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The language of gene ontology: a Zipf's law analysis.

Kalankesh LR, Stevens R, Brass A - BMC Bioinformatics (2012)

Bottom Line: Annotations from the Gene Ontology Annotation project were found to follow Zipf's law.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.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.

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Affiliation: School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK.

ABSTRACT

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.

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The power law exponent, β, as a function of the total number of distinct GO identifiers in each of the GO sub-ontologies referenced in table 4 as well as a number of other species datasets taken from Ensembl.
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Figure 2: The power law exponent, β, as a function of the total number of distinct GO identifiers in each of the GO sub-ontologies referenced in table 4 as well as a number of other species datasets taken from Ensembl.

Mentions: Using the data from Tables 1 and 3 it is possible to examine β as a function of both the total and distinct number of GO identifiers in each genomic annotation dataset. There is no clear correlation between the size of the data set and the power law exponent (Figure 2). This analysis includes data from a wide range of species data sets from the Ensembl database in addition to the GOA datasets.


The language of gene ontology: a Zipf's law analysis.

Kalankesh LR, Stevens R, Brass A - BMC Bioinformatics (2012)

The power law exponent, β, as a function of the total number of distinct GO identifiers in each of the GO sub-ontologies referenced in table 4 as well as a number of other species datasets taken from Ensembl.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3473240&req=5

Figure 2: The power law exponent, β, as a function of the total number of distinct GO identifiers in each of the GO sub-ontologies referenced in table 4 as well as a number of other species datasets taken from Ensembl.
Mentions: Using the data from Tables 1 and 3 it is possible to examine β as a function of both the total and distinct number of GO identifiers in each genomic annotation dataset. There is no clear correlation between the size of the data set and the power law exponent (Figure 2). This analysis includes data from a wide range of species data sets from the Ensembl database in addition to the GOA datasets.

Bottom Line: Annotations from the Gene Ontology Annotation project were found to follow Zipf's law.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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK.

ABSTRACT

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.

Show MeSH