. P., Davis, J. J., Edirisinghe, J. N., Taylor, R. C., Weisenhorn, P. B., Olson, R. D., Stevens, R., Rocha, M., Rocha, I., Best, A. A., DeJongh, M., Tintle, N. L., Parrelo, B., Overbeek, R., & Henry, C. S. (2016). Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation. Frontiers in Microbiology, 7, 1819. https://doi.org/10.3389/fmicb.2016.01819