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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Journal ArticleDOI
Global analysis of protein expression in yeast
Sina Ghaemmaghami,Won-Ki Huh,Kiowa Bower,Russell W. Howson,Archana Belle,Noah Dephoure,Erin K. O'Shea,Jonathan S. Weissman +7 more
TL;DR: A Saccharomyces cerevisiae fusion library is created where each open reading frame is tagged with a high-affinity epitope and expressed from its natural chromosomal location, and it is found that about 80% of the proteome is expressed during normal growth conditions.
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Semi-Supervised Learning
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Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation
TL;DR: This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments.
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Missing value estimation methods for DNA microarrays.
Olga G. Troyanskaya,Michael N. Cantor,Gavin Sherlock,Patrick O. Brown,Trevor Hastie,Robert Tibshirani,David Botstein,Russ B. Altman +7 more
TL;DR: It is shown that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVD Impute and KNN Impute surpass the commonly used row average method (as well as filling missing values with zeros).
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Using Bayesian networks to analyze expression data
TL;DR: A new framework for discovering interactions between genes based on multiple expression measurements is proposed and a method for recovering gene interactions from microarray data is described using tools for learning Bayesian networks.
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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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.