Percolation of annotation errors through hierarchically structured protein sequence databases

Walter R. Gilks, Benjamin Audit, Daniela De Angelis, Sophia Tsoka, Christos A. Ouzounis

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)

Abstract

Databases of protein sequences have grown rapidly in recent years as a result of genome sequencing projects. Annotating protein sequences with descriptions of their biological function ideally requires careful experimentation, but this work lags far behind. Instead, biological function is often imputed by copying annotations from similar protein sequences. This gives rise to annotation errors, and more seriously, to chains of misannotation. [Percolation of annotation errors in a database of protein sequences (2002)] developed a probabilistic framework for exploring the consequences of this percolation of errors through protein databases, and applied their theory to a simple database model. Here we apply the theory to hierarchically structured protein sequence databases, and draw conclusions about database quality at different levels of the hierarchy.

Original languageEnglish
Pages (from-to)223-234
Number of pages12
JournalMathematical Biosciences
Volume193
Issue number2
DOIs
Publication statusPublished - Feb 2005

Keywords

  • Annotation errors
  • Biological function
  • Database quality
  • Hierarchical classification
  • Homology
  • Probability model
  • Protein database
  • Protein sequence

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