Text-mining applied to autoimmune disease research: the Sjögren's syndrome knowledge base.

BACKGROUND: Sjögren's syndrome is a tissue-specific autoimmune disease that affects exocrine tissues, especially salivary glands and lacrimal glands. Despite a large body of evidence gathered over the past 60 years, significant gaps still exist in our understanding of Sjögren's syndrome. The goal of this study was to develop a database that collects and organizes gene and protein expression data from the existing literature for comparative analysis with future gene expression and proteomic studies of Sjögren's syndrome. DESCRIPTION: To catalog the existing knowledge in the field, we used text mining to generate the Sjögren's Syndrome Knowledge Base (SSKB) of published gene/protein data, which were extracted from PubMed using text mining of over 7,700 abstracts and listing approximately 500 potential genes/proteins. The raw data were manually evaluated to remove duplicates and false-positives and assign gene names. The data base was manually curated to 477 entries, including 377 potential functional genes, which were used for enrichment and pathway analysis using gene ontology and KEGG pathway analysis. CONCLUSIONS: The Sjögren's syndrome knowledge base ( http://sskb.umn.edu) can form the foundation for an informed search of existing knowledge in the field as new potential therapeutic targets are identified by conventional or high throughput experimental techniques.

Biogem: an effective tool-based approach for scaling up open source software development in bioinformatics.

SUMMARY: Biogem provides a software development environment for the Ruby programming language, which encourages community-based software development for bioinformatics while lowering the barrier to entry and encouraging best practices. Biogem, with its targeted modular and decentralized approach, software generator, tools and tight web integration, is an improved general model for scaling up collaborative open source software development in bioinformatics. AVAILABILITY: Biogem and modules are free and are OSS. Biogem runs on all systems that support recent versions of Ruby, including Linux, Mac OS X and Windows. Further information at http://www.biogems.info. A tutorial is available at http://www.biogems.info/howto.html CONTACT: bonnal@ingm.org.

An ontology of fungal subcellular traits.

UNLABELLED: • PREMISE OF THE STUDY: The Fungal Subcellular Ontology used in the Assembling the Fungal Tree of Life project is a taxon-wide ontology (controlled vocabulary for attributes) designed to clarify and integrate the broad range of subcellular characters and character states used in higher-level fungal systematics. As in the algae, cellular characters are important phylogenetic markers in kingdom Fungi. The Fungal Subcellular Ontology has been developed primarily to help researchers, especially systematists, in their search for information on subcellular characters across the Fungi, and it complements existing biological ontologies, including the Gene Ontology. • METHODS: The character and character state data set used in the Assembling the Fungal Tree of Life Structural and Biochemical Database (http://aftol.umn.edu) is the source of terms for generating the ontology. After the terms were accessioned and defined, they were combined in OBO-Edit file format, and the ontology was edited using OBO-Edit, an open source Java tool supported by the Gene Ontology project. • KEY RESULTS: The Fungal Subcellular Ontology covers both model and nonmodel fungi in great detail and is downloadable in OBO-Edit format at website http://aftol.umn.edu/ontology/fungal_subcellular.obo. • CONCLUSIONS: The ontology provides a controlled vocabulary of fungal subcellular terms and functions as an operating framework for the Assembling the Fungal Tree of Life Structural and Biochemical Database. An ontology-based design enhances reuse of data deposited in the Structural and Biochemical Database from other independent biological and genetic databases. Data integration approaches that advance access to data from the diversity of biological databases are imperative as interdisciplinary research gains importance. In this sense, the Fungal Subcellular Ontology becomes highly relevant to mycologists as well as nonmycologists because fungi interact actively as symbionts and parasites or passively with many other life forms.

Identification of imprinted genes subject to parent-of-origin specific expression in Arabidopsis thaliana seeds.

BACKGROUND: Epigenetic regulation of gene dosage by genomic imprinting of some autosomal genes facilitates normal reproductive development in both mammals and flowering plants. While many imprinted genes have been identified and intensively studied in mammals, smaller numbers have been characterized in flowering plants, mostly in Arabidopsis thaliana. Identification of additional imprinted loci in flowering plants by genome-wide screening for parent-of-origin specific uniparental expression in seed tissues will facilitate our understanding of the origins and functions of imprinted genes in flowering plants. RESULTS: cDNA-AFLP can detect allele-specific expression that is parent-of-origin dependent for expressed genes in which restriction site polymorphisms exist in the transcripts derived from each allele. Using a genome-wide cDNA-AFLP screen surveying allele-specific expression of 4500 transcript-derived fragments, we report the identification of 52 maternally expressed genes (MEGs) displaying parent-of-origin dependent expression patterns in Arabidopsis siliques containing F1 hybrid seeds (3, 4 and 5 days after pollination). We identified these MEGs by developing a bioinformatics tool (GenFrag) which can directly determine the identities of transcript-derived fragments from (i) their size and (ii) which selective nucleotides were added to the primers used to generate them. Hence, GenFrag facilitates increased throughput for genome-wide cDNA-AFLP fragment analyses. The 52 MEGs we identified were further filtered for high expression levels in the endosperm relative to the seed coat to identify the candidate genes most likely representing novel imprinted genes expressed in the endosperm of Arabidopsis thaliana. Expression in seed tissues of the three top-ranked candidate genes, ATCDC48, PDE120 and MS5-like, was confirmed by Laser-Capture Microdissection and qRT-PCR analysis. Maternal-specific expression of these genes in Arabidopsis thaliana F1 seeds was confirmed via allele-specific transcript analysis across a range of different accessions. Differentially methylated regions were identified adjacent to ATCDC48 and PDE120, which may represent candidate imprinting control regions. Finally, we demonstrate that expression levels of these three genes in vegetative tissues are MET1-dependent, while their uniparental maternal expression in the seed is not dependent on MET1. CONCLUSIONS: Using a cDNA-AFLP transcriptome profiling approach, we have identified three genes, ATCDC48, PDE120 and MS5-like which represent novel maternally expressed imprinted genes in the Arabidopsis thaliana seed. The extent of overlap between our cDNA-AFLP screen for maternally expressed imprinted genes, and other screens for imprinted and endosperm-expressed genes is discussed.