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Motivation: Annotations are a key feature of many biological databases, usedto convey our knowledge of a sequence to the reader. Ideally, annotations arecurated manually, however manual curation is costly, time consuming andrequires expert knowledge and training. Given these issues and the exponentialincrease of data, many databases implement automated annotation pipelines in anattempt to avoid un-annotated entries. Both manual and automated annotationsvary in quality between databases and annotators, making assessment ofannotation reliability problematic for users. The community lacks a genericmeasure for determining annotation quality and correctness, which we look ataddressing within this article. Specifically we investigate word reuse withinbulk textual annotations and relate this to Zipf's Principle of Least Effort.We use UniProt Knowledge Base (UniProtKB) as a case study to demonstrate thisapproach since it allows us to compare annotation change, both over time andbetween automated and manually curated annotations.  Results: By applying power-law distributions to word reuse in annotation, weshow clear trends in UniProtKB over time, which are consistent with existingstudies of quality on free text English. Further, we show a clear distinctionbetween manual and automated analysis and investigate cohorts of proteinrecords as they mature. These results suggest that this approach holds distinctpromise as a mechanism for judging annotation quality.  Availability: Source code is available at the authors website:http://homepages.cs.ncl.ac.uk/m.j.bell1/annotation.  Contact: phillip.lord@newcastle.ac.uk

An approach to describing and analysing bulk biological annotation quality: a case study using UniProtKB