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.

  • Michael J. Bell
  • Matthew Collison
  • Phillip Lord


Plain English Summary

There are many database resources which describe biological entities such as proteins, and genes available to the researcher. These are used by both biologists and medics to understand how biological systems work which has implications for many areas. These databases store information of various sorts, called annotation: some of this is highly organised or structured knowledge; some is free text, written in English.

The quantity of this material available means that having a computation method to check the annotation is desirable. The structured knowledge is easier to check because it is organised. The free text knowledge is much harder.

Most methods of analysing free text are based around “normal” English; biological annotation uses a highly specialised form of English, heavily controlled and with many jargon words. In this paper, we exploit this specialised form to infer provenance, to understand when sentences were first added to the database, and how they change over time. By analysing these patterns of provenance, we were able to identify patterns which are indicative of inconsistency or erroneous annotation.

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