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PubMed2006

Gene expression omnibus: microarray data storage, submission, retrieval, and analysis.

The Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information archives and freely distributes high-throughput molecular abundance data, predominantly gene expression data generated by DNA microarray technology. The database has a flexible design that can handle diverse styles of both unprocessed and processed data in a Minimum Information About a Microarray Experiment-supportive infrastructure that promotes fully annotated submissions. GEO currently stores about a billion individual gene expression measurements, derived from over 100 organisms, submitted by over 1500 laboratories, addressing a wide range of biological phenomena. To maximize the utility of these data, several user-friendly web-based interfaces and applications have been implemented that enable effective exploration, query, and visualization of these data at the level of individual genes or entire studies. This chapter describes how data are stored, submission procedures, and mechanisms for data retrieval and query. GEO is publicly accessible at http://www.ncbi.nlm.nih.gov/projects/geo/.

Animals↗
PubMed2006

Ensembl 2006.

The Ensembl (http://www.ensembl.org/) project provides a comprehensive and integrated source of annotation of large genome sequences. Over the last year the number of genomes available from the Ensembl site has increased from 4 to 19, with the addition of the mammalian genomes of Rhesus macaque and Opossum, the chordate genome of Ciona intestinalis and the import and integration of the yeast genome. The year has also seen extensive improvements to both data analysis and presentation, with the introduction of a redesigned website, the addition of RNA gene and regulatory annotation and substantial improvements to the integration of human genome variation data.

Animals↗
PubMed2002

Annotation of the Drosophila melanogaster euchromatic genome: a systematic review.

BACKGROUND: The recent completion of the Drosophila melanogaster genomic sequence to high quality and the availability of a greatly expanded set of Drosophila cDNA sequences, aligning to 78% of the predicted euchromatic genes, afforded FlyBase the opportunity to significantly improve genomic annotations. We made the annotation process more rigorous by inspecting each gene visually, utilizing a comprehensive set of curation rules, requiring traceable evidence for each gene model, and comparing each predicted peptide to SWISS-PROT and TrEMBL sequences. RESULTS: Although the number of predicted protein-coding genes in Drosophila remains essentially unchanged, the revised annotation significantly improves gene models, resulting in structural changes to 85% of the transcripts and 45% of the predicted proteins. We annotated transposable elements and non-protein-coding RNAs as new features, and extended the annotation of untranslated (UTR) sequences and alternative transcripts to include more than 70% and 20% of genes, respectively. Finally, cDNA sequence provided evidence for dicistronic transcripts, neighboring genes with overlapping UTRs on the same DNA sequence strand, alternatively spliced genes that encode distinct, non-overlapping peptides, and numerous nested genes. CONCLUSIONS: Identification of so many unusual gene models not only suggests that some mechanisms for gene regulation are more prevalent than previously believed, but also underscores the complex challenges of eukaryotic gene prediction. At present, experimental data and human curation remain essential to generate high-quality genome annotations.

Animals↗
PubMed2003

Integrative approach for computationally inferring protein domain interactions.

MOTIVATION: The current need for high-throughput protein interaction detection has resulted in interaction data being generated en masse through such experimental methods as yeast-two-hybrids and protein chips. Such data can be erroneous and they often do not provide adequate functional information for the detected interactions. Therefore, it is useful to develop an in silico approach to further validate and annotate the detected protein interactions. RESULTS: Given that protein-protein interactions involve physical interactions between protein domains, domain-domain interaction information can be useful for validating, annotating, and even predicting protein interactions. However, large-scale, experimentally determined domain-domain interaction data do not exist. Here, we describe an integrative approach to computationally derive putative domain interactions from multiple data sources, including protein interactions, protein complexes, and Rosetta Stone sequences. We further prove the usefulness of such an integrative approach by applying the derived domain interactions to predict and validate protein-protein interactions. AVAILABILITY: A database of putative protein domain interactions derived using the method described in this paper is available at http://interdom.lit.org.sg.

Algorithms↗
PubMed2006

Expanded protein information at SGD: new pages and proteome browser.

The recent explosion in protein data generated from both directed small-scale studies and large-scale proteomics efforts has greatly expanded the quantity of available protein information and has prompted the Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) to enhance the depth and accessibility of protein annotations. In particular, we have expanded ongoing efforts to improve the integration of experimental information and sequence-based predictions and have redesigned the protein information web pages. A key feature of this redesign is the development of a GBrowse-derived interactive Proteome Browser customized to improve the visualization of sequence-based protein information. This Proteome Browser has enabled SGD to unify the display of hidden Markov model (HMM) domains, protein family HMMs, motifs, transmembrane regions, signal peptides, hydropathy plots and profile hits using several popular prediction algorithms. In addition, a physico-chemical properties page has been introduced to provide easy access to basic protein information. Improvements to the layout of the Protein Information page and integration of the Proteome Browser will facilitate the ongoing expansion of sequence-specific experimental information captured in SGD, including post-translational modifications and other user-defined annotations. Finally, SGD continues to improve upon the availability of genetic and physical interaction data in an ongoing collaboration with BioGRID by providing direct access to more than 82,000 manually-curated interactions.

Computer Graphics↗
PubMed2004

Analysis of multiple genomic sequence alignments: a web resource, online tools, and lessons learned from analysis of mammalian SCL loci.

Comparative analysis of genomic sequences is becoming a standard technique for studying gene regulation. However, only a limited number of tools are currently available for the analysis of multiple genomic sequences. An extensive data set for the testing and training of such tools is provided by the SCL gene locus. Here we have expanded the data set to eight vertebrate species by sequencing the dog SCL locus and by annotating the dog and rat SCL loci. To provide a resource for the bioinformatics community, all SCL sequences and functional annotations, comprising a collation of the extensive experimental evidence pertaining to SCL regulation, have been made available via a Web server. A Web interface to new tools specifically designed for the display and analysis of multiple sequence alignments was also implemented. The unique SCL data set and new sequence comparison tools allowed us to perform a rigorous examination of the true benefits of multiple sequence comparisons. We demonstrate that multiple sequence alignments are, overall, superior to pairwise alignments for identification of mammalian regulatory regions. In the search for individual transcription factor binding sites, multiple alignments markedly increase the signal-to-noise ratio compared to pairwise alignments.

Animals↗
PubMed2006

Accurate and unambiguous tag-to-gene mapping in serial analysis of gene expression.

BACKGROUND: In this study, we present a robust and reliable computational method for tag-to-gene assignment in serial analysis of gene expression (SAGE). The method relies on current genome information and annotation, incorporation of several new features, and key improvements over alternative methods, all of which are important to determine gene expression levels more accurately. The method provides a complete annotation of potential virtual SAGE tags within a genome, along with an estimation of their confidence for experimental observation that ranks tags that present multiple matches in the genome. RESULTS: We applied this method to the Saccharomyces cerevisiae genome, producing the most thorough and accurate annotation of potential virtual SAGE tags that is available today for this organism. The usefulness of this method is exemplified by the significant reduction of ambiguous cases in existing experimental SAGE data. In addition, we report new insights from the analysis of existing SAGE data. First, we found that experimental SAGE tags mapping onto introns, intron-exon boundaries, and non-coding RNA elements are observed in all available SAGE data. Second, a significant fraction of experimental SAGE tags was found to map onto genomic regions currently annotated as intergenic. Third, a significant number of existing experimental SAGE tags for yeast has been derived from truncated cDNAs, which are synthesized through oligo-d(T) priming to internal poly-(A) regions during reverse transcription. CONCLUSION: We conclude that an accurate and unambiguous tag mapping process is essential to increase the quality and the amount of information that can be extracted from SAGE experiments. This is supported by the results obtained here and also by the large impact that the erroneous interpretation of these data could have on downstream applications.

Chromosome Mapping↗
PubMed2006

A system-based approach to interpret dose- and time-dependent microarray data: quantitative integration of gene ontology analysis for risk assessment.

Although microarray technology has emerged as a powerful tool to explore expression levels of thousands of genes or even complete genomes after exposure to toxicants, the functional interpretation of microarray data sets still represents a time-consuming and challenging task. Gene ontology (GO) and pathway mapping have both been shown to be powerful approaches to generate a global view of biological processes and cellular components impacted by toxicants. However, current methods only allow for comparisons across two experimental settings at one particular time point. In addition, the resulting annotations are presented in extensive gene lists with minimal or limited quantitative information, data that are crucial in the application of toxicogenomic data for risk assessment. To facilitate quantitative interpretation of dose- or time-dependent genomic data, we propose to use combined average raw gene expression values (e.g., intensity or ratio) of genes associated with specific functional categories derived from the GO database. We developed an extended program (GO-Quant) to extract quantitative gene expression values and to calculate the average intensity or ratio for those significantly altered by functional gene category based on MAPPFinder results. To demonstrate its application, we applied this approach to a previously published dose- and time-dependent toxicogenomic data set (J. F. Dillman et al., 2005, Chem. Res. Toxicol. 18, 28-34). Our results indicate that the above systems approach can describe quantitatively the degree to which functional gene systems change across dose or time. Additionally, this approach provides a robust measurement to illustrate results compared to single-gene assessments and enables the user to calculate the corresponding ED(50) for each specific functional GO term, important for risk assessment.

Animals↗
PubMed1993

Data interchange for clinical neurophysiology.

Clinical Neurophysiology is rapidly moving towards complete dependence on digital instrumentation and computer-based systems. ASTM E 1467-92 "Standard Specification for Transferring Digital Neurophysiological Data Between Independent Computer Systems" was developed to allow exchange of multichannel electrophysiological waveforms, embedded textual annotations, results of mathematical processing, and clinical interpretive reports between various instruments and systems. This will permit integration of the neurophysiology laboratory interaction with hospital and clinic informations systems, and sharing of data between institutions for clinical or research purposes.

Computer Systems↗
PubMed2006

Genomic pathways database and biological data management.

In this paper, we discuss the properties of biological data and challenges it poses for data management, and argue that, in order to meet the data management requirements for 'digital biology', careful integration of the existing technologies and the development of new data management techniques for biological data are needed. Based on this premise, we present PathCase: Case Pathways Database System. PathCase is an integrated set of software tools for modelling, storing, analysing, visualizing and querying biological pathways data at different levels of genetic, molecular, biochemical and organismal detail. The novel features of the system include: (i) genomic information integrated with other biological data and presented starting from pathways; (ii) design for biologists who are possibly unfamiliar with genomics, but whose research is essential for annotating gene and genome sequences with biological functions; (iii) database design, implementation and graphical tools which enable users to visualize pathways data in multiple abstraction levels and to pose exploratory queries; (iv) a wide range of different types of queries including, 'path' and 'neighbourhood queries' and graphical visualization of query outputs; and (v) an implementation that allows for web (XML)-based dissemination of query outputs (i.e. pathways data in BIOPAX format) to researchers in the community, giving them control on the use of pathways data.

Computational Biology↗
PubMed2004

COMe: the ontology of bioinorganic proteins.

BACKGROUND: Many characterised proteins contain metal ions, small organic molecules or modified residues. In contrast, the huge amount of data generated by genome projects consists exclusively of sequences with almost no annotation. One of the goals of the structural genomics initiative is to provide representative three-dimensional (3-D) structures for as many protein/domain folds as possible to allow successful homology modelling. However, important functional features such as metal co-ordination or a type of prosthetic group are not always conserved in homologous proteins. So far, the problem of correct annotation of bioinorganic proteins has been largely ignored by the bioinformatics community and information on bioinorganic centres obtained by methods other than crystallography or NMR is only available in literature databases. RESULTS: COMe (Co-Ordination of Metals) represents the ontology for bioinorganic and other small molecule centres in complex proteins. COMe consists of three types of entities: 'bioinorganic motif' (BIM), 'molecule' (MOL), and 'complex proteins' (PRX), with each entity being assigned a unique identifier. A BIM consists of at least one centre (metal atom, inorganic cluster, organic molecule) and two or more endogenous and/or exogenous ligands. BIMs are represented as one-dimensional (1-D) strings and 2-D diagrams. A MOL entity represents a 'small molecule' which, when in complex with one or more polypeptides, forms a functional protein. The PRX entities refer to the functional proteins as well as to separate protein domains and subunits. The complex proteins in COMe are subdivided into three categories: (i) metalloproteins, (ii) organic prosthetic group proteins and (iii) modified amino acid proteins. The data are currently stored in both XML format and a relational database and are available at http://www.ebi.ac.uk/come/. CONCLUSION: COMe provides the classification of proteins according to their 'bioinorganic' features and thus is orthogonal to other classification schemes, such as those based on sequence similarity, 3-D fold, enzyme activity, or biological process. The hierarchical organisation of the controlled vocabulary allows both for annotation and querying at different levels of granularity.

Binding Sites↗
PubMed2004

Are data from different gene expression microarray platforms comparable?

Many commercial and custom-made microarray formats are routinely used for large-scale gene expression surveys. Here, we sought to determine the level of concordance between microarray platforms by analyzing breast cancer cell lines with in situ synthesized oligonucleotide arrays (Affymetrix HG-U95v2), commercial cDNA microarrays (Agilent Human 1 cDNA), and custom-made cDNA microarrays from a sequence-validated 13K cDNA library. Gene expression data from the commercial platforms showed good correlations across the experiments (r = 0.78-0.86), whereas the correlations between the custom-made and either of the two commercial platforms were lower (r = 0.62-0.76). Discrepant findings were due to clone errors on the custom-made microarrays, old annotations, or unknown causes. Even within platform, there can be several ways to analyze data that may influence the correlation between platforms. Our results indicate that combining data from different microarray platforms is not straightforward. Variability of the data represents a challenge for developing future diagnostic applications of microarrays.

Cell Line, Tumor↗
PubMed2005

Metalloproteomics: high-throughput structural and functional annotation of proteins in structural genomics.

A high-throughput method for measuring transition metal content based on quantitation of X-ray fluorescence signals was used to analyze 654 proteins selected as targets by the New York Structural GenomiX Research Consortium. Over 10% showed the presence of transition metal atoms in stoichiometric amounts; these totals as well as the abundance distribution are similar to those of the Protein Data Bank. Bioinformatics analysis of the identified metalloproteins in most cases supported the metalloprotein annotation; identification of the conserved metal binding motif was also shown to be useful in verifying structural models of the proteins. Metalloproteomics provides a rapid structural and functional annotation for these sequences and is shown to be approximately 95% accurate in predicting the presence or absence of stoichiometric metal content. The project's goal is to assay at least 1 member from each Pfam family; approximately 500 Pfam families have been characterized with respect to transition metal content so far.

Binding Sites↗
PubMed2005

Inconsistencies over time in 5% of NetAffx probe-to-gene annotations.

BACKGROUND: DNA microarray probes are designed to match particular mRNA transcripts, often based on expressed sequences like ESTs, or cDNAs, many times incomplete. As a result, the relations between probes and genes can change as the sequence data are updated. However, it is frequent that the reported results of microarray analyses are given just as lists of genes without any reference to the underlying probes. RESULTS: We show for a particular commercial microarray design that the number of probes associated to some genes change with time. These changes concern approximately 5% of the probe sets across the history of annotation releases over a two year span. CONCLUSION: We recommend to report probe set identifiers when publishing microarray results, and to submit those analyses to microarray public databases to ensure that the interpretation of the data is updated with the latest set of annotations.

Animals↗
PubMed2004

HCVDB: hepatitis C virus sequences database.

UNLABELLED: To date, more than 30 000 hepatitis C virus (HCV) sequences have been deposited in the generalist databases DNA Data Bank of Japan (DDBJ), EMBL Nucleotide Sequence Database (EMBL) and GenBank. The main difficulties with HCV sequences in these databases are their retrieval, annotation and analyses. To help HCV researchers face the increasing needs of HCV sequence analyses, we developed a specialised database of computer-annotated HCV sequences, called HCVDB. HCVDB is re-built every month from an up-to-date EMBL database by an automated process. HCVDB provides key data about the HCV sequences (e.g. genotype, genomic region, protein names and functions, known 3-dimensional structures) and ensures consistency of the annotations, which enables reliable keyword queries. The database is highly integrated with sequence and structure analysis tools and the SRS (LION bioscience) keywords query system. Thus, any user can extract subsets of sequences matching particular criteria or enter their own sequences and analyse them with various bioinformatics programs available on the same server. AVAILABILITY: HCVDB is available from http://hepatitis.ibcp.fr.

Amino Acid Sequence↗
PubMed2003

Cytokine-related genes identified from the RIKEN full-length mouse cDNA data set.

To identify novel cytokine-related genes, we searched the set of 60,770 annotated RIKEN mouse cDNA clones (FANTOM2 clones), using keywords such as cytokine itself or cytokine names (such as interferon, interleukin, epidermal growth factor, fibroblast growth factor, and transforming growth factor). This search produced 108 known cytokines and cytokine-related products such as cytokine receptors, cytokine-associated genes, or their products (enhancers, accessory proteins, cytokine-induced genes). We found 15 clusters of FANTOM2 clones that are candidates for novel cytokine-related genes. These encoded products with strong sequence similarity to guanylate-binding protein (GBP-5), interleukin-1 receptor-associated kinase 2 (IRAK-2), interleukin 20 receptor alpha isoform 3, a member of the interferon-inducible proteins of the Ifi 200 cluster, four members of the membrane-associated family 1-8 of interferon-inducible proteins, one p27-like protein, and a hypothetical protein containing a Toll/Interleukin receptor domain. All four clones representing novel candidates of gene products from the family contain a novel highly conserved cross-species domain. Clones similar to growth factor-related products included transforming growth factor beta-inducible early growth response protein 2 (TIEG-2), TGFbeta-induced factor 2, integrin beta-like 1, latent TGF-binding protein 4S, and FGF receptor 4B. We performed a detailed sequence analysis of the candidate novel genes to elucidate their likely functional properties.

Amino Acid Sequence↗
PubMedDate not supplied

GFSWeb: a web tool for genome-based identification of proteins from mass spectrometric samples.

The interpretation of mass spectrometry data for protein identification has become a vital component of proteomics research. However, since most existing software tools rely on protein databases, their success is limited, especially as the pace of annotation efforts fails to keep pace with sequencing. We present a publicly available, web-based version of a software tool that maps peptide mass fingerprint data directly to their genomic origin, allowing for genome-based, annotation-independent protein identification.

Databases, Protein↗
PubMed2004

MIPS: analysis and annotation of proteins from whole genomes.

The Munich Information Center for Protein Sequences (MIPS-GSF), Neuherberg, Germany, provides protein sequence-related information based on whole-genome analysis. The main focus of the work is directed toward the systematic organization of sequence-related attributes as gathered by a variety of algorithms, primary information from experimental data together with information compiled from the scientific literature. MIPS maintains automatically generated and manually annotated genome-specific databases, develops systematic classification schemes for the functional annotation of protein sequences and provides tools for the comprehensive analysis of protein sequences. This report updates the information on the yeast genome (CYGD), the Neurospora crassa genome (MNCDB), the database of complete cDNAs (German Human Genome Project, NGFN), the database of mammalian protein-protein interactions (MPPI), the database of FASTA homologies (SIMAP), and the interface for the fast retrieval of protein-associated information (QUIPOS). The Arabidopsis thaliana database, the rice database, the plant EST databases (MATDB, MOsDB, SPUTNIK), as well as the databases for the comprehensive set of genomes (PEDANT genomes) are described elsewhere in the 2003 and 2004 NAR database issues, respectively. All databases described, and the detailed descriptions of our projects can be accessed through the MIPS web server (http://mips.gsf.de).

Animals↗