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


RESULTS
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.


CONCLUSIONS
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.
Department of Molecular Biology and Biochemistry, University of Málaga, Boulevard Louis Pasteur, Málaga, 29071, Spain.
Beatriz
B
Serrano-Solano
Department of Algebra, Geometry and Topology, University of Málaga, Boulevard Louis Pasteur, Málaga, 29071, Spain.
Antonio
A
Díaz Ramos
European Molecular Biology Laboratory, Meyerhofstrasse 1, Heidelberg, 69117, Germany.
Jean-Karim
JK
Hériché
Department of Molecular Biology and Biochemistry, University of Málaga, Boulevard Louis Pasteur, Málaga, 29071, Spain. ranea@uma.es.|CIBER de Enfermedades raras (CIBERER), Madrid, Spain. ranea@uma.es.
Juan A G
JAG
Ranea
D019540:Area Under Curve; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D010641:Phenotype; D034622:RNA Interference; D012372:ROC Curve
Biological network; Cellular phenotype; Cluster analysis; Ontology