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The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) is a scientific database of gene, protein and genomic information for the yeast Saccharomyces cerevisiae. SGD has recently developed two new resources that facilitate nucleotide and protein sequence comparisons between S.cerevisiae and other organisms. The Fungal BLAST tool provides directed searches against all fungal nucleotide and protein sequences available from GenBank, divided into categories according to organism, status of completeness and annotation, and source. The Model Organism BLASTP Best Hits resource displays, for each S.cerevisiae protein, the single most similar protein from several model organisms and presents links to the database pages of those proteins, facilitating access to curated information about potential orthologs of yeast proteins.

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 Gene Ontology (GO; http://www.geneontology.org) is a community-based bioinformatics resource that supplies information about gene product function using ontologies to represent biological knowledge. Here we describe improvements and expansions to several branches of the ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community. The Gene Ontology Consortium (GOC) has expanded areas of the ontology such as cilia-related terms, cell-cycle terms and multicellular organism processes. We have also implemented new tools for generating ontology terms based on a set of logical rules making use of templates, and we have made efforts to increase our use of logical definitions. The GOC has a new and improved web site summarizing new developments and documentation, serving as a portal to GO data. Users can perform GO enrichment analysis, and search the GO for terms, annotations to gene products, and associated metadata across multiple species using the all-new AmiGO 2 browser. We encourage and welcome the input of the research community in all biological areas in our continued effort to improve the Gene Ontology.

The Encyclopedia of DNA elements (ENCODE) project is an ongoing collaborative effort to create a catalog of genomic annotations. To date, the project has generated over 4000 experiments across more than 350 cell lines and tissues using a wide array of experimental techniques to study the chromatin structure, regulatory network and transcriptional landscape of the Homo sapiens and Mus musculus genomes. All ENCODE experimental data, metadata and associated computational analyses are submitted to the ENCODE Data Coordination Center (DCC) for validation, tracking, storage and distribution to community resources and the scientific community. As the volume of data increases, the organization of experimental details becomes increasingly complicated and demands careful curation to identify related experiments. Here, we describe the ENCODE DCC’s use of ontologies to standardize experimental metadata. We discuss how ontologies, when used to annotate metadata, provide improved searching capabilities and facilitate the ability to find connections within a set of experiments. Additionally, we provide examples of how ontologies are used to annotate ENCODE metadata and how the annotations can be identified via ontology-driven searches at the ENCODE portal. As genomic datasets grow larger and more interconnected, standardization of metadata becomes increasingly vital to allow for exploration and comparison of data between different scientific projects. Database URL: https://www.encodeproject.org/

Decomposing regulatory networks into functional modules is a first step toward deciphering the logical structure of complex networks. We propose a systematic approach to reconstructing transcription modules (defined by a transcription factor and its target genes) and identifying conditions/perturbations under which a particular transcription module is activated/deactivated. Our approach integrates information from regulatory sequences, genome-wide mRNA expression data, and functional annotation. We systematically analyzed gene expression profiling experiments in which the yeast cell was subjected to various environmental or genetic perturbations. We were able to construct transcription modules with high specificity and sensitivity for many transcription factors, and predict the activation of these modules under anticipated as well as unexpected conditions. These findings generate testable hypotheses when combined with existing knowledge on signaling pathways and protein–protein interactions. Correlating the activation of a module to a specific perturbation predicts links in the cell's regulatory networks, and examining coactivated modules suggests specific instances of crosstalk between regulatory pathways.

mRNA degradation provides a powerful means for controlling gene expression during growth, development, and many physiological transitions in plants and other systems. Rates of decay help define the steady state levels to which transcripts accumulate in the cytoplasm and determine the speed with which these levels change in response to the appropriate signals. When fast responses are to be achieved, rapid decay of mRNAs is necessary. Accordingly, genes with unstable transcripts often encode proteins that play important regulatory roles. Although detailed studies have been carried out on individual genes with unstable transcripts, there is limited knowledge regarding their nature and associations from a genomic perspective, or the physiological significance of rapid mRNA turnover in intact organisms. To address these problems, we have applied cDNA microarray analysis to identify and characterize genes with unstable transcripts in Arabidopsis thaliana ( AtGUTs ). Our studies showed that at least 1% of the 11,521 clones represented on Arabidopsis Functional Genomics Consortium microarrays correspond to transcripts that are rapidly degraded, with estimated half-lives of less than 60 min. AtGUTs encode proteins that are predicted to participate in a broad range of cellular processes, with transcriptional functions being over-represented relative to the whole Arabidopsis genome annotation. Analysis of public microarray expression data for these genes argues that mRNA instability is of high significance during plant responses to mechanical stimulation and is associated with specific genes controlled by the circadian clock.

Genome-wide expression profiling with DNA microarrays has and will provide a great deal of data to the plant scientific community. However, reliability concerns have required the development data quality tests for common systematic biases. Fortunately, most large-scale systematic biases are detectable and some are correctable by normalization. Technical replication experiments and statistical surveys indicate that these biases vary widely in severity and appearance. As a result, no single normalization or correction method currently available is able to address all the issues. However, careful sequence selection, array design, experimental design and experimental annotation can substantially improve the quality and biological of microarray data. In this review, we discuss these issues with reference to examples from the Arabidopsis Functional Genomics Consortium (AFGC) microarray project.

Nutrition leaders surmised graduate medical nutrition education was not well addressed because most medical and surgical specialties have insufficient resources to teach current nutrition practice. A needs assessment survey was constructed to determine resources and commitment for nutrition education from U.S. graduate medical educators to address this problem.An online survey of 36 questions was sent to 495 Accreditation Council for Graduate Medical Education (ACGME) Program Directors in anesthesia, family medicine, internal medicine, pediatrics, obstetrics/gynecology, and general surgery. Demographics, resources, and open-ended questions were included. There was a 14% response rate (72 programs), consistent with similar studies on the topic.Most (80%) of the program directors responding were from primary care programs, the rest surgical (17%) or anesthesia (3%). Program directors themselves lacked knowledge of nutrition. While some form of nutrition education was provided at 78% of programs, only 26% had a formal curriculum and physicians served as faculty at only 53%. Sixteen programs had no identifiable expert in nutrition and 10 programs stated that no nutrition training was provided. Training was variable, ranging from an hour of lecture to a month-long rotation. Seventy-seven percent of program directors stated that the required educational goals in nutrition were not met. The majority felt an advanced course in clinical nutrition should be required of residents now or in the future.Nutrition education in current graduate medical education is poor. Most programs lack the expertise or time commitment to teach a formal course but recognize the need to meet educational requirements. A broad-based, diverse universal program is needed for training in nutrition during residency.

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

The Saccharomyces Genome Database (SGD) collects and organizes information about the molecular biology and genetics of the yeast Saccharomyces cerevisiae. The latest protein structure and comparison tools available at SGD are presented here. With the completion of the yeast sequence and the Caenorhabditis elegans sequence soon to follow, comparison of proteins from complete eukaryotic proteomes will be an extremely powerful way to learn more about a particular protein's structure, its function, and its relationships with other proteins. SGD can be accessed through the World Wide Web at http://genome-www.stanford.edu/Saccharomyces/

AtDB, the Arabidopsis thaliana Database, has a primary role to provide public access to the collected genomic information for A. thaliana via the World Wide Web (URL: http://genome-www.stanford. edu/ ). AtDB presents interactive physical and genetics maps that are hyperlinked with detailed information about the clones and markers placed on these maps. A large literature collection on Arabidopsis , contact information on researchers worldwide, laboratory method manuals and other information useful to plant molecular biologists are also provided. This paper discusses the database-driven clickable displays that provide easy navigation within a variety of genomic maps, including those summarizing progress of the international Arabidopsis genomic sequencing effort, AGI (the Arabidopsis Genome Initiative). The interface uses client-side hyperlinked GIF-images that direct the user to detailed database-information. A new BLAST service is also described. This gives users access to the thousands of Arabidopsis BAC clone end-sequences and includes hyperlinked images summarizing the search results. The linking of genetic and physically mapped regions and their sequence into information for loci within that region is an ongoing goal for this project.

Some centers prefer CMV-IVIG over IVIG for the prophylaxis of EBV-related PTLD in solid organ transplant patients. Our objective was to compare the relative dose-related EBV ELISA antibody concentrations and cost of standard IVIG and CMV-IVIG. The concentration of EBV IgG to VCA was analyzed via ELISA in four lots of IVIG and four lots of CMV-IVIG. Relative EBV ELISA antibody concentrations and cost were compared assuming an IVIG dose of 0.5 gm/kg and CMV-IVIG dose of 0.15 gm/kg in a 50-kg patient. The price of IVIG was $70/gm and CMV-IVIG $430/gm. IVIG contains the same EBV antibody concentrations (20 790 ELISA antibody units/mL) than CMV-IVIG (17 430 ELISA antibody units/mL, p > 0.2) in the four lots of each product sampled. When factoring in the dosing scheme for a 50-kg patient, IVIG contains two times more EBV antibody than CMV-IVIG. Yet, CMV-IVIG is 1.8 times more expensive than IVIG ($3225 vs. $1750). In the four lots of each product sampled, IVIG contains more EBV antibodies and costs less than CMV-IVIG when factoring in the dosing scheme. Studies are needed to determine whether there is clinical efficacy of immunoglobulin products for EBV-related PTLD prophylaxis. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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

Model organisms are widely used for understanding basic biology and have significantly contributed to the study of human disease. In recent years, genomic analysis has provided extensive evidence of widespread conservation of gene sequence and function amongst eukaryotes, allowing insights from model organisms to help decipher gene function in a wider range of species. The InterMOD consortium is developing an infrastructure based around the InterMine data warehouse system to integrate genomic and functional data from a number of key model organisms, leading the way to improved cross-species research. So far including budding yeast, nematode worm, fruit fly, zebrafish, rat and mouse, the project has set up data warehouses, synchronized data models and created analysis tools and links between data from different species. The project unites a number of major model organism databases, improving both the consistency and accessibility of comparative research, to the benefit of the wider scientific community.

The Saccharomyces Genome Database (SGD) is a scientific database that provides researchers with high-quality curated data about the genes and gene products of Saccharomyces cerevisiae. To provide instant and easy access to this information on mobile devices, we have developed YeastGenome, a native application for the Apple iPhone and iPad. YeastGenome can be used to quickly find basic information about S. cerevisiae genes and chromosomal features regardless of internet connectivity. With or without network access, you can view basic information and Gene Ontology annotations about a gene of interest by searching gene names and gene descriptions or by browsing the database within the app to find the gene of interest. With internet access, the app provides more detailed information about the gene, including mutant phenotypes, references and protein and genetic interactions, as well as provides hyperlinks to retrieve detailed information by showing SGD pages and views of the genome browser. SGD provides online help describing basic ways to navigate the mobile version of SGD, highlights key features and answers frequently asked questions related to the app. The app is available from iTunes (http://itunes.com/apps/yeastgenome). The YeastGenome app is provided freely as a service to our community, as part of SGD’s mission to provide free and open access to all its data and annotations.

Although about 1 percent of surgeons are infected with hepatitis B virus (HBV), transmission from surgeons to patients is thought to be uncommon. In July 1992, a 47-year-old woman became ill with acute hepatitis B after undergoing a thymectomy in which a thoracic-surgery resident who had had acute hepatitis B six months earlier assisted.To determine whether the surgeon transmitted HBV to this patient and others, we conducted chart reviews, interviews, and serologic testing of thoracic-surgery patients at the two hospitals where the surgeon worked from July 1991 to July 1992. Hepatitis B surface antigen (HBsAg) subtypes and DNA sequences from the surgeon and from infected patients were determined.Of 144 susceptible patients in whose surgery the infected surgeon participated, 19 had evidence of recent HBV infection (13 percent). One of the hospitals was selected for additional study, and none of the 124 susceptible patients of the other thoracic surgeons at this hospital had evidence of recent HBV infection (relative risk, infinity; 95 percent confidence interval, 4.7 to infinity). No evidence was found for any common source of HBV other than the infected surgeon. The HBsAg subtype and the partial HBV DNA sequences from the surgeon were identical to those in the infected patients. Transmission of the infection was associated with cardiac transplantation (relative risk, 4.9; 95 percent confidence interval, 1.5 to 15.5) but not with other surgical procedures. The surgeon was positive for hepatitis B e antigen and had a high serum HBV DNA concentration (15 ng per milliliter). Our investigations identified no deficiencies in the surgeon's infection-control practices.In this outbreak there was surgeon-to-patient HBV transmission despite apparent compliance with recommended infection-control practices. We could not identify any specific events that led to transmission.

Ontologies have proven very useful for capturing knowledge as a hierarchy of terms and their interrelationships. In biology a major challenge has been to construct ontologies of gene function given incomplete biological knowledge and inconsistencies in how this knowledge is manually curated. Here we show that large networks of gene and protein interactions in Saccharomyces cerevisiae can be used to infer an ontology whose coverage and power are equivalent to those of the manually curated Gene Ontology (GO). The network-extracted ontology (NeXO) contains 4,123 biological terms and 5,766 term-term relations, capturing 58% of known cellular components. We also explore robust NeXO terms and term relations that were initially not cataloged in GO, a number of which have now been added based on our analysis. Using quantitative genetic interaction profiling and chemogenomics, we find further support for many of the uncharacterized terms identified by NeXO, including multisubunit structures related to protein trafficking or mitochondrial function. This work enables a shift from using ontologies to evaluate data to using data to construct and evaluate ontologies.

The Gene Ontology (GO) Consortium (GOC, http://www.geneontology.org) is a community-based bioinformatics resource that classifies gene product function through the use of structured, controlled vocabularies. Over the past year, the GOC has implemented several processes to increase the quantity, quality and specificity of GO annotations. First, the number of manual, literature-based annotations has grown at an increasing rate. Second, as a result of a new 'phylogenetic annotation' process, manually reviewed, homology-based annotations are becoming available for a broad range of species. Third, the quality of GO annotations has been improved through a streamlined process for, and automated quality checks of, GO annotations deposited by different annotation groups. Fourth, the consistency and correctness of the ontology itself has increased by using automated reasoning tools. Finally, the GO has been expanded not only to cover new areas of biology through focused interaction with experts, but also to capture greater specificity in all areas of the ontology using tools for adding new combinatorial terms. The GOC works closely with other ontology developers to support integrated use of terminologies. The GOC supports its user community through the use of e-mail lists, social media and web-based resources.

“Go to, let us go down, and there confound their language, that they may not understand one another's speech. …Therefore is the name of it called Babel; because the Lord did there confound the language of all the earth…” Genesis 11:7,9

[1] Current consensus on global climate change predicts warming trends driven by anthropogenic forcing, with maximum temperature changes projected in the Northern Hemisphere (NH) high latitudes during winter. Yet, global temperature trends show little warming over the most recent decade or so. For longer time periods appropriate to the assessment of trends, however, global temperatures have experienced significant warming trends for all seasons except winter, when cooling trends exist instead across large stretches of eastern North America and northern Eurasia. Hence, the most recent lapse in global warming is a seasonal phenomenon, prevalent only in boreal winter. Additionally, we show that the largest regional contributor to global temperature trends over the past two decades is land surface temperatures in the NH extratropics. Therefore, proposed mechanisms explaining the fluctuations in global annual temperatures should address this apparent seasonal asymmetry.

The set of annotations at the Saccharomyces Genome Database (SGD) that classifies the cellular function of S. cerevisiae gene products using Gene Ontology (GO) terms has become an important resource for facilitating experimental analysis. In addition to capturing and summarizing experimental results, the structured nature of GO annotations allows for functional comparison across organisms as well as propagation of functional predictions between related gene products. Due to their relevance to many areas of research, ensuring the accuracy and quality of these annotations is a priority at SGD. GO annotations are assigned either manually, by biocurators extracting experimental evidence from the scientific literature, or through automated methods that leverage computational algorithms to predict functional information. Here, we discuss the relationship between literature-based and computationally predicted GO annotations in SGD and extend a strategy whereby comparison of these two types of annotation identifies genes whose annotations need review. Our method, CvManGO (Computational versus Manual GO annotations), pairs literature-based GO annotations with computational GO predictions and evaluates the relationship of the two terms within GO, looking for instances of discrepancy. We found that this method will identify genes that require annotation updates, taking an important step towards finding ways to prioritize literature review. Additionally, we explored factors that may influence the effectiveness of CvManGO in identifying relevant gene targets to find in particular those genes that are missing literature-supported annotations, but our survey found that there are no immediately identifiable criteria by which one could enrich for these under-annotated genes. Finally, we discuss possible ways to improve this strategy, and the applicability of this method to other projects that use the GO for curation. Database URL: http://www.yeastgenome.org

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field. © 2012 Landes Bioscience.

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 present article proposes the adoption of a community-defined, uniform, generic description of the core attributes of biological databases, BioDBCore. The goals of these attributes are to provide a general overview of the database landscape, to encourage consistency and interoperability between resources; and to promote the use of semantic and syntactic standards. BioDBCore will make it easier for users to evaluate the scope and relevance of available resources. This new resource will increase the collective impact of the information present in biological databases.

The solution to the problem of os calcis fractures has not yet been found. While Boehler has done much to focus attention on the care of these fractures, it is doubtful if he has evolved a satisfactory method of treatment. His method carries with it certain dangers and appears to give but an indifferent end result. Other methods of treatment would appear worthy of trial.

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.

Following up 107 operations for varicose veins over a 2-year period, 89 have been re-examined and the results of operation reviewed. The results have been listed as “ good ” (67), “fair” (9), and “failure “’ (13). Where ulceration has been present the result of operation would appear to have been very gratifying, with 20 cured out of 25.

Objective To assess the current distribution and training patterns of pediatric nurse practitioners (PNPs). Study design Secondary data analysis from the National Association of Pediatric Nurse Practitioners and the 2008 US Census Bureau were used to estimate the distribution of PNPs per 100 000 children. Data on nurse practitioner (NP) graduation and specialty education programs were obtained from the American Association of Colleges of Nursing. Results PNPs have the greatest concentration in the New England and mid-Atlantic regions and a narrow band of Midwestern states. States that allow PNPs to practice or prescribe independently do not consistently have a higher density of PNPs per child population. There has been a slight decrease in the proportion of programs that offer PNP training. In the last decade, the proportion of NP graduates pursuing family nurse practitioner education has increased, and the proportion pursuing PNP education has decreased. Conclusion Workforce planning for the health care of children will require improved methods of assessment of the role of PNPs and the volume of care they provide. Increased use of PNPs in pediatrics will likely require greater effort at recruitment of NPs into the PNP specialty.

The Gene Ontology (GO) is a collaborative effort that provides structured vocabularies for annotating the molecular function, biological role, and cellular location of gene products in a highly systematic way and in a species-neutral manner with the aim of unifying the representation of gene function across different organisms. Each contributing member of the GO Consortium independently associates GO terms to gene products from the organism(s) they are annotating. Here we introduce the Reference Genome project, which brings together those independent efforts into a unified framework based on the evolutionary relationships between genes in these different organisms. The Reference Genome project has two primary goals: to increase the depth and breadth of annotations for genes in each of the organisms in the project, and to create data sets and tools that enable other genome annotation efforts to infer GO annotations for homologous genes in their organisms. In addition, the project has several important incidental benefits, such as increasing annotation consistency across genome databases, and providing important improvements to the GO's logical structure and biological content., Author Summary Biological research is increasingly dependent on the availability of well-structured representations of biological data with detailed, accurate descriptions provided by the curators of the data repositories. The Reference Genome project's goal is to provide comprehensive functional annotation for the genomes of human as well as eleven organisms that are important models in biomedical research. To achieve this, we have developed an approach that superposes experimentally-based annotations onto the leaves of phylogenetic trees and then we manually annotate the function of the common ancestors, predicated on the assumption that the ancestors possessed the experimentally determined functions that are held in common at these leaves, and that these functions are likely to be conserved in all other descendents of each family.

Suspension culture cells of cowpea (Vigna unguiculata) were examined using transmission electron microscopy to characterize morphological changes associated with hyperthermal stress. Cultures maintained at 26°C (unadapted cells) and 38°C (thermoadapted cells) were examined before and after exposure to elevated temperatures of 34°C and 45°C, respectively. Observations indicate that while there were significant ultrastructural differences between unadapted and thermoadapted cells, the following structural modifications in response to heat stress were observed in cells of both cultures: (a) almost a complete loss of polyribosomes, rough ER, and dictyosomes, (b) migration of intracellular waste material (presumably proteinaceous in composition) into the cell vacuole, (c) swelling of the nucleolus with assumed accumulation of preribosomal RNP granules, and (d) retraction of the tonoplast from the cytoplasm into the vacuoles of some cells. Heat shock granules (two size classes) were observed in the cytoplasm of stressed thermoadapted cells along with hollow-cored granules within the leucoplasts. Apart from a few minor differences, the morphological modifications that were made in apparent response to hyperthermal stress were remarkably similar in both cultures.

Selecting the best treatment(s) is a common probleme in agricultural research. The usual procedure is to rank all treatments means and to claim the best treatment(s) are the highest (or lowest) subset found not to differ significantly according to a traditional multiple comparison (TMC). A least significant difference (LSD) procedure is applied most frequently. Our objective is to introduce a somewhat new methodology called simply ranking and selection (R&S) by way of agronomic examples and to illustrte why R&S may be preferred over TMC for selecting the best (...)

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/

This unit outlines a variety of methods by which DNA sequences can be manipulated by computers. Procedures for entering sequence data into the computer and assembling raw sequence data into a contiguous sequence are described first, followed by a description of methods of analyzing and manipulating sequences--e.g., verifying sequences, constructing restriction maps, designing oligonucleotides, identifying protein-coding regions, and predicting secondary structures. This unit also provides information on the large amount of software available for sequence analysis. The appendix to this unit lists some of the commercial software, shareware, and free software related to DNA sequence manipulation. The goal of this unit is to serve as a starting point for researchers interested in utilizing the tremendous sequencing resources available to the computer-knowledgeable molecular biology laboratory.

This appendix presents useful basic information, including common abbreviations, useful measurements and data, characteristics of amino acids and nucleic acids, information on radioactivity and the safe use of radioisotopes and other hazardous chemicals, conversions for centrifuges and rotors, characteristics of common detergents, and common conversion factors.

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.

Motivation: The rate at which gene-related findings appear in the scientific literature makes it difficult if not impossible for biomedical scientists to keep fully informed and up to date. The importance of these findings argues for the development of automated methods that can find, extract and summarize this information. This article reports on methods for determining the molecular function claims that are being made in a scientific article, specifically those that are backed by experimental evidence.Results: The most significant result is that for molecular function claims based on direct assays, our methods achieved recall of 70.7% and precision of 65.7%. Furthermore, our methods correctly identified in the text 44.6% of the specific molecular function claims backed up by direct assays, but with a precision of only 0.92%, a disappointing outcome that led to an examination of the different kinds of errors. These results were based on an analysis of 1823 articles from the literature of Saccharomyces cerevisiae (budding yeast).Availability: The annotation files for S.cerevisiae are available from ftp://genome-ftp.stanford.edu/pub/yeast/data_download/literature_curation/gene_association.sgd.gz. The draft protocol vocabulary is available by request from the first author.Contact: crangle@converspeech.com

Many biochemical processes, and the proteins and cofactors involved, have been defined for the eukaryote Saccharomyces cerevisiae. This understanding has been largely derived through the awesome power of yeast genetics. The proteins responsible for the reactions that build complex molecules and generate energy for the cell have been integrated into web-based tools that provide classical views of pathways. The Yeast Pathways in the Saccharomyces Genome Database (SGD) is, however, the only database created from manually curated literature annotations. In this protocol, gene function is explored using phenotype annotations to enable hypotheses to be formulated about a gene’s action. A common use of the SGD is to understand more about a gene that was identified via a phenotypic screen or found to interact with a gene/protein of interest. There are still many genes that do not yet have an experimentally defined function and so the information currently available can be used to speculate about their potential function. Typically, computational annotations based on sequence similarity are used to predict gene function. In addition, annotations are sometimes available for phenotypes of mutations in the gene of interest. Integrated results for a few example genes will be explored in this protocol. This will be instructive for the exploration of details that aid the analysis of experimental results and the establishment of connections within the yeast literature.

The S. cerevisiae genome is the most well-characterized eukaryotic genome and one of the simplest in terms of identifying open reading frames (ORFs), yet its primary annotation has been updated continually in the decade since its initial release in 1996 (Goffeau et al., 1996). The Saccharomyces Genome Database (SGD; www.yeastgenome.org) (Hirschman et al., 2006), the community-designated repository for this reference genome, strives to ensure that the S. cerevisiae annotation is as accurate and useful as possible. At SGD, the S. cerevisiae genome sequence and annotation are treated as a working hypothesis, which must be repeatedly tested and refined. In this paper, in celebration of the tenth anniversary of the completion of the S. cerevisiae genome sequence, we discuss the ways in which the S. cerevisiae sequence and annotation have changed, consider the multiple sources of experimental and comparative data on which these changes are based, and describe our methods for evaluating, incorporating and documenting these new data.

We have recently shown that unilateral naris occlusion (UNO) causes an increase in olfactory marker protein (OMP) immunoreactivity (IR) in mouse olfactory sensory neurons (OSN) from the occluded side of the nasal cavity and a decrease in OMP-IR on the non-occluded side, relative to controls. Given OMP's demonstrated role in olfactory modulation, these OMP-IR changes have been interpreted as a compensatory response by OSNs to odor deprivation on the occluded side and to supernormal exposure to odor on the non-occluded side of the nasal cavity. In the current study, we examined the developmental timing and the regional distribution of this process throughout the nasal cavity using immunocytochemistry. Results demonstrate that OMP-IR diverges in OSNs from the occluded side relative to the non-occluded side of the nasal cavity within eleven days after UNO, with statistically significant differences measurable after 17 days (n=16). We also measured relative levels of the Type 4 phosphodiesterase (PDE4A), another potential olfactory modulator, in nasal cavity tissue from UNO (n=8) and untreated mice (n=9) using western blots and immunocytochemistry. Like OMP, PDE4A-IR increased on the occluded side of the nasal cavity after UNO. Finally, we used immunocytochemistry to assess relative levels of olfactory-specific adenylyl cyclase (ACIII, n=4) and G-protein (Golf, n=2) in OSNs from the occluded and non-occluded sides of the nasal cavity of UNO mice. Following UNO, ACIII but not Golf -IR levels diverged comparing the occluded to the non-occluded sides of the nasal cavity. Taken together, our findings provide support for the previously unknown phenomenon of compensatory responses by OSNs to odor environment.

The ciliate Tetrahymena thermophila is a model organism for molecular and cellular biology. Like other ciliates, this species has separate germline and soma functions that are embodied by distinct nuclei within a single cell. The germline-like micronucleus (MIC) has its genome held in reserve for sexual reproduction. The soma-like macronucleus (MAC), which possesses a genome processed from that of the MIC, is the center of gene expression and does not directly contribute DNA to sexual progeny. We report here the shotgun sequencing, assembly, and analysis of the MAC genome of T. thermophila, which is approximately 104 Mb in length and composed of approximately 225 chromosomes. Overall, the gene set is robust, with more than 27,000 predicted protein-coding genes, 15,000 of which have strong matches to genes in other organisms. The functional diversity encoded by these genes is substantial and reflects the complexity of processes required for a free-living, predatory, single-celled organism. This is highlighted by the abundance of lineage-specific duplications of genes with predicted roles in sensing and responding to environmental conditions (e.g., kinases), using diverse resources (e.g., proteases and transporters), and generating structural complexity (e.g., kinesins and dyneins). In contrast to the other lineages of alveolates (apicomplexans and dinoflagellates), no compelling evidence could be found for plastid-derived genes in the genome. UGA, the only T. thermophila stop codon, is used in some genes to encode selenocysteine, thus making this organism the first known with the potential to translate all 64 codons in nuclear genes into amino acids. We present genomic evidence supporting the hypothesis that the excision of DNA from the MIC to generate the MAC specifically targets foreign DNA as a form of genome self-defense. The combination of the genome sequence, the functional diversity encoded therein, and the presence of some pathways missing from other model organisms makes T. thermophila an ideal model for functional genomic studies to address biological, biomedical, and biotechnological questions of fundamental importance.