Simplifying MS1 and MS2 spectra to achieve lower mass error, more dynamic range, and higher peptide identification confidence on the Bruker timsTOF Pro
For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The dat...
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| Published in | PloS one Vol. 17; no. 7; p. e0271025 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
07.07.2022
Public Library of Science (PLoS) |
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| Online Access | Get full text |
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| Abstract | For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample's complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide's monoisotopic mass, which is critical for the peptide's identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instrument's detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). Data are available via ProteomeXchange with identifier PXD030706. |
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| AbstractList | For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample's complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide's monoisotopic mass, which is critical for the peptide's identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instrument's detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). Data are available via ProteomeXchange with identifier PXD030706. For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample’s complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide’s monoisotopic mass, which is critical for the peptide’s identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instrument’s detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126 ). Data are available via ProteomeXchange with identifier PXD030706. The primary goal of mass spectrometry data processing pipelines in the proteomic analysis of complex biological samples is to identify peptides accurately and comprehensively with abundance across a broad dynamic range. It has been reported that detection of low-abundance peptides for early-disease biomarkers in complex fluids is limited by the sensitivity of biomarker discovery platforms [ 1 ], the dynamic range of plasma abundance, which can exceed ten orders of magnitude [ 2 ], and the fact that lower abundance proteins provide the most insight in disease processes [ 3 ]. As mass spectrometry hardware improves, the corresponding increase in amounts of data for analysis pushes legacy software analysis methods out of their designed specification. Additionally, experimentation with new algorithms to analyse raw data produced by instruments such as the Bruker timsTOF Pro has been hampered by the paucity of modular, open-source software pipelines written in languages accessible by the large community of data scientists. Here we present several algorithms for simplifying MS1 and MS2 spectra that are written in Python. We show that these algorithms are effective to help improve the quality and accuracy of peptide identifications. For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample's complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide's monoisotopic mass, which is critical for the peptide's identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instrument's detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). Data are available via ProteomeXchange with identifier PXD030706.For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample's complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide's monoisotopic mass, which is critical for the peptide's identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instrument's detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). Data are available via ProteomeXchange with identifier PXD030706. For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample’s complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide’s monoisotopic mass, which is critical for the peptide’s identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instrument’s detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126 ). Data are available via ProteomeXchange with identifier PXD030706. |
| Audience | Academic |
| Author | Webb, Andrew I Dagley, Laura F Infusini, Giuseppe Spall, Sukhdeep K Wilding-McBride, Daryl |
| AuthorAffiliation | 3 Mass Dynamics, Melbourne, Victoria, Australia 2 Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia Aarhus University, DENMARK 1 The Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia |
| AuthorAffiliation_xml | – name: Aarhus University, DENMARK – name: 2 Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia – name: 3 Mass Dynamics, Melbourne, Victoria, Australia – name: 1 The Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia |
| Author_xml | – sequence: 1 givenname: Daryl orcidid: 0000-0003-3796-1297 surname: Wilding-McBride fullname: Wilding-McBride, Daryl organization: Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia – sequence: 2 givenname: Laura F orcidid: 0000-0003-4171-3712 surname: Dagley fullname: Dagley, Laura F organization: Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia – sequence: 3 givenname: Sukhdeep K surname: Spall fullname: Spall, Sukhdeep K organization: Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia – sequence: 4 givenname: Giuseppe orcidid: 0000-0001-5425-1698 surname: Infusini fullname: Infusini, Giuseppe organization: Mass Dynamics, Melbourne, Victoria, Australia – sequence: 5 givenname: Andrew I orcidid: 0000-0001-5061-6995 surname: Webb fullname: Webb, Andrew I organization: Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35797390$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1080_03639045_2024_2327475 |
| Cites_doi | 10.1007/s13361-017-1741-9 10.1021/pr1003856 10.1021/cr990076h 10.1038/nmeth.4256 10.1074/mcp.TIR120.002048 10.1007/s13361-017-1801-1 10.1093/nar/gky1106 10.1038/nmeth.1322 10.1074/mcp.M500230-MCP200 10.1021/ac303439m 10.1007/s00726-012-1289-8 10.1021/pr2003177 10.1016/j.chroma.2008.03.033 10.1016/j.jasms.2008.01.009 10.1074/mcp.TIR119.001720 10.1142/9789812701626_0023 10.1038/nbt.3685 10.1021/ac60214a047 10.1021/ac203255e 10.1007/s13361-014-0903-2 10.1080/14789450.2018.1450631 10.1074/mcp.TIR118.000900 10.1074/mcp.R200007-MCP200 10.1021/pr400034z 10.1093/bioinformatics/btl355 10.1007/s00216-007-1486-6 10.1016/j.ijms.2017.11.003 10.1093/jxb/eri068 10.1016/j.mcpro.2021.100149 10.1152/ajplung.00044.2008 10.1021/ac050980b 10.1186/1471-2105-9-504 10.1002/jms.2953 10.1021/pr100291q |
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| Copyright | COPYRIGHT 2022 Public Library of Science 2022 Wilding-McBride et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 Wilding-McBride et al 2022 Wilding-McBride et al |
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| DOI | 10.1371/journal.pone.0271025 |
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| References | P. Du (pone.0271025.ref019) 2006; 22 J. L. Wiza (pone.0271025.ref026) 1979 A. T. Kong (pone.0271025.ref007) 2017; 14 F. Meier (pone.0271025.ref005) 2018; 17 L. H. Negri (pone.0271025.ref025) pone.0271025.ref032 S. Pourshahian (pone.0271025.ref037) 2017; 28 I. Mitra (pone.0271025.ref039) 2012; 84 J. C. Silva (pone.0271025.ref033) 2006; 5 K. M. Åberg (pone.0271025.ref016) 2008; 1192 S. Houel (pone.0271025.ref012) 2010; 9 Y. Perez-Riverol (pone.0271025.ref048) 2019; 47 S. Cappadona (pone.0271025.ref004) 2012; 43 S. Willems (pone.0271025.ref049) 2021; 20 M. L. Toumi (pone.0271025.ref040) 2010; 9 P. Navarro (pone.0271025.ref042) 2016; 34 J. R. Wiśniewski (pone.0271025.ref043) 2009; 6 pone.0271025.ref047 pone.0271025.ref046 pone.0271025.ref045 pone.0271025.ref044 F. Yu (pone.0271025.ref008) 2020; 19 M. T. Strauss (pone.0271025.ref010) 2021 B. Kim (pone.0271025.ref001) 2018; 15 P. Schliekelman (pone.0271025.ref013) 2014; 13 N. Prianichnikov (pone.0271025.ref006) 2020; 19 J. Klein (pone.0271025.ref023) 2021 R. Aebersold (pone.0271025.ref034) 2001; 101 L. Sleno (pone.0271025.ref035) 2012; 47 J. Klein (pone.0271025.ref021) M. Bantscheff (pone.0271025.ref029) 2007; 389 D. A. Abdrakhimov (pone.0271025.ref009) 2021 pone.0271025.ref011 A. Ipsen (pone.0271025.ref014) 2017 M. Mann (pone.0271025.ref038) 1995 R. E. Gerszten (pone.0271025.ref003) 2008; 295 D. Valkenborg (pone.0271025.ref031) 2008; 19 E. Lange (pone.0271025.ref018) 2005 Oliver Raether (pone.0271025.ref027) 2021 R. Tautenhahn (pone.0271025.ref017) 2008; 9 R. Liu (pone.0271025.ref028) 2014; 25 A. V. Nefedov (pone.0271025.ref041) 2011; 10 P. Dittwald (pone.0271025.ref022) 2013; 85 K. K. Murray (pone.0271025.ref036) 2017; 28 J. Smedsgaard (pone.0271025.ref020) 2005; 56 N. L. Anderson (pone.0271025.ref002) 2002; 1 R. Stolt (pone.0271025.ref015) 2006; 78 Savitzky Abraham (pone.0271025.ref024) 1964; 36 A. Bilbao (pone.0271025.ref030) 2018; 427 |
| References_xml | – volume: 28 start-page: 1836 issue: 9 year: 2017 ident: pone.0271025.ref037 article-title: Mass Defect from Nuclear Physics to Mass Spectral Analysis publication-title: J. Am. Soc. Mass Spectrom. doi: 10.1007/s13361-017-1741-9 contributor: fullname: S. Pourshahian – volume: 9 start-page: 4152 issue: 8 year: 2010 ident: pone.0271025.ref012 article-title: Quantifying the impact of chimera MS/MS spectra on peptide identification in large scale proteomics studies publication-title: J. Proteome Res. doi: 10.1021/pr1003856 contributor: fullname: S. Houel – ident: pone.0271025.ref046 – volume: 101 start-page: 269 issue: 2 year: 2001 ident: pone.0271025.ref034 article-title: Mass Spectrometry in Proteomics publication-title: Chem. Rev. doi: 10.1021/cr990076h contributor: fullname: R. Aebersold – volume: 14 start-page: 513 issue: 5 year: 2017 ident: pone.0271025.ref007 article-title: MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics publication-title: Nat. Methods doi: 10.1038/nmeth.4256 contributor: fullname: A. T. Kong – volume: 19 start-page: 1575 issue: 9 year: 2020 ident: pone.0271025.ref008 article-title: Fast Quantitative Analysis of timsTOF PASEF Data with MSFragger and IonQuant publication-title: Mol. Cell. Proteomics doi: 10.1074/mcp.TIR120.002048 contributor: fullname: F. Yu – ident: pone.0271025.ref025 publication-title: PeakUtils: Peak detection utilities for 1D data contributor: fullname: L. H. Negri – volume: 28 start-page: 2724 issue: 12 year: 2017 ident: pone.0271025.ref036 article-title: Comment on: ‘Nominal Mass?’ by Athula B. Attygalle and Julius Pavlov, J. Am. Soc. Mass Spectrom. 28, 1737–1738 (2017) publication-title: J. Am. Soc. Mass Spectrom. doi: 10.1007/s13361-017-1801-1 contributor: fullname: K. K. Murray – volume: 47 start-page: D442 issue: D1 year: 2019 ident: pone.0271025.ref048 article-title: The PRIDE database and related tools and resources in 2019: improving support for quantification data publication-title: Nucleic Acids Res. doi: 10.1093/nar/gky1106 contributor: fullname: Y. Perez-Riverol – year: 2021 ident: pone.0271025.ref009 article-title: Biosaur: An open‐source Python software for liquid chromatography–mass spectrometry peptide feature detection with ion mobility support publication-title: Rapid Commun. Mass Spectrom. contributor: fullname: D. A. Abdrakhimov – volume: 6 start-page: 359 issue: 5 year: 2009 ident: pone.0271025.ref043 article-title: Universal sample preparation method for proteome analysis publication-title: Nat. Methods doi: 10.1038/nmeth.1322 contributor: fullname: J. R. Wiśniewski – volume: 5 start-page: 144 issue: 1 year: 2006 ident: pone.0271025.ref033 article-title: Absolute Quantification of Proteins by LCMSE publication-title: Mol. Cell. Proteomics doi: 10.1074/mcp.M500230-MCP200 contributor: fullname: J. C. Silva – year: 2021 ident: pone.0271025.ref023 publication-title: mobiusklein/brainpy contributor: fullname: J. Klein – start-page: 17 issue: 162 year: 1979 ident: pone.0271025.ref026 article-title: Microchannel Plate Detectors publication-title: Nucl. Instrum. Methods contributor: fullname: J. L. Wiza – volume: 85 start-page: 1991 issue: 4 year: 2013 ident: pone.0271025.ref022 article-title: BRAIN: A Universal Tool for High-Throughput Calculations of the Isotopic Distribution for Mass Spectrometry publication-title: Anal. Chem. doi: 10.1021/ac303439m contributor: fullname: P. Dittwald – volume: 43 start-page: 1087 issue: 3 year: 2012 ident: pone.0271025.ref004 article-title: Current challenges in software solutions for mass spectrometry-based quantitative proteomics publication-title: Amino Acids doi: 10.1007/s00726-012-1289-8 contributor: fullname: S. Cappadona – volume: 10 start-page: 4150 issue: 9 year: 2011 ident: pone.0271025.ref041 article-title: Examining Troughs in the Mass Distribution of All Theoretically Possible Tryptic Peptides publication-title: J. Proteome Res. doi: 10.1021/pr2003177 contributor: fullname: A. V. Nefedov – volume: 1192 start-page: 139 issue: 1 year: 2008 ident: pone.0271025.ref016 article-title: Feature detection and alignment of hyphenated chromatographic–mass spectrometric data: Extraction of pure ion chromatograms using Kalman tracking publication-title: J. Chromatogr. A doi: 10.1016/j.chroma.2008.03.033 contributor: fullname: K. M. Åberg – volume: 19 start-page: 703 issue: 5 year: 2008 ident: pone.0271025.ref031 article-title: A Model-Based Method for the Prediction of the Isotopic Distribution of Peptides publication-title: J. Am. Soc. Mass Spectrom. doi: 10.1016/j.jasms.2008.01.009 contributor: fullname: D. Valkenborg – volume-title: Proteomics Dynamic Range Standard Set—UPS2 Product Information ident: pone.0271025.ref032 – volume: 19 start-page: 1058 issue: 6 year: 2020 ident: pone.0271025.ref006 article-title: MaxQuant Software for Ion Mobility Enhanced Shotgun Proteomics * publication-title: Mol. Cell. Proteomics doi: 10.1074/mcp.TIR119.001720 contributor: fullname: N. Prianichnikov – ident: pone.0271025.ref045 – start-page: 243 year: 2005 ident: pone.0271025.ref018 article-title: HIGH-ACCURACY PEAK PICKING OF PROTEOMICS DATA USING WAVELET TECHNIQUES publication-title: Biocomputing 2006 doi: 10.1142/9789812701626_0023 contributor: fullname: E. Lange – volume: 34 start-page: 1130 issue: 11 year: 2016 ident: pone.0271025.ref042 article-title: A multicenter study benchmarks software tools for label-free proteome quantification publication-title: Nat. Biotechnol. doi: 10.1038/nbt.3685 contributor: fullname: P. Navarro – year: 2021 ident: pone.0271025.ref027 article-title: timsTOF detector contributor: fullname: Oliver Raether – volume: 36 start-page: 1627 issue: 8 year: 1964 ident: pone.0271025.ref024 article-title: Smoothing and Differentiation of Data by Simplified Least Squares Procedures. publication-title: Anal. Chem. doi: 10.1021/ac60214a047 contributor: fullname: Savitzky Abraham – volume: 84 start-page: 3026 issue: 6 year: 2012 ident: pone.0271025.ref039 article-title: Improved Mass Defect Model for Theoretical Tryptic Peptides publication-title: Anal. Chem. doi: 10.1021/ac203255e contributor: fullname: I. Mitra – volume: 25 start-page: 1374 issue: 8 year: 2014 ident: pone.0271025.ref028 article-title: Detection of large ions in time-of-flight mass spectrometry: effects of ion mass and acceleration voltage on microchannel plate detector response publication-title: J. Am. Soc. Mass Spectrom. doi: 10.1007/s13361-014-0903-2 contributor: fullname: R. Liu – volume: 15 start-page: 353 issue: 4 year: 2018 ident: pone.0271025.ref001 article-title: Affinity Enrichment for MS: Improving the yield of low abundance biomarkers publication-title: Expert Rev. Proteomics doi: 10.1080/14789450.2018.1450631 contributor: fullname: B. Kim – volume: 17 start-page: 2534 issue: 12 year: 2018 ident: pone.0271025.ref005 article-title: Online Parallel Accumulation–Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer publication-title: Mol. Cell. Proteomics MCP doi: 10.1074/mcp.TIR118.000900 contributor: fullname: F. Meier – volume: 1 start-page: 845 issue: 11 year: 2002 ident: pone.0271025.ref002 article-title: The Human Plasma Proteome: History, Character, and Diagnostic Prospects * publication-title: Mol. Cell. Proteomics doi: 10.1074/mcp.R200007-MCP200 contributor: fullname: N. L. Anderson – volume: 13 start-page: 348 issue: 2 year: 2014 ident: pone.0271025.ref013 article-title: Quantifying the Effect of Competition for Detection between Coeluting Peptides on Detection Probabilities in Mass-Spectrometry-Based Proteomics publication-title: J. Proteome Res. doi: 10.1021/pr400034z contributor: fullname: P. Schliekelman – ident: pone.0271025.ref021 article-title: ms_deisotope documentation contributor: fullname: J. Klein – volume: 22 start-page: 2059 issue: 17 year: 2006 ident: pone.0271025.ref019 article-title: Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl355 contributor: fullname: P. Du – volume: 389 start-page: 1017 issue: 4 year: 2007 ident: pone.0271025.ref029 article-title: Quantitative mass spectrometry in proteomics: a critical review publication-title: Anal. Bioanal. Chem. doi: 10.1007/s00216-007-1486-6 contributor: fullname: M. Bantscheff – ident: pone.0271025.ref044 – volume: 427 start-page: 91 year: 2018 ident: pone.0271025.ref030 article-title: An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies publication-title: Int. J. Mass Spectrom. doi: 10.1016/j.ijms.2017.11.003 contributor: fullname: A. Bilbao – volume: 56 start-page: 273 issue: 410 year: 2005 ident: pone.0271025.ref020 article-title: Metabolite profiling of fungi and yeast: from phenotype to metabolome by MS and informatics publication-title: J. Exp. Bot. doi: 10.1093/jxb/eri068 contributor: fullname: J. Smedsgaard – year: 2021 ident: pone.0271025.ref010 article-title: AlphaPept, a modern and open framework for MS-based proteomics publication-title: Bioinformatics contributor: fullname: M. T. Strauss – volume: 20 start-page: 100149 year: 2021 ident: pone.0271025.ref049 article-title: AlphaTims: Indexing Trapped Ion Mobility Spectrometry–TOF Data for Fast and Easy Accession and Visualization publication-title: Mol. Cell. Proteomics doi: 10.1016/j.mcpro.2021.100149 contributor: fullname: S. Willems – ident: pone.0271025.ref047 – volume: 295 start-page: L16 issue: 1 year: 2008 ident: pone.0271025.ref003 article-title: Challenges in translating plasma proteomics from bench to bedside: update from the NHLBI Clinical Proteomics Programs publication-title: Am. J. Physiol.-Lung Cell. Mol. Physiol. doi: 10.1152/ajplung.00044.2008 contributor: fullname: R. E. Gerszten – year: 1995 ident: pone.0271025.ref038 article-title: Useful Tables Of Possible And Probable Peptide Masses contributor: fullname: M. Mann – volume: 78 start-page: 975 issue: 4 year: 2006 ident: pone.0271025.ref015 article-title: Second-Order Peak Detection for Multicomponent High-Resolution LC/MS Data publication-title: Anal. Chem. doi: 10.1021/ac050980b contributor: fullname: R. Stolt – start-page: 10 year: 2017 ident: pone.0271025.ref014 article-title: Derivation of the Statistical Distribution of the Mass Peak Centroids of Mass Spectrometers Employing Analog-to-Digital Converters and Electron Multipliers publication-title: Anal Chem contributor: fullname: A. Ipsen – ident: pone.0271025.ref011 – volume: 9 start-page: 504 issue: 1 year: 2008 ident: pone.0271025.ref017 article-title: Highly sensitive feature detection for high resolution LC/MS publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-9-504 contributor: fullname: R. Tautenhahn – volume: 47 start-page: 226 issue: 2 year: 2012 ident: pone.0271025.ref035 article-title: The use of mass defect in modern mass spectrometry: Mass defect in mass spectrometry publication-title: J. Mass Spectrom. doi: 10.1002/jms.2953 contributor: fullname: L. Sleno – volume: 9 start-page: 5492 issue: 10 year: 2010 ident: pone.0271025.ref040 article-title: Improving Mass Defect Filters for Human Proteins publication-title: J. Proteome Res. doi: 10.1021/pr100291q contributor: fullname: M. L. Toumi |
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| SubjectTerms | Algorithms Analysis Biological properties Biological samples Biology and Life Sciences Biomarkers Chromatography, Liquid Clustering Complexity Computer and Information Sciences Data processing Dynamic range Electrical noise Engineering and Technology Experimentation Feature extraction Ionic mobility Isotopes Mass spectrometers Mass Spectrometry Mobility Noise Open source software Peptides Peptides - chemistry Physical Sciences Proteomics Public domain Research and Analysis Methods Retention Retention time Science Policy Scientific imaging Signal processing Software Spectra Spectrometers |
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| Title | Simplifying MS1 and MS2 spectra to achieve lower mass error, more dynamic range, and higher peptide identification confidence on the Bruker timsTOF Pro |
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