Abstract

As the quantity of data being depositing into biological databases continues to increase, it becomes ever more vital to develop methods that enable us to understand this data and ensure that the knowledge is correct. It is widely-held that data percolates between different databases, which causes particular concerns for data correctness; if this percolation occurs, incorrect data in one database may eventually affect many others while, conversely, corrections in one database may fail to percolate to others. In this paper, we test this widely-held belief by directly looking for sentence reuse both within and between databases. Further, we investigate patterns of how sentences are reused over time. Finally, we consider the limitations of this form of analysis and the implications that this may have for bioinformatics database design. We show that reuse of annotation is common within many different databases, and that also there is a detectable level of reuse between databases. In addition, we show that there are patterns of reuse that have previously been shown to be associated with percolation errors.

  • Michael J Bell
  • Phillip Lord


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.

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