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
Citations
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Towards reconstruction of gene networks from expression data by supervised learning
TL;DR: A supervised learning approach is used to build decision-tree-related classifiers, which predict gene expression from the expression data of other genes, which confirm the existing knowledge about cell-cycle gene expression, while hitherto unknown relationships can be treated as new hypotheses.
Journal ArticleDOI
Decoupled evolution of coding region and mRNA expression patterns after gene duplication: Implications for the neutralist-selectionist debate
TL;DR: Only a weak correlation is observed, suggesting that coding sequence and mRNA expression patterns of duplicate gene pairs evolve independently and at vastly different rates.
Journal ArticleDOI
Polarized growth and organelle segregation in yeast the tracks, motors, and receptors
TL;DR: In yeast, growth and organelle segregation requires formin-dependent assembly of polarized actin cables, which are used by myosin Vs to deliver secretory vesicles for cell growth, organelles for their segregation, and mRNA for fate determination.
Journal ArticleDOI
Integrative random forest for gene regulatory network inference.
TL;DR: iRafNet is a flexible, unified integrative framework that allows information from heterogeneous data, such as protein–protein interactions, transcription factor (TF)-DNA-binding, gene knock-down, to be jointly considered for GRN inference and improves the performance in predicting TF-target gene regulations and provides additional functional insights to the predicted gene regulations.
Journal ArticleDOI
Edge-based scoring and searching method for identifying condition-responsive protein–protein interaction sub-network
Zheng Guo,Yongjin Li,Xue Gong,Chen Yao,Wencai Ma,Dong Wang,Yanhui Li,Jing Zhu,Min Zhang,Da Yang,Jing Wang +10 more
TL;DR: A novel edge-based scoring and searching approach to extract a PPI sub-network responsive to conditions related to some investigated gene expression profiles and suggests a systematic approach to evaluate the biological relevance of the identified responsive sub- network by its ability of capturing condition-relevant functional modules.
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: 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.
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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.