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      Curated protein information in the Saccharomyces genome database

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          Abstract

          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

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          Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise.

          John R S Newman, Sina Ghaemmaghami, Jan Ihmels … (2006)
          A major goal of biology is to provide a quantitative description of cellular behaviour. This task, however, has been hampered by the difficulty in measuring protein abundances and their variation. Here we present a strategy that pairs high-throughput flow cytometry and a library of GFP-tagged yeast strains to monitor rapidly and precisely protein levels at single-cell resolution. Bulk protein abundance measurements of >2,500 proteins in rich and minimal media provide a detailed view of the cellular response to these conditions, and capture many changes not observed by DNA microarray analyses. Our single-cell data argue that noise in protein expression is dominated by the stochastic production/destruction of messenger RNAs. Beyond this global trend, there are dramatic protein-specific differences in noise that are strongly correlated with a protein's mode of transcription and its function. For example, proteins that respond to environmental changes are noisy whereas those involved in protein synthesis are quiet. Thus, these studies reveal a remarkable structure to biological noise and suggest that protein noise levels have been selected to reflect the costs and potential benefits of this variation.
          Article 0 comments Cited 557 times – based on 0 reviews     Review now
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            SGD: Saccharomyces Genome Database.

            J. Cherry (1998)
            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/
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              YeastMine—an integrated data warehouse for Saccharomyces cerevisiae data as a multipurpose tool-kit

              Rama Balakrishnan, Julie Park, Kalpana Karra … (2012)
              The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) provides high-quality curated genomic, genetic, and molecular information on the genes and their products of the budding yeast Saccharomyces cerevisiae. To accommodate the increasingly complex, diverse needs of researchers for searching and comparing data, SGD has implemented InterMine (http://www.InterMine.org), an open source data warehouse system with a sophisticated querying interface, to create YeastMine (http://yeastmine.yeastgenome.org). YeastMine is a multifaceted search and retrieval environment that provides access to diverse data types. Searches can be initiated with a list of genes, a list of Gene Ontology terms, or lists of many other data types. The results from queries can be combined for further analysis and saved or downloaded in customizable file formats. Queries themselves can be customized by modifying predefined templates or by creating a new template to access a combination of specific data types. YeastMine offers multiple scenarios in which it can be used such as a powerful search interface, a discovery tool, a curation aid and also a complex database presentation format. Database URL: http://yeastmine.yeastgenome.org
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                Author and article information

                Journal
                Journal ID (nlm-ta): Database (Oxford)
                Journal ID (iso-abbrev): Database (Oxford)
                Journal ID (publisher-id): databa
                Title: Database: The Journal of Biological Databases and Curation
                Publisher: Oxford University Press
                ISSN (Electronic): 1758-0463
                Publication date Collection: 2017
                Publication date (Electronic): 11 March 2017
                Publication date PMC-release: 11 March 2017
                Volume: 2017
                Electronic Location Identifier: bax011
                Affiliations
                Department of Genetics, Stanford University, Stanford, CA 94305, USA
                Author notes
                [* ] Corresponding author: Tel.: (650) 725-8956. Email: stacia@ 123456stanford.edu

                Citation details: Hellerstedt,S.T., Nash,R.S., Weng,S. et al. Curated protein information in the Saccharomyces genome database. Database (2017) Vol. 2017: article ID bax011; doi:10.1093/database/bax011

                Article
                Publisher ID: bax011
                DOI: 10.1093/database/bax011
                PMC ID: 5467551
                PubMed ID: 28365727
                SO-VID: d8ed6d45-195e-4970-8ec4-a26085ced366
                Copyright © © The Author(s) 2017. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 31 October 2016
                Date revision received : 12 January 2017
                Date accepted : 27 January 2017
                Page count
                Pages: 6
                Funding
                Funded by: National Human Genome Research Institute at the US National Institutes of Health
                Categories
                Subject: Original Article

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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