Rule-based knowledge aggregation for large-scale protein sequence analysis of influenza A viruses

Olivo Miotto, Tin Wee Tan, Vladimir Brusic

Research output: Contribution to journalArticle

Abstract

Background: The 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. Results: Over 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. Conclusion: To 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.

Original languageEnglish
Article numberS7
JournalBMC Bioinformatics
Volume9
Issue numberSUPPL. 1
DOIs
Publication statusPublished - Feb 13 2008
Externally publishedYes

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Influenza
Protein Sequence Analysis
Influenza A virus
Sequence Analysis
Protein Sequence
Metadata
Viruses
Semantics
Virus
Aggregation
Agglomeration
Databases
Proteins
Inconsistency
Human Influenza
Annotation
Nucleic Acid Databases
Cleaning
Intelligence
Isolation

ASJC Scopus subject areas

  • Medicine(all)
  • Structural Biology
  • Applied Mathematics

Cite this

Rule-based knowledge aggregation for large-scale protein sequence analysis of influenza A viruses. / Miotto, Olivo; Tan, Tin Wee; Brusic, Vladimir.

In: BMC Bioinformatics, Vol. 9, No. SUPPL. 1, S7, 13.02.2008.

Research output: Contribution to journalArticle

Miotto, Olivo ; Tan, Tin Wee ; Brusic, Vladimir. / Rule-based knowledge aggregation for large-scale protein sequence analysis of influenza A viruses. In: BMC Bioinformatics. 2008 ; Vol. 9, No. SUPPL. 1.
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