Exploring Approaches for Detecting Protein Functional Similarity within an Orthology-based Framework

Abstract Protein functional similarity based on gene ontology (GO) annotations serves as a powerful tool when comparing proteins on a functional level in applications such as protein-protein interaction prediction, gene prioritization, and disease gene discovery. Functional similarity (FS) is usuall...

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Autores principales:	Christian X. Weichenberger, Antonia Palermo, Peter P. Pramstaller, Francisco S. Domingues
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e3349d65cd56497ba631cc5306b0a9e7
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spelling	oai:doaj.org-article:e3349d65cd56497ba631cc5306b0a9e72021-12-02T16:06:31ZExploring Approaches for Detecting Protein Functional Similarity within an Orthology-based Framework10.1038/s41598-017-00465-52045-2322https://doaj.org/article/e3349d65cd56497ba631cc5306b0a9e72017-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-00465-5https://doaj.org/toc/2045-2322Abstract Protein functional similarity based on gene ontology (GO) annotations serves as a powerful tool when comparing proteins on a functional level in applications such as protein-protein interaction prediction, gene prioritization, and disease gene discovery. Functional similarity (FS) is usually quantified by combining the GO hierarchy with an annotation corpus that links genes and gene products to GO terms. One large group of algorithms involves calculation of GO term semantic similarity (SS) between all the terms annotating the two proteins, followed by a second step, described as “mixing strategy”, which involves combining the SS values to yield the final FS value. Due to the variability of protein annotation caused e.g. by annotation bias, this value cannot be reliably compared on an absolute scale. We therefore introduce a similarity z-score that takes into account the FS background distribution of each protein. For a selection of popular SS measures and mixing strategies we demonstrate moderate accuracy improvement when using z-scores in a benchmark that aims to separate orthologous cases from random gene pairs and discuss in this context the impact of annotation corpus choice. The approach has been implemented in Frela, a fast high-throughput public web server for protein FS calculation and interpretation.Christian X. WeichenbergerAntonia PalermoPeter P. PramstallerFrancisco S. DominguesNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-15 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Christian X. Weichenberger Antonia Palermo Peter P. Pramstaller Francisco S. Domingues Exploring Approaches for Detecting Protein Functional Similarity within an Orthology-based Framework
description	Abstract Protein functional similarity based on gene ontology (GO) annotations serves as a powerful tool when comparing proteins on a functional level in applications such as protein-protein interaction prediction, gene prioritization, and disease gene discovery. Functional similarity (FS) is usually quantified by combining the GO hierarchy with an annotation corpus that links genes and gene products to GO terms. One large group of algorithms involves calculation of GO term semantic similarity (SS) between all the terms annotating the two proteins, followed by a second step, described as “mixing strategy”, which involves combining the SS values to yield the final FS value. Due to the variability of protein annotation caused e.g. by annotation bias, this value cannot be reliably compared on an absolute scale. We therefore introduce a similarity z-score that takes into account the FS background distribution of each protein. For a selection of popular SS measures and mixing strategies we demonstrate moderate accuracy improvement when using z-scores in a benchmark that aims to separate orthologous cases from random gene pairs and discuss in this context the impact of annotation corpus choice. The approach has been implemented in Frela, a fast high-throughput public web server for protein FS calculation and interpretation.
format	article
author	Christian X. Weichenberger Antonia Palermo Peter P. Pramstaller Francisco S. Domingues
author_facet	Christian X. Weichenberger Antonia Palermo Peter P. Pramstaller Francisco S. Domingues
author_sort	Christian X. Weichenberger
title	Exploring Approaches for Detecting Protein Functional Similarity within an Orthology-based Framework
title_short	Exploring Approaches for Detecting Protein Functional Similarity within an Orthology-based Framework
title_full	Exploring Approaches for Detecting Protein Functional Similarity within an Orthology-based Framework
title_fullStr	Exploring Approaches for Detecting Protein Functional Similarity within an Orthology-based Framework
title_full_unstemmed	Exploring Approaches for Detecting Protein Functional Similarity within an Orthology-based Framework
title_sort	exploring approaches for detecting protein functional similarity within an orthology-based framework
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/e3349d65cd56497ba631cc5306b0a9e7
work_keys_str_mv	AT christianxweichenberger exploringapproachesfordetectingproteinfunctionalsimilaritywithinanorthologybasedframework AT antoniapalermo exploringapproachesfordetectingproteinfunctionalsimilaritywithinanorthologybasedframework AT peterppramstaller exploringapproachesfordetectingproteinfunctionalsimilaritywithinanorthologybasedframework AT franciscosdomingues exploringapproachesfordetectingproteinfunctionalsimilaritywithinanorthologybasedframework
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Exploring Approaches for Detecting Protein Functional Similarity within an Orthology-based Framework

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