Can inferred provenance and its visualisation be used to detect erroneous annotation? A case study using UniProtKB

TL;DR

This study investigates how visualising the provenance and propagation of annotations in UniProtKB can help identify erroneous or inconsistent biological data entries, highlighting the potential for improving annotation quality.

Contribution

The paper introduces a visualisation method for tracking sentence provenance in UniProtKB, enabling large-scale analysis of annotation reuse and propagation patterns.

Findings

High prevalence of sentence reuse in UniProtKB

Approximately 30% of propagated sentences are erroneous

35% of sentences are inconsistent

Abstract

A constant influx of new data poses a challenge in keeping the annotation in biological databases current. Most biological databases contain significant quantities of textual annotation, which often contains the richest source of knowledge. Many databases reuse existing knowledge, during the curation process annotations are often propagated between entries. However, this is often not made explicit. Therefore, it can be hard, potentially impossible, for a reader to identify where an annotation originated from. Within this work we attempt to identify annotation provenance and track its subsequent propagation. Specifically, we exploit annotation reuse within the UniProt Knowledgebase (UniProtKB), at the level of individual sentences. We describe a visualisation approach for the provenance and propagation of sentences in UniProtKB which enables a large-scale statistical analysis. Initially levels of sentence reuse within UniProtKB were analysed, showing that reuse is heavily prevalent, which enables the tracking of provenance and propagation. By analysing sentences throughout UniProtKB, a number of interesting propagation patterns were identified, covering over 100, 000 sentences. Over 8000 sentences remain in the database after they have been removed from the entries where they originally occurred. Analysing a subset of these sentences suggest that approximately 30% are erroneous, whilst 35% appear to be inconsistent. These results suggest that being able to visualise sentence propagation and provenance can aid in the determination of the accuracy and quality of textual annotation. Source code and supplementary data are available from the authors website.