The impact of focused Gene Ontology curation of specific mammalian systems.

UNLABELLED: The Gene Ontology (GO) resource provides dynamic controlled vocabularies to provide an information-rich resource to aid in the consistent description of the functional attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). System-focused projects, such as the Renal and Cardiovascular GO Annotation Initiatives, aim to provide detailed GO data for proteins implicated in specific organ development and function. Such projects support the rapid evaluation of new experimental data and aid in the generation of novel biological insights to help alleviate human disease. This paper describes the improvement of GO data for renal and cardiovascular research communities and demonstrates that the cardiovascular-focused GO annotations, created over the past three years, have led to an evident improvement of microarray interpretation. The reanalysis of cardiovascular microarray datasets confirms the need to continue to improve the annotation of the human proteome. AVAILABILITY: GO ANNOTATION DATA IS FREELY AVAILABLE FROM: ftp://ftp.geneontology.org/pub/go/gene-associations/

Transcriptomic analyses of murine resolution-phase macrophages.

Macrophages are either classically (M1) or alternatively-activated (M2). Whereas this nomenclature was generated from monocyte-derived macrophages treated in vitro with defined cytokine stimuli, the phenotype of in vivo-derived macrophages is less understood. We completed Affymetrix-based transcriptomic analysis of macrophages from the resolution phase of a zymosan-induced peritonitis. Compared with macrophages from hyperinflamed mice possessing a pro-inflammatory nature as well as naive macrophages from the uninflamed peritoneum, resolution-phase macrophages (rM) are similar to monocyte-derived dendritic cells (DCs), being CD209a positive but lacking CD11c. They are enriched for antigen processing/presentation (MHC class II [H2-Eb1, H2-Ab1, H2-Ob, H2-Aa], CD74, CD86), secrete T- and B-lymphocyte chemokines (Xcl1, Ccl5, Cxcl13) as well as factors that enhance macrophage/DC development, and promote DC/T cell synapse formation (Clec2i, Tnfsf4, Clcf1). rM are also enriched for cell cycle/proliferation genes as well as Alox15, Timd4, and Tgfb2, key systems in the termination of leukocyte trafficking and clearance of inflammatory cells. Finally, comparison with in vitro-derived M1/M2 shows that rM are neither classically nor alternatively activated but possess aspects of both definitions consistent with an immune regulatory phenotype. We propose that macrophages in situ cannot be rigidly categorized as they can express many shades of the inflammatory spectrum determined by tissue, stimulus, and phase of inflammation.

Access to immunology through the Gene Ontology.

The Gene Ontology (GO) is widely recognized as the premier tool for the organization and functional annotation of molecular aspects of cellular systems. However, for many immunologists the use of GO is a very foreign concept. Indeed, as a controlled vocabulary, GO can almost be considered a new language, and it can be difficult to appreciate the use and value of this approach for understanding the immune system. This review reflects on the application of GO to the field of immunology and explains the process of GO annotation. Finally, this review hopes to inspire immunologists to invest time and energy in improving both the content of the GO and the quality of GO annotations associated with genes of immunological interest.

AgBase: a functional genomics resource for agriculture.

BACKGROUND: Many agricultural species and their pathogens have sequenced genomes and more are in progress. Agricultural species provide food, fiber, xenotransplant tissues, biopharmaceuticals and biomedical models. Moreover, many agricultural microorganisms are human zoonoses. However, systems biology from functional genomics data is hindered in agricultural species because agricultural genome sequences have relatively poor structural and functional annotation and agricultural research communities are smaller with limited funding compared to many model organism communities. DESCRIPTION: To facilitate systems biology in these traditionally agricultural species we have established "AgBase", a curated, web-accessible, public resource http://www.agbase.msstate.edu for structural and functional annotation of agricultural genomes. The AgBase database includes a suite of computational tools to use GO annotations. We use standardized nomenclature following the Human Genome Organization Gene Nomenclature guidelines and are currently functionally annotating chicken, cow and sheep gene products using the Gene Ontology (GO). The computational tools we have developed accept and batch process data derived from different public databases (with different accession codes), return all existing GO annotations, provide a list of products without GO annotation, identify potential orthologs, model functional genomics data using GO and assist proteomics analysis of ESTs and EST assemblies. Our journal database helps prevent redundant manual GO curation. We encourage and publicly acknowledge GO annotations from researchers and provide a service for researchers interested in GO and analysis of functional genomics data. CONCLUSION: The AgBase database is the first database dedicated to functional genomics and systems biology analysis for agriculturally important species and their pathogens. We use experimental data to improve structural annotation of genomes and to functionally characterize gene products. AgBase is also directly relevant for researchers in fields as diverse as agricultural production, cancer biology, biopharmaceuticals, human health and evolutionary biology. Moreover, the experimental methods and bioinformatics tools we provide are widely applicable to many other species including model organisms.

An evaluation of GO annotation retrieval for BioCreAtIvE and GOA.

BACKGROUND: The Gene Ontology Annotation (GOA) database http://www.ebi.ac.uk/GOA aims to provide high-quality supplementary GO annotation to proteins in the UniProt Knowledgebase. Like many other biological databases, GOA gathers much of its content from the careful manual curation of literature. However, as both the volume of literature and of proteins requiring characterization increases, the manual processing capability can become overloaded. Consequently, semi-automated aids are often employed to expedite the curation process. Traditionally, electronic techniques in GOA depend largely on exploiting the knowledge in existing resources such as InterPro. However, in recent years, text mining has been hailed as a potentially useful tool to aid the curation process. To encourage the development of such tools, the GOA team at EBI agreed to take part in the functional annotation task of the BioCreAtIvE (Critical Assessment of Information Extraction systems in Biology) challenge. BioCreAtIvE task 2 was an experiment to test if automatically derived classification using information retrieval and extraction could assist expert biologists in the annotation of the GO vocabulary to the proteins in the UniProt Knowledgebase. GOA provided the training corpus of over 9000 manual GO annotations extracted from the literature. For the test set, we provided a corpus of 200 new Journal of Biological Chemistry articles used to annotate 286 human proteins with GO terms. A team of experts manually evaluated the results of 9 participating groups, each of which provided highlighted sentences to support their GO and protein annotation predictions. Here, we give a biological perspective on the evaluation, explain how we annotate GO using literature and offer some suggestions to improve the precision of future text-retrieval and extraction techniques. Finally, we provide the results of the first inter-annotator agreement study for manual GO curation, as well as an assessment of our current electronic GO annotation strategies. RESULTS: The GOA database currently extracts GO annotation from the literature with 91 to 100% precision, and at least 72% recall. This creates a particularly high threshold for text mining systems which in BioCreAtIvE task 2 (GO annotation extraction and retrieval) initial results precisely predicted GO terms only 10 to 20% of the time. CONCLUSION: Improvements in the performance and accuracy of text mining for GO terms should be expected in the next BioCreAtIvE challenge. In the meantime the manual and electronic GO annotation strategies already employed by GOA will provide high quality annotations.