Evaluating experimental bias and completeness in comparative phosphoproteomics analysis.

Unraveling the functional dynamics of phosphorylation networks is a crucial step in understanding the way in which biological networks form a living cell. Recently there has been an enormous increase in the number of measured phosphorylation events. Nevertheless, comparative and integrative analysis...

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Autores principales:	Jos Boekhorst, Paul J Boersema, Bastiaan B J Tops, Bas van Breukelen, Albert J R Heck, Berend Snel
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ae962c9391ae494fba7c3af2a1ad388b
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spelling	oai:doaj.org-article:ae962c9391ae494fba7c3af2a1ad388b2021-11-18T06:48:18ZEvaluating experimental bias and completeness in comparative phosphoproteomics analysis.1932-620310.1371/journal.pone.0023276https://doaj.org/article/ae962c9391ae494fba7c3af2a1ad388b2011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21853102/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Unraveling the functional dynamics of phosphorylation networks is a crucial step in understanding the way in which biological networks form a living cell. Recently there has been an enormous increase in the number of measured phosphorylation events. Nevertheless, comparative and integrative analysis of phosphoproteomes is confounded by incomplete coverage and biases introduced by different experimental workflows. As a result, we cannot differentiate whether phosphosites indentified in only one or two samples are the result of condition or species specific phosphorylation, or reflect missing data. Here, we evaluate the impact of incomplete phosphoproteomics datasets on comparative analysis, and we present bioinformatics strategies to quantify the impact of different experimental workflows on measured phosphoproteomes. We show that plotting the saturation in observed phosphosites in replicates provides a reproducible picture of the extent of a particular phosphoproteome. Still, we are still far away from a complete picture of the total human phosphoproteome. The impact of different experimental techniques on the similarity between phosphoproteomes can be estimated by comparing datasets from different experimental pipelines to a common reference. Our results show that comparative analysis is most powerful when datasets have been generated using the same experimental workflow. We show this experimentally by measuring the tyrosine phosphoproteome from Caenorhabditis elegans and comparing it to the tyrosine phosphoproteome of HeLa cells, resulting in an overlap of about 4%. This overlap between very different organisms represents a three-fold increase when compared to dataset of older studies, wherein different workflows were used. The strategies we suggest enable an estimation of the impact of differences in experimental workflows on the overlap between datasets. This will allow us to perform comparative analyses not only on datasets specifically generated for this purpose, but also to extract insights through comparative analysis of the ever-increasing wealth of publically available phosphorylation data.Jos BoekhorstPaul J BoersemaBastiaan B J TopsBas van BreukelenAlbert J R HeckBerend SnelPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 8, p e23276 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jos Boekhorst Paul J Boersema Bastiaan B J Tops Bas van Breukelen Albert J R Heck Berend Snel Evaluating experimental bias and completeness in comparative phosphoproteomics analysis.
description	Unraveling the functional dynamics of phosphorylation networks is a crucial step in understanding the way in which biological networks form a living cell. Recently there has been an enormous increase in the number of measured phosphorylation events. Nevertheless, comparative and integrative analysis of phosphoproteomes is confounded by incomplete coverage and biases introduced by different experimental workflows. As a result, we cannot differentiate whether phosphosites indentified in only one or two samples are the result of condition or species specific phosphorylation, or reflect missing data. Here, we evaluate the impact of incomplete phosphoproteomics datasets on comparative analysis, and we present bioinformatics strategies to quantify the impact of different experimental workflows on measured phosphoproteomes. We show that plotting the saturation in observed phosphosites in replicates provides a reproducible picture of the extent of a particular phosphoproteome. Still, we are still far away from a complete picture of the total human phosphoproteome. The impact of different experimental techniques on the similarity between phosphoproteomes can be estimated by comparing datasets from different experimental pipelines to a common reference. Our results show that comparative analysis is most powerful when datasets have been generated using the same experimental workflow. We show this experimentally by measuring the tyrosine phosphoproteome from Caenorhabditis elegans and comparing it to the tyrosine phosphoproteome of HeLa cells, resulting in an overlap of about 4%. This overlap between very different organisms represents a three-fold increase when compared to dataset of older studies, wherein different workflows were used. The strategies we suggest enable an estimation of the impact of differences in experimental workflows on the overlap between datasets. This will allow us to perform comparative analyses not only on datasets specifically generated for this purpose, but also to extract insights through comparative analysis of the ever-increasing wealth of publically available phosphorylation data.
format	article
author	Jos Boekhorst Paul J Boersema Bastiaan B J Tops Bas van Breukelen Albert J R Heck Berend Snel
author_facet	Jos Boekhorst Paul J Boersema Bastiaan B J Tops Bas van Breukelen Albert J R Heck Berend Snel
author_sort	Jos Boekhorst
title	Evaluating experimental bias and completeness in comparative phosphoproteomics analysis.
title_short	Evaluating experimental bias and completeness in comparative phosphoproteomics analysis.
title_full	Evaluating experimental bias and completeness in comparative phosphoproteomics analysis.
title_fullStr	Evaluating experimental bias and completeness in comparative phosphoproteomics analysis.
title_full_unstemmed	Evaluating experimental bias and completeness in comparative phosphoproteomics analysis.
title_sort	evaluating experimental bias and completeness in comparative phosphoproteomics analysis.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/ae962c9391ae494fba7c3af2a1ad388b
work_keys_str_mv	AT josboekhorst evaluatingexperimentalbiasandcompletenessincomparativephosphoproteomicsanalysis AT pauljboersema evaluatingexperimentalbiasandcompletenessincomparativephosphoproteomicsanalysis AT bastiaanbjtops evaluatingexperimentalbiasandcompletenessincomparativephosphoproteomicsanalysis AT basvanbreukelen evaluatingexperimentalbiasandcompletenessincomparativephosphoproteomicsanalysis AT albertjrheck evaluatingexperimentalbiasandcompletenessincomparativephosphoproteomicsanalysis AT berendsnel evaluatingexperimentalbiasandcompletenessincomparativephosphoproteomicsanalysis
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Evaluating experimental bias and completeness in comparative phosphoproteomics analysis.

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