Comprehensive Identification of Cell Cycle–regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization
Paul T. Spellman,Gavin Sherlock,Gavin Sherlock,Michael Q. Zhang,Vishwanath R. Iyer,Kirk R. Anders,Michael B. Eisen,Patrick O. Brown,Patrick O. Brown,David Botstein,Bruce Futcher +10 more
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TLDR
A comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle is created, and it is found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins.Abstract:
We sought to create a comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle. To this end, we used DNA microarrays and samples from yeast cultures sync...read more
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Causal Explorer: A Causal Probabilistic Network Learning Toolkit for Biomedical Discovery.
TL;DR: A software library that implements a suit of global, local and partial CPN induction algorithms, and emphasizes causal discovery algorithms that scale to thousands of variables and thus are particularly suitable for modeling in massive datasets.
Journal ArticleDOI
Sum1 and Ndt80 proteins compete for binding to middle sporulation element sequences that control meiotic gene expression.
Michael Pierce,Kirsten R. Benjamin,Sherwin P. Montano,Millie M. Georgiadis,Edward Winter,Andrew K. Vershon +5 more
TL;DR: It is shown that Sum1 and Ndt80 compete for binding to MSEs and that small changes in the sequence of an MSE can yield large differences in which protein is bound.
Proceedings Article
Mining for Putative Regulatory Elements in the Yeast Genome Using Gene Expression Data
TL;DR: A set of methods and tools for automatic discovery of putative regulatory signals in genome sequences and automatically derived consensus patterns of these patterns represent the results in a comprehensive way for a human investigator are developed.
Journal ArticleDOI
Integrating microarray data by consensus clustering
Vladimir Filkov,Steven Skiena +1 more
TL;DR: A general criterion to assess the potential benefit of integrating multiple heterogeneous data sets, i.e. whether the integrated data is more informative than the individual data sets is developed.
Journal ArticleDOI
Clustering gene expression time series data using an infinite Gaussian process mixture model.
Ian C. McDowell,Dinesh Manandhar,Christopher M. Vockley,Amy K. Schmid,Timothy E. Reddy,Barbara E. Engelhardt +5 more
TL;DR: This work presents a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models data clusters with a Dirich let process and temporal dependencies with Gaussian processes and demonstrates that jointly modeling cluster number and temporal dependency can reveal shared regulatory mechanisms.
References
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Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
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TL;DR: Unlike other quantitative PCR methods, real-time PCR does not require post-PCR sample handling, preventing potential PCR product carry-over contamination and resulting in much faster and higher throughput assays.
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Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale
TL;DR: DNA microarrays containing virtually every gene of Saccharomyces cerevisiae were used to carry out a comprehensive investigation of the temporal program of gene expression accompanying the metabolic shift from fermentation to respiration, and the expression patterns of many previously uncharacterized genes provided clues to their possible functions.
Book ChapterDOI
Getting started with yeast.
TL;DR: The yeast Saccharomyces cerevisiae is now recognized as a model system representing a simple eukaryote whose genome can be easily manipulated and made particularly accessible to gene cloning and genetic engineering techniques.
Journal ArticleDOI
A Genome-Wide Transcriptional Analysis of the Mitotic Cell Cycle
Raymond J. Cho,Michael J. Campbell,Elizabeth A. Winzeler,Lars M. Steinmetz,Andrew R. Conway,Lisa Wodicka,Tyra G. Wolfsberg,Andrei Gabrielian,David Landsman,David J. Lockhart,Ronald W. Davis +10 more
TL;DR: The genome-wide characterization of mRNA transcript levels during the cell cycle of the budding yeast S. cerevisiae indicates a mechanism for local chromosomal organization in global mRNA regulation and links a range of human genes to cell cycle period-specific biological functions.