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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Genetic network inference: from co-expression clustering to reverse engineering.
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K-Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data.
TL;DR: The nonlinear K-profiles clustering method is designed, which can be seen as the nonlinear counterpart of the K-means clustering algorithm, and has a built-in statistical testing procedure that ensures genes not belonging to any cluster do not impact the estimation of cluster profiles.
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Targets of the cyclin-dependent kinase Cdk1
Jeffrey A. Ubersax,Erika L. Woodbury,Erika L. Woodbury,Phuong N. Quang,Phuong N. Quang,Maria Paraz,Maria Paraz,Justin D. Blethrow,Justin D. Blethrow,Kavita Shah,Kevan M. Shokat,David O. Morgan,David O. Morgan +12 more
TL;DR: The identities of these substrates reveal that Cdk1 employs a global regulatory strategy involving phosphorylation of other regulatory molecules as well as phosphorylated of the molecular machines that drive cell-cycle events.
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A combined algorithm for genome-wide prediction of protein function
TL;DR: Proteins are grouped by correlated evolution, correlated messenger RNA expression patterns and patterns of domain fusion to determine functional relationships among the 6,217 proteins of the yeast Saccharomyces cerevisiae to discover pairwise links between functionally related yeast proteins.
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Discovering statistically significant biclusters in gene expression data.
TL;DR: A new method to detect significant biclusters in large expression datasets is proposed and is able to detect and relate finer tissue types than was previously possible in cancer data and outperforms the biclustering algorithm of Cheng and Church (2000).
References
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Journal ArticleDOI
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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Real time quantitative PCR.
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.
Journal ArticleDOI
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.