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

Abstract

Motivation: 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 un-annotated 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 article. Specifically we investigate word reuse within bulk textual annotations and relate this to Zipf's Principle of Least Effort. We use the UniProt Knowledgebase (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.Results: 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.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

Fitting Heavy Tailed Distributions: ThepoweRlawPackage

“…casualty numbers in armed conflicts (Bohorquez, Gourley, Dixon, Spagat, and Johnson 2009;Friedman 2015), Figure 1b; comparing manually curated databases with automatically curated biological databases (Bell, Gillespie, Swan, and Lord 2012), Figure 1c.…”

Human Variome Project Quality Assessment Criteria for Variation Databases

“…Despite the large number of biological and medical databases, there are a few papers focused on their quality, assessment, and control. There are some exceptions, for example, in controlling data deposition quality in the PRIDE proteomics repository [Csordas et al, 2012], and developing methods for assessing annotation quality at UniProtKB [Bell et al, 2012]. Otherwise, databases such as ClinVar [Landrum et al, 2014] typically include discussion of validation and standardization in more general papers.…”

On patterns and re-use in bioinformatics databases

“…The work described here shows the value and importance of historical records, and that this value is also relevant to the present. We have previously made extensive use of historical records when looking at trends in database word usage ( Bell et al , 2012 ), as have others to determine when a database might be complete ( Baumgartner et al , 2007 ), or to assay the accuracy of predictive tools ( Gross et al , 2009 ). These analyses have dealt with both the structured (GO) and unstructured (comments) components of annotation.…”