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Informed-Proteomics: Open Source Software Package for Top-down Proteomics
Top-down proteomics is the analysis of intact proteins in their endogenous form without proteolysis, preserving valuable information about post-translation modifications, isoforms and proteolytic processing. The quality of top-down LC-MS/MS datasets is rapidly increasing due to advances in instrumen...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5578875/ https://www.ncbi.nlm.nih.gov/pubmed/28783154 http://dx.doi.org/10.1038/nmeth.4388 |
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author | Park, Jungkap Piehowski, Paul D. Wilkins, Christopher Zhou, Mowei Mendoza, Joshua Fujimoto, Grant M Gibbons, Bryson C. Shaw, Jared B. Shen, Yufeng Shukla, Anil K. Moore, Ronald J. Liu, Tao Petyuk, Vladislav A Tolic, Nikola Pasa-Tolic, Ljiljana Smith, Richard D. Payne, Samuel H. Kim, Sangtae |
author_facet | Park, Jungkap Piehowski, Paul D. Wilkins, Christopher Zhou, Mowei Mendoza, Joshua Fujimoto, Grant M Gibbons, Bryson C. Shaw, Jared B. Shen, Yufeng Shukla, Anil K. Moore, Ronald J. Liu, Tao Petyuk, Vladislav A Tolic, Nikola Pasa-Tolic, Ljiljana Smith, Richard D. Payne, Samuel H. Kim, Sangtae |
author_sort | Park, Jungkap |
collection | PubMed |
description | Top-down proteomics is the analysis of intact proteins in their endogenous form without proteolysis, preserving valuable information about post-translation modifications, isoforms and proteolytic processing. The quality of top-down LC-MS/MS datasets is rapidly increasing due to advances in instrumentation and sample processing protocols. However, the top-down mass spectra are substantially more complex compared to conventional bottom-up data. To take full advantage of the increasing data quality, there is an urgent need to develop algorithms and software tools for confident proteoform identification and quantification. In this study, we present a new open source software suite for top-down proteomics analysis consisting of an LC-MS feature finding algorithm, a database search algorithm, and an interactive results viewer. The presented tool along with several other popular tools were evaluated using human-in-mouse xenograft luminal and basal breast tumor samples that are known to have significant differences in protein abundance based on bottom-up analysis. |
format | Online Article Text |
id | pubmed-5578875 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
record_format | MEDLINE/PubMed |
spelling | pubmed-55788752018-02-07 Informed-Proteomics: Open Source Software Package for Top-down Proteomics Park, Jungkap Piehowski, Paul D. Wilkins, Christopher Zhou, Mowei Mendoza, Joshua Fujimoto, Grant M Gibbons, Bryson C. Shaw, Jared B. Shen, Yufeng Shukla, Anil K. Moore, Ronald J. Liu, Tao Petyuk, Vladislav A Tolic, Nikola Pasa-Tolic, Ljiljana Smith, Richard D. Payne, Samuel H. Kim, Sangtae Nat Methods Article Top-down proteomics is the analysis of intact proteins in their endogenous form without proteolysis, preserving valuable information about post-translation modifications, isoforms and proteolytic processing. The quality of top-down LC-MS/MS datasets is rapidly increasing due to advances in instrumentation and sample processing protocols. However, the top-down mass spectra are substantially more complex compared to conventional bottom-up data. To take full advantage of the increasing data quality, there is an urgent need to develop algorithms and software tools for confident proteoform identification and quantification. In this study, we present a new open source software suite for top-down proteomics analysis consisting of an LC-MS feature finding algorithm, a database search algorithm, and an interactive results viewer. The presented tool along with several other popular tools were evaluated using human-in-mouse xenograft luminal and basal breast tumor samples that are known to have significant differences in protein abundance based on bottom-up analysis. 2017-08-07 2017-09 /pmc/articles/PMC5578875/ /pubmed/28783154 http://dx.doi.org/10.1038/nmeth.4388 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Park, Jungkap Piehowski, Paul D. Wilkins, Christopher Zhou, Mowei Mendoza, Joshua Fujimoto, Grant M Gibbons, Bryson C. Shaw, Jared B. Shen, Yufeng Shukla, Anil K. Moore, Ronald J. Liu, Tao Petyuk, Vladislav A Tolic, Nikola Pasa-Tolic, Ljiljana Smith, Richard D. Payne, Samuel H. Kim, Sangtae Informed-Proteomics: Open Source Software Package for Top-down Proteomics |
title | Informed-Proteomics: Open Source Software Package for Top-down Proteomics |
title_full | Informed-Proteomics: Open Source Software Package for Top-down Proteomics |
title_fullStr | Informed-Proteomics: Open Source Software Package for Top-down Proteomics |
title_full_unstemmed | Informed-Proteomics: Open Source Software Package for Top-down Proteomics |
title_short | Informed-Proteomics: Open Source Software Package for Top-down Proteomics |
title_sort | informed-proteomics: open source software package for top-down proteomics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5578875/ https://www.ncbi.nlm.nih.gov/pubmed/28783154 http://dx.doi.org/10.1038/nmeth.4388 |
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