Comprehensive Identification of Cell Cycle–regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization
01 Dec 1998-Molecular Biology of the Cell (American Society for Cell Biology)-Vol. 9, Iss: 12, pp 3273-3297
TL;DR: 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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TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Abstract: Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
35,225 citations
Cites background from "Comprehensive Identification of Cel..."
...Another use for GO ontologies that is gaining rapid adherence is the annotation of gene-expression data, especially after these have been clustered by similarities in pattern of gene expressio...
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TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
Abstract: Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
12,530 citations
TL;DR: Clustering algorithms for data sets appearing in statistics, computer science, and machine learning are surveyed, and their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts are illustrated.
Abstract: Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.
5,744 citations
Cites background or methods from "Comprehensive Identification of Cel..."
...Spellman et al. clustered 800 genes according to their expression during the yeast cell cycle [ 260 ]....
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...Generally, cluster analysis of gene expression data is composed of two aspects: clustering genes [80], [206], [ 260 ], [268], [283], [288] or clustering tissues or experiments [5], [109], [238]....
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TL;DR: The Perseus software platform was developed to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data and it is anticipated that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
Abstract: A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
5,165 citations
TL;DR: A general framework for `soft' thresholding that assigns a connection weight to each gene pair is described and several node connectivity measures are introduced and provided empirical evidence that they can be important for predicting the biological significance of a gene.
Abstract: Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for ;soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the ;weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its ;unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding. We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.
4,448 citations
Cites methods from "Comprehensive Identification of Cel..."
...This dataset recorded gene expression levels during different stages of cell cycles in yeasts and has been widely used before to illustrate clustering methods (Spellman et al., 1998)....
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References
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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.
Abstract: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is de- scribed that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be inter- preted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly charac- terized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
16,371 citations
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.
Abstract: We have developed a novel "real time" quantitative PCR method. The method measures PCR product accumulation through a dual-labeled fluorogenic probe (i.e., TaqMan Probe). This method provides very accurate and reproducible quantitation of gene copies. 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. The real-time PCR method has a very large dynamic range of starting target molecule determination (at least five orders of magnitude). Real-time quantitative PCR is extremely accurate and less labor-intensive than current quantitative PCR methods.
6,367 citations
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.
Abstract: 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. The expression profiles observed for genes with known metabolic functions pointed to features of the metabolic reprogramming that occur during the diauxic shift, and the expression patterns of many previously uncharacterized genes provided clues to their possible functions. The same DNA microarrays were also used to identify genes whose expression was affected by deletion of the transcriptional co-repressor TUP1 or overexpression of the transcriptional activator YAP1. These results demonstrate the feasibility and utility of this approach to genomewide exploration of gene expression patterns.
4,792 citations
"Comprehensive Identification of Cel..." refers background or methods in this paper
...Mixtures of labeled control and experimental cDNAs were competitively hybridized to individual microarrays containing essentially all yeast genes (DeRisi et al., 1997)....
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...DNAs were prepared and printed onto microarrays as described previously (Shalon et al., 1996; DeRisi et al., 1997 [http:/ /cmgm....
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...DNAs were prepared and printed onto microarrays as described previously (Shalon et al., 1996; DeRisi et al., 1997 [http://cmgm. stanford.edu/pbrown/]; Eisen and Brown, 1999) with 190-mm spacing between the centers of each element....
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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.
Abstract: Publisher Summary The yeast Saccharomyces cerevisiae is now recognized as a model system representing a simple eukaryote whose genome can be easily manipulated. Yeast has only a slightly greater genetic complexity than bacteria and shares many of the technical advantages that permitted rapid progress in the molecular genetics of prokaryotes and their viruses. Some of the properties that make yeast particularly suitable for biological studies include rapid growth, dispersed cells, the ease of replica plating and mutant isolation, a well-defined genetic system, and most important, a highly versatile DNA transformation system. Being nonpathogenic, yeast can be handled with little precautions. Large quantities of normal baker's yeast are commercially available and can provide a cheap source for biochemical studies. The development of DNA transformation has made yeast particularly accessible to gene cloning and genetic engineering techniques. Structural genes corresponding to virtually any genetic trait can be identified by complementation from plasmid libraries. Plasmids can be introduced into yeast cells either as replicating molecules or by integration into the genome. In contrast to most other organisms, integrative recombination of transforming DNA in yeast proceeds exclusively via homologous recombination. Cloned yeast sequences, accompanied by foreign sequences on plasmids, can therefore be directed at will to specific locations in the genome.
3,547 citations
"Comprehensive Identification of Cel..." refers background in this paper
...*Department of Genetics, Stanford University Medical Center, Stanford, California 94306-5120; ‡Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724-2209; §Department of Biochemistry, Stanford University Medical Center, Stanford, California 94306-5428; and \Howard Hughes Medical…...
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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.
2,232 citations
"Comprehensive Identification of Cel..." refers background or methods in this paper
...Molecular Biology of the Cell Vol. 9, 3273–3297, December 1998 Comprehensive Identification of Cell Cycle–regulated Genes of the Yeast Saccharomyces cerevisiae by...
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...The RNA was reverse transcribed using TAQman reverse transcription reagents (PE Applied Biosystems, Foster City, CA) consisting of 2.5 mM oligo-dT 16 mer, 1.25 U/ml MultiScribe reverse transcriptase, 0.5 mM dGTP, dATP, dTTP, and dCTP, 0.4 U/ml RNase inhibitor, 50 mM KCl, 10 mM Tris-HCl (pH 8.3)....
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...The microarrays were allowed to hybridize 4–6 h. Microarrays were removed from the chambers and placed in standard histochemistry slide holders where they were washed by plunging 30 times in each of the following solutions, respectively: 23 SSC, 0.2% SDS; 0.43 SSC; and 0.23 SSC....
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