Dataset Information

FluKB: A Knowledge-Based System for Influenza Vaccine Target Discovery and Analysis of the Immunological Properties of Influenza Viruses.

ABSTRACT: FluKB is a knowledge-based system focusing on data and analytical tools for influenza vaccine discovery. The main goal of FluKB is to provide access to curated influenza sequence and epitope data and enhance the analysis of influenza sequence diversity and the analysis of targets of immune responses. FluKB consists of more than 400,000 influenza protein sequences, known epitope data (357 verified T-cell epitopes, 685 HLA binders, and 16 naturally processed MHC ligands), and a collection of 28 influenza antibodies and their structurally defined B-cell epitopes. FluKB was built using a modular framework allowing the implementation of analytical workflows and includes standard search tools, such as keyword search and sequence similarity queries, as well as advanced tools for the analysis of sequence variability. The advanced analytical tools for vaccine discovery include visual mapping of T- and B-cell vaccine targets and assessment of neutralizing antibody coverage. FluKB supports the discovery of vaccine targets and the analysis of viral diversity and its implications for vaccine discovery as well as potential T-cell breadth and antibody cross neutralization involving multiple strains. FluKB is representation of a new generation of databases that integrates data, analytical tools, and analytical workflows that enable comprehensive analysis and automatic generation of analysis reports.

SUBMITTER: Simon C

PROVIDER: S-EPMC4609449 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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FluKB: A Knowledge-Based System for Influenza Vaccine Target Discovery and Analysis of the Immunological Properties of Influenza Viruses.

Simon Christian C Kudahl Ulrich J UJ Sun Jing J Olsen Lars Rønn LR Zhang Guang Lan GL Reinherz Ellis L EL Brusic Vladimir V

Journal of immunology research 20151004

FluKB is a knowledge-based system focusing on data and analytical tools for influenza vaccine discovery. The main goal of FluKB is to provide access to curated influenza sequence and epitope data and enhance the analysis of influenza sequence diversity and the analysis of targets of immune responses. FluKB consists of more than 400,000 influenza protein sequences, known epitope data (357 verified T-cell epitopes, 685 HLA binders, and 16 naturally processed MHC ligands), and a collection of 28 in ...[more]

PMID: 26504853

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Project description:BackgroundThe explosive growth of biological data provides opportunities for new statistical and comparative analyses of large information sets, such as alignments comprising tens of thousands of sequences. In such studies, sequence annotations frequently play an essential role, and reliable results depend on metadata quality. However, the semantic heterogeneity and annotation inconsistencies in biological databases greatly increase the complexity of aggregating and cleaning metadata. Manual curation of datasets, traditionally favoured by life scientists, is impractical for studies involving thousands of records. In this study, we investigate quality issues that affect major public databases, and quantify the effectiveness of an automated metadata extraction approach that combines structural and semantic rules. We applied this approach to more than 90,000 influenza A records, to annotate sequences with protein name, virus subtype, isolate, host, geographic origin, and year of isolation.ResultsOver 40,000 annotated Influenza A protein sequences were collected by combining information from more than 90,000 documents from NCBI public databases. Metadata values were automatically extracted, aggregated and reconciled from several document fields by applying user-defined structural rules. For each property, values were recovered from >/=88.8% of records, with accuracy exceeding 96% in most cases. Because of semantic heterogeneity, each property required up to six different structural rules to be combined. Significant quality differences between databases were found: GenBank documents yield values more reliably than documents extracted from GenPept. Using a simple set of semantic rules and a reasoner, we reconstructed relationships between sequences from the same isolate, thus identifying 7640 isolates. Validation of isolate metadata against a simple ontology highlighted more than 400 inconsistencies, leading to over 3,000 property value corrections.ConclusionTo overcome the quality issues inherent in public databases, automated knowledge aggregation with embedded intelligence is needed for large-scale analyses. Our results show that user-controlled intuitive approaches, based on combination of simple rules, can reliably automate various curation tasks, reducing the need for manual corrections to approximately 5% of the records. Emerging semantic technologies possess desirable features to support today's knowledge aggregation tasks, with a potential to bring immediate benefits to this field.

Project description:BackgroundThe burden of influenza is increasingly recognised in Africa. The WHO recommends introducing influenza vaccination to high-risk groups: pregnant women, children <5 years, and the elderly. The Gambia currently has no influenza vaccination policy, but the NASIMMUNE study, a clinical trial of intranasal live attenuated influenza vaccines (LAIV) in young children provided an opportunity to study maternal attitudes towards LAIV for the first time in sub-Saharan Africa. We assess acceptability of LAIV, influenza knowledge and attitudes towards influenza vaccination in Gambian women. Additionally, we investigate predictors of willingness to receive influenza vaccine (intent) in pregnancy or seasonally for children <5.MethodsA cross-sectional survey was conducted in Gambian women at two urban health facilities. To assess LAIV acceptability, the exposure group (women whose children had received LAIV during the NASIMMUNE study) were compared to a control group (women whose children were not enrolled in the NASIMMUNE study). Demographics and health belief constructs were analysed as predictors of influenza knowledge and vaccine intent.FindingsThe exposure group (n = 150) expressed a higher preference for a nasal spray vaccine than an injection compared to the control group (n = 304) (93.3% vs. 34.9%, OR = 26.15, p < 0.0001). Those in the exposure group who preferred the nasal spray found it less distressing, safer or equally safe, and easier or equally easy to give (all p < 0.001) than injections. Influenza knowledge increased with education level (p = 0.006 for higher education vs. none), and varied between sites (p = 0.0005). Vaccine intent was >98%, but no association with influenza knowledge or difference between groups was observed. Various health belief constructs were associated with vaccine intent.ConclusionLAIV acceptability was higher in those with first-hand experience. Influenza vaccine intent was also high. Incorporation of seasonal LAIV into the childhood immunisation schedule in The Gambia would be feasible, particularly if combined with community-based health education.

Dataset Information

FluKB: A Knowledge-Based System for Influenza Vaccine Target Discovery and Analysis of the Immunological Properties of Influenza Viruses.

Publications

FluKB: A Knowledge-Based System for Influenza Vaccine Target Discovery and Analysis of the Immunological Properties of Influenza Viruses.

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