Improvement of PubMed Literature Searching using Biomedical Ontology

PubMed articles are annotated using the Medical Subject Headings (MeSH) to increase search efficiency. However, MeSH contains limited information on many biomedical domains (e.g., vaccine). Biomedical ontologies may be used to improve PubMed searching capability. This study demonstrates that Vaccine Ontology (VO) can be used to significantly improve PubMed searching efficacy in the vaccine domain. The recall and precision of the ontology-based literature mining approach are analyzed and discussed

Bioinformatics analysis of Brucella vaccines and vaccine targets using VIOLIN

Abstract Background Brucella spp. are Gram-negative, facultative intracellular bacteria that cause brucellosis, one of the commonest zoonotic diseases found worldwide in humans and a variety of animal species. While several animal vaccines are available, there is no effective and safe vaccine for prevention of brucellosis in humans. VIOLIN (http://www.violinet.org) is a web-based vaccine database and analysis system that curates, stores, and analyzes published data of commercialized vaccines, and vaccines in clinical trials or in research. VIOLIN contains information for 454 vaccines or vaccine candidates for 73 pathogens. VIOLIN also contains many bioinformatics tools for vaccine data analysis, data integration, and vaccine target prediction. To demonstrate the applicability of VIOLIN for vaccine research, VIOLIN was used for bioinformatics analysis of existing Brucella vaccines and prediction of new Brucella vaccine targets. Results VIOLIN contains many literature mining programs (e.g., Vaxmesh) that provide in-depth analysis of Brucella vaccine literature. As a result of manual literature curation, VIOLIN contains information for 38 Brucella vaccines or vaccine candidates, 14 protective Brucella antigens, and 68 host response studies to Brucella vaccines from 97 peer-reviewed articles. These Brucella vaccines are classified in the Vaccine Ontology (VO) system and used for different ontological applications. The web-based VIOLIN vaccine target prediction program Vaxign was used to predict new Brucella vaccine targets. Vaxign identified 14 outer membrane proteins that are conserved in six virulent strains from B. abortus, B. melitensis, and B. suis that are pathogenic in humans. Of the 14 membrane proteins, two proteins (Omp2b and Omp31-1) are not present in B. ovis, a Brucella species that is not pathogenic in humans. Brucella vaccine data stored in VIOLIN were compared and analyzed using the VIOLIN query system. Conclusions Bioinformatics curation and ontological representation of Brucella vaccines promotes classification and analysis of existing Brucella vaccines and vaccine candidates. Computational prediction of Brucella vaccine targets provides more candidates for rational vaccine development. The use of VIOLIN provides a general approach that can be applied for analyses of vaccines against other pathogens and infection diseases.http://deepblue.lib.umich.edu/bitstream/2027.42/78263/1/1745-7580-6-S1-S5.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78263/2/1745-7580-6-S1-S5.pdfPeer Reviewe

OntoFox:

OntoFox ("http://ontofox.hegroup.org/":http://ontofox.hegroup.org/) is a web server that facilitates ontology development by automatically fetching ontology terms and their annotations from existing ontologies and saving the results in importable RDF/OWL format. OntoFox is developed based on the MIREOT principle. Currently OntoFox can fetch ontology annotations from >10 existing ontologies. OntoFox provides an efficient approach to promote ontology sharing and interoperability

PHIDIAS: a pathogen-host interaction data integration and analysis system

PHIDIAS is a web-based database system serving as a centralized source to search, compare and analyse integrated genome sequences, conserved domains and transcriptional data related to pathogen-host interactions

BBP: Brucella genome annotation with literature mining and curation

BACKGROUND: Brucella species are Gram-negative, facultative intracellular bacteria that cause brucellosis in humans and animals. Sequences of four Brucella genomes have been published, and various Brucella gene and genome data and analysis resources exist. A web gateway to integrate these resources will greatly facilitate Brucella research. Brucella genome data in current databases is largely derived from computational analysis without experimental validation typically found in peer-reviewed publications. It is partially due to the lack of a literature mining and curation system able to efficiently incorporate the large amount of literature data into genome annotation. It is further hypothesized that literature-based Brucella gene annotation would increase understanding of complicated Brucella pathogenesis mechanisms. RESULTS: The Brucella Bioinformatics Portal (BBP) is developed to integrate existing Brucella genome data and analysis tools with literature mining and curation. The BBP InterBru database and Brucella Genome Browser allow users to search and analyze genes of 4 currently available Brucella genomes and link to more than 20 existing databases and analysis programs. Brucella literature publications in PubMed are extracted and can be searched by a TextPresso-powered natural language processing method, a MeSH browser, a keywords search, and an automatic literature update service. To efficiently annotate Brucella genes using the large amount of literature publications, a literature mining and curation system coined Limix is developed to integrate computational literature mining methods with a PubSearch-powered manual curation and management system. The Limix system is used to quickly find and confirm 107 Brucella gene mutations including 75 genes shown to be essential for Brucella virulence. The 75 genes are further clustered using COG. In addition, 62 Brucella genetic interactions are extracted from literature publications. These results make possible more comprehensive investigation of Brucella pathogenesis. Other BBP features include publication email alert service, Brucella researchers' contact database, and discussion forum. CONCLUSION: BBP is a gateway for Brucella researchers to search, analyze, and curate Brucella genome data originated from public databases and literature. Brucella gene mutations and genetic interactions are annotated using Limix leading to better understanding of Brucella pathogenesis

Brucellosis Ontology (IDOBRU) as an extension of the Infectious Disease Ontology

<p>Abstract</p> <p>Background</p> <p>Caused by intracellular Gram-negative bacteria <it>Brucella </it>spp., brucellosis is the most common bacterial zoonotic disease. Extensive studies in brucellosis have yielded a large number of publications and data covering various topics ranging from basic <it>Brucella </it>genetic study to vaccine clinical trials. To support data interoperability and reasoning, a community-based brucellosis-specific biomedical ontology is needed.</p> <p>Results</p> <p>The Brucellosis Ontology (IDOBRU: <url>http://sourceforge.net/projects/idobru</url>), a biomedical ontology in the brucellosis domain, is an extension ontology of the core Infectious Disease Ontology (IDO-core) and follows OBO Foundry principles. Currently IDOBRU contains 1503 ontology terms, which includes 739 <it>Brucella</it>-specific terms, 414 IDO-core terms, and 350 terms imported from 10 existing ontologies. IDOBRU has been used to model different aspects of brucellosis, including host infection, zoonotic disease transmission, symptoms, virulence factors and pathogenesis, diagnosis, intentional release, vaccine prevention, and treatment. Case studies are typically used in our IDOBRU modeling. For example, diurnal temperature variation in <it>Brucella </it>patients, a <it>Brucella</it>-specific PCR method, and a WHO-recommended brucellosis treatment were selected as use cases to model brucellosis symptom, diagnosis, and treatment, respectively. Developed using OWL, IDOBRU supports OWL-based ontological reasoning. For example, by performing a Description Logic (DL) query in the OWL editor Protégé 4 or a SPARQL query in an IDOBRU SPARQL server, a check of <it>Brucella </it>virulence factors showed that eight of them are known protective antigens based on the biological knowledge captured within the ontology.</p> <p>Conclusions</p> <p>IDOBRU is the first reported bacterial infectious disease ontology developed to represent different disease aspects in a formal logical format. It serves as a brucellosis knowledgebase and supports brucellosis data integration and automated reasoning.</p

Ontology-based representation and analysis of host-Brucella interactions

Abstract Background Biomedical ontologies are representations of classes of entities in the biomedical domain and how these classes are related in computer- and human-interpretable formats. Ontologies support data standardization and exchange and provide a basis for computer-assisted automated reasoning. IDOBRU is an ontology in the domain of Brucella and brucellosis. Brucella is a Gram-negative intracellular bacterium that causes brucellosis, the most common zoonotic disease in the world. In this study, IDOBRU is used as a platform to model and analyze how the hosts, especially host macrophages, interact with virulent Brucella strains or live attenuated Brucella vaccine strains. Such a study allows us to better integrate and understand intricate Brucella pathogenesis and host immunity mechanisms. Results Different levels of host-Brucella interactions based on different host cell types and Brucella strains were first defined ontologically. Three important processes of virulent Brucella interacting with host macrophages were represented: Brucella entry into macrophage, intracellular trafficking, and intracellular replication. Two Brucella pathogenesis mechanisms were ontologically represented: Brucella Type IV secretion system that supports intracellular trafficking and replication, and Brucella erythritol metabolism that participates in Brucella intracellular survival and pathogenesis. The host cell death pathway is critical to the outcome of host-Brucella interactions. For better survival and replication, virulent Brucella prevents macrophage cell death. However, live attenuated B. abortus vaccine strain RB51 induces caspase-2-mediated proinflammatory cell death. Brucella-associated cell death processes are represented in IDOBRU. The gene and protein information of 432 manually annotated Brucella virulence factors were represented using the Ontology of Genes and Genomes (OGG) and Protein Ontology (PRO), respectively. Seven inference rules were defined to capture the knowledge of host-Brucella interactions and implemented in IDOBRU. Current IDOBRU includes 3611 ontology terms. SPARQL queries identified many results that are critical to the host-Brucella interactions. For example, out of 269 protein virulence factors related to macrophage-Brucella interactions, 81 are critical to Brucella intracellular replication inside macrophages. A SPARQL query also identified 11 biological processes important for Brucella virulence. Conclusions To systematically represent and analyze fundamental host-pathogen interaction mechanisms, we provided for the first time comprehensive ontological modeling of host-pathogen interactions using Brucella as the pathogen model. The methods and ontology representations used in our study are generic and can be broadened to study the interactions between hosts and other pathogens.http://deepblue.lib.umich.edu/bitstream/2027.42/113668/1/13326_2015_Article_36.pd

Ontology-based Brucella vaccine literature indexing and systematic analysis of gene-vaccine association network

<p>Abstract</p> <p>Background</p> <p>Vaccine literature indexing is poorly performed in PubMed due to limited hierarchy of Medical Subject Headings (MeSH) annotation in the vaccine field. Vaccine Ontology (VO) is a community-based biomedical ontology that represents various vaccines and their relations. SciMiner is an in-house literature mining system that supports literature indexing and gene name tagging. We hypothesize that application of VO in SciMiner will aid vaccine literature indexing and mining of vaccine-gene interaction networks. As a test case, we have examined vaccines for <it>Brucella</it>, the causative agent of brucellosis in humans and animals.</p> <p>Results</p> <p>The VO-based SciMiner (VO-SciMiner) was developed to incorporate a total of 67 <it>Brucella </it>vaccine terms. A set of rules for term expansion of VO terms were learned from training data, consisting of 90 biomedical articles related to <it>Brucella </it>vaccine terms. VO-SciMiner demonstrated high recall (91%) and precision (99%) from testing a separate set of 100 manually selected biomedical articles. VO-SciMiner indexing exhibited superior performance in retrieving <it>Brucella </it>vaccine-related papers over that obtained with MeSH-based PubMed literature search. For example, a VO-SciMiner search of "live attenuated <it>Brucella </it>vaccine" returned 922 hits as of April 20, 2011, while a PubMed search of the same query resulted in only 74 hits. Using the abstracts of 14,947 <it>Brucella</it>-related papers, VO-SciMiner identified 140 <it>Brucella </it>genes associated with <it>Brucella </it>vaccines. These genes included known protective antigens, virulence factors, and genes closely related to <it>Brucella </it>vaccines. These VO-interacting <it>Brucella </it>genes were significantly over-represented in biological functional categories, including metabolite transport and metabolism, replication and repair, cell wall biogenesis, intracellular trafficking and secretion, posttranslational modification, and chaperones. Furthermore, a comprehensive interaction network of <it>Brucella </it>vaccines and genes were identified. The asserted and inferred VO hierarchies provide semantic support for inferring novel knowledge of association of vaccines and genes from the retrieved data. New hypotheses were generated based on this analysis approach.</p> <p>Conclusion</p> <p>VO-SciMiner can be used to improve the efficiency for PubMed searching in the vaccine domain.</p