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NCBI genetic resources supporting immunogenetic research.

The NCBI creates and maintains a set of integrated bibliographic, sequence, map, structure and other database resources to promote the efficient retrieval of information and the discovery of novel relationships. The connections made between elements of these resources permit researchers to start a search from a wide spectrum of entry points. These multiple dimensions of data can be roughly categorized by primary content as text or bibliographic (PubMed, PubMedCentral, OMIM, LocusLink), sequence (GenBank, Reference Sequence Project (RefSeq), dbSNP, MMDB), protein structure (MMDB) or map position (MapView). They can also becategorized by level of expert curation, which may range from validation of submissions from external groups (GenBank, PubMed, PubMedCentral,), to automatic computation (HomoloGene, UniGene), and to highly reviewed and corrected (LocusLink, MMDB, OMIM, RefSeq). Searches can be made by words (in an article title, key words, sequence annotation, database value, author) by sequence (BLAST or e-PCR against multiple sequence databases), or by map coordinates. By computing or curating bi-directional links between related objects, NCBI can represent content on the genetics, molecular biology, and clinical considerations of interest to immunogeneticists. There is also an emerging resource developed by the NCBI in collaboration with the IHWG devoted to the presentation of MHC data (dbMHC). How dbMHC will augment existing resources at the NCBI is described.

Computational Biology↗

Cell signaling networks ontology.

Although databases for cell signaling pathways include numbers of reaction data of the pathways, the reaction data cannot be used yet to deduce biological functions from them. For the deduction, we need systematic and consistent interpretation of biological functions of reactions in cell signaling pathways in the context of "information transmission". To address this issue, we have developed a functional ontology for cell signaling pathways, Cell Signaling Network Ontology (CSN-Ontology), which provides framework for the functional interpretation presenting some important concepts as information, selectivity, movability, and signaling rules including passage of time.

Cells↗

Notes on the use of ontologies in the biochemical domain.

In this paper we aim at presenting the main flavours and uses that are given to the term ontology in the bio-domains. The paper does not intend to be a thorough review of the existing work in the area. It highlights the uses that are given to ontologies in the Scientific Databases and Visualisation Group at EML Research, in Heidelberg.

Biochemical Phenomena↗

[The application of human mutation databases].

Researches on genome mutation are becoming more and more important with the finish of human genome DNA draft. This review is to classify the existing human mutation databases, including mutation database, SNP(single nucleotide polymorphisms) databases, mutation databases about disease, mutation databases about proteins, mutation databases about map and mutation information about specific gene. We also give advice on how to utilize these mutation databases, and discuss problems of existing databases.

Databases, Factual↗

Ontological integration of data models for cell signaling pathways by defining a factor of causality called 'signal'.

Databases have collected masses of information concerning cell signaling pathways that includes information on pathways, molecular interactions as well as molecular complexes. However we have no general data model to represent comprehensive properties of cell signaling pathways, so that this type of information has been represented by two different data models that we call 'binary relation' and 'state transition'. The disagreement between the existing models derives from lack of consensus about a factor of causality in reactions in cell signaling pathways, which is often called 'signal'. We developed an ontology named CSNO (Cell Signaling Networks Ontology) based on device ontology. As device ontology is a research product of knowledge engineering, CSNO is the first application of it to biological knowledge. CSNO defines the factor of causality called 'signal', offers an integrative viewpoint for the two different data models, explicates intrinsic distinctions between signaling and metabolic pathways, and eliminates ambiguity from representation of complex molecules.

Cell Physiological Phenomena↗

Systematic DNA-binding domain classification of transcription factors.

Based on the manual annotation of transcription factors stored in the TRANSFAC database, we developed a library of hidden Markov models (HMM) to represent their DNA-binding domains and used it for a comprehensive classification. The models constructed were applied on the UniProt/Swiss-Prot database, leading to a systematic classification of further DNA-binding protein entries. The HMM library obtained can be used to classify any newly discovered transcription factor according to its DNA-binding domain and, thus, to generate hypotheses about its DNA-binding specificity.

Binding Sites↗

Clustering of database sequences for fast homology search using upper bounds on alignment score.

Homology data are among the most important information used to predict the functions of unknown proteins and thus fast and accurate methods are needed. In this paper, we propose a new approach for fast and accurate homology search using pre-computed all-against-all similarity scores in a target database. We previously developed a method for derivation of an upper bound of the Smith-Waterman score (SW-score) between a query and a homolog candidate sequence using the SW-score between the candidate and a sequence similar to the query. In this paper, by using this upper bound, we first cluster the sequences in the target database so that upper bounds of SW-scores for all the members in the clusters are less than a given value and select representative sequences for respective clusters. Then, the query sequence is searched against the representative sequences and the upper bounds of SW-scores for respective clusters are estimated. Only if the upper bound is higher than a given threshold, SW-alignments are computed for all the sequences in the cluster. We performed computational experiments to test efficiency of the proposed method for the KEGG/GENES database using the KEGG/SSDB. The results suggest that our method is efficient for redundant databases that include multiple closely related species.

Algorithms↗

Genestrace: phenomic knowledge discovery via structured terminology.

The era of applied genomic medicine is quickly approaching accompanied by the increasing availability of detailed genetic information. Understanding the genetic etiology behind complex, multi-gene diseases remains an important challenge. In order to uncover the putative genetic etiology of complex diseases, we designed a method that explores the relationships between two major terminological and ontological resources: the Unified Medical Language System (UMLS) and the Gene Ontology (GO). The UMLS has a mainly clinical emphasis; Gene Ontology has become the standard for biological annotations of genes and gene products. Using statistical and semantic relationships within and between the two resources, we are able to infer relationships between disease concepts in the UMLS and gene products annotated using GO and its associated databases. We validated our inferences by comparing them to the known gene-disease relationships, as defined in the Online Mendelian Inheritance in Man's morbidmap (OMIM). The proof-of-concept methods presented here are unique in that they bypass the ambiguity of the direct extraction of gene or disease term from MEDLINE. Additionally, our methods provide direct links to clinically significant diseases through established terminologies or ontologies. The preliminary results presented here indicate the potential utility of exploiting the existing, manually curated relationships in biomedical resources as a tool for the discovery of potentially valuable new gene-disease relationships.

Computational Biology↗

Exploratory visual analysis of pharmacogenomic results.

Comprehensive analysis of expansive pharmacogenomic datasets is a daunting challenge. A thorough exploration of experimental results requires both statistical and annotative information. Therefore, appropriate analysis tools must bring a readily-accessible, flexible combination of statistics and biological annotation to the user's desktop. We present the Exploratory Visual Analysis (EVA) software and database as such a tool and demonstrate its utility in replicating the findings of an earlier pharmacogenomic study as well as elucidating novel biologically plausible hypotheses. EVA brings all of the often disparate pieces of analysis together in an infinitely flexible visual display that is amenable to any type of statistical result and biological question. Here, we describe the motivations for developing EVA, detail the database and custom graphical user interface (GUI), provide an example of its application to a publicly available pharmacogenomic dataset, and discuss the broad utility of the EVA tool for the pharmacogenomics community.

Computational Biology↗

Linking experimental results, biological networks and sequence analysis methods using Ontologies and Generalised Data Structures.

The structure of a closely integrated data warehouse is described that is designed to link different types and varying numbers of biological networks, sequence analysis methods and experimental results such as those coming from microarrays. The data schema is inspired by a combination of graph based methods and generalised data structures and makes use of ontologies and meta-data. The core idea is to consider and store biological networks as graphs, and to use generalised data structures (GDS) for the storage of further relevant information. This is possible because many biological networks can be stored as graphs: protein interactions, signal transduction networks, metabolic pathways, gene regulatory networks etc. Nodes in biological graphs represent entities such as promoters, proteins, genes and transcripts whereas the edges of such graphs specify how the nodes are related. The semantics of the nodes and edges are defined using ontologies of node and relation types. Besides generic attributes that most biological entities possess (name, attribute description), further information is stored using generalised data structures. By directly linking to underlying sequences (exons, introns, promoters, amino acid sequences) in a systematic way, close interoperability to sequence analysis methods can be achieved. This approach allows us to store, query and update a wide variety of biological information in a way that is semantically compact without requiring changes at the database schema level when new kinds of biological information is added. We describe how this datawarehouse is being implemented by extending the text-mining framework ONDEX to link, support and complement different bioinformatics applications and research activities such as microarray analysis, sequence analysis and modelling/simulation of biological systems. The system is developed under the GPL license and can be downloaded from http://sourceforge.net/projects/ondex/

Algorithms↗

Deriving an ontology for human gene expression sources from the CYTOMER database on human organs and cell types.

CYTOMER is a relational database of organs/tissues, cell types, physiological systems and developmental stages that currently focuses on the human system. From this database, we have derived an ontology for anatomical and morphological structures for the human organism which includes all embryonal stages and the cell types constituting these structures. The ontology has been transferred to the OWL format and is freely available for download at http://cytomer/bioinf.med.uni-goettingen.de.

Animals↗

Analysis of gene ontology features in microarray data using the Proteome BioKnowledge Library.

Microarray technology has resulted in an explosion of complex, valuable data. Integrating data analysis tools with a comprehensive underlying database would allow efficient identification of common properties among differentially regulated genes. In this study we sought to compare the utility of various databases in microarray analysis. The Proteome BioKnowledge Library (BKL), a manually curated, proteome-wide compilation of the scientific literature, was used to generate a list of Gene Ontology (GO) Biological Process (BP) terms enriched among proteins involved in cardiovascular disease. Analysis of DNA microarray data generated in a study of rat vascular smooth muscle cell responses revealed significant enrichment in a number of GO BPs that were also enriched among cardiovascular disease-related proteins. Using annotation from LocusLink and chip annotation from the Gene Expression Omnibus yielded fewer enriched cardiovascular disease-associated GO BP terms. Data sets of orthologous genes from mouse and human were generated using the BKL Retriever. Analysis of these sets focusing on BKL Disease annotation, revealed a significant association of these genes with cardiovascular disease. These results and the extensive presence of experimental evidence for BKL GO and Disease features, underscore the benefits of using this database for microarray analysis.

Animals↗

Iceland's plan for genomics research: facts and implications.

The government of Iceland has authorized a private, for-profit firm, deCODE Genetics, to construct a database of the population's medical records as part of a larger plan by deCODE for human genetics research. This article presents the background for genetics research in Iceland, the history of deCODE, and the terms of the law authorizing the database. It then examines five objections to the law, based on commercialization, lack of informed consent, risks to privacy, the effects of other research, and financial unfairness. It concludes that the Icelandic model is not a good precedent for similar research elsewhere.

Confidentiality↗

A distributed computation of Interpro Pfam, PROSITE and ProDom for protein annotation.

Interpro is a widely used tool for protein annotation in genome sequencing projects, demanding a large amount of computation and representing a huge time-consuming step. We present a strategy to execute programs using databases Pfam, PROSITE and ProDom of Interpro in a distributed environment using a Java-based messaging system. We developed a two-layer scheduling architecture of the distributed infrastructure. Then, we made experiments and analyzed the results. Our distributed system gave much better results than Interpro Pfam, PROSITE and ProDom running in a centralized platform. This approach seems to be appropriate and promising for highly demanding computational tools used for biological applications.

Computational Biology↗

DoD: Database of Databases--updated molecular biology databases.

Database of Databases (DoD) is a collection of molecular biology databases extracted from Nucleic Acids Research, 2005 Database issue. DoD is constructed using javascript and html code. The 14 categories of 719 databases are provided with a search option, keyword help and database description linked to respective pages. Keyword help lists the search strings used to perform an individual search against categorized databases. Database description provides brief information about the database with a link to full-text article and main web page. DoD is available online and can be accessed at http://www.progenebio.in/DoD/DoD.htm.

Computational Biology↗

Human genetic databases and liberty.

This paper examines an act of the Icelandic Parliament on health-sector databases. Both the legislation itself and the manner in which it was presented by the Government to the Parliament and the general public raise various questions about democratic parliamentary procedures, community consultation, autonomy, privacy, professional confidence, control of health data in hospitals and business relationships between medical doctors and biotechnology corporations. A major question to be asked is: In whose interest is it that such sensitive data are handed over to for-profit corporations? Furthermore, is it within the authority of the legislature to authorize politically appointed boards of health institutes to transfer such data without the direct informed consent of the patient and without the relevant physicians' having a say? Does experience teach us to entrust private companies with sensitive personal data? Should the Government be involved in the research policy-making of the biotechnology companies that have been given access to the genetic data of a population, or should the profit motive be the sole deciding influence? That is, should the interest of the shareholders of the companies prevail over the interest of underprivileged groups who are most in need of new methods or medicine to alleviate their situation due to incurable diseases? Or is the invisible hand of the market the only competent decision-maker? Finally, will the proliferation of databases containing sensitive personal data, such as human genetic data, limit our personal liberty?

Biological Specimen Banks↗

PDB-Metrics: a web tool for exploring the PDB contents.

PDB-Metrics (http://sms.cbi.cnptia.embrapa.br/SMS/pdb_metrics/index.html) is a component of the Diamond STING suite of programs for the analysis of protein sequence, structure and function. It summarizes the characteristics of the collection of protein structure descriptions deposited in the Protein Data Bank (PDB) and provides a Web interface to search and browse the PDB, using a variety of alternative criteria. PDB-Metrics is a powerful tool for bioinformaticians to examine the data span in the PDB from several perspectives. Although other Web sites offer some similar resources to explore the PDB contents, PDB-Metrics is among those with the most complete set of such facilities, integrated into a single Web site. This program has been developed using SQLite, a C library that provides all the query facilities of a database management system.

Computer Graphics↗