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The identification and accurate quantitation of protein abundance has been a major objective of proteomics research. Abundance studies have the potential to provide users with data that can be used to gain a deeper understanding of protein function and regulation and can also help identify cellular pathways and modules that operate under various environmental stress conditions. One of the central missions of the Saccharomyces Genome Database (SGD; https://www.yeastgenome.org) is to work with researchers to identify and incorporate datasets of interest to the wider scientific community, thereby enabling hypothesis-driven research. A large number of studies have detailed efforts to generate proteome-wide abundance data, but deeper analyses of these data have been hampered by the inability to compare results between studies. Recently, a unified protein abundance dataset was generated through the evaluation of more than 20 abundance datasets, which were normalized and converted to common measurement units, in this case molecules per cell. We have incorporated these normalized protein abundance data and associated metadata into the SGD database, as well as the SGD YeastMine data warehouse, resulting in the addition of 56 487 values for untreated cells grown in either rich or defined media and 28 335 values for cells treated with environmental stressors. Abundance data for protein-coding genes are displayed in a sortable, filterable table on Protein pages, available through Locus Summary pages. A median abundance value was incorporated, and a median absolute deviation was calculated for each protein-coding gene and incorporated into SGD. These values are displayed in the Protein section of the Locus Summary page. The inclusion of these data has enhanced the quality and quantity of protein experimental information presented at SGD and provides opportunities for researchers to access and utilize the data to further their research.

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is an expertly curated database of literature-derived functional information for the model organism budding yeast, Saccharomyces cerevisiae. SGD constantly strives to synergize new types of experimental data and bioinformatics predictions with existing data, and to organize them into a comprehensive and up-to-date information resource. The primary mission of SGD is to facilitate research into the biology of yeast and to provide this wealth of information to advance, in many ways, research on other organisms, even those as evolutionarily distant as humans. To build such a bridge between biological kingdoms, SGD is curating data regarding yeast-human complementation, in which a human gene can successfully replace the function of a yeast gene, and/or vice versa. These data are manually curated from published literature, made available for download, and incorporated into a variety of analysis tools provided by SGD.

Proteins seldom function individually. Instead, they interact with other proteins or nucleic acids to form stable macromolecular complexes that play key roles in important cellular processes and pathways. One of the goals of Saccharomyces Genome Database (SGD; www.yeastgenome.org) is to provide a complete picture of budding yeast biological processes. To this end, we have collaborated with the Molecular Interactions team that provides the Complex Portal database at EMBL-EBI to manually curate the complete yeast complexome. These data, from a total of 589 complexes, were previously available only in SGD’s YeastMine data warehouse (yeastmine.yeastgenome.org) and the Complex Portal (www.ebi.ac.uk/complexportal). We have now incorporated these macromolecular complex data into the SGD core database and designed complex-specific reports to make these data easily available to researchers. These web pages contain referenced summaries focused on the composition and function of individual complexes. In addition, detailed information about how subunits interact within the complex, their stoichiometry and the physical structure are displayed when such information is available. Finally, we generate network diagrams displaying subunits and Gene Ontology annotations that are shared between complexes. Information on macromolecular complexes will continue to be updated in collaboration with the Complex Portal team and curated as more data become available.

The Saccharomyces Genome Database (SGD) is a well-established, key resource for researchers studying Saccharomyces cerevisiae. In addition to updating and maintaining the official genomic sequence of this highly studied organism, SGD provides integrated data regarding gene functions and phenotypes, which are extracted from the published literature. The vast amount and variety of data housed in the database can prove challenging to navigate for the first-time user. Therefore, this chapter serves as an introduction describing how to search the database in order to discover new information. We introduce the different types of pages on the website, and describe how to manipulate the tables and diagrams therein to display, download, or analyze the data using various SGD tools.

The Saccharomyces Genome Database (SGD) provides comprehensive, integrated biological information for the budding yeast Saccharomyces cerevisiae, along with search and analysis tools to explore these data, enabling the discovery of functional relationships between sequence and gene products in fungi and higher organisms. We have recently expanded our data model for regulation curation to address regulation at the protein level in addition to transcription, and are presenting the expanded data on the ‘Regulation’ pages at SGD. These pages include a summary describing the context under which the regulator acts, manually curated and high-throughput annotations showing the regulatory relationships for that gene and a graphical visualization of its regulatory network and connected networks. For genes whose products regulate other genes or proteins, the Regulation page includes Gene Ontology enrichment analysis of the biological processes in which those targets participate. For DNA-binding transcription factors, we also provide other information relevant to their regulatory function, such as DNA binding site motifs and protein domains. As with other data types at SGD, all regulatory relationships and accompanying data are available through YeastMine, SGD’s data warehouse based on InterMine. Database URL: http://www.yeastgenome.org

Due to recent advancements in the production of experimental proteomic data, the Saccharomyces genome database (SGD; www.yeastgenome.org) has been expanding our protein curation activities to make new data types available to our users. Because of broad interest in post-translational modifications (PTM) and their importance to protein function and regulation, we have recently started incorporating expertly curated PTM information on individual protein pages. Here we also present the inclusion of new abundance and protein half-life data obtained from high-throughput proteome studies. These new data types have been included with the aim to facilitate cellular biology research. Database URL: www.yeastgenome.org

The Saccharomyces Genome Database (SGD; www.yeastgenome.org ), the primary genetics and genomics resource for the budding yeast S. cerevisiae , provides free public access to expertly curated information about the yeast genome and its gene products. As the central hub for the yeast research community, SGD engages in a variety of social outreach efforts to inform our users about new developments, promote collaboration, increase public awareness of the importance of yeast to biomedical research, and facilitate scientific discovery. Here we describe these various outreach methods, from networking at scientific conferences to the use of online media such as blog posts and webinars, and include our perspectives on the benefits provided by outreach activities for model organism databases. Database url http://www.yeastgenome.org.

Elucidating the function of individual proteins will help to create a comprehensive picture of cell biology, as well as shed light on human disease mechanisms, possible treatments, and cures. Due to its compact genome, and extensive history of experimentation and annotation, the budding yeast Saccharomyces cerevisiae is an ideal model organism in which to determine protein function. This information can then be leveraged to infer functions of human homologs. Despite the large amount of research and biological data about S. cerevisiae, many proteins' functions remain unknown. Here, we explore ways to use the Saccharomyces Genome Database (SGD; http://www.yeastgenome.org ) to predict the function of proteins and gain insight into their roles in various cellular processes.

The Saccharomyces Genome Database (SGD; http:// www.yeastgenome.org) is a scientific database for the molecular biology and genetics of the yeast Saccharomyces cerevisiae, which is commonly known as baker’s or budding yeast. The information in SGD includes functional annotations, mapping and sequence information, protein domains and structure, expression data, mutant phenotypes, physical and genetic interactions and the primary literature from which these data are derived. Here we describe how published phenotypes and genetic interaction data are annotated and displayed in SGD.

The quest to characterize each of the genes of the yeast Saccharomyces cerevisiae has propelled the development and application of novel high-throughput (HTP) experimental techniques. To handle the enormous amount of information generated by these techniques, new bioinformatics tools and resources are needed. Gene Ontology (GO) annotations curated by the Saccharomyces Genome Database (SGD) have facilitated the development of algorithms that analyze HTP data and help predict functions for poorly characterized genes in S. cerevisiae and other organisms. Here, we describe how published results are incorporated into GO annotations at SGD and why researchers can benefit from using these resources wisely to analyze their HTP data and predict gene functions.

A set of 37,325 unique literature citations was identified from 120,078 literature-based annotations in the Saccharomyces Genome Database (SGD). The citations, gene products, and related Gene Ontology (GO) annotations were analyzed to quantify unique articles, journals, genes, and to rank by publication year, language, and GO term frequency. GO terms, MeSH indexing terms, MeSH Journal Descriptors, and SGD Literature Topics were quantified and analyzed to assess their potential utility for results set clustering. Results: Bradford’s Law of Scattering was shown to hold for the citations, journals, gene products, and GO annotations. Only the MeSH terms and article title/abstract pairs had significant numbers of term co-occurrence. Multiple term types may be useful for faceted searching and clustered results set browsing if the strengths of each are leveraged.

The Saccharomyces Genome Database (SGD) is the main community repository of information for the budding yeast, Saccharomyces cerevisiae. The SGD has collected published results on chromosomal features, including genes and their products, and has become an encyclopedia of information on the biology of the yeast cell. This information includes gene and gene product function, phenotype, interactions, regulation, complexes, and pathways. All information has been integrated into a unique web resource, accessible via http://yeastgenome.org. The website also provides custom tools to allow useful searches and visualization of data. The experimentally defined functions of genes, mutant phenotypes, and sequence homologies archived in the SGD provide a platform for understanding many fields of biological research. The mission of SGD is to provide public access to all published experimental results on yeast to aid life science students, educators, and researchers. As such, the SGD has become an essential tool for the design of experiments and for the analysis of experimental results.

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.

The Saccharomyces Genome Database (SGD) is compiling and annotating a comprehensive catalogue of functional sequence elements identified in the budding yeast genome. Recent advances in deep sequencing technologies have enabled for example, global analyses of transcription profiling and assembly of maps of transcription factor occupancy and higher order chromatin organization, at nucleotide level resolution. With this growing influx of published genome-scale data, come new challenges for their storage, display, analysis and integration. Here, we describe SGD's progress in the creation of a consolidated resource for genome sequence elements in the budding yeast, the considerations taken in its design and the lessons learned thus far. The data within this collection can be accessed at http://browse.yeastgenome.org and downloaded from http://downloads.yeastgenome.org. Database URL: http://www.yeastgenome.org

Analysis of genomic data requires access to software tools that place the sequence-derived information in the context of biology. The Saccharomyces Genome Database (SGD) integrates functional information about budding yeast genes and their products with a set of analysis tools that facilitate exploring their biological details. This unit describes how the various types of functional data available at SGD can be searched, retrieved, and analyzed. Starting with the guided tour of the SGD Home page and Locus Summary page, this unit highlights how to retrieve data using YeastMine, how to visualize genomic information with GBrowse, how to explore gene expression patterns with SPELL, and how to use Gene Ontology tools to characterize large-scale datasets.

Background Key to the success of e-Science is the ability to computationally evaluate expert-composed hypotheses for validity against experimental data. Researchers face the challenge of collecting, evaluating and integrating large amounts of diverse information to compose and evaluate a hypothesis. Confronted with rapidly accumulating data, researchers currently do not have the software tools to undertake the required information integration tasks. Results We present HyQue, a Semantic Web tool for querying scientific knowledge bases with the purpose of evaluating user submitted hypotheses. HyQue features a knowledge model to accommodate diverse hypotheses structured as events and represented using Semantic Web languages (RDF/OWL). Hypothesis validity is evaluated against experimental and literature-sourced evidence through a combination of SPARQL queries and evaluation rules. Inference over OWL ontologies (for type specifications, subclass assertions and parthood relations) and retrieval of facts stored as Bio2RDF linked data provide support for a given hypothesis. We evaluate hypotheses of varying levels of detail about the genetic network controlling galactose metabolism in Saccharomyces cerevisiae to demonstrate the feasibility of deploying such semantic computing tools over a growing body of structured knowledge in Bio2RDF. Conclusions HyQue is a query-based hypothesis evaluation system that can currently evaluate hypotheses about the galactose metabolism in S. cerevisiae. Hypotheses as well as the supporting or refuting data are represented in RDF and directly linked to one another allowing scientists to browse from data to hypothesis and vice versa. HyQue hypotheses and data are available at http://semanticscience.org/projects/hyque.

Background Mannoproteins construct the outer cover of the fungal cell wall. The covalently linked cell wall protein Ccw12p is an abundant mannoprotein. It is considered as crucial structural cell wall component since in baker's yeast the lack of CCW12 results in severe cell wall damage and reduced mating efficiency. Results In order to explore the function of CCW12, we performed a Synthetic Genetic Analysis (SGA) and identified genes that are essential in the absence of CCW12. The resulting interaction network identified 21 genes involved in cell wall integrity, chitin synthesis, cell polarity, vesicular transport and endocytosis. Among those are PFD1, WHI3, SRN2, PAC10, FEN1 and YDR417C, which have not been related to cell wall integrity before. We correlated our results with genetic interaction networks of genes involved in glucan and chitin synthesis. A core of genes essential to maintain cell integrity in response to cell wall stress was identified. In addition, we performed a large-scale transcriptional analysis and compared the transcriptional changes observed in mutant ccw12 Δ with transcriptomes from studies investigating responses to constitutive or acute cell wall damage. We identified a set of genes that are highly induced in the majority of the mutants/conditions and are directly related to the cell wall integrity pathway and cell wall compensatory responses. Among those are BCK1, CHS3, EDE1, PFD1, SLT2 and SLA1 that were also identified in the SGA. In contrast, a specific feature of mutant ccw12 Δ is the transcriptional repression of genes involved in mating. Physiological experiments substantiate this finding. Further, we demonstrate that Ccw12p is present at the cell periphery and highly concentrated at the presumptive budding site, around the bud, at the septum and at the tip of the mating projection. Conclusions The combination of high throughput screenings, phenotypic analyses and localization studies provides new insight into the function of Ccw12p. A compensatory response, culminating in cell wall remodelling and transport/recycling pathways is required to buffer the loss of CCW12. Moreover, the enrichment of Ccw12p in bud, septum and mating projection is consistent with a role of Ccw12p in preserving cell wall integrity at sites of active growth. The microarray data produced in this analysis have been submitted to NCBI GEO database and GSE22649 record was assigned., BMC Genomics, 12, ISSN:1471-2164

Background Glucose inhibition of gluconeogenic growth suppressor 2 protein (Gis2p) and zinc-finger protein 9 (ZNF9) are conserved yeast and human zinc-finger proteins. The function of yeast Gis2p is unknown, but human ZNF9 has been reported to bind nucleic acids, and mutations in the ZNF9 gene cause the neuromuscular disease myotonic dystrophy type 2. To explore the impact of these proteins on RNA regulation, we undertook a systematic analysis of the RNA targets and of the global implications for gene expression. Results Hundreds of mRNAs were associated with Gis2p, mainly coding for RNA processing factors, chromatin modifiers and GTPases. Target mRNAs contained stretches of G(A/U)(A/U) trinucleotide repeats located in coding sequences, which are sufficient for binding to both Gis2p and ZNF9, thus implying strong structural conservation. Predicted ZNF9 targets belong to the same functional categories as seen in yeast, indicating functional conservation, which is further supported by complementation of the large cell-size phenotype of gis2 mutants with ZNF9. We further applied a matched-sample proteome-transcriptome analysis suggesting that Gis2p differentially coordinates expression of RNA regulons, primarily by reducing mRNA and protein levels of genes required for ribosome assembly and by selectively up-regulating protein levels of myosins. Conclusions This integrated systematic exploration of RNA targets for homologous RNA-binding proteins indicates an unexpectedly high conservation of the RNA-binding properties and of potential targets, thus predicting conserved RNA regulons. We also predict regulation of muscle-specific genes by ZNF9, adding a potential link to the myotonic dystrophy related phenotypes seen in ZNF9 mouse models., Genome Biology, 12 (1), ISSN:1474-760X

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) organizes and displays molecular and genetic information about the genes and proteins of baker's yeast, Saccharomyces cerevisiae. Mutant phenotype screens have been the starting point for a large proportion of yeast molecular biological studies, and are still used today to elucidate the functions of uncharacterized genes and discover new roles for previously studied genes. To greatly facilitate searching and comparison of mutant phenotypes across genes, we have devised a new controlled-vocabulary system for capturing phenotype information. Each phenotype annotation is represented as an ‘observable’, which is the entity, or process that is observed, and a ‘qualifier’ that describes the change in that entity or process in the mutant (e.g. decreased, increased, or abnormal). Additional information about the mutant, such as strain background, allele name, conditions under which the phenotype is observed, or the identity of relevant chemicals, is captured in separate fields. For each gene, a summary of the mutant phenotype information is displayed on the Locus Summary page, and the complete information is displayed in tabular format on the Phenotype Details Page. All of the information is searchable and may also be downloaded in bulk using SGD's Batch Download Tool or Download Data Files Page. In the future, phenotypes will be integrated with other curated data to allow searching across different types of functional information, such as genetic and physical interaction data and Gene Ontology annotations. Database URL: http://www.yeastgenome.org/

In recent years, thousands of Saccharomyces cerevisiae genomes have been sequenced to varying degrees of completion. The Saccharomyces Genome Database (SGD) has long been the keeper of the original eukaryotic reference genome sequence, which was derived primarily from S. cerevisiae strain S288C. Because new technologies are pushing S. cerevisiae annotation past the limits of any system based exclusively on a single reference sequence, SGD is actively working to expand the original S. cerevisiae systematic reference sequence from a single genome to a multi-genome reference panel. We first commissioned the sequencing of additional genomes and their automated analysis using the AGAPE pipeline. Here we describe our curation strategy to produce manually reviewed high-quality genome annotations in order to elevate 11 of these additional genomes to Reference status. Database URL: http://www.yeastgenome.org/

The Saccharomyces Genome Database (SGD: http:// genome-www.stanford.edu/Saccharomyces/) has recently developed new resources to provide more complete information about proteins from the budding yeast Saccharomyces cerevisiae. The PDB Homologs page provides structural information from the Protein Data Bank (PDB) about yeast proteins and/or their homologs. SGD has also created a resource that utilizes the eMOTIF database for motif information about a given protein. A third new resource is the Protein Information page, which contains protein physical and chemical properties, such as molecular weight and hydropathicity scores, predicted from the translated ORF sequence.

A number of techniques have been developed in order to address issues such as genome, trascriptome and proteome analysis. However, a time and cost effective technique for interactome analysis is still lacking. Lots of methods for the predicion of protein - protein interacions have been developed: some of them are based on high quality alignment of sequences, others are based on the tridimensional features of proteins, but they all bear strong limitations that make impossible their large scale application. Recently, an SVM-based machine learning approach has been used to address this topic. Although the method was able to correctly classify 80% of the test samples, it was not applied to the prediction of yet unknown interactions. In this work, we address this topic and show that an optimized, SVM-based machine learning approach trained with combinations of shuffled sequences as examples of lack of interaction is unable to make large scale predictions of interaction. © Springer-Verlag Berlin Heidelberg 2003.

Yeast is a eukaryotic, single-celled organism that, at the biochemical level, is very similar to all other eukaryotic cells. Indeed, much yeast research of recent times has used the organism as a model for more complex eukaryotes because of its ease of culture, the high level of genetic understanding, including the complete genome structure (Goffeau et al., 1996), and the ease of manipulation rather than an intrinsic interest in the industrial fermentation properties of the organism. We thus have a very considerable amount of laboratory-derived information on the biochemistry and molecular biology of yeast, and the aim of this chapter is to discuss aspects that are important in the production of beer rather than attempting an overall survey of the topic. The background material is widely available in general textbooks (Stryer; 1995; Lodish et al., 1995; Zubay, 1998), in books that address yeasts in a more specific manner (Strathern, Jones and Broach, 1982; Walker, 1998; Dickinson and Schweizer, 1999) and at a number of web sites for example, the Saccharomyces Genome Database at http://genome-www.stanford.edu/Saccharomyces or the many facilities for Saccharomyces genomics and proteomics available at http:// scientistcentral.com.

We introduce clustering with overlapping neighborhood expansion (ClusterONE), a method for detecting potentially overlapping protein complexes from protein-protein interaction data. ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence (MIPS) catalog and complexes derived from the Saccharomyces Genome Database (SGD) than the results of seven popular methods. The results also showed a high extent of functional homogeneity.

Publisher Summary The goal of the Saccharomyces Genome Database (SGD) is to provide information about the genome of this yeast, the genes it encodes, and their biological functions. The genome sequence of S. cerevisiae provides the structure around which information in SGD is organized; value is added to the sequence by careful biological annotation drawn from a number of sources. SGD curates and stores information about budding yeast DNA and protein sequences, genetics, cell biology, and the associated community of researchers. SGD also provides search and analysis tools designed to help researchers mine the data for pieces or patterns of biological information relevant to their interests. A continuing challenge for the staff of SGD is to present up-to-date information about yeast genes in a format that is intuitive and useful to biomedical researchers, while responding to the needs of this community by providing resources and tools for exploring the data in new ways. This chapter describes the organization of SGD, the sources of the data stored in SGD, some methods for retrieving information from the database, connections SGD has with outside databases and non-yeast research communities, and SGD's repository of yeast community information.

The Saccharomyces Genome Database (SGD) resources, ranging from genetic and physical maps to genome-wide analysis tools, reflect the scientific progress in identifying genes and their functions over the last decade. As emphasis shifts from identification of the genes to identification of the role of their gene products in the cell, SGD seeks to provide its users with annotations that will allow relationships to be made between gene products, both within Saccharomyces cerevisiae and across species. To this end, SGD is annotating genes to the Gene Ontology (GO), a structured representation of biological knowledge that can be shared across species. The GO consists of three separate ontologies describing molecular function, biological process and cellular component. The goal is to use published information to associate each characterized S.cerevisiae gene product with one or more GO terms from each of the three ontologies. To be useful, this must be done in a manner that allows accurate associations based on experimental evidence, modifications to GO when necessary, and careful documentation of the annotations through evidence codes for given citations. Reaching this goal is an ongoing process at SGD. For information on the current progress of GO annotations at SGD and other participating databases, as well as a description of each of the three ontologies, please visit the GO Consortium page at http://www.geneontology.org. SGD gene associations to GO can be found by visiting our site at http:// genome-www.stanford.edu/Saccharomyces/.

The Saccharomyces Genome Database (SGD) provides Internet access to the complete Saccharomyces cerevisiae genomic sequence, its genes and their products, the phenotypes of its mutants, and the literature supporting these data. The amount of information and the number of features provided by SGD have increased greatly following the release of the S.cerevisiae genomic sequence, which is currently the only complete sequence of a eukaryotic genome. SGD aids researchers by providing not only basic information, but also tools such as sequence similarity searching that lead to detailed information about features of the genome and relationships between genes. SGD presents information using a variety of user-friendly, dynamically created graphical displays illustrating physical, genetic and sequence feature maps. SGD can be accessed via the World Wide Web at http://genome-www.stanford.edu/Saccharomyces/