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.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Miotto O,Tan TW,Brusic Vdoi
10.1186/1471-2105-9-S1-S7subject
Has Abstractpub_date
2008-01-01 00:00:00pages
S7issn
1471-2105pii
1471-2105-9-S1-S7journal_volume
9 Suppl 1pub_type
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journal_title:BMC bioinformatics
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更新日期:2020-03-12 00:00:00
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更新日期:2009-03-26 00:00:00
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journal_title:BMC bioinformatics
pub_type: 杂志文章
doi:10.1186/1471-2105-8-216
更新日期:2007-06-23 00:00:00
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journal_title:BMC bioinformatics
pub_type: 杂志文章
doi:10.1186/1471-2105-9-S12-S7
更新日期:2008-12-12 00:00:00
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pub_type: 杂志文章
doi:10.1186/1471-2105-10-186
更新日期:2009-06-18 00:00:00
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更新日期:2010-08-09 00:00:00
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pub_type: 杂志文章
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journal_title:BMC bioinformatics
pub_type: 杂志文章
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更新日期:2019-06-20 00:00:00