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Background Population‐wide reduction in mean blood pressure is proposed as a key strategy for primary prevention of cardiovascular disease. We evaluated the effectiveness of a task‐sharing strategy involving frontline health workers in the primary prevention of elevated blood pressure. Methods and Results We conducted DISHA (Diet and lifestyle Interventions for Hypertension Risk reduction through Anganwadi Workers and Accredited Social Health Activists) study, a cluster randomized controlled trial involving 12 villages each from 4 states in India. Frontline health workers delivered a custom‐made and structured lifestyle modification intervention in the selected villages. A baseline survey was conducted in 23 and 24 clusters in the control (n=6663) and intervention (n=7150) groups, respectively. The baseline characteristics were similar between control and intervention clusters. In total 5616 participants from 23 clusters in the control area and 5699 participants from 24 clusters in the intervention area participated in a repeat cross‐sectional survey conducted immediately after the intervention phase of 18‐months. The mean (SD) systolic blood pressure increased from 125.7 (18.1) mm Hg to 126.1 (16.8) mm Hg in the control clusters, and it increased from 124.4 (17.8) mm Hg to 126.7 (17.5) mm Hg in the intervention clusters. The population average adjusted mean difference in difference in systolic blood pressure was 1.75 mm Hg (95% CI, −0.21 to 3.70). Conclusions Task‐sharing interventions involving minimally trained nonphysician health workers are not effective in reducing population average blood pressure in India. Expanding the scope of task sharing and intensive training of health workers such as nurses, nutritionists, or health counselors in management of cardiovascular risk at the population level may be more effective in primary prevention of cardiovascular disease. Registration URL: https://www.ctri.nic.in ; Unique identifier: CTRI/2013/10/004049.

Over recent years, there has been exciting growth in collaboration between academia and industry in the life sciences to make data more Findable, Accessible, Interoperable and Reusable (FAIR) to achieve greater value. Despite considerable progress, the transformative shift from an application-centric to a data-centric perspective, enabled by FAIR implementation, remains very much a work in progress on the 'FAIR journey'. In this review, we consider use cases for FAIR implementation. These can be deployed alongside assessment of data quality to maximize the value of data generated from research, clinical trials, and real-world healthcare data, which are essential for the discovery and development of new medical treatments by biopharma.

Background: Identifying patients with high-risk of progression or death is important in developing novel treatment strategies in diffuse large B-cell lymphoma (DLBCL). The International Prognostic Index (IPI) is a commonly used score to classify the prognostic risk of previously untreated (1L) DLBCL patients. Although perceived as important in understanding the health status of patients, patient-reported health-related quality-of-life (HRQoL) measures have not been studied extensively as prognostic factors in DLBCL. In this study, we explored the prognostic value of pretreatment HRQoL in progression free survival (PFS) and overall survival (OS) in 1L DLBCL patients, using data from the phase III GOYA study (NCT01287741, Obinutuzumab-CHOP vs Rituximab-CHOP). Method: Four preselected pretreatment HRQoL subscale scores (lymphoma specific [LYMS], physical functioning [PF2], role functioning [RF2], and fatigue [FA]) were derived from two HRQoL questionnaires (EORTC-QLQ C30 and FACT-Lym) in the GOYA study. Each subscale was dichotomized to indicate low or high HRQoL based on their respective median scores (table). Sensitivity analyses were similarly evaluated. The prognostic value of each HRQoL subscale was evaluated using Cox proportional hazard models, adjusted for the five components of IPI. Table.Summary of prognostic value for the four pre-treatment HRQoL subscalesSummary3-year OS estimate2 (95% CI)Cox proportional hazard modelSubscale(n1)QuestionnaireMedian (min-max)Low HRQoLHigh HRQoLOS HR3 (95% CI)PFS HR3(95% CI)Lymphoma specific(n = 1246)FACT-LYM47 (7-60)0.78 (0.74, 0.81)0.85 (0.82, 0.87)0.7 (0.51, 0.95)0.81(0.63, 1.03)Physical functioning(n = 1254)EORTC-QLQ C3087(0-100)0.77 (0.74, 0.80)0.86 (0.83, 0.89)0.6 (0.43, 0.85)0.72(0.56, 0.93)Role functioning(n = 1256)EORTC-QLQ C3083(0-100)0.78 (0.75, 0.81)0.84 (0.81, 0.88)0.72(0.52, 0.99)0.84(0.65, 1.07)Fatigue(n = 1256)EORTC-QLQ C3033(0-100)0.74(0.70, 0.78)0.84 (0.82, 0.87)0.68 (0.5, 0.92)0.94(0.73, 1.2)1Only patients with valid respective pretreatment HRQoL scores were included in each analysis2OS estimated using the Kaplan-Meier method3HR = hazard ratio representing high HRQoL vs. low HRQoL, adjusted for the five components of IPI: age(≤60 vs >60), ECOG PS(0-1 vs 2-3), lactate dehydrogenase level (LDH)(≤1 normal vs >1 normal), Ann Arbor stage (Stage I or II vs III or IV disease), and extranodal sites(≤1 vs >1 extranodal site) Results: All four HRQoL subscales contributed independent prognostic value to patient outcome (table). Results show that high HRQoL is associated with better survival outcome (higher 3-year OS estimate and lower risk [HR]) compared with low HRQoL. Among the four subscales, PF2 had the highest estimated contribution to prognosis (OS: HR = 0.59, 95% CI: [0.42, 0.84], PFS: HR = 0.71, 95% CI [0.55, 0.93]). Conclusion: Our findings demonstrate the potential of patient-reported HRQoL measures in providing prognostic value in addition to IPI, which may contribute to improved risk stratification and inform treatment decisions for DLBCL patients. Citation Format: Huang Huang, Asim Dayte, Ming Fan, Andrea Knapp, Rama Balakrishnan, Sandhya Balasubramanian, Julia Chae, Emma Roth, Tina Nielsen, Joseph N. Paulson, Peter Trask. Listen to the patients: Assessing the prognostic value of pre-treatment health-related quality of life in 1L DLBCL patients [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2035.

In this review, we provide a summary of recent progress in ontology mapping (OM) at a crucial time when biomedical research is under a deluge of an increasing amount and variety of data. This is particularly important for realising the full potential of semantically enabled or enriched applications and for meaningful insights, such as drug discovery, using machine-learning technologies. We discuss challenges and solutions for better ontology mappings, as well as how to select ontologies before their application. In addition, we describe tools and algorithms for ontology mapping, including evaluation of tool capability and quality of mappings. Finally, we outline the requirements for an ontology mapping service (OMS) and the progress being made towards implementation of such sustainable services.

The Gene Ontology Consortium (GOC) is a community-based bioinformatics project that classifies gene product function through the use of structured controlled vocabularies. A fundamental application of the Gene Ontology (GO) is in the creation of gene product annotations, evidence-based associations between GO definitions and experimental or sequence-based analysis. Currently, the GOC disseminates 126 million annotations covering >374,000 species including all the kingdoms of life. This number includes two classes of GO annotations: those created manually by experienced biocurators reviewing the literature or by examination of biological data (1.1 million annotations covering 2226 species) and those generated computationally via automated methods. As manual annotations are often used to propagate functional predictions between related proteins within and between genomes, it is critical to provide accurate consistent manual annotations. Toward this goal, we present here the conventions defined by the GOC for the creation of manual annotation. This guide represents the best practices for manual annotation as established by the GOC project over the past 12 years. We hope this guide will encourage research communities to annotate gene products of their interest to enhance the corpus of GO annotations available to all. DATABASE URL: http://www.geneontology.org.

Summary InterMine is a data integration warehouse and analysis software system developed for large and complex biological data sets. Designed for integrative analysis, it can be accessed through a user-friendly web interface. For bioinformaticians, extensive web services as well as programming interfaces for most common scripting languages support access to all features. The web interface includes a useful identifier look-up system, and both simple and sophisticated search options. Interactive results tables enable exploration, and data can be filtered, summarized, and browsed. A set of graphical analysis tools provide a rich environment for data exploration including statistical enrichment of sets of genes or other entities. InterMine databases have been developed for the major model organisms, budding yeast, nematode worm, fruit fly, zebrafish, mouse, and rat together with a newly developed human database. Here, we describe how this has facilitated interoperation and development of cross-organism analysis tools and reports. InterMine as a data exploration and analysis tool is also described. All the InterMine-based systems described in this article are resources freely available to the scientific community. genesis 53:547–560, 2015. © 2015 Wiley Periodicals, Inc.

We have developed a general progressive procedure, Active Interaction Mapping, to guide assembly of the hierarchy of functions encoding any biologicalsystem. Using this process, we assemble an ontology of functions comprising autophagy, a central recycling process implicated in numerous diseases. A first-generation model, built from existing gene networks in Saccharomyces, captures most known autophagy components in broad relation to vesicle transport, cell cycle, and stress response. Systematic analysis identifies synthetic-lethal interactions as most informative for further experiments; consequently, we saturate the model with 156,364 such measurements across autophagy-activating conditions. These targeted interactions provide more information about autophagy than all previous datasets, producing a second-generation ontology of 220 functions. Approximately half are previously unknown; we confirm roles for Gyp1 at the phagophore-assembly site, Atg24 in cargo engulfment, Atg26 in cytoplasm-to-vacuole targeting, and Ssd1, Did4, and others in selective and non-selective autophagy. The procedure and autophagy hierarchy are at http://atgo.ucsd.edu/.

The genome of the budding yeast Saccharomyces cerevisiae was the first completely sequenced from a eukaryote. It was released in 1996 as the work of a worldwide effort of hundreds of researchers. In the time since, the yeast genome has been intensively studied by geneticists, molecular biologists, and computational scientists all over the world. Maintenance and annotation of the genome sequence have long been provided by the Saccharomyces Genome Database, one of the original model organism databases. To deepen our understanding of the eukaryotic genome, the S. cerevisiae strain S288C reference genome sequence was updated recently in its first major update since 1996. The new version, called “S288C 2010,” was determined from a single yeast colony using modern sequencing technologies and serves as the anchor for further innovations in yeast genomic science.

Introduction: Diffuse large B-cell lymphoma (DLBCL), is a genetically and clinically heterogeneous disease. Building on existing clinical prognostic scores in the era of genomic risk modeling is critical to improve patient (pt) outcomes and inform future trials. With advances in next-generation sequencing, the interrogation of genetic drivers to refine prognostic subgroups over and above histological cell of origin (COO) subtypes has been of keen interest. The application of an unsupervised prognostic clustering approach, non-negative matrix factorization (NMF) algorithm, to whole exome sequence (WES) data has led to identification of six distinct molecular clusters with potential clinical relevance (Chapuy et al. Nature Med, 2018). Here, we applied the NMF clustering approach to targeted exome-sequencing data utilizing the FoundationOne HemeTM panel and analyzed RNA gene expression data from the randomized Phase 3 GOYA and Phase 1b/2 CAVALLI studies in de novo DLBCL. Methods: Biopsy samples, clinical outcomes (overall survival [OS], progression-free survival [PFS]), and targeted exome-sequencing data were available for 499 pts from the GOYA trial (NCT01287741; ITT=1418; obinutuzumab [G]-CHOP vs rituximab [R]-CHOP) and 106 pts from the CAVALLI trial (NCT02055820; ITT=267, venetoclax+[G/R]-CHOP). The FoundationOne HemeTM panel of 465 genes (F1H, Foundation Medicine Inc. [FMI]) was used to define single nucleotide variants, copy number amplifications, and rearrangements. Clustering methodology was implemented using the R package NMF (Gaujoux and Seoighe, BMC Bioinformatics 2010). All models were adjusted for treatment (where applicable), age, International Prognostic Index (IPI), sex, COO (NanoString), and BCL2 by IHC. Results: Applying the NMF algorithm, de novo pts in GOYA clustered similarly into four of the six groups recently reported by Chapuy et al.: MYD88/CD79B, BCL2/EZH2, NOTCH2/TNFAIP3, and no mutations (Fig 1A). The mutation profiles and COO subset distribution were also similar to those previously described. Furthermore, the MYD88/CD79B and BCL2/EZH2 groups remained associated with inferior clinical outcomes (Fig 1B), with observed poor prognosis over the NOTCH2/TNFAIP3 and no mutations groups (OS: hazard ratio [HR], 2.0; 95% CI: 0.97, 3.90; PFS: HR, 1.60; 95% CI: 0.93, 2.80), even when adjusting for known prognostic factors. Application of NMF to pts treated with the BCL2-inhibitor venetoclax in combination with R-CHOP (CAVALLI) yielded only MYD88/CD79B- and BCL2/EZH2-like groups due to enrollment. However, in contrast to the BCL2/EZH2 subgroup reported in both Chapuy et al. and GOYA, these pts demonstrated a trend towards improved outcomes over MYD88/CD79B (OS: HR, 0.12; 95% CI: 0.01, 1.60; PFS: HR, 0.11; 95% CI: 0.01, 1.10; Fig 1D). The difference between studies is potentially explained by the ability of venetoclax treatment to overcome the adverse prognostic impact of BCL2 over-expression. BCL2 IHC positive (BCL2+) pts were enriched in high-risk groups in GOYA (MYD88/CD79B and BCL2/EZH2) (odds ratio [OR], 5.0; 95% CI: 3.13, 8.33) and CAVALLI (MYD88/CD79B) (OR, 2.0; 95% CI: 0.63, 6.67). In GOYA, a prognostic trend for BCL2+ status is observed within low-risk pts (OS BCL2+ HR, 1.9; 95% CI: 0.55, 6.3; PFS BCL2+ HR, 2.7; 95% CI: 1.0, 6.8), but not within high-risk groups (OS BCL2+ HR, 0.55; 95% CI: 0.29, 1.00; PFS BCL2+ HR, 0.76; 95% CI: 0.45, 1.30; Fig 1C). Additional genetic risk models will be presented, e.g. as per Reddy et al. (Cell, 2017), as well as differential expression analysis. Conclusions: Important findings from the present work include: 1) utility of targeted mutational data from F1H in recapitulating molecular clusters, previously identified using WES, that further subclassify established prognostic groups such as COO and IPI; and 2) a trend towards improved survival outcomes for patients in the BCL2/EZH2 cluster upon treatment with the BCL2 inhibitor venetoclax. This highlights the potential for utilizing novel genetic signatures as a means of identifying patients suitable for targeted therapies in the era of personalized health care. Disclosures Kim: Genentech, Inc.: Employment. Fan:Genentech, Inc.: Employment. Bolen:F. Hoffmann-La Roche: Equity Ownership; Genentech, Inc.: Employment. Bazeos:Roche Products Ltd: Employment, Equity Ownership. Jiang:Genentech: Employment, Equity Ownership; F. Hoffman-La Roche: Equity Ownership. Balasubramanian:Roche-Genentech: Employment; F. Hoffman-La Roche: Equity Ownership. Balakrishnan:Genentech, Inc.: Employment. Knapp:F. Hoffmann-La Roche Ltd: Employment. Humphrey:F. Hoffmann-La Roche Ltd: Employment. Nielsen:F. Hoffmann-La Roche Ltd: Employment, Equity Ownership. Venstrom:F. Hoffmann-La Roche Ltd: Employment. Hernandez:Genentech, Inc.: Employment, Equity Ownership. Paulson:F. Hoffman-La Roche: Equity Ownership; Genentech: Employment.

The Saccharomyces Genome Database (SGD, http://www.yeastgenome.org) is the community resource for the budding yeast Saccharomyces cerevisiae. The SGD project provides the highest-quality manually curated information from peer-reviewed literature. The experimental results reported in the literature are extracted and integrated within a well-developed database. These data are combined with quality high-throughput results and provided through Locus Summary pages, a powerful query engine and rich genome browser. The acquisition, integration and retrieval of these data allow SGD to facilitate experimental design and analysis by providing an encyclopedia of the yeast genome, its chromosomal features, their functions and interactions. Public access to these data is provided to researchers and educators via web pages designed for optimal ease of use.

The Gene Ontology (GO) (http://www.geneontology.org) is a community bioinformatics resource that represents gene product function through the use of structured, controlled vocabularies. The number of GO annotations of gene products has increased due to curation efforts among GO Consortium (GOC) groups, including focused literature-based annotation and ortholog-based functional inference. The GO ontologies continue to expand and improve as a result of targeted ontology development, including the introduction of computable logical definitions and development of new tools for the streamlined addition of terms to the ontology. The GOC continues to support its user community through the use of e-mail lists, social media and web-based resources.

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.

MicroRNA regulation of developmental and cellular processes is a relatively new field of study, and the available research data have not been organized to enable its inclusion in pathway and network analysis tools. The association of gene products with terms from the Gene Ontology is an effective method to analyze functional data, but until recently there has been no substantial effort dedicated to applying Gene Ontology terms to microRNAs. Consequently, when performing functional analysis of microRNA data sets, researchers have had to rely instead on the functional annotations associated with the genes encoding microRNA targets. In consultation with experts in the field of microRNA research, we have created comprehensive recommendations for the Gene Ontology curation of microRNAs. This curation manual will enable provision of a high-quality, reliable set of functional annotations for the advancement of microRNA research. Here we describe the key aspects of the work, including development of the Gene Ontology to represent this data, standards for describing the data, and guidelines to support curators making these annotations. The full microRNA curation guidelines are available on the GO Consortium wiki (http://wiki.geneontology.org/index.php/MicroRNA_GO_annotation_manual).

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 Evidence Ontology (ECO) is a structured, controlled vocabulary for capturing evidence in biological research. ECO includes diverse terms for categorizing evidence that supports annotation assertions including experimental types, computational methods, author statements and curator inferences. Using ECO, annotation assertions can be distinguished according to the evidence they are based on such as those made by curators versus those automatically computed or those made via high-throughput data review versus single test experiments. Originally created for capturing evidence associated with Gene Ontology annotations, ECO is now used in other capacities by many additional annotation resources including UniProt, Mouse Genome Informatics, Saccharomyces Genome Database, PomBase, the Protein Information Resource and others. Information on the development and use of ECO can be found at http://evidenceontology.org. The ontology is freely available under Creative Commons license (CC BY-SA 3.0), and can be downloaded in both Open Biological Ontologies and Web Ontology Language formats at http://code.google.com/p/evidenceontology. Also at this site is a tracker for user submission of term requests and questions. ECO remains under active development in response to user-requested terms and in collaborations with other ontologies and database resources. Database URL: Evidence Ontology Web site: http://evidenceontology.org.

As the pace of life science discovery increases, so do the demands on researchers. To remain competitive in the life science industry, researchers must use every tool at their disposal to keep up with new products, protocols, news, and literature in their field. While there are now myriad Web sites that assist researchers with this problem, many suffer from confusing user interfaces, poorly designed search engines, and a narrow information focus. Here, we present LabVelocity™, a user-friendly Web site that provides a free multidisciplinary information-gathering service for the life science research community. Using LabVelocity, a researcher can quickly find the products, protocols, technical references, news, MEDLINE® abstracts, and interactive software tools necessary for an experiment. This aggregation of information can streamline experimental planning and is especially useful when researchers want to set up a new laboratory or to venture outside their field of expertise.

CORRIGENDUM: InterMOD: integrated data and tools for the unification of model organism research

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.

The Gene Ontology (GO) describes molecular functions, biological processes, and cellular components of gene products using controlled-vocabulary terms that are related to each other in a structure that facilitates computing on GO annotations within and across species. Experimentally-based GO annotations that are manually curated from the literature are often used to predict the functions of related uncharacterized proteins. The accuracy of such annotations is thus critically important, particularly for a well-studied model organism such as Saccharomyces cerevisiae which is frequently used as the source of the experimental data.Comparison of experimentally-based annotations with those predicted by computational methods for the same gene products may reveal inaccuracies in curation of the experimental data, and could additionally be used to evaluate and improve the computational methods. We will present the results of an analysis at SGD that identified four major reasons for discrepancies between the two kinds of annotation. Some discrepancies revealed cases in which human error led to errors or omissions in the manual curation, prompting prioritization for review and correction. In another category, the computational annotations were not supported or were refuted by the literature, thereby suggesting ways in which the accuracy of the prediction methods could be improved. Yet another type of discrepancy resulted from issues with the GO structure, such as missing parentage for certain terms, leading to reexamination and improvement of the ontology. Finally, some discrepancies arose because the computational predictions were entirely novel, and no relevant experimental evidence was available. These cases highlight potential interesting new avenues for experimentation.

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) is the authoritative community resource for the Saccharomyces cerevisiae reference genome sequence and its annotation. To provide a wider scope of genetic and phenotypic variation in yeast, the genome sequences and their corresponding annotations from 11 alternative S. cerevisiae reference strains have been integrated into SGD. Genomic and protein sequence information for genes from these strains are now available on the Sequence and Protein tab of the corresponding Locus Summary pages. We illustrate how these genome sequences can be utilized to aid our understanding of strain-specific functional and phenotypic differences. Database URL: www.yeastgenome.org

The Saccharomyces Genome Database (SGD; http:// www.yeastgenome.org/) collects and organizes biological information about the chromosomal features and gene products of the budding yeast Saccharomyces cerevisiae. Although published data from traditional experimental methods are the primary sources of evidence supporting Gene Ontology (GO) annotations for a gene product, high-throughput experiments and computational predictions can also provide valuable insights in the absence of an extensive body of literature. Therefore, GO annotations available at SGD now include high-throughput data as well as computational predictions provided by the GO Annotation Project (GOA UniProt; http://www.ebi.ac.uk/GOA/). Because the annotation method used to assign GO annotations varies by data source, GO resources at SGD have been modified to distinguish data sources and annotation methods. In addition to providing information for genes that have not been experimentally characterized, GO annotations from independent sources can be compared to those made by SGD to help keep the literature-based GO annotations current.

The budding yeast, Saccharomyces cerevisiae, has been experimentally manipulated for several decades. Much of the information generated is available in the Saccharomyces genome database (SGD, http://www.yeastgenome.org/). SGD contains large datasets of both genomic and proteomic information, as well as tools for data analysis. This paper will highlight three datasets that are maintained by SGD. First, a large dataset of hand-curated information is provided in machine readable format for each gene of the Saccharomyces genome. These hand-curated annotations use the gene ontology (GO) controlled vocabularies for biological process, molecular function and cellular component and each contains categorical evidence codes and literature references. A second area of focus is on metabolic pathways. A new dataset of hand-curated information on metabolic pathways within budding yeast was released in May 2003. This resource can be searched to view biochemical reactions and pathways and their component gene products. This resource also maps data from genome-wide expression analyses onto the pathway overview providing a visualization of the changes in gene expression in the context of cellular metabolism. These pathways are created and edited using the pathway tools software but the content is reviewed and updated by SGD. A third dataset has recently become available as the result of two comparative genomic analyses. Two groups sequenced the genomes of several yeasts closely related to S. cerevisiae, and then completed a gene-by-gene comparison of these genomes. These genome comparisons were combined with available experimental evidence by SGD. Using these data the annotations for the S.cerevisiae reference genome were improved. All these datasets are freely available from the SGD ftp site.

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.

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.

Annotation using Gene Ontology (GO) terms is one of the most important ways in which biological information about specific gene products can be expressed in a searchable, computable form that may be compared across genomes and organisms. Because literature-based GO annotations are often used to propagate functional predictions between related proteins, their accuracy is critically important. We present a strategy that employs a comparison of literature-based annotations with computational predictions to identify and prioritize genes whose annotations need review. Using this method, we show that comparison of manually assigned 'unknown' annotations in the Saccharomyces Genome Database (SGD) with InterPro-based predictions can identify annotations that need to be updated. A survey of literature-based annotations and computational predictions made by the Gene Ontology Annotation (GOA) project at the European Bioinformatics Institute (EBI) across several other databases shows that this comparison strategy could be used to maintain and improve the quality of GO annotations for other organisms besides yeast. The survey also shows that although GOA-assigned predictions are the most comprehensive source of functional information for many genomes, a large proportion of genes in a variety of different organisms entirely lack these predictions but do have manual annotations. This underscores the critical need for manually performed, literature-based curation to provide functional information about genes that are outside the scope of widely used computational methods. Thus, the combination of manual and computational methods is essential to provide the most accurate and complete functional annotation of a genome. Database URL: http://www.yeastgenome.org.

The Gene Ontology (GO) project (http://www. geneontology.org/) provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences. Many model organism databases and genome annotation groups use the GO and contribute their annotation sets to the GO resource. The GO database integrates the vocabularies and contributed annotations and provides full access to this information in several formats. Members of the GO Consortium continually work collectively, involving outside experts as needed, to expand and update the GO vocabularies. The GO Web resource also provides access to extensive documentation about the GO project and links to applications that use GO data for functional analyses.

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.

Sequencing and annotation of the entire Saccharomyces cerevisiae genome has made it pos- sible to gain a genome-wide perspective on yeast genes and gene products. To make this information available on an ongoing basis, the Saccharomyces 20Genome 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 fea- 25tures of S.cerevisiae. The information is organized into two categories: (i) number of each type of chro- mosomal feature annotated in the genome and (ii) number and distribution of genes annotated to Gene Ontology terms. Detailed lists are accessible 30through 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/).