http://precedings.nature.com/collections/biocuration-09/ Recently posted documents in 3rd International Biocuration Conference 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.2009.3936.1 IntAct provides a freely available, open source database system and analysis tools for protein interaction data. All interactions are derived from literature curation or direct user submission and all experimental information relating to binary protein-proteininteractions is entered into the IntAct database by curators, via a web-based editor. Interaction information is added to the SUBUNIT comment and the RP line of the relevant publication within the UniProtKB entry. There may be a single INTERACTION comment present within a UniProtKB entry, which conveys information relevant to binary protein-protein interactions. This is automatically derived from the IntAct database and is updated on a triweekly basis. Interactions can be derived by any appropriate experimental method but must be confirmed by a second interaction if resulting from a single yeast2hybrid experiment. For large-scale experiments, interactions are considered if a high confidence score is assigned by the authors. The INTERACTION line contains a direct link to IntAct that provides detailed information for the experimental support. These lines are not changed manually and any discrepancy is reported to IntAct for updates. There is also a database crossreference line within the UniProtKB entry i.e.: DR IntAct _UniProtKB AC, which directs the user to additional interaction data for that molecule. UniProt is supported by grants from the National Institutes of Health, European Commission, Swiss Federal Government and PATRIC BRC.IntAct is funded by the European Commission under FELICS, contract number 021902 (RII3) within the Research Infrastructure Action of the FP6 “Structuring the European Research Area” Programme. http://dx.doi.org/10.1038/npre.2009.3936.1 Tue, 10 Nov 2009 15:11:08 UTC The Relationship between the UniProt Knowledgebase (UniProtKB) and the IntAct Molecular Interaction Databases doi:10.1038/npre.2009.3936.1 2009-11-10 Yasmin Alam-Faruque Nature Precedings 2009-11-10T15:11:08Z Poster 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.2009.3379.1 The Protein Data Bank (PDB) is the worldwide repository for experimentally determined 3D structures of biological macromolecules. Established in 1971 with just seven structures, it presently includes more than 56,000 entries. To maintain the highest standards in curation and processing, the members of the worldwide Protein Data Bank (wwPDB) collaborate in data annotation and the development of procedures, tools, and resources. Annotation-related issues, particularly those impacted by new developmentsin structural biology, are critically reviewed at in-person and virtual meetings regularly and frequently. Comprehensive documentation of the procedures, formats, and related data dictionaries used in data annotation are available at the wwPDB website(www.wwpdb.org).Mindful of the impact that changes in annotation procedures or data format may have on users, changes are carefully managed and communicated in a timely fashion. In cases involving complex scientific or policy issues, input is sought from advisory committees, standing task forces, experimental method developers, and community experts. This is exemplified by creation of the recently-released version of the PDB archive which updates and further standardizes database references, small molecule chemistry, biological assemblies, and active sites. http://dx.doi.org/10.1038/npre.2009.3379.1 Tue, 30 Jun 2009 08:24:32 UTC Annotation and Curation of the Protein Data Bank doi:10.1038/npre.2009.3379.1 2009-06-30 Jasmine Young Nature Precedings 2009-06-30T08:24:32Z Poster Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3322.1 The identification of a chemical compound solely based on its name requires comprehensive chemical knowledge and often extensive searches in chemical databases. However, it is crucial for the integration of biochemical data extracted from the literature, since many publications exclusively describe a compound by its name. We have developed an application which matches synonymic names of chemical compounds and thereby facilitates the bundling of corresponding data referring to the same compound.The tool that we have developed is based on natural language processing (NLP) methods and applies rules to systematically normalize chemical compound names. Matching of synonymous names is achieved by comparison of the normalized name forms. It is capable of normalizing a given name of a chemical compound and matching it against names in (bio-)chemical databases (e.g. SABIO-RK, ChEBI or PubChem), even when there is no exact name-to-name-match. The tool is also able to match a complete list of compound names against these databases which makes it useful for the automatic annotation of chemical data.This normalization and matching of various synonyms of a chemical compound constitutes a platform for the unambiguous identification of compounds described in the literature or in databases. http://dx.doi.org/10.1038/npre.2009.3322.1 Fri, 05 Jun 2009 20:06:57 UTC Normalization and Matching of Chemical Compound Names doi:10.1038/npre.2009.3322.1 2009-06-05 Martin Golebiewski Nature Precedings 2009-06-05T20:06:57Z Poster Chemistry 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.2009.3302.1 Ten years ago WormBase1 started as a repository for sequence data for the modelorganism Caenorhabditis elegans and has since striven to include the curation of allgenetic and molecular data published for this nematode. With a publication rate in the C.elegans field of approximately 800 papers per year, WormBase (WB) has the opportunity to include information from every paper published. Currently there are ~11,000 full text research papers (mid-1970’s to the present) downloaded into the WB curation database, from which over 27 data types (i.e. genetic interactions, transgene objects, gene expression patterns, mutant phenotypes etc.) are extracted by curators. Textpresso2 is an open source text-mining tool capable of rapid searches for keywords, as well as concepts, from the full text of research papers. Curators at WB use Textpresso on a daily basis for many aspects of literature curation, from simple keyword searches to semi- or fully automated entity and fact extraction, which feed into curation pipelines or directly into the curation database itself. In addition, Textpresso greatly aids prioritization of literature curation by retrieving papers based on their full contents rather than solely on their abstracts. Such retrievable contents can range from the very particular (such as a gene simply being mentioned in the Materials and Methods section of a paper) to the complex (such as molecular functions that involve cellular components). As WB expands to incorporate the genomes of other nematodes, we will be working with Textpresso developers to set up a library of literature for related nematodes. We expect Textpresso to be crucial for most efficiently directing our efforts in literature curation, and for most quickly providing data to users searching the literature. In this workshop we will show how we use Textpresso in our curation pipeline to help with literature queries, to prioritize our workflow, and to automate data and fact extraction.1 WormBase2 Textpresso http://dx.doi.org/10.1038/npre.2009.3302.1 Tue, 02 Jun 2009 14:49:35 UTC Using Textpresso for Information Retrieval, Fact Extraction doi:10.1038/npre.2009.3302.1 2009-06-02 Kimberly Van Auken Nature Precedings 2009-06-02T14:49:35Z Presentation Genetics & Genomics 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.2009.3287.1 The National Center for Biotechnology Information (NCBI) provides curation support for many genomes, and disseminates information in several resources including Entrez Gene, reference sequences (RefSeq), the Consensus CDS (CCDS) database, and the Genome Reference Consortium (GRC). These projects are supported by several collaborations to provide:1) support to the international consortium maintaining the assemblies for human and mouse (GRC); 2) sequence standards for chromosomes, genes, transcripts and proteins (RefSeq); 3) reports of integrated information including nomenclature, publications, phenotypes and diseases, sequences, ontologies, interactions (Gene); and 4) identification of proteins that are consistently annotated on the human and mouse reference genomes, and consistently updated by collaborating members (CCDS). NCBI curation of any one data type (e.g., a gene) is closely integrated with evaluation of the genome assembly, and determining annotation by way of RefSeq transcript and protein sequences. Database and work-flow infrastructure is designed to support reporting and tracking issues with the assembly, gene, or evidence data to collaborating groups, and to support collaborative review and discussions of issues that arise. Curation depends on publicly available information to represent the gene extent, alternatively spliced transcripts, and protein isoforms. Scientific consults occur regularly and wet-bench validation needs are supported by some of the collaborations. Curation of genome annotation results in improved data presentation at the three major genome browser sites (Ensembl, NCBI, UCSC) and has resulted in efforts to define common curation guidelines to maximize consistency and minimize conflicts.The presentation focuses on curation of the human genome, genes, and RefSeq sequence standards. http://dx.doi.org/10.1038/npre.2009.3287.1 Wed, 27 May 2009 22:41:09 UTC Curation at the NCBI: Genomes, Genes, & Sequence Standards doi:10.1038/npre.2009.3287.1 2009-05-27 Kim D. Pruitt Nature Precedings 2009-05-27T22:41:09Z Poster Genetics & Genomics Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3257.1 Curation of biomedical data has come to encompass a broad range of activities and considerations, which include the building of digital archives, making decisions on the relative value and longevity of one dataset vs another, editing data records manually, performing or assessing computational processes over very large sets of data, and grappling with issues of web usability and data standards. People who consider themselves biological curators may range from a single domain expert, who develops a collection which reflects their personal judgments and priorities, to groups of people supporting large, long term public resources such as GenBank, RefSeq, or PubMed, and everything in between. Finding the right balance between objective measures of quality and personal judgment, between computational measures and manual curation, between published results in journals and active curation of databases varies by project but some common themes and considerations recur in our experiences of the past two decades at NCBI. http://dx.doi.org/10.1038/npre.2009.3257.1 Tue, 26 May 2009 16:52:13 UTC Digital BioCuration: A Question of Balance doi:10.1038/npre.2009.3257.1 2009-05-26 James Ostell Nature Precedings 2009-05-26T16:52:13Z Presentation Genetics & Genomics Immunology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3262.1 A major challenge for the development of resources for functional and comparative genomics is the extraction of data from the biomedical literature. Although text retrieval and extraction for biological data is an active research field, few applications have been integrated into production literature curation systems such as those of the model organism databases.In September 2008, Mouse Genome Informatics (MGI) at The Jackson Lab initiated a search for dictionary-based text mining tools that we could integrate into our curation workflow. MGI has rigorous document triage and annotation procedures designed to identify articles about mouse genome biology and determine whether those articles should be curated. We currently screens approximately 1000 journal articles a month for Gene Ontology terms, gene mapping, gene expression, phenotype data and other key biological information. Although we don’t foresee that human curation tasks can be fully automated in the near future, we are eager to implement entity name recognition and gene tagging tools that can help streamline our curation workflow and simplify gene indexing tasks in the MGI system. In this presentation, we discuss our search process and the steps we took to identify a short list of potential tools for further evaluation. We present our performance metrics and success criteria, and pilot projects in progress. The primary applications under current review are Fraunhofer SCAI’s ProMiner and NCBO’s Open-Biomedical Annotator. http://dx.doi.org/10.1038/npre.2009.3262.1 Wed, 20 May 2009 21:16:19 UTC Integrating Text Mining into the MGI Biocuration Workflow doi:10.1038/npre.2009.3262.1 2009-05-20 Karen G. Dowell Nature Precedings 2009-05-20T21:16:19Z Presentation Bioinformatics http://creativecommons.org/licenses/by/3.0/