Result: A practical guide to interpreting and generating bottom-up proteomics data visualizations.
Title:
A practical guide to interpreting and generating bottom-up proteomics data visualizations.
Authors:
Schessner JP; Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Planegg, Germany., Voytik E; Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Planegg, Germany., Bludau I; Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Planegg, Germany.
Source:
Proteomics [Proteomics] 2022 Apr; Vol. 22 (8), pp. e2100103. Date of Electronic Publication: 2022 Feb 15.
Publication Type:
Journal Article; Review; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 101092707 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1615-9861 (Electronic) Linking ISSN: 16159853 NLM ISO Abbreviation: Proteomics Subsets: MEDLINE
Imprint Name(s):
Original Publication: Weinheim, Germany : Wiley-VCH,
MeSH Terms:
References:
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Contributed Indexing:
Keywords: bottom-up proteomics; data visualization; open science; science communication
Substance Nomenclature:
0 (Peptides)
Entry Date(s):
Date Created: 20220202 Date Completed: 20220415 Latest Revision: 20220516
Update Code:
20250114
DOI:
10.1002/pmic.202100103
PMID:
35107884
Database:
MEDLINE
Further Information
Mass-spectrometry based bottom-up proteomics is the main method to analyze proteomes comprehensively and the rapid evolution of instrumentation and data analysis has made the technology widely available. Data visualization is an integral part of the analysis process and it is crucial for the communication of results. This is a major challenge due to the immense complexity of MS data. In this review, we provide an overview of commonly used visualizations, starting with raw data of traditional and novel MS technologies, then basic peptide and protein level analyses, and finally visualization of highly complex datasets and networks. We specifically provide guidance on how to critically interpret and discuss the multitude of different proteomics data visualizations. Furthermore, we highlight Python-based libraries and other open science tools that can be applied for independent and transparent generation of customized visualizations. To further encourage programmatic data visualization, we provide the Python code used to generate all data figures in this review on GitHub (https://github.com/MannLabs/ProteomicsVisualization).
(© 2022 The Authors. Proteomics published by Wiley-VCH GmbH.)