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Deep representation features from DreamDIA(XMBD) improve the analysis of data-independent acquisition proteomics
We developed DreamDIA(XMBD) (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves consid...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8517002/ https://www.ncbi.nlm.nih.gov/pubmed/34650228 http://dx.doi.org/10.1038/s42003-021-02726-6 |
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author | Gao, Mingxuan Yang, Wenxian Li, Chenxin Chang, Yuqing Liu, Yachen He, Qingzu Zhong, Chuan-Qi Shuai, Jianwei Yu, Rongshan Han, Jiahuai |
author_facet | Gao, Mingxuan Yang, Wenxian Li, Chenxin Chang, Yuqing Liu, Yachen He, Qingzu Zhong, Chuan-Qi Shuai, Jianwei Yu, Rongshan Han, Jiahuai |
author_sort | Gao, Mingxuan |
collection | PubMed |
description | We developed DreamDIA(XMBD) (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. Specifically, in contrast to existing methods which use only 6 to 10 selected fragment ions from spectral libraries, DreamDIA extracts additional features from hundreds of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by nonlinear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA is publicly available at https://github.com/xmuyulab/DreamDIA-XMBD for high coverage and accuracy DIA data analysis. |
format | Online Article Text |
id | pubmed-8517002 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-85170022021-10-29 Deep representation features from DreamDIA(XMBD) improve the analysis of data-independent acquisition proteomics Gao, Mingxuan Yang, Wenxian Li, Chenxin Chang, Yuqing Liu, Yachen He, Qingzu Zhong, Chuan-Qi Shuai, Jianwei Yu, Rongshan Han, Jiahuai Commun Biol Article We developed DreamDIA(XMBD) (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. Specifically, in contrast to existing methods which use only 6 to 10 selected fragment ions from spectral libraries, DreamDIA extracts additional features from hundreds of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by nonlinear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA is publicly available at https://github.com/xmuyulab/DreamDIA-XMBD for high coverage and accuracy DIA data analysis. Nature Publishing Group UK 2021-10-14 /pmc/articles/PMC8517002/ /pubmed/34650228 http://dx.doi.org/10.1038/s42003-021-02726-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Gao, Mingxuan Yang, Wenxian Li, Chenxin Chang, Yuqing Liu, Yachen He, Qingzu Zhong, Chuan-Qi Shuai, Jianwei Yu, Rongshan Han, Jiahuai Deep representation features from DreamDIA(XMBD) improve the analysis of data-independent acquisition proteomics |
title | Deep representation features from DreamDIA(XMBD) improve the analysis of data-independent acquisition proteomics |
title_full | Deep representation features from DreamDIA(XMBD) improve the analysis of data-independent acquisition proteomics |
title_fullStr | Deep representation features from DreamDIA(XMBD) improve the analysis of data-independent acquisition proteomics |
title_full_unstemmed | Deep representation features from DreamDIA(XMBD) improve the analysis of data-independent acquisition proteomics |
title_short | Deep representation features from DreamDIA(XMBD) improve the analysis of data-independent acquisition proteomics |
title_sort | deep representation features from dreamdia(xmbd) improve the analysis of data-independent acquisition proteomics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8517002/ https://www.ncbi.nlm.nih.gov/pubmed/34650228 http://dx.doi.org/10.1038/s42003-021-02726-6 |
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