Hybrid Spectral Library Combining DIA-MS Data and a Targeted Virtual Library Substantially Deepens the Proteome Coverage
Data-independent acquisition mass spectrometry (DIA-MS) is a powerful technique that enables relatively deep proteomic profiling with superior quantification reproducibility. DIA data mining predominantly relies on a spectral library of sufficient proteome coverage that, in most cases, is built on d...
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| Published in | iScience Vol. 23; no. 3; p. 100903 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
27.03.2020
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2589-0042 2589-0042 |
| DOI | 10.1016/j.isci.2020.100903 |
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| Abstract | Data-independent acquisition mass spectrometry (DIA-MS) is a powerful technique that enables relatively deep proteomic profiling with superior quantification reproducibility. DIA data mining predominantly relies on a spectral library of sufficient proteome coverage that, in most cases, is built on data-dependent acquisition-based analysis of the same sample. To expand the proteome coverage for a pre-determined protein family, we report herein on the construction of a hybrid spectral library that supplements a DIA experiment-derived library with a protein family-targeted virtual library predicted by deep learning. Leveraging this DIA hybrid library substantially deepens the coverage of three transmembrane protein families (G protein-coupled receptors, ion channels, and transporters) in mouse brain tissues with increases in protein identification of 37%–87% and peptide identification of 58%–161%. Moreover, of the 412 novel GPCR peptides exclusively identified with the DIA hybrid library strategy, 53.6% were validated as present in mouse brain tissues based on orthogonal experimental measurement.
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•A virtual library is built for a selected protein family using deep learning models•The hybrid library strategy vastly deepens the coverage for the targeted protein family•About 53.6% of novel GPCR peptides identified with the DIA hybrid library are validated•Extend the strategy to deep mapping of multiple transmembrane protein families
Analytical Chemistry; Biological Sciences; Classification of Proteins; Proteomics |
|---|---|
| AbstractList | Data-independent acquisition mass spectrometry (DIA-MS) is a powerful technique that enables relatively deep proteomic profiling with superior quantification reproducibility. DIA data mining predominantly relies on a spectral library of sufficient proteome coverage that, in most cases, is built on data-dependent acquisition-based analysis of the same sample. To expand the proteome coverage for a pre-determined protein family, we report herein on the construction of a hybrid spectral library that supplements a DIA experiment-derived library with a protein family-targeted virtual library predicted by deep learning. Leveraging this DIA hybrid library substantially deepens the coverage of three transmembrane protein families (G protein-coupled receptors, ion channels, and transporters) in mouse brain tissues with increases in protein identification of 37%-87% and peptide identification of 58%-161%. Moreover, of the 412 novel GPCR peptides exclusively identified with the DIA hybrid library strategy, 53.6% were validated as present in mouse brain tissues based on orthogonal experimental measurement.Data-independent acquisition mass spectrometry (DIA-MS) is a powerful technique that enables relatively deep proteomic profiling with superior quantification reproducibility. DIA data mining predominantly relies on a spectral library of sufficient proteome coverage that, in most cases, is built on data-dependent acquisition-based analysis of the same sample. To expand the proteome coverage for a pre-determined protein family, we report herein on the construction of a hybrid spectral library that supplements a DIA experiment-derived library with a protein family-targeted virtual library predicted by deep learning. Leveraging this DIA hybrid library substantially deepens the coverage of three transmembrane protein families (G protein-coupled receptors, ion channels, and transporters) in mouse brain tissues with increases in protein identification of 37%-87% and peptide identification of 58%-161%. Moreover, of the 412 novel GPCR peptides exclusively identified with the DIA hybrid library strategy, 53.6% were validated as present in mouse brain tissues based on orthogonal experimental measurement. Data-independent acquisition mass spectrometry (DIA-MS) is a powerful technique that enables relatively deep proteomic profiling with superior quantification reproducibility. DIA data mining predominantly relies on a spectral library of sufficient proteome coverage that, in most cases, is built on data-dependent acquisition-based analysis of the same sample. To expand the proteome coverage for a pre-determined protein family, we report herein on the construction of a hybrid spectral library that supplements a DIA experiment-derived library with a protein family-targeted virtual library predicted by deep learning. Leveraging this DIA hybrid library substantially deepens the coverage of three transmembrane protein families (G protein-coupled receptors, ion channels, and transporters) in mouse brain tissues with increases in protein identification of 37%–87% and peptide identification of 58%–161%. Moreover, of the 412 novel GPCR peptides exclusively identified with the DIA hybrid library strategy, 53.6% were validated as present in mouse brain tissues based on orthogonal experimental measurement. • A virtual library is built for a selected protein family using deep learning models • The hybrid library strategy vastly deepens the coverage for the targeted protein family • About 53.6% of novel GPCR peptides identified with the DIA hybrid library are validated • Extend the strategy to deep mapping of multiple transmembrane protein families Analytical Chemistry; Biological Sciences; Classification of Proteins; Proteomics Data-independent acquisition mass spectrometry (DIA-MS) is a powerful technique that enables relatively deep proteomic profiling with superior quantification reproducibility. DIA data mining predominantly relies on a spectral library of sufficient proteome coverage that, in most cases, is built on data-dependent acquisition-based analysis of the same sample. To expand the proteome coverage for a pre-determined protein family, we report herein on the construction of a hybrid spectral library that supplements a DIA experiment-derived library with a protein family-targeted virtual library predicted by deep learning. Leveraging this DIA hybrid library substantially deepens the coverage of three transmembrane protein families (G protein-coupled receptors, ion channels, and transporters) in mouse brain tissues with increases in protein identification of 37%–87% and peptide identification of 58%–161%. Moreover, of the 412 novel GPCR peptides exclusively identified with the DIA hybrid library strategy, 53.6% were validated as present in mouse brain tissues based on orthogonal experimental measurement. [Display omitted] •A virtual library is built for a selected protein family using deep learning models•The hybrid library strategy vastly deepens the coverage for the targeted protein family•About 53.6% of novel GPCR peptides identified with the DIA hybrid library are validated•Extend the strategy to deep mapping of multiple transmembrane protein families Analytical Chemistry; Biological Sciences; Classification of Proteins; Proteomics Data-independent acquisition mass spectrometry (DIA-MS) is a powerful technique that enables relatively deep proteomic profiling with superior quantification reproducibility. DIA data mining predominantly relies on a spectral library of sufficient proteome coverage that, in most cases, is built on data-dependent acquisition-based analysis of the same sample. To expand the proteome coverage for a pre-determined protein family, we report herein on the construction of a hybrid spectral library that supplements a DIA experiment-derived library with a protein family-targeted virtual library predicted by deep learning. Leveraging this DIA hybrid library substantially deepens the coverage of three transmembrane protein families (G protein-coupled receptors, ion channels, and transporters) in mouse brain tissues with increases in protein identification of 37%-87% and peptide identification of 58%-161%. Moreover, of the 412 novel GPCR peptides exclusively identified with the DIA hybrid library strategy, 53.6% were validated as present in mouse brain tissues based on orthogonal experimental measurement. Data-independent acquisition mass spectrometry (DIA-MS) is a powerful technique that enables relatively deep proteomic profiling with superior quantification reproducibility. DIA data mining predominantly relies on a spectral library of sufficient proteome coverage that, in most cases, is built on data-dependent acquisition-based analysis of the same sample. To expand the proteome coverage for a pre-determined protein family, we report herein on the construction of a hybrid spectral library that supplements a DIA experiment-derived library with a protein family-targeted virtual library predicted by deep learning. Leveraging this DIA hybrid library substantially deepens the coverage of three transmembrane protein families (G protein-coupled receptors, ion channels, and transporters) in mouse brain tissues with increases in protein identification of 37%–87% and peptide identification of 58%–161%. Moreover, of the 412 novel GPCR peptides exclusively identified with the DIA hybrid library strategy, 53.6% were validated as present in mouse brain tissues based on orthogonal experimental measurement. : Analytical Chemistry; Biological Sciences; Classification of Proteins; Proteomics Subject Areas: Analytical Chemistry, Biological Sciences, Classification of Proteins, Proteomics |
| ArticleNumber | 100903 |
| Author | Tang, Pan Li, Yunxia Ding, Kang Lou, Ronghui Zhang, Yaoyang Shui, Wenqing Tian, Cuiping Zhao, Suwen Li, Shanshan |
| AuthorAffiliation | 4 Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China 1 iHuman Institute, ShanghaiTech University, Shanghai 201210, China 2 School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China 3 University of Chinese Academy of Sciences, Beijing 100049, China |
| AuthorAffiliation_xml | – name: 2 School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China – name: 4 Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China – name: 1 iHuman Institute, ShanghaiTech University, Shanghai 201210, China – name: 3 University of Chinese Academy of Sciences, Beijing 100049, China |
| Author_xml | – sequence: 1 givenname: Ronghui surname: Lou fullname: Lou, Ronghui organization: iHuman Institute, ShanghaiTech University, Shanghai 201210, China – sequence: 2 givenname: Pan surname: Tang fullname: Tang, Pan organization: iHuman Institute, ShanghaiTech University, Shanghai 201210, China – sequence: 3 givenname: Kang surname: Ding fullname: Ding, Kang organization: iHuman Institute, ShanghaiTech University, Shanghai 201210, China – sequence: 4 givenname: Shanshan surname: Li fullname: Li, Shanshan organization: iHuman Institute, ShanghaiTech University, Shanghai 201210, China – sequence: 5 givenname: Cuiping surname: Tian fullname: Tian, Cuiping organization: iHuman Institute, ShanghaiTech University, Shanghai 201210, China – sequence: 6 givenname: Yunxia surname: Li fullname: Li, Yunxia organization: Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China – sequence: 7 givenname: Suwen surname: Zhao fullname: Zhao, Suwen email: zhaosw@shanghaitech.edu.cn organization: iHuman Institute, ShanghaiTech University, Shanghai 201210, China – sequence: 8 givenname: Yaoyang surname: Zhang fullname: Zhang, Yaoyang email: zyy@sioc.ac.cn organization: Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 201210, China – sequence: 9 givenname: Wenqing surname: Shui fullname: Shui, Wenqing email: shuiwq@shanghaitech.edu.cn organization: iHuman Institute, ShanghaiTech University, Shanghai 201210, China |
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| Cites_doi | 10.1074/mcp.RA117.000314 10.1038/nprot.2015.015 10.15252/msb.20178126 10.1146/annurev-pharmtox-032112-135923 10.1038/nmeth.4390 10.1074/mcp.O111.016717 10.1074/mcp.M114.044305 10.1074/mcp.O114.047035 10.1093/nar/gky869 10.1016/j.cels.2017.05.009 10.1038/s41592-019-0426-7 10.1016/j.cell.2016.06.041 10.1021/acs.analchem.8b02386 10.1038/nmeth.2015 10.1038/nmeth.4153 10.1038/s41592-019-0427-6 10.1016/j.coph.2017.02.001 10.1109/TKDE.2009.191 10.1038/nmeth.4398 10.1021/acs.analchem.7b02566 10.1038/nature13319 |
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| References | Bruderer, Bernhardt, Gandhi, Xuan, Sondermann, Schmidt, Gomez-Varela, Reiter (bib3) 2017; 16 Bruderer, Bernhardt, Gandhi, Miladinovic, Cheng, Messner, Ehrenberger, Zanotelli, Butscheid, Escher (bib2) 2015; 14 Tsou, Avtonomov, Larsen, Tucholska, Choi, Gingras, Nesvizhskii (bib20) 2015; 12 Gessulat, Schmidt, Zolg, Samaras, Schnatbaum, Zerweck, Knaute, Rechenberger, Delanghe, Huhmer (bib4) 2019; 16 Ting, Egertson, Bollinger, Searle, Payne, Noble, MacCoss (bib17) 2017; 14 Picotti, Aebersold (bib13) 2012; 9 Ting, Egertson, Payne, Kim, MacLean, Kall, Aebersold, Smith, Noble, MacCoss (bib18) 2015; 14 Kusebauch, Campbell, Deutsch, Chu, Spicer, Brusniak, Slagel, Sun, Stevens, Grimes (bib8) 2016; 166 Ludwig, Gillet, Rosenberger, Amon, Collins, Aebersold (bib9) 2018; 14 Katritch, Cherezov, Stevens (bib7) 2013; 53 Ma, Chen, Wu, Yang, Bai, Shu, Li, Zhang, Jin, He (bib11) 2019; 47 Rosenberger, Bludau, Schmitt, Heusel, Hunter, Liu, MacCoss, MacLean, Nesvizhskii, Pedrioli (bib14) 2017; 14 Wilhelm, Schlegl, Hahne, Gholami, Lieberenz, Savitski, Ziegler, Butzmann, Gessulat, Marx (bib21) 2014; 509 Zolg, Wilhelm, Schnatbaum, Zerweck, Knaute, Delanghe, Bailey, Gessulat, Ehrlich, Weininger (bib24) 2017; 14 Ma, Ren, Yang, Ren, Yang, Liu (bib10) 2018; 90 Tiwary, Levy, Gutenbrunner, Salinas Soto, Palaniappan, Deming, Berndl, Brant, Cimermancic, Cox (bib19) 2019; 16 Zhou, Zeng, Chi, Luo, Liu, Zhan, He, Zhang (bib23) 2017; 89 Bekker-Jensen, Kelstrup, Batth, Larsen, Haldrup, Bramsen, Sorensen, Hoyer, Orntoft, Andersen (bib1) 2017; 4 Pan, Yang (bib12) 2010; 22 Schubert, Gillet, Collins, Navarro, Rosenberger, Wolski, Lam, Amodei, Mallick, MacLean (bib15) 2015; 10 Gillet, Navarro, Tate, Rost, Selevsek, Reiter, Bonner, Aebersold (bib5) 2012; 11 Huang, Todd, Thathiah (bib6) 2017; 32 Picotti (10.1016/j.isci.2020.100903_bib13) 2012; 9 Zhou (10.1016/j.isci.2020.100903_bib23) 2017; 89 Rosenberger (10.1016/j.isci.2020.100903_bib14) 2017; 14 Gessulat (10.1016/j.isci.2020.100903_bib4) 2019; 16 Ludwig (10.1016/j.isci.2020.100903_bib9) 2018; 14 Ting (10.1016/j.isci.2020.100903_bib18) 2015; 14 Gillet (10.1016/j.isci.2020.100903_bib5) 2012; 11 Katritch (10.1016/j.isci.2020.100903_bib7) 2013; 53 Ting (10.1016/j.isci.2020.100903_bib17) 2017; 14 Huang (10.1016/j.isci.2020.100903_bib6) 2017; 32 Kusebauch (10.1016/j.isci.2020.100903_bib8) 2016; 166 Ma (10.1016/j.isci.2020.100903_bib10) 2018; 90 Ma (10.1016/j.isci.2020.100903_bib11) 2019; 47 Bruderer (10.1016/j.isci.2020.100903_bib2) 2015; 14 Tsou (10.1016/j.isci.2020.100903_bib20) 2015; 12 Pan (10.1016/j.isci.2020.100903_bib12) 2010; 22 Bekker-Jensen (10.1016/j.isci.2020.100903_bib1) 2017; 4 Tiwary (10.1016/j.isci.2020.100903_bib19) 2019; 16 Wilhelm (10.1016/j.isci.2020.100903_bib21) 2014; 509 Zolg (10.1016/j.isci.2020.100903_bib24) 2017; 14 Schubert (10.1016/j.isci.2020.100903_bib15) 2015; 10 Bruderer (10.1016/j.isci.2020.100903_bib3) 2017; 16 |
| References_xml | – volume: 22 start-page: 1345 year: 2010 end-page: 1359 ident: bib12 article-title: A survey on transfer learning publication-title: IEEE Trans. Knowl. Data Eng. – volume: 32 start-page: 96 year: 2017 end-page: 110 ident: bib6 article-title: The role of GPCRs in neurodegenerative diseases: avenues for therapeutic intervention publication-title: Curr. Opin. Pharmacol. – volume: 89 start-page: 12690 year: 2017 end-page: 12697 ident: bib23 article-title: pDeep: predicting MS/MS spectra of peptides with deep learning publication-title: Anal. Chem. – volume: 14 start-page: e8126 year: 2018 ident: bib9 article-title: Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial publication-title: Mol. Syst. Biol. – volume: 11 year: 2012 ident: bib5 article-title: Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis publication-title: Mol. Cell. Proteomics – volume: 14 start-page: 903 year: 2017 end-page: 908 ident: bib17 article-title: PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data publication-title: Nat. Methods – volume: 14 start-page: 1400 year: 2015 end-page: 1410 ident: bib2 article-title: Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues publication-title: Mol. Cell. Proteomics – volume: 10 start-page: 426 year: 2015 end-page: 441 ident: bib15 article-title: Building high-quality assay libraries for targeted analysis of SWATH MS data publication-title: Nat. Protoc. – volume: 14 start-page: 2301 year: 2015 end-page: 2307 ident: bib18 article-title: Peptide-centric proteome analysis: an alternative strategy for the analysis of tandem mass spectrometry data publication-title: Mol. Cell. Proteomics – volume: 4 start-page: 587 year: 2017 end-page: 599.e4 ident: bib1 article-title: An optimized shotgun strategy for the rapid generation of comprehensive human proteomes publication-title: Cell Syst. – volume: 12 start-page: 258 year: 2015 end-page: 264 ident: bib20 article-title: DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics publication-title: Nat. Methods – volume: 16 start-page: 2296 year: 2017 end-page: 2309 ident: bib3 article-title: Optimization of experimental parameters in data-independent mass spectrometry significantly increases depth and reproducibility of results publication-title: Mol. Cell. Proteomics – volume: 16 start-page: 519 year: 2019 end-page: 525 ident: bib19 article-title: High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis publication-title: Nat. Methods – volume: 509 start-page: 582 year: 2014 end-page: 587 ident: bib21 article-title: Mass-spectrometry-based draft of the human proteome publication-title: Nature – volume: 53 start-page: 531 year: 2013 end-page: 556 ident: bib7 article-title: Structure-function of the G protein-coupled receptor superfamily publication-title: Annu. Rev. Pharmacol. Toxicol. – volume: 90 start-page: 10881 year: 2018 end-page: 10888 ident: bib10 article-title: Improved peptide retention time prediction in liquid chromatography through deep learning publication-title: Anal Chem. – volume: 14 start-page: 259 year: 2017 end-page: 262 ident: bib24 article-title: Building ProteomeTools based on a complete synthetic human proteome publication-title: Nat. Methods – volume: 9 start-page: 555 year: 2012 end-page: 566 ident: bib13 article-title: Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions publication-title: Nat. Methods – volume: 166 start-page: 766 year: 2016 end-page: 778 ident: bib8 article-title: Human SRMAtlas: a resource of targeted assays to quantify the complete human proteome publication-title: Cell – volume: 16 start-page: 509 year: 2019 end-page: 518 ident: bib4 article-title: Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning publication-title: Nat. Methods – volume: 14 start-page: 921 year: 2017 end-page: 927 ident: bib14 article-title: Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses publication-title: Nat. Methods – volume: 47 start-page: D1211 year: 2019 end-page: D1217 ident: bib11 article-title: iProX: an integrated proteome resource publication-title: Nucleic Acids Res. – volume: 16 start-page: 2296 year: 2017 ident: 10.1016/j.isci.2020.100903_bib3 article-title: Optimization of experimental parameters in data-independent mass spectrometry significantly increases depth and reproducibility of results publication-title: Mol. Cell. Proteomics doi: 10.1074/mcp.RA117.000314 – volume: 10 start-page: 426 year: 2015 ident: 10.1016/j.isci.2020.100903_bib15 article-title: Building high-quality assay libraries for targeted analysis of SWATH MS data publication-title: Nat. Protoc. doi: 10.1038/nprot.2015.015 – volume: 14 start-page: e8126 year: 2018 ident: 10.1016/j.isci.2020.100903_bib9 article-title: Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial publication-title: Mol. Syst. Biol. doi: 10.15252/msb.20178126 – volume: 53 start-page: 531 year: 2013 ident: 10.1016/j.isci.2020.100903_bib7 article-title: Structure-function of the G protein-coupled receptor superfamily publication-title: Annu. Rev. Pharmacol. Toxicol. doi: 10.1146/annurev-pharmtox-032112-135923 – volume: 14 start-page: 903 year: 2017 ident: 10.1016/j.isci.2020.100903_bib17 article-title: PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data publication-title: Nat. Methods doi: 10.1038/nmeth.4390 – volume: 11 year: 2012 ident: 10.1016/j.isci.2020.100903_bib5 article-title: Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis publication-title: Mol. Cell. Proteomics doi: 10.1074/mcp.O111.016717 – volume: 14 start-page: 1400 year: 2015 ident: 10.1016/j.isci.2020.100903_bib2 article-title: Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues publication-title: Mol. Cell. Proteomics doi: 10.1074/mcp.M114.044305 – volume: 14 start-page: 2301 year: 2015 ident: 10.1016/j.isci.2020.100903_bib18 article-title: Peptide-centric proteome analysis: an alternative strategy for the analysis of tandem mass spectrometry data publication-title: Mol. Cell. Proteomics doi: 10.1074/mcp.O114.047035 – volume: 47 start-page: D1211 year: 2019 ident: 10.1016/j.isci.2020.100903_bib11 article-title: iProX: an integrated proteome resource publication-title: Nucleic Acids Res. doi: 10.1093/nar/gky869 – volume: 4 start-page: 587 year: 2017 ident: 10.1016/j.isci.2020.100903_bib1 article-title: An optimized shotgun strategy for the rapid generation of comprehensive human proteomes publication-title: Cell Syst. doi: 10.1016/j.cels.2017.05.009 – volume: 12 start-page: 258 year: 2015 ident: 10.1016/j.isci.2020.100903_bib20 article-title: DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics publication-title: Nat. Methods – volume: 16 start-page: 509 year: 2019 ident: 10.1016/j.isci.2020.100903_bib4 article-title: Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning publication-title: Nat. Methods doi: 10.1038/s41592-019-0426-7 – volume: 166 start-page: 766 year: 2016 ident: 10.1016/j.isci.2020.100903_bib8 article-title: Human SRMAtlas: a resource of targeted assays to quantify the complete human proteome publication-title: Cell doi: 10.1016/j.cell.2016.06.041 – volume: 90 start-page: 10881 year: 2018 ident: 10.1016/j.isci.2020.100903_bib10 article-title: Improved peptide retention time prediction in liquid chromatography through deep learning publication-title: Anal Chem. doi: 10.1021/acs.analchem.8b02386 – volume: 9 start-page: 555 year: 2012 ident: 10.1016/j.isci.2020.100903_bib13 article-title: Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions publication-title: Nat. Methods doi: 10.1038/nmeth.2015 – volume: 14 start-page: 259 year: 2017 ident: 10.1016/j.isci.2020.100903_bib24 article-title: Building ProteomeTools based on a complete synthetic human proteome publication-title: Nat. Methods doi: 10.1038/nmeth.4153 – volume: 16 start-page: 519 year: 2019 ident: 10.1016/j.isci.2020.100903_bib19 article-title: High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis publication-title: Nat. Methods doi: 10.1038/s41592-019-0427-6 – volume: 32 start-page: 96 year: 2017 ident: 10.1016/j.isci.2020.100903_bib6 article-title: The role of GPCRs in neurodegenerative diseases: avenues for therapeutic intervention publication-title: Curr. Opin. Pharmacol. doi: 10.1016/j.coph.2017.02.001 – volume: 22 start-page: 1345 year: 2010 ident: 10.1016/j.isci.2020.100903_bib12 article-title: A survey on transfer learning publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2009.191 – volume: 14 start-page: 921 year: 2017 ident: 10.1016/j.isci.2020.100903_bib14 article-title: Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses publication-title: Nat. Methods doi: 10.1038/nmeth.4398 – volume: 89 start-page: 12690 year: 2017 ident: 10.1016/j.isci.2020.100903_bib23 article-title: pDeep: predicting MS/MS spectra of peptides with deep learning publication-title: Anal. Chem. doi: 10.1021/acs.analchem.7b02566 – volume: 509 start-page: 582 year: 2014 ident: 10.1016/j.isci.2020.100903_bib21 article-title: Mass-spectrometry-based draft of the human proteome publication-title: Nature doi: 10.1038/nature13319 |
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| Title | Hybrid Spectral Library Combining DIA-MS Data and a Targeted Virtual Library Substantially Deepens the Proteome Coverage |
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