Systematic detection of functional proteoform groups from bottom-up proteomic datasets

Many proteins exist in various proteoforms but detecting these variants by bottom-up proteomics remains difficult. Here, the authors present a computational approach based on peptide correlation analysis to identify and characterize proteoforms from bottom-up proteomics data.

Bibliographic Details
Main Authors:	Isabell Bludau, Max Frank, Christian Dörig, Yujia Cai, Moritz Heusel, George Rosenberger, Paola Picotti, Ben C. Collins, Hannes Röst, Ruedi Aebersold
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-06-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-021-24030-x

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spelling	doaj-4bc622ef871944e7b4a94176e06f11442021-06-27T11:11:21ZengNature Publishing GroupNature Communications2041-17232021-06-0112111810.1038/s41467-021-24030-xSystematic detection of functional proteoform groups from bottom-up proteomic datasetsIsabell Bludau0Max Frank1Christian Dörig2Yujia Cai3Moritz Heusel4George Rosenberger5Paola Picotti6Ben C. Collins7Hannes Röst8Ruedi Aebersold9Department of Biology, Institute of Molecular Systems Biology, ETH ZurichDepartment of Biology, Institute of Molecular Systems Biology, ETH ZurichDepartment of Biology, Institute of Molecular Systems Biology, ETH ZurichDepartment of Molecular Genetics, University of TorontoDepartment of Biology, Institute of Molecular Systems Biology, ETH ZurichDepartment of Biology, Institute of Molecular Systems Biology, ETH ZurichDepartment of Biology, Institute of Molecular Systems Biology, ETH ZurichDepartment of Biology, Institute of Molecular Systems Biology, ETH ZurichDepartment of Molecular Genetics, University of TorontoDepartment of Biology, Institute of Molecular Systems Biology, ETH ZurichMany proteins exist in various proteoforms but detecting these variants by bottom-up proteomics remains difficult. Here, the authors present a computational approach based on peptide correlation analysis to identify and characterize proteoforms from bottom-up proteomics data.https://doi.org/10.1038/s41467-021-24030-x
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Isabell Bludau Max Frank Christian Dörig Yujia Cai Moritz Heusel George Rosenberger Paola Picotti Ben C. Collins Hannes Röst Ruedi Aebersold
spellingShingle	Isabell Bludau Max Frank Christian Dörig Yujia Cai Moritz Heusel George Rosenberger Paola Picotti Ben C. Collins Hannes Röst Ruedi Aebersold Systematic detection of functional proteoform groups from bottom-up proteomic datasets Nature Communications
author_facet	Isabell Bludau Max Frank Christian Dörig Yujia Cai Moritz Heusel George Rosenberger Paola Picotti Ben C. Collins Hannes Röst Ruedi Aebersold
author_sort	Isabell Bludau
title	Systematic detection of functional proteoform groups from bottom-up proteomic datasets
title_short	Systematic detection of functional proteoform groups from bottom-up proteomic datasets
title_full	Systematic detection of functional proteoform groups from bottom-up proteomic datasets
title_fullStr	Systematic detection of functional proteoform groups from bottom-up proteomic datasets
title_full_unstemmed	Systematic detection of functional proteoform groups from bottom-up proteomic datasets
title_sort	systematic detection of functional proteoform groups from bottom-up proteomic datasets
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2021-06-01
description	Many proteins exist in various proteoforms but detecting these variants by bottom-up proteomics remains difficult. Here, the authors present a computational approach based on peptide correlation analysis to identify and characterize proteoforms from bottom-up proteomics data.
url	https://doi.org/10.1038/s41467-021-24030-x
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Systematic detection of functional proteoform groups from bottom-up proteomic datasets

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