COMPUTING SCIENCE An approach to describing and analysing bulk biological annotation quality: A case study using UniProtKB

Annotations are a key feature of many biological databases, used to convey our knowledge of a sequence to the reader. Ideally, annotations are curated manually, however manual curation is costly, time consuming and requires expert knowledge and training. Given these issues and the exponential increase of data, many databases implement automated annotation pipelines in an attempt to avoid unannotated entries. Both manual and automated annotations vary in quality between databases and annotators, making assessment of annotation reliability problematic for users. The community lacks a generic measure for determining annotation quality and correctness, which we look at addressing within this paper. Specifically we investigate word reuse within bulk textual annotations and relate this to Zipf’s Principle of Least Effort. We use UniProtKB as a case study to demonstrate this approach since it allows us to compare annotation change, both over time and between automated and manually-curated annotations. By applying power-law distributions to word reuse in annotation, we show clear trends in UniProtKB over time, which are consistent with existing studies of quality on free text English. Further, we show a clear distinction between manual and automated analysis and investigate cohorts of protein records as they mature. These results suggest that this approach holds distinct promise as a mechanism for judging annotation quality. Source code and supplementary data are available at: http://homepages.cs.ncl.ac.uk/m.j.bell1/annotation/ © 2012 Newcastle University. Printed and published by Newcastle University, Computing Science, Claremont Tower, Claremont Road, Newcastle upon Tyne, NE1 7RU, England. Bibliographical details BELL, M.J., GILLESPIE, C.S., SWAN, D., LORD, P. An approach to describing and analysing bulk biological annotation quality: A case study using UniProtKB [By] M. J. Bell, C. S. Gillespie, D. Swan, P. Lord Newcastle upon Tyne: Newcastle University: Computing Science, 2012. (Newcastle University, Computing Science, Technical Report Series, No. CS-TR-1336)