Gutenberg Open Science: Correcting for the study bias associated with protein-protein interaction measurements reveals differences between protein degree distributions from different cancer types

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-7168

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DC Field	Value	Language
dc.contributor.author	Schaefer, Martin H.	-
dc.contributor.author	Serrano, Luis	-
dc.contributor.author	Andrade, Miguel	-
dc.date.accessioned	2022-06-20T08:03:10Z	-
dc.date.available	2022-06-20T08:03:10Z	-
dc.date.issued	2015
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/7182	-
dc.description.abstract	Protein-protein interaction (PPI) networks are associated with multiple types of biases partly rooted in technical limitations of the experimental techniques. Another source of bias are the different frequencies with which proteins have been studied for interaction partners. It is generally believed that proteins with a large number of interaction partners tend to be essential, evolutionarily conserved, and involved in disease. It has been repeatedly reported that proteins driving tumor formation have a higher number of PPI partners. However, it has been noticed before that the degree distribution of PPI networks is biased toward disease proteins, which tend to have been studied more often than non-disease proteins. At the same time, for many poorly characterized proteins no interactions have been reported yet. It is unclear to which extent this study bias affects the observation that cancer proteins tend to have more PPI partners. Here, we show that the degree of a protein is a function of the number of times it has been screened for interaction partners. We present a randomization-based method that controls for this bias to decide whether a group of proteins is associated with significantly more PPI partners than the proteomic background. We apply our method to cancer proteins and observe, in contrast to previous studies, no conclusive evidence for a significantly higher degree distribution associated with cancer proteins as compared to non-cancer proteins when we compare them to proteins that have been equally often studied as bait proteins. Comparing proteins from different tumor types, a more complex picture emerges in which proteins of certain cancer classes have significantly more interaction partners while others are associated with a smaller degree. For example, proteins of several hematological cancers tend to be associated with a higher number of interaction partners as expected by chance. Solid tumors, in contrast, are usually associated with a degree distribution similar to those of equally often studied random protein sets. We discuss the biological implications of these findings. Our work shows that accounting for biases in the PPI network is possible and increases the value of PPI data.	en_GB
dc.description.sponsorship	DFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin	de
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	570 Biowissenschaften	de_DE
dc.subject.ddc	570 Life sciences	en_GB
dc.title	Correcting for the study bias associated with protein-protein interaction measurements reveals differences between protein degree distributions from different cancer types	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-7168	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 10 Biologie	de
jgu.organisation.number	7970	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Frontiers in genetics	de
jgu.journal.volume	6	de
jgu.pages.alternative	Art. 260	de
jgu.publisher.year	2015	-
jgu.publisher.name	Frontiers Media	de
jgu.publisher.place	Lausanne	de
jgu.publisher.uri	http://dx.doi.org/10.3389/fgene.2015.00260	de
jgu.publisher.issn	1664-8021	de
jgu.notes.public	Andrade, Miguel veröffentlicht unter: Andrade-Navarro, Miguel A.	de
jgu.organisation.place	Mainz	-
jgu.identifier.pmid	26300911
jgu.subject.ddccode	570	de
opus.date.modified	2018-09-05T09:10:25Z
opus.subject.dfgcode	00-000
opus.organisation.string	FB 10: Biologie: Zentrum für Bioinformatik	de_DE
opus.identifier.opusid	51958
opus.importsource	pubmed
opus.institute.number	1010
opus.metadataonly	false
opus.type.contenttype	Keine	de_DE
opus.type.contenttype	None	en_EN
opus.affiliated	Andrade, Miguel
jgu.publisher.doi	10.3389/fgene.2015.00260	de
jgu.organisation.ror	https://ror.org/023b0x485
Appears in collections:	DFG-OA-Publizieren (2012 - 2017)

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