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PubMed2003

Viewing and annotating sequence data with Artemis.

Artemis is a widely used software tool for annotating and viewing sequence data. No database is required to use Artemis. Instead, individual sequence data files can be analysed with little or no formatting, making it particularly suited to the study of small genomes and chromosomes, and straightforward for a novice user to get started. Since its release in 1999, Artemis has been used to annotate a diverse collection of prokaryotic and eukaryotic genomes, ranging from Streptomyces coelicolor to, more recently, a large proportion of the Plasmodium falciparum genome. Artemis allows annotated genomes to be easily browsed and makes it simple to add useful biological information to raw sequence data. This paper gives an overview of some of the features of Artemis and includes how it facilitates manual gene prediction and can provide an overview of entire chromosomes or small compact genomes--useful for uncovering unusual features such as pathogenicity islands.

Animals↗

PubMed2003

PCAS--a precomputed proteome annotation database resource.

BACKGROUND: Many model proteomes or "complete" sets of proteins of given organisms are now publicly available. Much effort has been invested in computational annotation of those "draft" proteomes. Motif or domain based algorithms play a pivotal role in functional classification of proteins. Employing most available computational algorithms, mainly motif or domain recognition algorithms, we set up to develop an online proteome annotation system with integrated proteome annotation data to complement existing resources. RESULTS: We report here the development of PCAS (ProteinCentric Annotation System) as an online resource of pre-computed proteome annotation data. We applied most available motif or domain databases and their analysis methods, including hmmpfam search of HMMs in Pfam, SMART and TIGRFAM, RPS-PSIBLAST search of PSSMs in CDD, pfscan of PROSITE patterns and profiles, as well as PSI-BLAST search of SUPERFAMILY PSSMs. In addition, signal peptide and TM are predicted using SignalP and TMHMM respectively. We mapped SUPERFAMILY and COGs to InterPro, so the motif or domain databases are integrated through InterPro. PCAS displays table summaries of pre-computed data and a graphical presentation of motifs or domains relative to the protein. As of now, PCAS contains human IPI, mouse IPI, and rat IPI, A. thaliana, C. elegans, D. melanogaster, S. cerevisiae, and S. pombe proteome.PCAS is available at http://pak.cbi.pku.edu.cn/proteome/gca.php CONCLUSION: PCAS gives better annotation coverage for model proteomes by employing a wider collection of available algorithms. Besides presenting the most confident annotation data, PCAS also allows customized query so users can inspect statistically less significant boundary information as well. Therefore, besides providing general annotation information, PCAS could be used as a discovery platform. We plan to update PCAS twice a year. We will upgrade PCAS when new proteome annotation algorithms identified.

Algorithms↗

PubMed2000

Annotating sequence data using Genotator.

In this postgenomic era, it is no longer necessary to argue the need for automated methods for sequence annotation. Many researchers have designed tools for analyzing DNA sequences, but running multiple tools and interpreting the results can be tedious and confusing. In the last few years, many analysis workbenches have been developed to help streamline the process of sequence annotation. Genotator, developed in 1996, is still a popular choice owing to its ease of use and its configurability. This article will review annotating sequence data using the Genotator.

Amino Acid Sequence↗

PubMed2005

MiMiR: a comprehensive solution for storage, annotation and exchange of microarray data.

BACKGROUND: The generation of large amounts of microarray data presents challenges for data collection, annotation, exchange and analysis. Although there are now widely accepted formats, minimum standards for data content and ontologies for microarray data, only a few groups are using them together to build and populate large-scale databases. Structured environments for data management are crucial for making full use of these data. DESCRIPTION: The MiMiR database provides a comprehensive infrastructure for microarray data annotation, storage and exchange and is based on the MAGE format. MiMiR is MIAME-supportive, customised for use with data generated on the Affymetrix platform and includes a tool for data annotation using ontologies. Detailed information on the experiment, methods, reagents and signal intensity data can be captured in a systematic format. Reports screens permit the user to query the database, to view annotation on individual experiments and provide summary statistics. MiMiR has tools for automatic upload of the data from the microarray scanner and export to databases using MAGE-ML. CONCLUSION: MiMiR facilitates microarray data management, annotation and exchange, in line with international guidelines. The database is valuable for underpinning research activities and promotes a systematic approach to data handling. Copies of MiMiR are freely available to academic groups under licence.

Data Collection↗

PubMed2002

Microarray data representation, annotation and storage.

Management and analysis of the huge amounts of data produced by microarray experiments is becoming one of the major bottlenecks in the utilization of this high-throughput technology. We describe the basic design of a microarray gene expression database to help microarray users and their informatics teams to set up their information services. We describe two data models--a simpler one called ArrayExpressB and the complete model ArrayExpressC, and discuss some implementation issues. For latest developments see http: wwwebi.ac.uk/arrayexpress

Algorithms↗

PubMed2003

An extensible application for assembling annotation for genomic data.

SUMMARY: AnnBuilder is an R package for assembling genomic annotation data. The system currently provides parsers to process annotation data from LocusLink, Gene Ontology Consortium, and Human Gene Project and can be extended to new data sources via user defined parsers. AnnBuilder differs from other existing systems in that it provides users with unlimited ability to assemble data from user selected sources. The products of AnnBuilder are files in XML format that can be easily used by different systems. AVAILABILITY: (http://www.bioconductor.org). Open source.

Database Management Systems↗

PubMed2001

Use of mass spectrometry-derived data to annotate nucleotide and protein sequence databases.

Mass spectrometry-based proteomic methodologies can be used to annotate both nucleotide and protein sequence databases. Because such data have to be derived from proteins, they can be used to identify coding regions of the genome as well as provide the complete primary sequence of proteins and their expression patterns and post-translational modifications.

Animals↗

PubMed2001

PlasmoDB: An integrative database of the Plasmodium falciparum genome. Tools for accessing and analyzing finished and unfinished sequence data. The Plasmodium Genome Database Collaborative.

The PLASMODIUM: falciparum Genome Database (http://PlasmoDB.org) integrates sequence information, automated analyses and annotation data emerging from the P.falciparum genome sequencing consortium. To date, raw sequence coverage is available for >90% of the genome, and two chromosomes have been finished and annotated. Data in PlasmoDB are organized by chromosome (1-14), and can be accessed using a variety of tools for graphical and text-based browsing or downloaded in various file formats. The GUS (Genomics Unified Schema) implementation of PlasmoDB provides a multi-species genomic relational database, incorporating data from human and mouse, as well as P.falciparum. The relational schema uses a highly structured format to accommodate diverse data sets related to genomic sequence and gene expression. Tools have been designed to facilitate complex biological queries, including many that are specific to PLASMODIUM: parasites and malaria as a disease. Additional projects seek to integrate genomic information with the rich data sets now becoming available for RNA transcription, protein expression, metabolic pathways, genetic and physical mapping, antigenic and population diversity, and phylogenetic relationships with other apicomplexan parasites. The overall goal of PlasmoDB is to facilitate Internet- and CD-ROM-based access to both finished and unfinished sequence information by the global malaria research community.

Animals↗

PubMed2005

Probabilistic model of the human protein-protein interaction network.

A catalog of all human protein-protein interactions would provide scientists with a framework to study protein deregulation in complex diseases such as cancer. Here we demonstrate that a probabilistic analysis integrating model organism interactome data, protein domain data, genome-wide gene expression data and functional annotation data predicts nearly 40,000 protein-protein interactions in humans-a result comparable to those obtained with experimental and computational approaches in model organisms. We validated the accuracy of the predictive model on an independent test set of known interactions and also experimentally confirmed two predicted interactions relevant to human cancer, implicating uncharacterized proteins into definitive pathways. We also applied the human interactome network to cancer genomics data and identified several interaction subnetworks activated in cancer. This integrative analysis provides a comprehensive framework for exploring the human protein interaction network.

Chromosome Mapping↗

PubMed2006

Genome-wide prediction of C. elegans genetic interactions.

To obtain a global view of functional interactions among genes in a metazoan genome, we computationally integrated interactome data, gene expression data, phenotype data, and functional annotation data from three model organisms-Saccharomyces cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster-and predicted genome-wide genetic interactions in C. elegans. The resulting genetic interaction network (consisting of 18,183 interactions) provides a framework for system-level understanding of gene functions. We experimentally tested the predicted interactions for two human disease-related genes and identified 14 new modifiers.

Algorithms↗

PubMed2005

Integrating protein annotation resources through the Distributed Annotation System.

Using the Distributed Annotation System (DAS) we have created a protein annotation resource available at our web page: http://www.cbs.dtu.dk, as a part of the BioSapiens Network of Excellence EU FP6 project. The DAS protocol allows us to gather layers of annotation data for a given sequence and thereby gain an overview of the sequence's features. A user-friendly graphical client has also been developed (http://www.cbs.dtu.dk/cgi-bin/das), which demonstrates the possibility of integrating DAS annotation data from multiple sources into a simple graphical view. The client displays protein feature annotations from the Center for Biological Sequence Analysis as well as from the BioSapiens reference UniProt server (http://www.ebi.ac.uk/das-srv/uniprot/das) at the European Bioinformatics Institute. Other DAS data sources for protein annotation will be added as they become available.

Computer Graphics↗

PubMed1987

[Technical ambulatory ECG innovations in performance evaluation].

Evaluating the performances of the instrumentation for Ambulatory ECG (AECG) analysis is a need largely recognized by both manufacturers and users. The most generally accepted method is the beat-by-beat comparison with annotated data bases, representing the different ECG abnormalities. Available data bases are aimed at arrhythmias detection evaluation, while it is recognized that the AECG ST-T changes detection has a great relevance in the analysis of ischemic heart disease. A concerted action of the European Community on Ambulatory Monitoring has been approved for achieving a comprehensive reference standard for assessing the quality of AECG instrumentation. The European project has been concentrated on the problem of ST-T analysis. An annotated data base is being developed with the contribution of european experts. A pilot study has been performed for establishing the criteria related to the development and annotation; particularly the definition of significant ST-T changes has been established. The data base will include 2-hour double channel AECG records, which contain at least one ST-T episode. Each record is annotated beat by beat according to the established scheme, identifying arrhythmic beats, rhythm changes, ST-T changes and noisy segments. A coordinating center has been established for interacting with the participating groups and for performing the operations related to the generation and management of the data base. For the time being 14 Groups of 8 Countries are participating to the annotation of the data base. A minimum number of 100 records is planned within June 1988.

Algorithms↗

PubMed1998

The complete genome of Bacillus subtilis: from sequence annotation to data management and analysis.

The completion of the entire 4.2-Mb genome sequence of the gram-positive bacterium Bacillus subtilis has been a milestone for biological studies on this model organism. This paper describes bioinformatics work related to this joint European and Japanese project: methods and strategies for gene annotation and detection of sequencing errors, using an integrated cooperative computer environment (Imagene); construction of a specialized database for data management and a WWW server for data retrieval (SubtiList); DNA sequence analysis, yielding striking results on oligonucleotide bias, repeated sequences, and codon usage, all landmarks of evolutionary events shaping the B. subtilis genome.

Amino Acid Sequence↗

PubMed2004

KARMA: a web server application for comparing and annotating heterogeneous microarray platforms.

We have developed a universal web server application (KARMA) that allows comparison and annotation of user-defined pairs of microarray platforms based on diverse types of genome annotation data (across different species) collected from multiple sources. The application is an effective tool for diverse microarray platforms, including arrays that are provided by (i) the Keck Microarray Resource at Yale, (ii) commercially available Affymetrix GeneChips and spotted arrays and (iii) custom arrays made by individual academics. The tool provides a web interface that allows users to input pairs of test files that represent diverse array platforms for either single or multiple species. The program dynamically identifies analogous DNA fragments spotted or synthesized on multiple microarray platforms based on the following types of information: (i) NCBI-Unigene identifiers, if the platforms being compared are within the same species or (ii) NCBI-Homologene data, if they are cross-species. The single-species comparison is implemented based on set operations: intersection, union and difference. Other forms of retrievable annotation data, including LocusLink, SwissProt and Gene Ontology (GO), are collected from multiple remote sites and stored in an integrated fashion using an Oracle database. The KARMA database, which is updated periodically, is available on line at the following URL: http://ymd.med.yale.edu/karma/cgi-bin/karma.pl.

Gene Expression Profiling↗

PubMed2005

goCluster integrates statistical analysis and functional interpretation of microarray expression data.

MOTIVATION: Several tools that facilitate the interpretation of transcriptional profiles using gene annotation data are available but most of them combine a particular statistical analysis strategy with functional information. goCluster extends this concept by providing a modular framework that facilitates integration of statistical and functional microarray data analysis with data interpretation. RESULTS: goCluster enables scientists to employ annotation information, clustering algorithms and visualization tools in their array data analysis and interpretation strategy. The package provides four clustering algorithms and GeneOntology terms as prototype annotation data. The functional analysis is based on the hypergeometric distribution whereby the Bonferroni correction or the false discovery rate can be used to correct for multiple testing. The approach implemented in goCluster was successfully applied to interpret the results of complex mammalian and yeast expression data obtained with high density oligonucleotide microarrays (GeneChips). AVAILABILITY: goCluster is available via the BioConductor portal at www.bioconductor.org. The software package, detailed documentation, user- and developer guides as well as other background information are also accessible via a web portal at http://www.bioz.unibas.ch/gocluster CONTACT: michael.primig@unibas.ch

Algorithms↗

PubMed2005

Integration of data for gene annotation using the BioMediator system.

Gene annotation requires integration of data from multiple sources in order to functionally classify genes. We are using BioMediator, a general purpose data-integration solution, to develop a gene annotation system to automate the process of collecting data from disparate genomic databases. Integration of annotation data from multiple sources into a single format will facilitate use of analytic tools for the proper functional classification of genes.

Base Sequence↗

PubMed2006

The Arabidopsis unannotated secreted peptide database, a resource for plant peptidomics.

In the era of genomics, if a gene is not annotated, it is not investigated. Due to their small size, genes encoding peptides are often missed in genome annotations. Secreted peptides are important regulators of plant growth, development, and physiology. Identification of additional peptide signals by sequence homology searches has had limited success due to sequence heterogeneity. A bioinformatics approach was taken to find unannotated Arabidopsis (Arabidopsis thaliana) peptides. Arabidopsis chromosome sequences were searched for all open reading frames (ORFs) encoding peptides and small proteins between 25 and 250 amino acids in length. The translated ORFs were then sequentially queried for the presence of an amino-terminal cleavable signal peptide, the absence of transmembrane domains, and the absence of endoplasmic reticulum lumenal retention sequences. Next, the ORFs were filtered against the The Arabidopsis Information Resource 6.0 annotated Arabidopsis genes to remove those ORFs overlapping known genes. The remaining 33,809 ORFs were placed in a relational database to which additional annotation data were deposited. Genome-wide tiling array data were compared with the coordinates of the ORFs, supporting the possibility that many of the ORFs may be expressed. In addition, clustering and sequence similarity analyses revealed that many of the putative peptides are in gene families and/or appear to be present in the rice (Oryza sativa) genome. A subset of the ORFs was evaluated by reverse transcription-PCR and, for one-fifth of those, expression was detected. These results support the idea that the number and diversity of plant peptides is broader than currently assumed. The peptides identified and their annotation data may be viewed or downloaded through a searchable Web interface at peptidome.missouri.edu.

Arabidopsis↗

PubMed2006

Ensemble attribute profile clustering: discovering and characterizing groups of genes with similar patterns of biological features.

BACKGROUND: Ensemble attribute profile clustering is a novel, text-based strategy for analyzing a user-defined list of genes and/or proteins. The strategy exploits annotation data present in gene-centered corpora and utilizes ideas from statistical information retrieval to discover and characterize properties shared by subsets of the list. The practical utility of this method is demonstrated by employing it in a retrospective study of two non-overlapping sets of genes defined by a published investigation as markers for normal human breast luminal epithelial cells and myoepithelial cells. RESULTS: Each genetic locus was characterized using a finite set of biological properties and represented as a vector of features indicating attributes associated with the locus (a gene attribute profile). In this study, the vector space models for a pre-defined list of genes were constructed from the Gene Ontology (GO) terms and the Conserved Domain Database (CDD) protein domain terms assigned to the loci by the gene-centered corpus LocusLink. This data set of GO- and CDD-based gene attribute profiles, vectors of binary random variables, was used to estimate multiple finite mixture models and each ensuing model utilized to partition the profiles into clusters. The resultant partitionings were combined using a unanimous voting scheme to produce consensus clusters, sets of profiles that co-occurred consistently in the same cluster. Attributes that were important in defining the genes assigned to a consensus cluster were identified. The clusters and their attributes were inspected to ascertain the GO and CDD terms most associated with subsets of genes and in conjunction with external knowledge such as chromosomal location, used to gain functional insights into human breast biology. The 52 luminal epithelial cell markers and 89 myoepithelial cell markers are disjoint sets of genes. Ensemble attribute profile clustering-based analysis indicated that both lists contained groups of genes with the functional properties of membrane receptor biology/signal transduction and nucleic acid binding/transcription. A subset of the luminal markers was associated with metabolic and oxidoreductase activities, whereas a subset of myoepithelial markers was associated with protein hydrolase activity. CONCLUSION: Given a set of genes and/or proteins associated with a phenomenon, process or system of interest, ensemble attribute profile clustering provides a simple method for collating and sythesizing the annotation data pertaining to them that are present in text-based, gene-centered corpora. The results provide information about properties common and unique to subsets of the list and hence insights into the biology of the problem under investigation.

Algorithms↗