  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.
