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PubMed2003

Large-scale delineation of secreted protein biomarkers overexpressed in cancer tissue and serum.

Genetic alterations in tumor cells often lead to the emergence of growth-stimulatory autocrine and paracrine signals, involving overexpression of secreted peptide growth factors, cytokines, and hormones. Increased levels of these soluble proteins may be exploited for cancer diagnosis and management or as points of therapeutic intervention. Here, we combined the use of controlled vocabulary terms and sequence-based algorithms to predict genes encoding secreted proteins from among approximately 12,500 sequences represented on oligonucleotide microarrays. Expression of these genes was queried in 150 carcinomas from 10 anatomic sites of origin and compared with 46 normal tissues derived from the corresponding sites of tumor origin and other body tissues and organs. Of 74 different genes identified as overexpressed in cancer tissues, several encode proteins with demonstrated clinical diagnostic application, such as alpha-fetoprotein in liver carcinoma, and kallikreins 6 and 10 in ovarian cancer, or therapeutic utility, such as gastrin-releasing peptide/bombesin in lung carcinomas. We show that several of the other candidate genes encode proteins with high levels of tumor-associated expression by immunohistochemistry on tissue microarrays and further demonstrate significantly elevated levels of another novel candidate protein, macrophage inhibitory cytokine 1, a distant member of the transforming growth factor-beta superfamily, in the serum of patients with metastatic prostate, breast, and colorectal carcinomas. Our results suggest that the combination of annotation/protein sequence analysis, transcript profiling, immunohistochemistry, and immunoassay is a powerful approach for delineating candidate biomarkers with potential clinical significance and may be broadly applicable to other human diseases.

Biomarkers, Tumor↗

PubMed2003

BEME Guide no 3: systematic searching for evidence in medical education--Part 1: Sources of information.

Searching for evidence to inform best practice in medical education is a complex undertaking. With very few information sources dedicated to medical education itself, one is forced to consult a wide range of often enormous sources--and these are dedicated to either medicine or education, making a medical education search all the more challenging. This guide provides a comprehensive overview of relevant information sources and methods (including bibliographic databases, grey literature, hand searching and the Internet) and describes when they should be consulted. The process of constructing a search is explained: identifying and combining core concepts, using Boolean algebra and search syntax, limiting results sets, and making best use of databases' controlled vocabularies. This process is illustrated with images from search screens and is followed by numerous examples designed to reinforce skills and concepts covered. The guide has been developed from the ongoing experience gained from the systematic searches conducted for the Best Evidence Medical Education Collaboration, and concludes by looking ahead to initiatives that will shape future searching for medical education evidence.

Benchmarking↗

PubMed2003

BEME guide no. 3: systematic searching for evidence in medical education--part 2: constructing searches.

Searching for evidence to inform best practice in medical education is a complex undertaking. With very few information sources dedicated to medical education itself, one is forced to consult a wide range of often enormous sources-and these are dedicated to either medicine or education, making a medical education search all the more challenging. This guide provides a comprehensive overview of relevant information sources and methods (including bibliographic databases, grey literature, hand searching and the Internet) and describes when they should be consulted. The process of constructing a search is explained:identifying and combining core concepts, using Boolean algebra and search syntax, limiting results sets, and making best use of databases' controlled vocabularies. This process is illustrated with images from search screens and is followed by numerous examples designed to reinforce skills and concepts covered. The guide has been developed from the ongoing experience gained from the systematic searches conducted for the Best Evidence Medical Education Collaboration, and concludes by looking ahead to initiatives that will shape future searching for medical education evidence.

Abstracting and Indexing↗

PubMedDate not supplied

Use of information-seeking strategies for developing systematic reviews and engaging in evidence-based practice: the application of traditional and comprehensive Pearl Growing. A review.

BACKGROUND: Efficient library searches for research evidence are critical to practitioners who wish to engage in evidence-based practice (EBP) as well as researchers who seek to develop systematic reviews. AIMS: This review will propose the benefits of the search technique 'Pearl Growing' ('Traditional Pearl Growing') as well as an adaptation of this technique ('Comprehensive Pearl Growing'), until now ignored by the literature on EBP and systematic reviews, to aid in the retrieval of research evidence. These search techniques are illustrated with examples from the field of augmentative and alternative communication. MAIN CONTRIBUTIONS: Traditional Pearl Growing is proposed as an important addition to the arsenal of EBP search strategies for practitioners. The literature on Traditional Pearl Growing is extended in that EBP presents a newly identified purpose for this technique and the benefits in identifying appropriate quality filter goes beyond its previously exclusive focus on keywords. Comprehensive Pearl Growing is projected as a new strategy for researchers searching for studies to be included in systematic reviews. Not only does it provide data-based guidance in selecting effective keywords and quality filters, but also it provides appropriate databases. CONCLUSIONS: Although the techniques Traditional Pearl Growing and Comprehensive Pearl Growing are believed to be useful for locating research evidence in any field, it may be particularly important for interdisciplinary topics where the use of effective controlled vocabulary plays a greater role in bringing together evidence that may be scattered across databases.

Communication Disorders↗

PubMed2001

A comprehensive information resource on traditional, complementary, and alternative medicine: toward an international collaboration.

OBJECTIVES: A prototype for a comprehensive information resource for traditional complementary and alternative medicine (TCAM) has been developed to fill the considerable needs of a broad audience for worldwide access to TCAM information. The proposed resource is to be a comprehensive, vocabulary-controlled, integrated, standardized, multimedia information resource for TCAM. It will facilitate international cooperation, promote synergistic development of individual resources, promote dissemination of TCAM knowledge, and map the interrelationships among the TCAM traditions. METHODS: We organized two workshops for representatives of international databases that contain significant information on various aspects of alternative medicine. For the first workshop, we prepared and demonstrated a prototype named Complementary and Alternative Medicine Digital Library (CAMed) to illustrate the anticipated structure, content, and functionality of the comprehensive resource. We then constructed a second prototype to demonstrate the possibilities of searching across the collaborating databases and presented it to the representatives at the second workshop. OUTCOMES: Representatives of nine international databases attended the two workshops, in Bangalore, India (1998), and in Seoul, Korea (1999). We presented the prototypes at the workshops. Prototype I uses a Web interface, and supports browsing and searching from a variety of access points. Prototype II demonstrates a functional system that provides simultaneous access to selected represented databases by searching thesauri of these databases through our system. The group formalized itself as the International Collaboration for Information on Complementary and Traditional Medicine (IC2TM) with a goal of fully realizing the potential of the project.

Complementary Therapies↗

PubMed2006

Data standards: a call to action.

Access to data is something that every molecular biologist takes for granted nowadays, but data alone is of little use unless it is made available in a useable form through the development and global uptake of data standards. The challenge of standards development has been taken up by grass-roots movements working within several different branches of the biomedical research community. Many of these initiatives are proving extremely successful; for example, the Gene Ontology, which provides a controlled vocabulary for describing the properties of gene products, the Microarray Gene Expression Data Society's standards for describing microarray experiments, and the emerging standards developed by the Proteomics Standards Initiative are gaining broad acceptance. Standards development now faces its greatest ever challenge--the integration of diverse data types to fulfill the goals of systems biology. Now is the time for the communities that are developing these standards, the funding bodies that have invested so heavily in high-throughput data generation, and the publishers of biomedical research papers to cooperate fully to make the goals of integrated data analysis a reality.

Animals↗

PubMed2005

Get ready to GO! A biologist's guide to the Gene Ontology.

The Gene Ontology (GO) project provides a controlled vocabulary to facilitate high-quality functional gene annotation for all species. Genes in biological databases are linked to GO terms, allowing biologists to ask questions about gene function in a manner independent of species. This tutorial provides an introduction for biologists to the GO resources and covers three of the most common methods of querying GO: by individual gene, by gene function and by using a list of genes. [For the sake of brevity, the term 'gene' is used throughout this paper to refer to genes and their products (proteins and RNAs). GO annotations are always based on the characteristics of gene products, even though it may be the gene that is cited in the annotation.].

Abstracting and Indexing↗

PubMed2006

Metabolomics Standards Workshop and the development of international standards for reporting metabolomics experimental results.

Informatics standards and controlled vocabularies are essential for allowing information technology to help exchange, manage, interpret and compare large data collections. In a rapidly evolving field, the challenge is to work out how best to describe, but not prescribe, the use of these technologies and methods. A Metabolomics Standards Workshop was held by the US National Institutes of Health (NIH) to bring together multiple ongoing standards efforts in metabolomics with the NIH research community. The goals were to discuss metabolomics workflows (methods, technologies and data treatments) and the needs, challenges and potential approaches to developing a Metabolomics Standards Initiative that will help facilitate this rapidly growing field which has been a focus of the NIH roadmap effort. This report highlights specific aspects of what was presented and discussed at the 1st and 2nd August 2005 Metabolomics Standards Workshop.

Cell Physiological Phenomena↗

PubMed2025

argNorm: normalization of antibiotic resistance gene annotations to the Antibiotic Resistance Ontology (ARO).

SUMMARY: Currently available and frequently used tools for annotating antibiotic resistance genes (ARGs) in genomes and metagenomes provide results using inconsistent nomenclature. This makes the comparison of different ARG annotation outputs challenging. The comparability of ARG annotation outputs can be improved by mapping gene names and their categories to a common controlled vocabulary such as the Antibiotic Resistance Ontology (ARO). We developed argNorm, a command line tool and Python library, to normalize all detected genes across six ARG annotation tools (eight databases) to the ARO. argNorm also adds information to the outputs using the same ARG categorization so that they are comparable across tools. AVAILABILITY AND IMPLEMENTATION: argNorm is available as an open-source tool at: https://github.com/BigDataBiology/argNorm. It can also be downloaded as a PyPI package and is available on Bioconda and as an nf-core module.

Molecular Sequence Annotation↗

PubMed2004

Pedro: a configurable data entry tool for XML.

UNLABELLED: Pedro is a Java application that dynamically generates data entry forms for data models expressed in XML Schema, producing XML data files that validate against this schema. The software uses an intuitive tree-based navigation system, can supply context-sensitive help to users and features a sophisticated interface for populating data fields with terms from controlled vocabularies. The software also has the ability to import records from tab delimited text files and features various validation routines. AVAILABILITY: The application, source code, example models from several domains and tutorials can be downloaded from http://pedro.man.ac.uk/.

Computer Graphics↗

PubMed2005

Expert knowledge without the expert: integrated analysis of gene expression and literature to derive active functional contexts.

MOTIVATION: The interpretation of expression data without appropriate expert knowledge is difficult and usually limited to exploratory data analysis, such as clustering and detecting differentially regulated genes. However, comparing experimental results against manually compiled knowledge resources might limit or bias the perspective on the data. Thus, manual analysis by experts is required to obtain confident predictions about involved processes. RESULTS: We present an algorithm to simultaneously derive interpretations of expression measurements together with biological hypotheses from biomedical publications. It identifies active functional contexts ('concepts'), i.e. gene clusters that exhibit both a significant gene expression as well as a coherent literature profile. Manual intervention by an expert in specifying prior knowledge is not required. The approach scales to realistic applications and does not rely on controlled vocabularies or pathway resources. We validated our algorithm by analyzing a current juvenile arthritis dataset. A number of gene clusters and accompanying literature topics are identified as an interpretation of the data that coincide well with the phenotype and biological processes known to be involved in the disease. We demonstrate that generated clusters are both more sensitive and more specific than Gene Ontology categories detected on the same data. The method allows for in-depth investigation of subsets of genes, the associated literature topics and publications. AVAILABILITY: Supplementary data on clusters is available upon request.

Expert Systems↗

PubMed2004

Bloader--a batch loader application for MIAMExpress.

UNLABELLED: BLoader is a client server application for annotating and loading large amounts of Microarray data into a local installation of the MIAMExpress database. A set of nested spreadsheets is used to collect the required MIAME annotation. Controlled vocabularies are downloaded from MIAMExpress and ArrayExpress databases to guarantee MIAME compliance. AVAILABILITY: The application is available from the author at http://www.ansorge-group.embl.de/bloader CONTACT: schwager@embl.de SUPPLEMENTARY INFORMATION: For more details on BLoader visit the above web page.

Computer Graphics↗

PubMed2005

On the quality of tree-based protein classification.

MOTIVATION: Phylogenetic analysis of protein sequences is widely used in protein function classification and delineation of subfamilies within larger families. In addition, the recent increase in the number of protein sequence entries with controlled vocabulary terms describing function (e.g. the Gene Ontology) suggests that it may be possible to overlay these terms onto phylogenetic trees to automatically locate functional divergence events in protein family evolution. Phylogenetic analysis of large datasets requires fast algorithms; and even 'fast', approximate distance matrix-based phylogenetic algorithms are slow on large datasets since they involve calculating maximum likelihood estimates of pairwise evolutionary distances. There have been many attempts to classify protein sequences on the family and subfamily level without reconstructing phylogenetic trees, but using hierarchical clustering with simpler distance measures, which also produce trees or dendrograms. How can these trees be compared in their ability to accurately classify protein sequences? RESULTS: Given a 'reference classification' or 'group membership labels' for a set of related protein sequences as well as a tree describing their relationships (e.g. a phylogenetic tree), we propose a method for dividing the tree into monophyletic or paraphyletic groups so as to optimize the correspondence between the reference groups and the tree-derived groups. We call the achieved optimal correspondence the 'accuracy of a tree-based classification (TBC)', which measures the ability of a tree to separate proteins of similar function into monophyletic or paraphyletic groups. We apply this measure to compare classical NJ and UPGMA phylogenetic trees with the trees obtained from hierarchical clustering using different protein similarity measures. Our preliminary analysis on a set of expert-curated protein families and alignments suggests that there is no uniformly superior algorithm, and that simple protein similarity measures combined with hierarchical clustering produce trees with reasonable and often the most accurate TBC. We used our measure to help us to design TIPS, a tree-building algorithm, based on agglomerative clustering with a similarity measure derived from profile scoring. TIPS is comparable with phylogenetic algorithms in terms of classification accuracy and is much faster on large protein families. Due to its time scalability and acceptable accuracy, TIPS is being used in the large-scale PANTHER protein classification project. The trees produced by different algorithms for different protein families can be viewed at http://panther.appliedbiosystems.com/pub/tree_quality/trees.jsp. For every tree and every level of classification granularity we provide the optimal TBC along with the reference classification. AVAILABILITY: The script that evaluates the accuracy of TBC is available at http://panther.appliedbiosystems.com/pub/tree_quality/index.jsp

Algorithms↗

PubMed2005

Automatic assignment of biomedical categories: toward a generic approach.

MOTIVATION: We report on the development of a generic text categorization system designed to automatically assign biomedical categories to any input text. Unlike usual automatic text categorization systems, which rely on data-intensive models extracted from large sets of training data, our categorizer is largely data-independent. METHODS: In order to evaluate the robustness of our approach we test the system on two different biomedical terminologies: the Medical Subject Headings (MeSH) and the Gene Ontology (GO). Our lightweight categorizer, based on two ranking modules, combines a pattern matcher and a vector space retrieval engine, and uses both stems and linguistically-motivated indexing units. RESULTS AND CONCLUSION: Results show the effectiveness of phrase indexing for both GO and MeSH categorization, but we observe the categorization power of the tool depends on the controlled vocabulary: precision at high ranks ranges from above 90% for MeSH to <20% for GO, establishing a new baseline for categorizers based on retrieval methods.

Abstracting and Indexing↗

PubMed2000

Integrating functional genomic information into the Saccharomyces genome database.

The Saccharomyces Genome Database (SGD) stores and organizes information about the nearly 6200 genes in the yeast genome. The information is organized around the 'locus page' and directs users to the detailed information they seek. SGD is endeavoring to integrate the existing information about yeast genes with the large volume of data generated by functional analyses that are beginning to appear in the literature and on web sites. New features will include searches of systematic analyses and Gene Summary Paragraphs that succinctly review the literature for each gene. In addition to current information, such as gene product and phenotype descriptions, the new locus page will also describe a gene product's cellular process, function and localization using a controlled vocabulary developed in collaboration with two other model organism databases. We describe these developments in SGD through the newly reorganized locus page. The SGD is accessible via the WWW at http://genome-www.stanford.edu/Saccharomyces/

Database Management Systems↗

PubMed2001

Protein Information Resource: a community resource for expert annotation of protein data.

The Protein Information Resource, in collaboration with the Munich Information Center for Protein Sequences (MIPS) and the Japan International Protein Information Database (JIPID), produces the most comprehensive and expertly annotated protein sequence database in the public domain, the PIR-International Protein Sequence Database. To provide timely and high quality annotation and promote database interoperability, the PIR-International employs rule-based and classification-driven procedures based on controlled vocabulary and standard nomenclature and includes status tags to distinguish experimentally determined from predicted protein features. The database contains about 200,000 non-redundant protein sequences, which are classified into families and superfamilies and their domains and motifs identified. Entries are extensively cross-referenced to other sequence, classification, genome, structure and activity databases. The PIR web site features search engines that use sequence similarity and database annotation to facilitate the analysis and functional identification of proteins. The PIR-Inter-national databases and search tools are accessible on the PIR web site at http://pir.georgetown.edu/ and at the MIPS web site at http://www.mips.biochem.mpg.de. The PIR-International Protein Sequence Database and other files are also available by FTP.

Computational Biology↗

PubMed2001

The Mouse Gene Expression Database (GXD).

The Gene Expression Database (GXD) is a community resource of gene expression information for the laboratory mouse. By combining the different types of expression data, GXD aims to provide increasingly complete information about the expression profiles of genes in different mouse strains and mutants, thus enabling valuable insights into the molecular networks that underlie normal development and disease. GXD is integrated with the Mouse Genome Database (MGD). Extensive interconnections with sequence databases and with databases from other species, and the development and use of shared controlled vocabularies extend GXD's utility for the analysis of gene expression information. GXD is accessible through the Mouse Genome Informatics web site at http://www.informatics.jax.org/ or directly at http://www.informatics.jax.org/menus/expression_menu. shtml.

Animals↗

PubMed2002

Gramene: a resource for comparative grass genomics.

Gramene (http://www.gramene.org) is a comparative genome mapping database for grasses and a community resource for rice. Rice, in addition to being an economically important crop, is also a model monocot for understanding other agronomically important grass genomes. Gramene replaces the existing AceDB database 'RiceGenes' with a relational database based on Oracle. Gramene provides curated and integrative information about maps, sequence, genes, genetic markers, mutants, QTLs, controlled vocabularies and publications. Its aims are to use the rice genetic, physical and sequence maps as fundamental organizing units, to provide a common denominator for moving from one crop grass to another and is to serve as a portal for interconnecting with other web-based crop grass resources. This paper describes the initial steps we have taken towards realizing these goals.

Chromosome Mapping↗