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How can functional annotations be derived from profiles of phenotypic annotations?

Overview of attention for article published in BMC Bioinformatics, February 2017
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Title
How can functional annotations be derived from profiles of phenotypic annotations?
Published in
BMC Bioinformatics, February 2017
DOI 10.1186/s12859-017-1503-5
Pubmed ID
Authors

Beatriz Serrano-Solano, Antonio Díaz Ramos, Jean-Karim Hériché, Juan A. G. Ranea

Abstract

Loss-of-function phenotypes are widely used to infer gene function using the principle that similar phenotypes are indicative of similar functions. However, converting phenotypic to functional annotations requires careful interpretation of phenotypic descriptions and assessment of phenotypic similarity. Understanding how functions and phenotypes are linked will be crucial for the development of methods for the automatic conversion of gene loss-of-function phenotypes to gene functional annotations. We explored the relation between cellular phenotypes from RNAi-based screens in human cells and gene annotations of cellular functions as provided by the Gene Ontology (GO). Comparing different similarity measures, we found that information content-based measures of phenotypic similarity were the best at capturing gene functional similarity. However, phenotypic similarities did not map to the Gene Ontology organization of gene function but to functions defined as groups of GO terms with shared gene annotations. Our observations have implications for the use and interpretation of phenotypic similarities as a proxy for gene functions both in RNAi screen data analysis and curation and in the prediction of disease genes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 4%
Netherlands 1 4%
Germany 1 4%
Unknown 21 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 25%
Student > Ph. D. Student 6 25%
Student > Bachelor 2 8%
Student > Master 2 8%
Student > Doctoral Student 1 4%
Other 2 8%
Unknown 5 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 33%
Biochemistry, Genetics and Molecular Biology 6 25%
Computer Science 2 8%
Business, Management and Accounting 1 4%
Immunology and Microbiology 1 4%
Other 1 4%
Unknown 5 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 February 2017.
All research outputs
#20,403,545
of 22,953,506 outputs
Outputs from BMC Bioinformatics
#6,882
of 7,308 outputs
Outputs of similar age
#357,924
of 422,694 outputs
Outputs of similar age from BMC Bioinformatics
#127
of 148 outputs
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