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PubMed1996

Molecular probe data base (MPDB).

The molecular probe data base (MPDB) contains detailed information on synthetic oligonucleotides, including their identification, target genes, applications and bibliographic references. It is available on-line through Internet and can be searched by using Network Information Retrieval tools. In this article the most recent enhancements of MPDB, both in terms of data contents and new ways of access, are described. These include a recently established collaboration with EMBL Data Library, in the sphere of SRSWWW network browser, in view of a better integration of MPDB with other molecular biology databases.

Computer Communication Networks↗

PubMed2004

ConSite: web-based prediction of regulatory elements using cross-species comparison.

ConSite is a user-friendly, web-based tool for finding cis-regulatory elements in genomic sequences. Predictions are based on the integration of binding site prediction generated with high-quality transcription factor models and cross-species comparison filtering (phylogenetic footprinting). By incorporating evolutionary constraints, selectivity is increased by an order of magnitude as compared to single-sequence analysis. ConSite offers several unique features, including an interactive expert system for retrieving orthologous regulatory sequences. Programming modules and biological databases that form the foundation of the ConSite service are freely available to the research community. ConSite is available at http:/www.phylofoot.org/consite.

Animals↗

PubMed2006

FASTSNP: an always up-to-date and extendable service for SNP function analysis and prioritization.

Single nucleotide polymorphism (SNP) prioritization based on the phenotypic risk is essential for association studies. Assessment of the risk requires access to a variety of heterogeneous biological databases and analytical tools. FASTSNP (function analysis and selection tool for single nucleotide polymorphisms) is a web server that allows users to efficiently identify and prioritize high-risk SNPs according to their phenotypic risks and putative functional effects. A unique feature of FASTSNP is that the functional effect information used for SNP prioritization is always up-to-date, because FASTSNP extracts the information from 11 external web servers at query time using a team of web wrapper agents. Moreover, FASTSNP is extendable by simply deploying more Web wrapper agents. To validate the results of our prioritization, we analyzed 1569 SNPs from the SNP500Cancer database. The results show that SNPs with a high predicted risk exhibit low allele frequencies for the minor alleles, consistent with a well-known finding that a strong selective pressure exists for functional polymorphisms. We have been using FASTSNP for 2 years and FASTSNP enables us to discover a novel promoter polymorphism. FASTSNP is available at http://fastsnp.ibms.sinica.edu.tw.

Gene Frequency↗

PubMedDate not supplied

Mapping information roadways from sequence to phenotype and across species.

Expressing genetic concepts in ways that allow computerization of detailed genetic information and connectivitity with molecular biology databases is discussed. Interconnections between and among organismal and molecular databases are illustrated with examples from E. coli and maize databases on the World Wide Web connecting with GenBank, SwissProt, and other databases. Issues in development, maintenance, and widespread public use of such information are discussed.

DNA↗

PubMed2005

Plant genome resources at the national center for biotechnology information.

The National Center for Biotechnology Information (NCBI) integrates data from more than 20 biological databases through a flexible search and retrieval system called Entrez. A core Entrez database, Entrez Nucleotide, includes GenBank and is tightly linked to the NCBI Taxonomy database, the Entrez Protein database, and the scientific literature in PubMed. A suite of more specialized databases for genomes, genes, gene families, gene expression, gene variation, and protein domains dovetails with the core databases to make Entrez a powerful system for genomic research. Linked to the full range of Entrez databases is the NCBI Map Viewer, which displays aligned genetic, physical, and sequence maps for eukaryotic genomes including those of many plants. A specialized plant query page allow maps from all plant genomes covered by the Map Viewer to be searched in tandem to produce a display of aligned maps from several species. PlantBLAST searches against the sequences shown in the Map Viewer allow BLAST alignments to be viewed within a genomic context. In addition, precomputed sequence similarities, such as those for proteins offered by BLAST Link, enable fluid navigation from unannotated to annotated sequences, quickening the pace of discovery. NCBI Web pages for plants, such as Plant Genome Central, complete the system by providing centralized access to NCBI's genomic resources as well as links to organism-specific Web pages beyond NCBI.

Biotechnology↗

PubMed2006

Whole-plant growth stage ontology for angiosperms and its application in plant biology.

Plant growth stages are identified as distinct morphological landmarks in a continuous developmental process. The terms describing these developmental stages record the morphological appearance of the plant at a specific point in its life cycle. The widely differing morphology of plant species consequently gave rise to heterogeneous vocabularies describing growth and development. Each species or family specific community developed distinct terminologies for describing whole-plant growth stages. This semantic heterogeneity made it impossible to use growth stage description contained within plant biology databases to make meaningful computational comparisons. The Plant Ontology Consortium (http://www.plantontology.org) was founded to develop standard ontologies describing plant anatomical as well as growth and developmental stages that can be used for annotation of gene expression patterns and phenotypes of all flowering plants. In this article, we describe the development of a generic whole-plant growth stage ontology that describes the spatiotemporal stages of plant growth as a set of landmark events that progress from germination to senescence. This ontology represents a synthesis and integration of terms and concepts from a variety of species-specific vocabularies previously used for describing phenotypes and genomic information. It provides a common platform for annotating gene function and gene expression in relation to the developmental trajectory of a plant described at the organismal level. As proof of concept the Plant Ontology Consortium used the plant ontology growth stage ontology to annotate genes and phenotypes in plants with initial emphasis on those represented in The Arabidopsis Information Resource, Gramene database, and MaizeGDB.

Arabidopsis↗

PubMed2002

Modeling the heart--from genes to cells to the whole organ.

Successful physiological analysis requires an understanding of the functional interactions between the key components of cells, organs, and systems, as well as how these interactions change in disease states. This information resides neither in the genome nor even in the individual proteins that genes code for. It lies at the level of protein interactions within the context of subcellular, cellular, tissue, organ, and system structures. There is therefore no alternative to copying nature and computing these interactions to determine the logic of healthy and diseased states. The rapid growth in biological databases; models of cells, tissues, and organs; and the development of powerful computing hardware and algorithms have made it possible to explore functionality in a quantitative manner all the way from the level of genes to the physiological function of whole organs and regulatory systems. This review illustrates this development in the case of the heart. Systems physiology of the 21st century is set to become highly quantitative and, therefore, one of the most computer-intensive disciplines.

Animals↗

PubMed1994

Parallel computing in biomedical research.

Scalable parallel computer architectures provide the computational performance needed for advanced biomedical computing problems. The National Institutes of Health have developed a number of parallel algorithms and techniques useful in determining biological structure and function. These applications include processing electron micrographs to determine the three-dimensional structure of viruses, calculating the solvent-accessible surface area of proteins to help predict the three-dimensional conformation of these molecules from their primary structures, and searching for homologous DNA or amino acid sequences in large biological databases. Timing results demonstrate substantial performance improvements with parallel implementations compared with conventional sequential systems.

Algorithms↗

PubMed2006

Inconsistencies between human genetic cytolocations and those derived using genomic sequence.

One result of the publishing of the human genome sequence is the ability to define objects through their position on the consensus sequence. While this has simplified the process of creating order maps for genes on a chromosome, it has created discrepancies between the published cytolocations of human genes, as presented through genetic references, and those locations derived computationally from the genomic sequence. For the 6,830 records with HUGO gene symbols shared between the online version of Mendelian Inheritance in Man and Ensembl, 18% of the records have a discrepancy of at least one cytogenetic band between the datasets. Discordance between data sets at this frequency would have a significant impact on the utility of datasets created by the amalgamation of numerous biological databases.

Base Sequence↗

PubMed2005

DynGO: a tool for visualizing and mining of Gene Ontology and its associations.

BACKGROUND: A large volume of data and information about genes and gene products has been stored in various molecular biology databases. A major challenge for knowledge discovery using these databases is to identify related genes and gene products in disparate databases. The development of Gene Ontology (GO) as a common vocabulary for annotation allows integrated queries across multiple databases and identification of semantically related genes and gene products (i.e., genes and gene products that have similar GO annotations). Meanwhile, dozens of tools have been developed for browsing, mining or editing GO terms, their hierarchical relationships, or their "associated" genes and gene products (i.e., genes and gene products annotated with GO terms). Tools that allow users to directly search and inspect relations among all GO terms and their associated genes and gene products from multiple databases are needed. RESULTS: We present a standalone package called DynGO, which provides several advanced functionalities in addition to the standard browsing capability of the official GO browsing tool (AmiGO). DynGO allows users to conduct batch retrieval of GO annotations for a list of genes and gene products, and semantic retrieval of genes and gene products sharing similar GO annotations. The result are shown in an association tree organized according to GO hierarchies and supported with many dynamic display options such as sorting tree nodes or changing orientation of the tree. For GO curators and frequent GO users, DynGO provides fast and convenient access to GO annotation data. DynGO is generally applicable to any data set where the records are annotated with GO terms, as illustrated by two examples. CONCLUSION: We have presented a standalone package DynGO that provides functionalities to search and browse GO and its association databases as well as several additional functions such as batch retrieval and semantic retrieval. The complete documentation and software are freely available for download from the website http://biocreative.ifsm.umbc.edu/dyngo.

Computer Graphics↗

PubMed2006

Meta-All: a system for managing metabolic pathway information.

BACKGROUND: Many attempts are being made to understand biological subjects at a systems level. A major resource for these approaches are biological databases, storing manifold information about DNA, RNA and protein sequences including their functional and structural motifs, molecular markers, mRNA expression levels, metabolite concentrations, protein-protein interactions, phenotypic traits or taxonomic relationships. The use of these databases is often hampered by the fact that they are designed for special application areas and thus lack universality. Databases on metabolic pathways, which provide an increasingly important foundation for many analyses of biochemical processes at a systems level, are no exception from the rule. Data stored in central databases such as KEGG, BRENDA or SABIO-RK is often limited to read-only access. If experimentalists want to store their own data, possibly still under investigation, there are two possibilities. They can either develop their own information system for managing that own data, which is very time-consuming and costly, or they can try to store their data in existing systems, which is often restricted. Hence, an out-of-the-box information system for managing metabolic pathway data is needed. RESULTS: We have designed META-ALL, an information system that allows the management of metabolic pathways, including reaction kinetics, detailed locations, environmental factors and taxonomic information. Data can be stored together with quality tags and in different parallel versions. META-ALL uses Oracle DBMS and Oracle Application Express. We provide the META-ALL information system for download and use. In this paper, we describe the database structure and give information about the tools for submitting and accessing the data. As a first application of META-ALL, we show how the information contained in a detailed kinetic model can be stored and accessed. CONCLUSION: META-ALL is a system for managing information about metabolic pathways. It facilitates the handling of pathway-related data and is designed to help biochemists and molecular biologists in their daily research. It is available on the Web at http://bic-gh.de/meta-all and can be downloaded free of charge and installed locally.

Database Management Systems↗

PubMed2006

EST-PAC a web package for EST annotation and protein sequence prediction.

With the decreasing cost of DNA sequencing technology and the vast diversity of biological resources, researchers increasingly face the basic challenge of annotating a larger number of expressed sequences tags (EST) from a variety of species. This typically consists of a series of repetitive tasks, which should be automated and easy to use. The results of these annotation tasks need to be stored and organized in a consistent way. All these operations should be self-installing, platform independent, easy to customize and amenable to using distributed bioinformatics resources available on the Internet. In order to address these issues, we present EST-PAC a web oriented multi-platform software package for expressed sequences tag (EST) annotation. EST-PAC provides a solution for the administration of EST and protein sequence annotations accessible through a web interface. Three aspects of EST annotation are automated: 1) searching local or remote biological databases for sequence similarities using Blast services, 2) predicting protein coding sequence from EST data and, 3) annotating predicted protein sequences with functional domain predictions. In practice, EST-PAC integrates the BLASTALL suite, EST-Scan2 and HMMER in a relational database system accessible through a simple web interface. EST-PAC also takes advantage of the relational database to allow consistent storage, powerful queries of results and, management of the annotation process. The system allows users to customize annotation strategies and provides an open-source data-management environment for research and education in bioinformatics.

Journal Article↗

PubMed2004

Gene Ontology: looking backwards and forwards.

The Gene Ontology consortium began six years ago with a group of scientists who decided to connect our data by sharing the same language for describing it. Its most significant achievement lies in uniting many independent biological database efforts into a cooperative force.

Animals↗

PubMed2005

An ontology for cell types.

We describe an ontology for cell types that covers the prokaryotic, fungal, animal and plant worlds. It includes over 680 cell types. These cell types are classified under several generic categories and are organized as a directed acyclic graph. The ontology is available in the formats adopted by the Open Biological Ontologies umbrella and is designed to be used in the context of model organism genome and other biological databases. The ontology is freely available at http://obo.sourceforge.net/ and can be viewed using standard ontology visualization tools such as OBO-Edit and COBrA.

Animals↗

PubMed2006

Quantitative assessment of dictionary-based protein named entity tagging.

OBJECTIVE: Natural language processing (NLP) approaches have been explored to manage and mine information recorded in biological literature. A critical step for biological literature mining is biological named entity tagging (BNET) that identifies names mentioned in text and normalizes them with entries in biological databases. The aim of this study was to provide quantitative assessment of the complexity of BNET on protein entities through BioThesaurus, a thesaurus of gene/protein names for UniProt knowledgebase (UniProtKB) entries that was acquired using online resources. METHODS: We evaluated the complexity through several perspectives: ambiguity (i.e., the number of genes/proteins represented by one name), synonymy (i.e., the number of names associated with the same gene/protein), and coverage (i.e., the percentage of gene/protein names in text included in the thesaurus). We also normalized names in BioThesaurus and measures were obtained twice, once before normalization and once after. RESULTS: The current version of BioThesaurus has over 2.6 million names or 2.1 million normalized names covering more than 1.8 million UniProtKB entries. The average synonymy is 3.53 (2.86 after normalization), ambiguity is 2.31 before normalization and 2.32 after, while the coverage is 94.0% based on the BioCreAtive data set comprising MEDLINE abstracts containing genes/proteins. CONCLUSION: The study indicated that names for genes/proteins are highly ambiguous and there are usually multiple names for the same gene or protein. It also demonstrated that most gene/protein names appearing in text can be found in BioThesaurus.

Dictionaries as Topic↗

PubMedDate not supplied

Representing and analysing molecular and cellular function using the computer.

Determining the biological function of a myriad of genes, and understanding how they interact to yield a living cell, is the major challenge of the post genome-sequencing era. The complexity of biological systems is such that this cannot be envisaged without the help of powerful computer systems capable of representing and analysing the intricate networks of physical and functional interactions between the different cellular components. In this review we try to provide the reader with an appreciation of where we stand in this regard. We discuss some of the inherent problems in describing the different facets of biological function, give an overview of how information on function is currently represented in the major biological databases, and describe different systems for organising and categorising the functions of gene products. In a second part, we present a new general data model, currently under development, which describes information on molecular function and cellular processes in a rigorous manner. The model is capable of representing a large variety of biochemical processes, including metabolic pathways, regulation of gene expression and signal transduction. It also incorporates taxonomies for categorising molecular entities, interactions and processes, and it offers means of viewing the information at different levels of resolution, and dealing with incomplete knowledge. The data model has been implemented in the database on protein function and cellular processes 'aMAZE' (http://www.ebi.ac.uk/research/pfbp/), which presently covers metabolic pathways and their regulation. Several tools for querying, displaying, and performing analyses on such pathways are briefly described in order to illustrate the practical applications enabled by the model.

Cell Physiological Phenomena↗

PubMed2005

Computational prediction of SEG (single exon gene) function in humans.

Human genes are often interrupted by non-coding, intragenic sequences called introns. Hence, the gene sequence is divided into exons (coding segments) and introns (non-coding segments). Consequently, a majority of them are multi exon genes (MEG). However, a considerable amount of single exon genes (SEG) are present in the human genome (approximately 12%). This amount is sizeable and it is important to probe their molecular function and cellular role. Hence, we performed a genome wide functional assignment to 3750 SEG sequences using PFAM (protein family database), PROSITE (database of biologically meaningful signatures or motifs) and SUPERFAMILY (a library covering all proteins of known 3 dimensional structure). PFAM assigned 13% SEG to trans-membrane receptor genes of the G-protein coupled receptor (GPCR) family and showed that a majority of SEG proteins have DNA binding function. PROSITE identified 336 unique motif types in them and this accounts for 25% of all known patterns, with a majority having PHOSPHORYLATION and ACETYLATION signals. SUPERFAMILY assigned 33% SEG to the membrane all alpha (proteins containing alpha helix structural elements according to SCOP (structural classification of proteins) definition). Functional assignment of SEG proteins at multiple levels (sequence signals, sequence families, 3D structures) using PFAM, PROSITE and SUPERFAMILY is envisioned to suggest their selective and predominant molecular function in cellular systems. Their function as DNA binding, phosphorylating, acetylating and house-keeping agents is intriguing. The analysis also showed evidence of SEG expression and retro-transposition. However, this information is inadequate to draw concerted conclusion on the prevalent role played by these proteins in cellular biology. A complete understanding of SEG function will help to explore their role in cellular environment. The derived datasets from these analyses are available at http://sege.ntu.edu.sg/wester/intronless/human/.

Computational Biology↗

PubMed2001

Advances in high-throughput mass spectrometry.

The evolution of high-throughput drug discovery is readily apparent as the pharmaceutical industry continues to stress the rapid progression of new chemical entities and biological agents through drug discovery and development pipelines. Mass spectrometry and high performance liquid chromatography-mass spectrometry have played an instrumental role in the support and advancement of all facets of high-throughput drug discovery. The introduction of new instrumentation has extended the breadth of mass spectrometric-based capabilities from the characterization of high-throughput organic synthesis products to early adsorption, distribution, metabolism and excretion profiling. Additionally, advances in the capacity and throughput of mass spectrometry systems have concurrently led to the introduction of data management tools to address automated data reduction, archival and mining, as well as analytical data integration to chemical and biological databases.

Chromatography, High Pressure Liquid↗