Plain English Summary
Bioinformaticians store large amounts of data about proteins in their databases which we call annotation. This annotation is often repetitive; this happens a database might store information about proteins from different organisms and these organisms have very similar proteins. Additionally, there are many databases which store different but related information and these often have repetitive information.
We have previously look at this repetitiveness within one database, and shown that it can lead to problems where one copy will be updated but another will not. We can detect this by looking for certain patterns of reuse.
In this paper, we explictly study the repetition between databases; in some cases, databases are extremely repetitive containing less than 1% of original sentences. More over, we can detect text that is shared between databases and find the same patterns in these that we previously used to detect errors.
This paper opens up new possibilities using bulk data analysis to help improve the quality of knowledge in these databases.