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MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes
Isotope-labeling-based mass spectrometry (MS) is widely used in quantitative proteomic studies. With this technique, the relative abundance of thousands of proteins can be efficiently profiled in parallel, greatly facilitating the detection of proteins differentially expressed across samples. Howeve...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6796874/ https://www.ncbi.nlm.nih.gov/pubmed/31636953 http://dx.doi.org/10.1038/s41421-019-0107-9 |
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author | Li, Mushan Tu, Shiqi Li, Zijia Tan, Fengxiang Liu, Jian Wang, Qian Zhang, Yuannyu Xu, Jian Zhang, Yijing Zhou, Feng Shao, Zhen |
author_facet | Li, Mushan Tu, Shiqi Li, Zijia Tan, Fengxiang Liu, Jian Wang, Qian Zhang, Yuannyu Xu, Jian Zhang, Yijing Zhou, Feng Shao, Zhen |
author_sort | Li, Mushan |
collection | PubMed |
description | Isotope-labeling-based mass spectrometry (MS) is widely used in quantitative proteomic studies. With this technique, the relative abundance of thousands of proteins can be efficiently profiled in parallel, greatly facilitating the detection of proteins differentially expressed across samples. However, this task remains computationally challenging. Here we present a new approach, termed Model-based Analysis of Proteomic data (MAP), for this task. Unlike many existing methods, MAP does not require technical replicates to model technical and systematic errors, and instead utilizes a novel step-by-step regression analysis to directly assess the significance of observed protein abundance changes. We applied MAP to compare the proteomic profiles of undifferentiated and differentiated mouse embryonic stem cells (mESCs), and found it has superior performance compared with existing tools in detecting proteins differentially expressed during mESC differentiation. A web-based application of MAP is provided for online data processing at http://bioinfo.sibs.ac.cn/shaolab/MAP. |
format | Online Article Text |
id | pubmed-6796874 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-67968742019-10-21 MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes Li, Mushan Tu, Shiqi Li, Zijia Tan, Fengxiang Liu, Jian Wang, Qian Zhang, Yuannyu Xu, Jian Zhang, Yijing Zhou, Feng Shao, Zhen Cell Discov Article Isotope-labeling-based mass spectrometry (MS) is widely used in quantitative proteomic studies. With this technique, the relative abundance of thousands of proteins can be efficiently profiled in parallel, greatly facilitating the detection of proteins differentially expressed across samples. However, this task remains computationally challenging. Here we present a new approach, termed Model-based Analysis of Proteomic data (MAP), for this task. Unlike many existing methods, MAP does not require technical replicates to model technical and systematic errors, and instead utilizes a novel step-by-step regression analysis to directly assess the significance of observed protein abundance changes. We applied MAP to compare the proteomic profiles of undifferentiated and differentiated mouse embryonic stem cells (mESCs), and found it has superior performance compared with existing tools in detecting proteins differentially expressed during mESC differentiation. A web-based application of MAP is provided for online data processing at http://bioinfo.sibs.ac.cn/shaolab/MAP. Nature Publishing Group UK 2019-08-13 /pmc/articles/PMC6796874/ /pubmed/31636953 http://dx.doi.org/10.1038/s41421-019-0107-9 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Li, Mushan Tu, Shiqi Li, Zijia Tan, Fengxiang Liu, Jian Wang, Qian Zhang, Yuannyu Xu, Jian Zhang, Yijing Zhou, Feng Shao, Zhen MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes |
title | MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes |
title_full | MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes |
title_fullStr | MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes |
title_full_unstemmed | MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes |
title_short | MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes |
title_sort | map: model-based analysis of proteomic data to detect proteins with significant abundance changes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6796874/ https://www.ncbi.nlm.nih.gov/pubmed/31636953 http://dx.doi.org/10.1038/s41421-019-0107-9 |
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