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Open AccessJournal ArticleDOI

Comprehensive Identification of Cell Cycle–regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization

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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...

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Citations
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Journal ArticleDOI

Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling

Hiroyuki Toh, +1 more
- 01 Feb 2002 - 
TL;DR: A method combining the cluster analysis with GGM for the inference of the genetic network from the expression profile data and the accuracy of the inferred network was examined by the agreement of the results with the cumulative results of experimental studies.
Journal ArticleDOI

Asymmetric Sequence Divergence of Duplicate Genes

TL;DR: This work shows that 20%-30% of duplicate gene pairs show asymmetric evolution in the amino acid sequence of their protein products, which indicates that most asymmetric divergence may be caused by relaxed selective constraints on one of the duplicates.
Journal ArticleDOI

Prions as protein-based genetic elements.

TL;DR: The ability to become a prion appears to be evolutionarily conserved in two cases, and [PSI(+)] provides a mechanism for genetic variation and phenotypic diversity in response to changing environments.
Book

Kernel Methods and Machine Learning

Sun-Yuan Kung
TL;DR: This chapter discusses kernel methods for estimation, prediction, and system identification, as well as kNN, PNN, and Bayes classifiers, and their applications in machine learning and cluster discovery.
Journal ArticleDOI

Protein network inference from multiple genomic data: a supervised approach

TL;DR: A new method to infer protein networks from multiple types of genomic data based on a variant of kernel canonical correlation analysis is presented, which is shown to outperform other unsupervised protein network inference methods.
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.
Journal ArticleDOI

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

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.
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