http://precedings.nature.com/tags/annotation Recently posted documents tagged with 'annotation' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://dx.doi.org/10.1038/npre.2009.3569.1 We are developing software applications to perform meta-analysis of microarray experiments based on standardized experiment annotations aiming to identify similar experiments and cluster experiments. The applications were tested on files obtained from the ArrayExpress public repository. Annotation terms were used to compute experiment dissimilarities to find experiments related to a query experiment. These applications may motivate efforts of bench biologists to better annotate experiments. http://dx.doi.org/10.1038/npre.2009.3569.1 Thu, 06 Aug 2009 20:01:12 UTC Annotation-based meta-analysis of microarray experiments doi:10.1038/npre.2009.3569.1 2009-08-06 Jie Zheng Nature Precedings 2009-08-06T20:01:12Z Poster Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3556.1 We are creating a gold-standard corpus of manually annotated full-text biomedical journal articles toward natural-language-processing applications. Central to this is our use of entire ontologies of the Open Biomedical Ontologies initiative as well as other terminologies as term sources, in contrast to most other such annotation projects, which have used small, ad hoc schemas. In addition to the standard difficulties in such annotation projects, each of the terminologies we have used has idiosyncrasies and ambiguities that present further challenges to consistent, high-quality annotation of these articles. In this paper we present and discuss the most salient of these with regard to the Gene Ontology that we have encountered and addressed in our annotation guidelines and training. The utility of these guidelines can be seen in the high and still-increasing interannotator-agreement statistics that we continually monitor. http://dx.doi.org/10.1038/npre.2009.3556.1 Thu, 06 Aug 2009 14:33:25 UTC Using the Gene Ontology to Annotate Biomedical Journal Articles doi:10.1038/npre.2009.3556.1 2009-08-06 Michael Bada Nature Precedings 2009-08-06T14:33:25Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3531/version/1 There is a plethora of disease ontologies available, all potentially useful for the annotation of biological datasets. We define seven desirable features for such ontologies and examine whether or not these features are supported by eleven disease ontologies. The four ontologies most closely aligned with our desiderata are Disease Ontology, SNOMED CT, NCI thesaurus and UMLS. http://precedings.nature.com/documents/3531/version/1 Mon, 03 Aug 2009 14:04:38 UTC Desiderata for an ontology of diseases for the annotation of biological datasets. hdl:10101/npre.2009.3531.1 2009-08-03 Olivier Bodenreider Nature Precedings 2009-08-03T14:04:38Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3457.1 The Genoscope annotation workflow for eukaryote genomes relies on evidence from ab initio gene models predictions combined with homology searches, using collections of expressed sequences – full length cDNAs, ESTs or massive-scale mRNA sequences from the same or closely related organisms – proteins or other genomic sequences. Global analysis of these drafts or complete sequences are then combining both approaches in the form of gene prediction data integration using GAZE, capable to identify a majority of the existing gene features. Although of very good quality, gene-modelling remains still tentative at the end of the process. Even though computational predictors are useful on large scale annotation for global genomics analysis, there is no complete genome for which all gene structures, in terms of exons, introns and coding regions, have been experimentally confirmed.Finished genomes can provide exciting insights into the genome organization and evolution. Additional experimental data generated by genome sequencing projects give assistance to genome annotation aiming to a better understanding of the biology of the organism. Therefore, gene models and annotation can be improved by human curation to find errors or to resolve incongruous evidence on the automatic annotation of the genome. We now provide to collaborators carrying sequencing projects with a distributed annotation platform allowing expert evaluation of the annotation, in addition to our automated gene prediction pipeline.To ensure at most the participation of the scientific community, an annotation tool for revising annotations has been set up using components of the Generic Model Organism Database toolkit, which provides tools for managing organism databases. A CHADO database, linked to an Apollo graphical interface, permit users to correct gene structures and store them in a dedicated organism database, as we will show on a few examples. Such a tool would facilitate connecting and comparing predicted annotations with existing biological data, becoming the repository of complete annotated finished genome sequence. http://dx.doi.org/10.1038/npre.2009.3457.1 Fri, 24 Jul 2009 15:28:24 UTC The Eukaryote Genome Annotation Platform at Genoscope doi:10.1038/npre.2009.3457.1 2009-07-24 Betina M. Porcel Nature Precedings 2009-07-24T15:28:24Z Poster Genetics & Genomics Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3444.1 The BioModels database contains biochemical network models in SBML format, in which the biochemical meaning of elements is specified by MIRIAM-compliant RDF annotations. We used these annotations to define a similarity measure for models, scoring the overlap of the biochemical systems described. Based on this score, we used two-way clustering to detect groups of similar models and groups of co-occuring model elements. To recognize and compare biochemical elements, we used routines from the software semanticSBML. A Python script extracts all MIRIAM annotations (regardless of their qualifiers) using the semanticSBML annotation classes. The result is a matrix in which the rows represent the models (e.g. BioModel 001), while the columns represent specific annotations (e.g. urn:miriam:reactome:REACT_15422). A matrix element is set to 1 if an identifier occurs in a model and to 0 otherwise. This matrix was used as an input for a hierarchical clustering algorithm (implemented in Matlab) and the clustered matrix was visualized. Model clustering allows to detect models describing similar biochemical processes (e.g. glycolysis) and their specific common elements. This may help to find candidate models for completing a given initial model, which could then be merged using semanticSBML. http://dx.doi.org/10.1038/npre.2009.3444.1 Mon, 20 Jul 2009 18:16:55 UTC A simple clustering of the BioModels database using semanticSBML doi:10.1038/npre.2009.3444.1 2009-07-20 Wolfram Liebermeister Nature Precedings 2009-07-20T18:16:55Z Poster Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3313.1 Experienced biocurators can outperform automated systems on specific genes once they determine which pieces of evidence should drive annotation, and which annotations should be spread. The annotation logic may weigh both homology evidence (BLAST matches or HMM hits) and non-homology evidence (neighboring genes, metabolic context, taxonomic group). Unfortunately, the expertise developed to annotate each gene is short-lived, and is mostly lost if the logic driving the annotation is not captured. We report the development of BrainGrab, an interface added to the MANATEE manual annotation tool for prokaryotic genomes. The curator can specify evidence scenarios that should always lead to equivalent annotation for similar genes in similar contexts, and thus create new annotation rules while the expertise is fresh. No special knowledge of programming or protein family construction is required. BrainGrab rules can mix and match evidence types from the large array of existing protein family definitions such as Pfam families, sequence analyses such as SignalP, and contextual clues, that is, the same types of evidence already familiar to experienced biocurators. We have now created an infrastructure for collecting, distributing, interpreting, and applying BrainGrab rules for automated annotation. A rules interpreter combines queries of existing evidence with specified new searches to determine if a rule must fire. If so, the interpreter writes a new piece of rule-based evidence. Once deposited, BrainGrab/RuleBase evidence can provide automated annotation, pathway reconstruction, and even input data for other rules. We demonstrate the system with sets of rules for annotating proteins and pathways of siderophore biosynthesis in human pathogens, for annotating common fusion proteins, and for applying the proper nomenclature to bacterial ribosomal proteins. The chance to harness curatorial expertise for building rules creates a promising avenue for community contributions to improved annotation pipelines. http://dx.doi.org/10.1038/npre.2009.3313.1 Wed, 03 Jun 2009 15:52:03 UTC BrainGrab: Capturing Curator Expertise as Reusable Annotation Rules doi:10.1038/npre.2009.3313.1 2009-06-03 Daniel H. Haft Nature Precedings 2009-06-03T15:52:03Z Presentation Genetics & Genomics Microbiology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3297.1 The GenitoUrinary Development Molecular Anatomy Project (GUDMAP) is a consortium of laboratories working to provide the scientific and medical community with gene expression data and tools to facilitate research (see www.gudmap.org). The data provided by GUDMAP includes large in situ hybridization screens (wholemount and section) and expression microarray analysis of components of the developing mouse urogenital system (including laser-captured material and FACS-isolated cells from transgenic reporter mice). In addition, a high-resolution anatomy ontology has been developed by members of the GUDMAP consortium to describe the subcompartments of the developing murine genitourinary tract. The GUDMAP Database Development Team and Editorial Office – both based in Edinburgh – function to ensure submission, curation, storage and presentation of the data submitted by the GUDMAP consortium. Our collective aim is twofold: 1) to simplify the process of submission so that data is publically available as soon as it is produced; and 2) to organize this information in a database and ensure that the online interface is continuously available and easy to use. Thus far, we have developed a range of tools that help both the submitter and the end user. These include: an online annotation tool that simplifies in situ data submission through an ontology-based graphical user interface; a database interface that allows users to browse and query expression data, and to filter data by organ system; a heat-map display of microarray data and analyses. Furthermore, the Edinburgh team has developed a GUDMAP Disease Database that queries associations between genes, genitourinary diseases, and renal/urinary and reproductive phenotypes. In collaboration with GUDMAP consortium members at the CCHMC (Cincinnati Children’s Hospital Medical Center), the Disease Database is being extended to include mammalian phenotypes mapped to OMIM entries. By virtue of its impressive dataset and its ease of use we hope that the GUDMAP Website will continue to serve as a powerful resource for biologists, clinicians and bioinformaticians with an interest in the urogenital system. http://dx.doi.org/10.1038/npre.2009.3297.1 Sat, 30 May 2009 14:17:58 UTC GUDMAP – An Online GenitoUrinary Resource doi:10.1038/npre.2009.3297.1 2009-05-30 Simon Harding Nature Precedings 2009-05-30T14:17:58Z Poster Developmental Biology Genetics & Genomics Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3251.1 H-Invitational Database (H-InvDB; http://www.h-invitational.jp/) is an integrated database of human genes and transcripts. By extensive analyses of all human transcripts, we provide curated annotations of human genes and transcripts that include gene structures, alternative splicing isoforms, non-coding functional RNAs, protein functions, functional domains, sub-cellular localizations, metabolic pathways, protein 3D structure, genetic polymorphisms, relation with diseases, gene expression profiling, molecular evolutionary features, protein-protein interactions (PPIs) and gene families/groups. The latest release of H-InvDB (release 6.0) provide annotation for 219,765 human transcripts in 43,159 human gene clusters based on human FLcDNAs and mRNAs.H-InvDB consists of two main views, the Transcript view and the Locus view, and six auxiliary databases with web-based viewers; G-integra, H-ANGEL, DiseaseInfo Viewer, Evola, PPI view and Gene Family/Group view. We also provides several data mining tools such as “Navi search”: consists of 16 search contents each of which includes items for the search condition (http://www.h-invitational.jp/hinv/c-search/hinvNaviTop.jsp), “PANDA”: Priority ANalysis for Disease Association (PANDA) system (http://www.h-invitational.jp/panda/app), H-InvDB now provides web service APIs of SOAP and REST to use H-InvDB data in programs. (http://www.h-invitational.jp/hinv/hws/doc/) http://dx.doi.org/10.1038/npre.2009.3251.1 Thu, 14 May 2009 21:30:10 UTC H-InvDB release 6, a comprehensive annotation resource for human genes and transcripts doi:10.1038/npre.2009.3251.1 2009-05-14 Chisato Yamasaki Nature Precedings 2009-05-14T21:30:10Z Poster Genetics & Genomics Bioinformatics Evolutionary Biology http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3225.1 Data curation has been critical in the development of biology from Darwin and Linnaeus to UniProt, the careful collection and organisation of data has been the spring from which new hypotheses and understanding have emerged. In this presentation, I will describe how we have used data curation in my own research group – and also present an overview of curation at the EBI. With new technical developments and the move towards the semantic web, the role of curation in the future needs to develop to take advantage of these new opportunities. This will be discussed. http://dx.doi.org/10.1038/npre.2009.3225.1 Tue, 12 May 2009 13:19:11 UTC Data Curation in Biology – Past, Present and Future doi:10.1038/npre.2009.3225.1 2009-05-12 Nature Precedings 2009-05-12T13:19:11Z Presentation Genetics & Genomics Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3215.1 The development of next generation sequencing technologies promises a massive increase in the rate of submission of new protein sequences to sequence databases such as the Universal Protein Resource Knowledge Base, UniProtKB. At UniProtKB/Swiss-Prot we propose to meet this challenge by continuing to expand and develop systems for the automatic propagation of existing annotation to newly submitted protein sequences. These developments will promote the standardization of ortholog annotation both across and within kingdoms and significantly enhance our ability to accurately annotate new protein sequences which are being produced at an ever increasing rate. http://dx.doi.org/10.1038/npre.2009.3215.1 Fri, 08 May 2009 15:41:19 UTC Automatisation in UniProtKB / Swiss-Prot Annotation: New Rules and Tools doi:10.1038/npre.2009.3215.1 2009-05-08 Alan Bridge Nature Precedings 2009-05-08T15:41:19Z Poster Bioinformatics http://creativecommons.org/licenses/by/3.0/