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Jane Y Yoo

Publications and source records attributed to Jane Y Yoo.

2 recordsLinked to original sources

Combined global localization analysis and transcriptome data identify genes that are directly coregulated by Adr1 and Cat8.

In Saccharomyces cerevisiae, glucose depletion causes a profound alteration in metabolism, mediated in part by global transcriptional changes. Many of the transcription factors that regulate these changes act combinatorially. We have analyzed combinatorial regulation by Adr1 and Cat8, two transcription factors that act during glucose depletion, by combining genome-wide expression and genome-wide binding data. We identified 32 genes that are directly activated by Adr1, 28 genes that are directly activated by Cat8, and 14 genes that are directly regulated by both. Our analysis also uncovered promoters that Adr1 binds but does not regulate and promoters that are indirectly regulated by Cat8, stressing the advantage of combining global expression and global localization analysis to find directly regulated targets. At most of the coregulated promoters, the in vivo binding of one factor is independent of the other, but Adr1 is required for optimal Cat8 binding at two promoters with a poor match to the Cat8 binding consensus. In addition, Cat8 is required for Adr1 binding at promoters where Adr1 is not required for transcription. These data provide a comprehensive analysis of the direct, indirect, and combinatorial requirements for these two global transcription factors.

Chromatin Immunoprecipitation↗

Computational discovery of gene modules and regulatory networks.

We describe an algorithm for discovering regulatory networks of gene modules, GRAM (Genetic Regulatory Modules), that combines information from genome-wide location and expression data sets. A gene module is defined as a set of coexpressed genes to which the same set of transcription factors binds. Unlike previous approaches that relied primarily on functional information from expression data, the GRAM algorithm explicitly links genes to the factors that regulate them by incorporating DNA binding data, which provide direct physical evidence of regulatory interactions. We use the GRAM algorithm to describe a genome-wide regulatory network in Saccharomyces cerevisiae using binding information for 106 transcription factors profiled in rich medium conditions data from over 500 expression experiments. We also present a genome-wide location analysis data set for regulators in yeast cells treated with rapamycin, and use the GRAM algorithm to provide biological insights into this regulatory network

Algorithms↗