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

View Article: PubMed Central - PubMed

ABSTRACT

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.

Electronic supplementary material: The online version of this article (doi:10.1186/s12859-017-1503-5) contains supplementary material, which is available to authorized users.

No MeSH data available.


Distributions of functional and phenotypic similarities. The box represents the upper and lower quartiles and the median is represented by the black line inside the box. a Phenotypic similarity in CMPO versus functional similarity in GO. b Functional similarity in GO versus phenotypic similarity in CMPO
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Fig3: Distributions of functional and phenotypic similarities. The box represents the upper and lower quartiles and the median is represented by the black line inside the box. a Phenotypic similarity in CMPO versus functional similarity in GO. b Functional similarity in GO versus phenotypic similarity in CMPO

Mentions: Having identified a suitable measure of phenotypic similarity, we set to explore how gene functions relate to phenotypes more directly. If phenotypes are predictive of biological functions, we expect that pairs of genes with similar phenotypes will have similar functions. Since gene functions have been standardized using the Gene Ontology, gene functional similarity was computed using Resnik’s semantic similarity between GO terms, a measure generally found to be the best for this purpose [37]. To assess links between gene phenotypic similarity and gene semantic similarity in GO, we plotted GO semantic similarities versus CMPO semantic similarities for the RNAi screen data (Fig. 3a), excluding genes with no functional annotation in GO. The distribution of functional similarity values is the same for all levels of phenotypic similarity except the highest, which showed a trend towards higher functional similarity. Although weak, this effect is robust as it is still observed when removing up to 30% of the phenotypic annotations (see Additional file 5: Figure S2) and does not appear to be due to chance because random assignment of GO similarity values to high-scoring CMPO gene pairs resulted in a lower average GO similarity (Additional file 6: Figure 3). While this matched our expectation that specific phenotypes are associated with specific functions, this represented only a small fraction of the genes (20/4198) and for most genes, phenoypes do not appear to be good indicators of function.Fig. 3


How can functional annotations be derived from profiles of phenotypic annotations?
Distributions of functional and phenotypic similarities. The box represents the upper and lower quartiles and the median is represented by the black line inside the box. a Phenotypic similarity in CMPO versus functional similarity in GO. b Functional similarity in GO versus phenotypic similarity in CMPO
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5304448&req=5

Fig3: Distributions of functional and phenotypic similarities. The box represents the upper and lower quartiles and the median is represented by the black line inside the box. a Phenotypic similarity in CMPO versus functional similarity in GO. b Functional similarity in GO versus phenotypic similarity in CMPO
Mentions: Having identified a suitable measure of phenotypic similarity, we set to explore how gene functions relate to phenotypes more directly. If phenotypes are predictive of biological functions, we expect that pairs of genes with similar phenotypes will have similar functions. Since gene functions have been standardized using the Gene Ontology, gene functional similarity was computed using Resnik’s semantic similarity between GO terms, a measure generally found to be the best for this purpose [37]. To assess links between gene phenotypic similarity and gene semantic similarity in GO, we plotted GO semantic similarities versus CMPO semantic similarities for the RNAi screen data (Fig. 3a), excluding genes with no functional annotation in GO. The distribution of functional similarity values is the same for all levels of phenotypic similarity except the highest, which showed a trend towards higher functional similarity. Although weak, this effect is robust as it is still observed when removing up to 30% of the phenotypic annotations (see Additional file 5: Figure S2) and does not appear to be due to chance because random assignment of GO similarity values to high-scoring CMPO gene pairs resulted in a lower average GO similarity (Additional file 6: Figure 3). While this matched our expectation that specific phenotypes are associated with specific functions, this represented only a small fraction of the genes (20/4198) and for most genes, phenoypes do not appear to be good indicators of function.Fig. 3

View Article: PubMed Central - PubMed

ABSTRACT

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.

Electronic supplementary material: The online version of this article (doi:10.1186/s12859-017-1503-5) contains supplementary material, which is available to authorized users.

No MeSH data available.