The language of gene ontology: a Zipf's law analysis.
BACKGROUND
Most major genome projects and sequence databases provide a GO annotation of their data, either automatically or through human annotators, creating a large corpus of data written in the language of GO. Texts written in natural language show a statistical power law behaviour, Zipf's law, the exponent of which can provide useful information on the nature of the language being used. We have therefore explored the hypothesis that collections of GO annotations will show similar statistical behaviours to natural language.


RESULTS
Annotations from the Gene Ontology Annotation project were found to follow Zipf's law. Surprisingly, the measured power law exponents were consistently different between annotation captured using the three GO sub-ontologies in the corpora (function, process and component). On filtering the corpora using GO evidence codes we found that the value of the measured power law exponent responded in a predictable way as a function of the evidence codes used to support the annotation.


CONCLUSIONS
Techniques from computational linguistics can provide new insights into the annotation process. GO annotations show similar statistical behaviours to those seen in natural language with measured exponents that provide a signal which correlates with the nature of the evidence codes used to support the annotations, suggesting that the measured exponent might provide a signal regarding the information content of the annotation.
School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK.
Leila Ranandeh
LR
Kalankesh
Robert
R
Stevens
Andy
A
Brass
D030541:Databases, Genetic; D005796:Genes; D023281:Genomics / Q000379:methods; D058977:Molecular Sequence Annotation; D018875:Vocabulary, Controlled