Genome-wide phenotypic screens in the budding yeast Saccharomyces cerevisiae , enabled by its knockout collection, have produced the largest, richest, and most systematic phenotypic description of any organism. However, integrative analyses of this rich data source have been virtually impossible because of the lack of a central data repository and consistent metadata annotations. Here, we describe the aggregation, harmonization, and analysis of ~14,500 yeast knockout screens, which we call Yeast Phenome. Using this unique dataset, we characterized two unknown genes ( YHR045W and YGL117W ) and showed that tryptophan starvation is a by-product of many chemical treatments. Furthermore, we uncovered an exponential relationship between phenotypic similarity and intergenic distance, which suggests that gene positions in both yeast and human genomes are optimized for function.

Acetyl-CoA carboxylase (ACCase) catalyzes the first committed step in thede novosynthesis of fatty acids. The multisubunit ACCase in the chloroplast is activated by a shift to pH 8 upon light adaptation and is inhibited by a shift to pH 7 upon dark adaptation. Here, titrations with the purified ACCase biotin attachment domain-containing (BADC) and biotin carboxyl carrier protein (BCCP) subunits fromArabidopsisindicated that they can competently and independently bind biotin carboxylase (BC) but differ in responses to pH changes representing those in the plastid stroma during light or dark conditions. At pH 7 in phosphate buffer, BADC1 and BADC2 gain an advantage over BCCP1 and BCCP2 in affinity for BC. At pH 8 in KCl solution, however, BCCP1 and BCCP2 had more than 10-fold higher affinity for BC than did BADC1. The pH-modulated shifts in BC preferences for BCCP and BADC partners suggest they contribute to light-dependent regulation of heteromeric ACCase. Using NMR spectroscopy, we found evidence for increased intrinsic disorder of the BADC and BCCP subunits at pH 7. We propose that this intrinsic disorder potentially promotes fast association with BC through a ?fly-casting mechanism.? We hypothesize that the pH effects on the BADC and BCCP subunits attenuate ACCase activity by night and enhance it by day. Consistent with this hypothesis,Arabidopsis badc1 badc3mutant lines grown in a light?dark cycle synthesized more fatty acids in their seeds. In summary, our findings provide evidence that the BADC and BCCP subunits function as pH sensors required for light-dependent switching of heteromeric ACCase activity.

The Drosophila cerebrum originates from about 100 neuroblasts per hemisphere, with each neuroblast producing a characteristic set of neurons. Neurons from a neuroblast are often so diverse that many neuron types remain unexplored. We developed new genetic tools that target neuroblasts and their diverse descendants, increasing our ability to study fly brain structure and development. Common enhancer-based drivers label neurons on the basis of terminal identities rather than origins, which provides limited labeling in the heterogeneous neuronal lineages. We successfully converted conventional drivers that are temporarily expressed in neuroblasts, into drivers expressed in all subsequent neuroblast progeny. One technique involves immortalizing GAL4 expression in neuroblasts and their descendants. Another depends on loss of the GAL4 repressor, GAL80, from neuroblasts during early neurogenesis. Furthermore, we expanded the diversity of MARCM-based reagents and established another site-specific mitotic recombination system. Our transgenic tools can be combined to map individual neurons in specific lineages of various genotypes.

Hung-Hsiang Yu,1,3,5 Takeshi Awasaki,1,3,6 Mark David Schroeder,1,4 Fuhui Long,1,4 Jacob S. Yang,1,4 Yisheng He,2 Peng Ding,2 Jui-Chun Kao,2 Gloria Yueh-Yi Wu,1 Hanchuan Peng,1 Gene Myers,1 and Tzumin Lee1,2,* 1Howard Hughes Medical Institute, Janelia Farm Research Campus, 19700 Helix Drive, Ashburn, VA 20147, USA 2Department of Neurobiology, University of Massachusetts, 364 Plantation Street, Worcester, MA 01605, USA

BACKGROUND:The insect brain can be divided into neuropils that are formed by neurites of both local and remote origin. The complexity of the interconnections obscures how these neuropils are established and interconnected through development. The Drosophila central brain develops from a fixed number of neuroblasts (NBs) that deposit neurons in regional clusters.RESULTS:By determining individual NB clones and pursuing their projections into specific neuropils, we unravel the regional development of the brain neural network. Exhaustive clonal analysis revealed 95 stereotyped neuronal lineages with characteristic cell-body locations and neurite trajectories. Most clones show complex projection patterns, but despite the complexity, neighboring clones often coinnervate the same local neuropil or neuropils and further target a restricted set of distant neuropils.CONCLUSIONS:These observations argue for regional clonal development of both neuropils and neuropil connectivity throughout the Drosophila central brain.

The generation of metameric body plans is a key process in development. In Drosophila segmentation, periodicity is established rapidly through the complex transcriptional regulation of the pair-rule genes. The ‘primary’ pair-rule genes generate their 7-stripe expression through stripe-specific cis-regulatory elements controlled by the preceding non-periodic maternal and gap gene patterns, whereas ‘secondary’ pair-rule genes are thought to rely on 7-stripe elements that read off the already periodic primary pair-rule patterns. Using a combination of computational and experimental approaches, we have conducted a comprehensive systems-level examination of the regulatory architecture underlying pair-rule stripe formation. We find that runt (run), fushi tarazu (ftz) and odd skipped (odd) establish most of their pattern through stripe-specific elements, arguing for a reclassification of ftz and odd as primary pair-rule genes. In the case of run, we observe long-range cis-regulation across multiple intervening genes. The 7-stripe elements of run, ftz and odd are active concurrently with the stripe-specific elements, indicating that maternal/gap-mediated control and pair-rule gene cross-regulation are closely integrated. Stripe-specific elements fall into three distinct classes based on their principal repressive gap factor input; stripe positions along the gap gradients correlate with the strength of predicted input. The prevalence of cis-elements that generate two stripes and their genomic organization suggest that single-stripe elements arose by splitting and subfunctionalization of ancestral dual-stripe elements. Overall, our study provides a greatly improved understanding of how periodic patterns are established in the Drosophila embryo.

The establishment of complex expression patterns at precise times and locations is key to metazoan development, yet a mechanistic understanding of the underlying transcription control networks is still missing. Here we describe a novel thermodynamic model that computes expression patterns as a function of cis-regulatory sequence and of the binding-site preferences and expression of participating transcription factors. We apply this model to the segmentation gene network of Drosophila melanogaster and find that it predicts expression patterns of cis-regulatory modules with remarkable accuracy, demonstrating that positional information is encoded in the regulatory sequence and input factor distribution. Our analysis reveals that both strong and weaker binding sites contribute, leading to high occupancy of the module DNA, and conferring robustness against mutation; short-range homotypic clustering of weaker sites facilitates cooperative binding, which is necessary to sharpen the patterns. Our computational framework is generally applicable to most protein-DNA interaction systems.

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) collects and organizes biological information about the genes, proteins, and chromosomal features of S. cerevisiae, and presents this information on Locus Summary pages for every feature. Locus Summary pages contain such information as descriptions of the function and role of the feature, mutant phenotype data, summaries of physical and genetic interaction data, and results of large-scale gene expression studies. Through this extensive annotation SGD has become a powerful resource for studies of related proteins in other organisms. SGD has developed web-based tools that integrate searching among these diverse types of data to discover functional connections among sets of genes. These tools, such as the GO Term Finder, allow for analysis of gene list results from high-throughput assays to identify cellular localizations or biological roles that a set of proteins shares in common. Use of comparison resources such as the Model Organism BLASTP Best Hits identifies proteins in other organisms that may be orthologs to those identified in the assay. Examples describing the use of these tools will be discussed. The SGD project is an ongoing effort and we welcome feedback from users. SGD is funded as a National Genomic Resource by the National Human Genome Research Institute at the National Institutes of Health.

The recent explosion in protein data generated from both directed small-scale studies and large-scale proteomics efforts has greatly expanded the quantity of available protein information and has prompted the Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) to enhance the depth and accessibility of protein annotations. In particular, we have expanded ongoing efforts to improve the integration of experimental information and sequence-based predictions and have redesigned the protein information web pages. A key feature of this redesign is the development of a GBrowse-derived interactive Proteome Browser customized to improve the visualization of sequence-based protein information. This Proteome Browser has enabled SGD to unify the display of hidden Markov model (HMM) domains, protein family HMMs, motifs, transmembrane regions, signal peptides, hydropathy plots and profile hits using several popular prediction algorithms. In addition, a physico-chemical properties page has been introduced to provide easy access to basic protein information. Improvements to the layout of the Protein Information page and integration of the Proteome Browser will facilitate the ongoing expansion of sequence-specific experimental information captured in SGD, including post-translational modifications and other user-defined annotations. Finally, SGD continues to improve upon the availability of genetic and physical interaction data in an ongoing collaboration with BioGRID by providing direct access to more than 82,000 manually-curated interactions.

Sequencing and annotation of the entire Saccharomyces cerevisiae genome has made it possible to gain a genome-wide perspective on yeast genes and gene products.To make this information available on an ongoing basis, the Saccharomyces Genome Database (SGD) (http://www.yeastgenome.org/) has created the Genome Snapshot (http://db.yeastgenome.org/cgi-bin/genomeSnapShot.pl).The Genome Snapshot summarizes the current state of knowledge about the genes and chromosomal features of S.cerevisiae.The information is organized into two categories: (i) number of each type of chromosomal feature annotated in the genome and (ii) number and distribution of genes annotated to Gene Ontology terms.Detailed lists are accessible through SGD's Advanced Search tool (http://db.yeastgenome.org/cgi-bin/search/featureSearch),and all the data presented on this page are available from the SGD ftp site (ftp://ftp.yeastgenome.org/yeast/).

BACKGROUND:The discovery of cis-regulatory modules in metazoan genomes is crucial for understanding the connection between genes and organism diversity. It is important to quantify how comparative genomics can improve computational detection of such modules.RESULTS:We run the Stubb software on the entire D. melanogaster genome, to obtain predictions of modules involved in segmentation of the embryo. Stubb uses a probabilistic model to score sequences for clustering of transcription factor binding sites, and can exploit multiple species data within the same probabilistic framework. The predictions are evaluated using publicly available gene expression data for thousands of genes, after careful manual annotation. We demonstrate that the use of a second genome (D. pseudoobscura) for cross-species comparison significantly improves the prediction accuracy of Stubb, and is a more sensitive approach than intersecting the results of separate runs over the two genomes. The entire list of predictions is made available online.CONCLUSION:Evolutionary conservation of modules serves as a filter to improve their detection in silico. The future availability of additional fruitfly genomes therefore carries the prospect of highly specific genome-wide predictions using Stubb.

The segmentation gene network of Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross-) regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules) with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a uniform set of criteria, permitting the definition of basic composition rules. The study demonstrates that computational methods are a powerful complement to experimental approaches in the analysis of transcription networks.

A scientific database can be a powerful tool for biologists in an era where large-scale genomic analysis, combined with smaller-scale scientific results, provides new insights into the roles of genes and their products in the cell. However, the collection and assimilation of data is, in itself, not enough to make a database useful. The data must be incorporated into the database and presented to the user in an intuitive and biologically significant manner. Most importantly, this presentation must be driven by the user's point of view; that is, from a biological perspective. The success of a scientific database can therefore be measured by the response of its users - statistically, by usage numbers and, in a less quantifiable way, by its relationship with the community it serves and its ability to serve as a model for similar projects. Since its inception ten years ago, the Saccharomyces Genome Database (SGD) has seen a dramatic increase in its usage, has developed and maintained a positive working relationship with the yeast research community, and has served as a template for at least one other database. The success of SGD, as measured by these criteria, is due in large part to philosophies that have guided its mission and organisation since it was established in 1993. This paper aims to detail these philosophies and how they shape the organisation and presentation of the database.

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The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/), a scientific database of the molecular biology and genetics of the yeast Saccharomyces cerevisiae, has recently developed several new resources that allow the comparison and integration of information on a genome-wide scale, enabling the user not only to find detailed information about individual genes, but also to make connections across groups of genes with common features and across different species. The Fungal Alignment Viewer displays alignments of sequences from multiple fungal genomes, while the Sequence Similarity Query tool displays PSI-BLAST alignments of each S.cerevisiae protein with similar proteins from any species whose sequences are contained in the non-redundant (nr) protein data set at NCBI. The Yeast Biochemical Pathways tool integrates groups of genes by their common roles in metabolism and displays the metabolic pathways in a graphical form. Finally, the Find Chromosomal Features search interface provides a versatile tool for querying multiple types of information in SGD.

Although mRNA decay rates are a key determinant of the steady-state concentration for any given mRNA species, relatively little is known, on a population level, about what factors influence turnover rates and how these rates are integrated into cellular decisions. We decided to measure mRNA decay rates in two human cell lines with high-density oligonucleotide arrays that enable the measurement of decay rates simultaneously for thousands of mRNA species. Using existing annotation and the Gene Ontology hierarchy of biological processes, we assign mRNAs to functional classes at various levels of resolution and compare the decay rate statistics between these classes. The results show statistically significant organizational principles in the variation of decay rates among functional classes. In particular, transcription factor mRNAs have increased average decay rates compared with other transcripts and are enriched in “fast-decaying” mRNAs with half-lives <2 h. In contrast, we find that mRNAs for biosynthetic proteins have decreased average decay rates and are deficient in fast-decaying mRNAs. Our analysis of data from a previously published study of Saccharomyces cerevisiae mRNA decay shows the same functional organization of decay rates, implying that it is a general organizational scheme for eukaryotes. Additionally, we investigated the dependence of decay rates on sequence composition, that is, the presence or absence of short mRNA motifs in various regions of the mRNA transcript. Our analysis recovers the positive correlation of mRNA decay with known AU-rich mRNA motifs, but we also uncover further short mRNA motifs that show statistically significant correlation with decay. However, we also note that none of these motifs are strong predictors of mRNA decay rate, indicating that the regulation of mRNA decay is more complex and may involve the cooperative binding of several RNA-binding proteins at different sites.

Chapter 7 Computational Methods and Bioinformatic Tools Dr. Paul Cullen, Ogham GmbH, Mendelstr. 11, 48149 Münster, GermanySearch for more papers by this authorDr. Stefan Lorkowski, Institute of Arteriosclerosis Research, University of Münster, Domagkstr. 3, 48149 Münster, GermanySearch for more papers by this authorSteffen Hennig, Max Planck Institut für Molekulare Genetik, Berlin (Germany)Search for more papers by this authorAlbert Poustka, Max Planck Institut für Molekulare Genetik, Berlin (Germany)Search for more papers by this authorGeorgia Panopoulou, Max Planck Institut für Molekulare Genetik, Berlin (Germany)Search for more papers by this authorHans Lehrach, Max Planck Institut für Molekulare Genetik, Berlin (Germany)Search for more papers by this authorEberhard Korsching, University of Münster, Münster (Germany)Search for more papers by this authorOxana Pickeral, Human Genome Sciences, Inc., Rockville (USA)Search for more papers by this authorRalf Herwig, Max Planck Institut für Molekulare Genetik, Berlin (Germany)Search for more papers by this authorAlexander Kel, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorDmitrij Tchekmenev, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorEdgar Wingender, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorJohannes Streicher, University of Vienna, Vienna (Austria)Search for more papers by this authorGerd B. Müller, University of Vienna, Vienna (Austria)Search for more papers by this authorTakeshi Kawashima, Kyoto University, Kyoto (Japan)Search for more papers by this authorKazuhiro W. Makabe, Kyoto University, Kyoto (Japan)Search for more papers by this authorInna Dubchak, Lawrence Berkeley National Laboratory, Berkeley (USA)Search for more papers by this authorHongkai Ji, Tsinghua University, Beijing (People's Republic of China)Search for more papers by this authorKousaku Okubo, Kyushu University, Fukuoka (Japan)Search for more papers by this authorShoko Kawamoto, Medical Institute for Bioregulation, Fukuoka (Japan)Search for more papers by this authorEllen Fricke, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorDagmar Karas, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorMartin Haubrock, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorSigrid Land, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorStella Rotert, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorXin Chen, The National Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing (People's Republic of China)Search for more papers by this authorJoan Pontius, National Center for Biotechnology Information, National Institute of Health, Bethesda (USA)Search for more papers by this authorEric Eveno, Genexpress, Centre National de la Recherche Scientifique, Villejuif Cedex (France)Search for more papers by this authorCharles Auffray, Genexpress, Centre National de la Recherche Scientifique, Villejuif Cedex (France)Search for more papers by this authorCharles Decraene, Commisariat á l'Ènergie Atomique (CEA), Evry Cedex (France)Search for more papers by this authorClaude Chelala, Genexpress, Centre National de la Recherche Scientifique, Villejuif Cedex (France)Search for more papers by this authorGeneviève Piétu, Commisariat á l'Ènergie Atomique (CEA), Evry Cedex (France)Search for more papers by this authorMarie-Dominique Devignes, LORIA – Langue et dialogue, Vandoeuvre les Nancy (France)Search for more papers by this authorRégine Mariage-Samson, Genexpress, Centre National de la Recherche Scientifique, Villejuif Cedex (France)Search for more papers by this authorSandrine Imbeaud, Genexpress, Centre National de la Recherche Scientifique, Villejuif Cedex (France)Search for more papers by this authorSylvie Bortoli, Commisariat á l'Ènergie Atomique (CEA), Evry Cedex (France)Search for more papers by this authorAlon Amit, Compugen Ltd., Jamesburg (USA)Search for more papers by this authorMartin Ringwald, The Jackson Laboratory, Bar Harbor (USA)Search for more papers by this authorMichael J. de Veer, The Walter and Eliza Hall Institute of Medical Research, The Royal Melbourne Hospital, Victoria (Australia)Search for more papers by this authorBryan R. G. Williams, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland (USA)Search for more papers by this authorEldon M. Walker, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland (USA)Search for more papers by this authorJamie A. Davies, University of Edinburgh, Medical School, Edinburgh (UK)Search for more papers by this authorChristoph Grunau, Institute de Génétique Humain, Montpellier (France)Search for more papers by this authorRichard Baldock, Western General Hospital, Edinburgh (UK)Search for more papers by this authorDuncan R. Davidson, Western General Hospital, Edinburgh (UK)Search for more papers by this authorChristian J. Stoeckert Jr., University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorAngel Pizarro, University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorElisabetta Manduchi, University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorGregory R. Grant, University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorJonathan Crabtree, Center for Bioinformatics, University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorJunmin Liu, University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorPhuc V. Le, University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorShannon K. McWeeney, University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorStephen Welle, University of Rochester, Rochester (USA)Search for more papers by this authorCatherine A. Ball, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorDavid Botstein, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorGail Binkley, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorGavin J. Sherlock, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorJ. Michael Cherry, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorKara Dolinski, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorLaurie Issel-Tarver, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorMark Schroeder, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorSelina S. Dwight, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorShuai Wenig, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorJohn C. Matese, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorHeng Jin, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorJeremy Gollub, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorJoan Hebert, Center for Clinical Sciences Research, Stanford (USA)Search for more papers by this authorMiroslava Kaloper, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorPatrick O. Brown, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorTina Hernandez-Boussard, Center for Clinical Sciences Research, Stanford (USA)Search for more papers by this authorAnuj Kumar, Cellular and Developmental Biology, Yale University, New Haven (USA)Search for more papers by this authorKei-Hoi Cheung, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorLuis Marenco, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorMichael Snyder, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorNick Tosches, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorPaul Bertone, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorPerry Miller, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorPeter Masiar, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorYang Liu, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorGraziano Pesole, University of Milan, Milan (Italy)Search for more papers by this authorPhilippe Marc, Ecole Normale Supérieure, Paris (France)Search for more papers by this authorMargaret Biswas, ViaLactia Biosciences Ltd., Auckland (New Zealand)Search for more papers by this authorPaul Kersey, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton (UK)Search for more papers by this authorRolf Apweiler, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton (UK)Search for more papers by this authorChristine Hoogland, Swiss Institute of Bioinformatics, Geneva (Switzerland)Search for more papers by this author Dr. Paul Cullen, Ogham GmbH, Mendelstr. 11, 48149 Münster, GermanySearch for more papers by this authorDr. Stefan Lorkowski, Institute of Arteriosclerosis Research, University of Münster, Domagkstr. 3, 48149 Münster, GermanySearch for more papers by this authorSteffen Hennig, Max Planck Institut für Molekulare Genetik, Berlin (Germany)Search for more papers by this authorAlbert Poustka, Max Planck Institut für Molekulare Genetik, Berlin (Germany)Search for more papers by this authorGeorgia Panopoulou, Max Planck Institut für Molekulare Genetik, Berlin (Germany)Search for more papers by this authorHans Lehrach, Max Planck Institut für Molekulare Genetik, Berlin (Germany)Search for more papers by this authorEberhard Korsching, University of Münster, Münster (Germany)Search for more papers by this authorOxana Pickeral, Human Genome Sciences, Inc., Rockville (USA)Search for more papers by this authorRalf Herwig, Max Planck Institut für Molekulare Genetik, Berlin (Germany)Search for more papers by this authorAlexander Kel, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorDmitrij Tchekmenev, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorEdgar Wingender, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorJohannes Streicher, University of Vienna, Vienna (Austria)Search for more papers by this authorGerd B. Müller, University of Vienna, Vienna (Austria)Search for more papers by this authorTakeshi Kawashima, Kyoto University, Kyoto (Japan)Search for more papers by this authorKazuhiro W. Makabe, Kyoto University, Kyoto (Japan)Search for more papers by this authorInna Dubchak, Lawrence Berkeley National Laboratory, Berkeley (USA)Search for more papers by this authorHongkai Ji, Tsinghua University, Beijing (People's Republic of China)Search for more papers by this authorKousaku Okubo, Kyushu University, Fukuoka (Japan)Search for more papers by this authorShoko Kawamoto, Medical Institute for Bioregulation, Fukuoka (Japan)Search for more papers by this authorEllen Fricke, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorDagmar Karas, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorMartin Haubrock, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorSigrid Land, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorStella Rotert, BIOBASE GmbH, Wolfenbüttel (Germany)Search for more papers by this authorXin Chen, The National Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing (People's Republic of China)Search for more papers by this authorJoan Pontius, National Center for Biotechnology Information, National Institute of Health, Bethesda (USA)Search for more papers by this authorEric Eveno, Genexpress, Centre National de la Recherche Scientifique, Villejuif Cedex (France)Search for more papers by this authorCharles Auffray, Genexpress, Centre National de la Recherche Scientifique, Villejuif Cedex (France)Search for more papers by this authorCharles Decraene, Commisariat á l'Ènergie Atomique (CEA), Evry Cedex (France)Search for more papers by this authorClaude Chelala, Genexpress, Centre National de la Recherche Scientifique, Villejuif Cedex (France)Search for more papers by this authorGeneviève Piétu, Commisariat á l'Ènergie Atomique (CEA), Evry Cedex (France)Search for more papers by this authorMarie-Dominique Devignes, LORIA – Langue et dialogue, Vandoeuvre les Nancy (France)Search for more papers by this authorRégine Mariage-Samson, Genexpress, Centre National de la Recherche Scientifique, Villejuif Cedex (France)Search for more papers by this authorSandrine Imbeaud, Genexpress, Centre National de la Recherche Scientifique, Villejuif Cedex (France)Search for more papers by this authorSylvie Bortoli, Commisariat á l'Ènergie Atomique (CEA), Evry Cedex (France)Search for more papers by this authorAlon Amit, Compugen Ltd., Jamesburg (USA)Search for more papers by this authorMartin Ringwald, The Jackson Laboratory, Bar Harbor (USA)Search for more papers by this authorMichael J. de Veer, The Walter and Eliza Hall Institute of Medical Research, The Royal Melbourne Hospital, Victoria (Australia)Search for more papers by this authorBryan R. G. Williams, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland (USA)Search for more papers by this authorEldon M. Walker, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland (USA)Search for more papers by this authorJamie A. Davies, University of Edinburgh, Medical School, Edinburgh (UK)Search for more papers by this authorChristoph Grunau, Institute de Génétique Humain, Montpellier (France)Search for more papers by this authorRichard Baldock, Western General Hospital, Edinburgh (UK)Search for more papers by this authorDuncan R. Davidson, Western General Hospital, Edinburgh (UK)Search for more papers by this authorChristian J. Stoeckert Jr., University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorAngel Pizarro, University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorElisabetta Manduchi, University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorGregory R. Grant, University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorJonathan Crabtree, Center for Bioinformatics, University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorJunmin Liu, University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorPhuc V. Le, University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorShannon K. McWeeney, University of Pennsylvania, Philadelphia (USA)Search for more papers by this authorStephen Welle, University of Rochester, Rochester (USA)Search for more papers by this authorCatherine A. Ball, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorDavid Botstein, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorGail Binkley, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorGavin J. Sherlock, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorJ. Michael Cherry, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorKara Dolinski, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorLaurie Issel-Tarver, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorMark Schroeder, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorSelina S. Dwight, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorShuai Wenig, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorJohn C. Matese, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorHeng Jin, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorJeremy Gollub, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorJoan Hebert, Center for Clinical Sciences Research, Stanford (USA)Search for more papers by this authorMiroslava Kaloper, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorPatrick O. Brown, School of Medicine, Stanford University, Stanford (USA)Search for more papers by this authorTina Hernandez-Boussard, Center for Clinical Sciences Research, Stanford (USA)Search for more papers by this authorAnuj Kumar, Cellular and Developmental Biology, Yale University, New Haven (USA)Search for more papers by this authorKei-Hoi Cheung, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorLuis Marenco, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorMichael Snyder, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorNick Tosches, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorPaul Bertone, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorPerry Miller, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorPeter Masiar, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorYang Liu, School of Medicine, Yale University, New Haven (USA)Search for more papers by this authorGraziano Pesole, University of Milan, Milan (Italy)Search for more papers by this authorPhilippe Marc, Ecole Normale Supérieure, Paris (France)Search for more papers by this authorMargaret Biswas, ViaLactia Biosciences Ltd., Auckland (New Zealand)Search for more papers by this authorPaul Kersey, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton (UK)Search for more papers by this authorRolf Apweiler, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton (UK)Search for more papers by this authorChristine Hoogland, Swiss Institute of Bioinformatics, Geneva (Switzerland)Search for more papers by this author Book Editor(s):Dr. Stefan Lorkowski, Institute of Arteriosclerosis Research, University of Münster, Domagkstr. 3, 48149 Münster, GermanySearch for more papers by this authorDr. Paul Cullen, Ogham GmbH, Mendelstr. 11, 48149 Münster, GermanySearch for more papers by this author First published: 01 December 2002 https://doi.org/10.1002/352760149X.ch7 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Summary This chapter contains sections titled: Introduction Comparative expressed sequence tag analysis Introduction Processing expressed sequence tags prior to content analysis Gene content and annotation of expressed sequence tags Expressed sequence tags in comparative genomics In silico subtraction using clustered sets of expressed sequence tags Expressed sequence tag data repositories and cDNA clone distribution centres Data management and data mining Introduction Current situation Future development Taking part in bioinformatics Hardware and software demands Data types, structures and processing Communication structures Building a test scenario Microarray experiments Analysing the workflow – getting things done Designing the question and choosing the right tools for the answer Scaling up Strategies of data mining Data evaluation and representation Principles of query languages Data mining Custom solutions Summary Integration of heterogeneous high-throughput gene expression data Introduction Steps towards data integration Initial steps in realising data integration Conclusions Cluster analysis of gene expression profiles Introduction Information content of gene expression clusters Similarity matrices and gene expression matrices Clustering algorithms Hierarchical clustering Self-organising maps K-means Gene shaving Evaluation of gene expression clusters Conclusion Promoter finding in eukaryotic genomes Introduction Transcription regulation in eukaryotes Promoter structure Transcription factors Combinatorial nature of transcription regulation Databases on transcriptional regulation In silico study of gene transcription regulation Recognition of cis-regulatory elements Recognition of composite regulatory elements Recognition of promoters Conclusions GeneEMAC – Three-dimensional visualisation of gene expression Introduction Principles and basics of the GeneEMAC concept Specimen preparation Whole-mount in situ hybridisation Embedding Introduction of external markers Capturing of a reference image Histological sectioning Microscopy and digital image processing Image capturing Image congruencing Image segmentation Generation of a three-dimensional model Visualisations of models Examples Discussion RNA-based gene expression databases and analyses tools Introduction ASDB – The Alternative Splicing Database AsMamDB – The Alternative Splice Database of Mammals BodyMap – An anatomical gene expression database of human and mouse The CYTOMER® Gene Expression Database on human organs and cell types Database of three-dimensional visualisation of gene expression dbEST – The Database of Expressed Sequence Tags DDD – Digital Differential Display The Genexpress IMAGE Knowledge Base of the Human Genome and Transcriptomes The GencartaTM Database GEO – Gene Expression Omnibus Database GXD – The Mouse Gene Expression Database ISG Database – Interferon-Stimulated Gene Database The Kidney Development Database MAGEST – The Maboya Gene Expression Patterns and Sequence Tags Database MethDB – The DNA Methylation Database EMAGE – The Edinburgh Mouse Atlas Gene Expression Database RAD – The RNA Abundance Database The Rochester Muscle Database SAGEmap – The serial analysis of gene expression tag to gene mapping database SGD – The Saccharomyces Genome Database and its Expression Connection SMD – The Stanford Microarray Database TRIPLES – The Database of Transposon-Insertion Phenotypes, Localisation, and Expression in Saccharomyces cerevisiae UTRdb and UTRsite – The Specialised Databases of Sequences and Functional Elements of 5′ and 3′-Untranslated Regions of Eukaryotic mRNAs yMGV – The Yeast Microarray Global Viewer Protein-based gene expression databases and analyses tools Introduction to protein-based gene expression databases The Proteome Analysis Database SWISS-2DPAGE – A two-dimensional polyacrylamide gel electrophoresis database Further gene-expression databases in the internet Summary References Analysing Gene Expression: A Handbook of Methods: Possibilities and Pitfalls RelatedInformation