Literature consistency of bioinformatics sequence databases is effective for assessing record quality
AuthorBouadjenek, MR; Verspoor, K; Zobel, J
Source TitleDatabase: the journal of biological databases and curation
PublisherOXFORD UNIV PRESS
Computing and Information Systems
Document TypeJournal Article
CitationsBouadjenek, M. R., Verspoor, K. & Zobel, J. (2017). Literature consistency of bioinformatics sequence databases is effective for assessing record quality. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION, 2017 (1), https://doi.org/10.1093/database/bax021.
Access StatusOpen Access
ARC Grant codeARC/DP150101550
Bioinformatics sequence databases such as Genbank or UniProt contain hundreds of millions of records of genomic data. These records are derived from direct submissions from individual laboratories, as well as from bulk submissions from large-scale sequencing centres; their diversity and scale means that they suffer from a range of data quality issues including errors, discrepancies, redundancies, ambiguities, incompleteness and inconsistencies with the published literature. In this work, we seek to investigate and analyze the data quality of sequence databases from the perspective of a curator, who must detect anomalous and suspicious records. Specifically, we emphasize the detection of inconsistent records with respect to the literature. Focusing on GenBank, we propose a set of 24 quality indicators, which are based on treating a record as a query into the published literature, and then use query quality predictors. We then carry out an analysis that shows that the proposed quality indicators and the quality of the records have a mutual relationship, in which one depends on the other. We propose to represent record-literature consistency as a vector of these quality indicators. By reducing the dimensionality of this representation for visualization purposes using principal component analysis, we show that records which have been reported as inconsistent with the literature fall roughly in the same area, and therefore share similar characteristics. By manually analyzing records not previously known to be erroneous that fall in the same area than records know to be inconsistent, we show that one record out of four is inconsistent with respect to the literature. This high density of inconsistent record opens the way towards the development of automatic methods for the detection of faulty records. We conclude that literature inconsistency is a meaningful strategy for identifying suspicious records. Database URL: https://github.com/rbouadjenek/DQBioinformatics.
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