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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

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

Beatriz Serrano-Solano¹, Antonio Díaz Ramos², Jean-Karim Hériché³

¹Department of Molecular Biology and Biochemistry, University of Málaga, Boulevard Louis Pasteur, Málaga, 29071, Spain.

BMC Bioinformatics

|February 11, 2017

Summary

This summary is machine-generated.

Similar cellular phenotypes from RNA interference (RNAi) screens can indicate similar gene functions. Information content measures best capture this link, though it aligns with grouped Gene Ontology (GO) terms, not GO organization.

Keywords:

Biological network Cellular phenotype Cluster analysis Ontology

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Area of Science:

Genetics and Genomics
Systems Biology
Bioinformatics

Background:

Loss-of-function phenotypes are commonly used to infer gene function.
Accurate conversion of phenotypic to functional annotations requires careful interpretation and similarity assessment.
Understanding the phenotype-function link is vital for automated annotation methods.

Purpose of the Study:

To explore the relationship between cellular phenotypes from RNA interference (RNAi) screens in human cells and gene functional annotations.
To identify optimal methods for measuring phenotypic similarity to infer gene function.

Main Methods:

Utilized data from RNAi-based screens in human cells.
Compared various similarity measures to assess phenotypic similarity.
Correlated phenotypic similarity with gene functional annotations from the Gene Ontology (GO).

Main Results:

Information content-based measures demonstrated the highest efficacy in capturing gene functional similarity from phenotypic data.
Phenotypic similarities did not align with the hierarchical structure of the Gene Ontology.
Phenotypic similarities mapped to functional groups defined by shared gene annotations within the GO.

Conclusions:

Phenotypic similarity serves as a valuable proxy for gene function, particularly when analyzed using information content measures.
Findings impact the analysis and curation of RNAi screening data.
The results are relevant for predicting disease genes based on phenotypic data.