Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 1,002

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Data‐independent acquisition‐based SWATH ‐ MS for quantitative proteomics: a tutorial

Open access
Ludwig, Christina, Gillet, Ludovic, Rosenberger, George, Amon, Sabine, Collins, Ben C, Aebersold, Ruedi·2018·Molecular Systems Biology
DOI 10.15252/msb.20178126
Open this paper in CoChat — free account

Abstract

Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH-MS is a specific variant of data-independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH-MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH-MS data, a strategy based on peptide-centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH-MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH-MS data using peptide-centric scoring. Furthermore, concepts on how to improve SWATH-MS data acquisition, potential trade-offs of parameter settings and alternative data analysis strategies are discussed.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.15252/msb.20178126

Cites

31 of 31 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 31 references

Research works printed but not linked: 100

  1. Ahrne E, Glatter T, Vigano C, Schubert C, Nigg EA, Schmidt A (2016) Evaluation and improvement of quantification accuracy in isobaric mass tag‐based protein quantification experiments. J Proteome Res 15: 2537–2547 · DOI · Paper page
  2. Bereman MS, Beri J, Sharma V, Nathe C, Eckels J, MacLean B, MacCoss MJ (2016) An automated pipeline to monitor system performance in liquid chromatography‐tandem mass spectrometry proteomic experiments. J Proteome Res 15: 4763–4769 · DOI
  3. Bilbao A, Varesio E, Luban J, Strambio‐De‐Castillia C, Hopfgartner G, Muller M, Lisacek F (2015) Processing strategies and software solutions for data‐independent acquisition in mass spectrometry. Proteomics 15: 964–980 · DOI · Paper page
  4. Blainey P, Krzywinski M, Altman N (2014) Points of significance: replication. Nat Methods 11: 879–880 · DOI · Paper page
  5. Bruderer R, Bernhardt O, Gandhi T, Reiter L (2016) High precision iRT retention time prediction in the targeted analysis of data‐independent acquisition and its impact on identification and quantitation. Proteomics 16: 2246–2256 · DOI
  6. Bruderer R, Bernhardt OM, Gandhi T, Xuan Y, Sondermann J, Schmidt M, Gomez‐Varela D, Reiter L (2017) Optimization of experimental parameters in data‐independent mass spectrometry significantly increases depth and reproducibility of results. Mol Cell Proteomics 16: 2296–2309 · DOI · Paper page
  7. Chang CY, Picotti P, Huttenhain R, Heinzelmann‐Schwarz V, Jovanovic M, Aebersold R, Vitek O (2012) Protein significance analysis in selected reaction monitoring (SRM) measurements. Mol Cell Proteomics 11: M111.014662 · DOI
  8. Chapman JD, Goodlett DR, Masselon CD (2014) Multiplexed and data‐independent tandem mass spectrometry for global proteome profiling. Mass Spectrom Rev 33: 452–470 · DOI · Paper page
  9. Chiva C, Olivella R, Borras E, Espadas G, Pastor O, Sole A, Sabido E (2018) QCloud: a cloud‐based quality control system for mass spectrometry‐based proteomics laboratories. PLoS ONE 13: e0189209 · DOI · Paper page
  10. Choi M, Chang CY, Clough T, Broudy D, Killeen T, MacLean B, Vitek O (2014) MSstats: an R package for statistical analysis of quantitative mass spectrometry‐based proteomic experiments. Bioinformatics 30: 2524–2526 · DOI · Paper page
  11. Craig R, Cortens JP, Beavis RC (2004) Open source system for analyzing, validating, and storing protein identification data. J Proteome Res 3: 1234–1242 · DOI · Paper page
  12. Deutsch EW, Lam H, Aebersold R (2008) PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep 9: 429–434 · DOI · Paper page
  13. Deutsch EW, Mendoza L, Shteynberg D, Farrah T, Lam H, Tasman N, Sun Z, Nilsson E, Pratt B, Prazen B, Eng JK, Martin DB, Nesvizhskii AI, Aebersold R (2010) A guided tour of the trans‐proteomic pipeline. Proteomics 10: 1150–1159 · DOI · Paper page
  14. Egertson JD, Kuehn A, Merrihew GE, Bateman NW, MacLean BX, Ting YS, Canterbury JD, Marsh DM, Kellmann M, Zabrouskov V, Wu CC, MacCoss MJ (2013) Multiplexed MS/MS for improved data‐independent acquisition. Nat Methods 10: 744–746 · DOI · Paper page
  15. Egertson JD, MacLean B, Johnson R, Xuan Y, MacCoss MJ (2015) Multiplexed peptide analysis using data‐independent acquisition and skyline. Nat Protoc 10: 887–903 · DOI · Paper page
  16. Escher C, Reiter L, MacLean B, Ossola R, Herzog F, Chilton J, MacCoss MJ, Rinner O (2012) Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 12: 1111–1121 · DOI · Paper page
  17. Frejno M, Zenezini Chiozzi R, Wilhelm M, Koch H, Zheng R, Klaeger S, Ruprecht B, Meng C, Kramer K, Jarzab A, Heinzlmeir S, Johnstone E, Domingo E, Kerr D, Jesinghaus M, Slotta‐Huspenina J, Weichert W, Knapp S, Feller SM, Kuster B (2017) Pharmacoproteomic characterisation of human colon and rectal cancer. Mol Syst Biol 13: 951 · DOI
  18. Geiger T, Cox J, Mann M (2010) Proteomics on an Orbitrap benchtop mass spectrometer using all‐ion fragmentation. Mol Cell Proteomics 9: 2252–2261 · DOI · Paper page
  19. de Graaf EL, Altelaar AF, van Breukelen B, Mohammed S, Heck AJ (2011) Improving SRM assay development: a global comparison between triple quadrupole, ion trap, and higher energy CID peptide fragmentation spectra. J Proteome Res 10: 4334–4341 · DOI
  20. Karlsson C, Malmstrom L, Aebersold R, Malmstrom J (2012) Proteome‐wide selected reaction monitoring assays for the human pathogen Streptococcus pyogenes. Nat Commun 3: 1301 · DOI
  21. Keam SP, Gulati T, Gamell C, Caramia F, Huang C, Schittenhelm RB, Kleifeld O, Neeson PJ, Haupt Y, Williams SG (2018) Exploring the oncoproteomic response of human prostate cancer to therapeutic radiation using data‐independent acquisition (DIA) mass spectrometry. Prostate 78: 563–575 · DOI
  22. Keller A, Bader SL, Shteynberg D, Hood L, Moritz RL (2015) Automated validation of results and removal of fragment ion interferences in targeted analysis of data‐independent acquisition mass spectrometry (MS) using SWATHProphet. Mol Cell Proteomics 14: 1411–1418 · DOI
  23. Keller A, Bader SL, Kusebauch U, Shteynberg D, Hood L, Moritz RL (2016) Opening a SWATH window on posttranslational modifications: automated pursuit of modified peptides. Mol Cell Proteomics 15: 1151–1163 · DOI
  24. Kelstrup CD, Bekker‐Jensen DB, Arrey TN, Hogrebe A, Harder A, Olsen JV (2018) Performance evaluation of the Q exactive HF‐X for shotgun proteomics. J Proteome Res 17: 727–738 · DOI · Paper page
  25. Kong AT, Leprevost FV, Avtonomov DM, Mellacheruvu D, Nesvizhskii AI (2017) MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry‐based proteomics. Nat Methods 14: 513–520 · DOI · Paper page
  26. Krautkramer KA, Reiter L, Denu JM, Dowell JA (2015) Quantification of SAHA‐dependent changes in histone modifications using data‐independent acquisition mass spectrometry. J Proteome Res 14: 3252–3262 · DOI
  27. Krzywinski M, Altman N (2013) Points of significance: power and sample size. Nat Methods 10: 1139–1140 · DOI · Paper page
  28. Krzywinski M, Altman N (2014a) Points of significance: analysis of variance and blocking. Nat Methods 11: 699–700 · DOI
  29. Krzywinski M, Altman N (2014b) Points of view: designing comparative experiments. Nat Methods 11: 597–598 · DOI
  30. Kulkarni S, Koller A, Mani KM, Wen R, Alfieri A, Saha S, Wang J, Patel P, Bandeira N, Guha C, Chen EI (2016) Identifying urinary and serum exosome biomarkers for radiation exposure using a data dependent acquisition and SWATH‐MS combined workflow. Int J Radiat Oncol Biol Phys 96: 566–577 · DOI
  31. Kusebauch U, Campbell DS, Deutsch EW, Chu CS, Spicer DA, Brusniak MY, Slagel J, Sun Z, Stevens J, Grimes B, Shteynberg D, Hoopmann MR, Blattmann P, Ratushny AV, Rinner O, Picotti P, Carapito C, Huang CY, Kapousouz M, Lam H et al (2016) Human SRMAtlas: a resource of targeted assays to quantify the complete human proteome. Cell 166: 766–778 · DOI · Paper page
  32. Kuster B, Schirle M, Mallick P, Aebersold R (2005) Scoring proteomes with proteotypic peptide probes. Nat Rev Mol Cell Biol 6: 577–583 · DOI · Paper page
  33. Lange V, Picotti P, Domon B, Aebersold R (2008) Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol 4: 222 · DOI · Paper page
  34. Lawrence RT, Perez EM, Hernandez D, Miller CP, Haas KM, Irie HY, Lee SI, Blau CA, Villen J (2015) The proteomic landscape of triple‐negative breast cancer. Cell Rep 11: 630–644 · DOI · Paper page
  35. Lawrence RT, Searle BC, Llovet A, Villen J (2016) Plug‐and‐play analysis of the human phosphoproteome by targeted high‐resolution mass spectrometry. Nat Methods 13: 431–434 · DOI
  36. Li Y, Zhong CQ, Xu X, Cai S, Wu X, Zhang Y, Chen J, Shi J, Lin S, Han J (2015) Group‐DIA: analyzing multiple data‐independent acquisition mass spectrometry data files. Nat Methods 12: 1105–1106 · DOI
  37. Litichevskiy L, Peckner R, Abelin JG, Asiedu JK, Creech AL, Davis JF, Davison D, Dunning CM, Egertson JD, Egri S, Gould J, Ko T, Johnson SA, Lahr DL, Lam D, Liu Z, Lyons NJ, Lu X, MacLean BX, Mungenast AE et al (2018) A library of phosphoproteomic and chromatin signatures for characterizing cellular responses to drug perturbations. Cell Syst 6: 424–443.e7 · DOI
  38. Liu Y, Huttenhain R, Surinova S, Gillet LC, Mouritsen J, Brunner R, Navarro P, Aebersold R (2013) Quantitative measurements of N‐linked glycoproteins in human plasma by SWATH‐MS. Proteomics 13: 1247–1256 · DOI · Paper page
  39. Liu Y, Chen J, Sethi A, Li QK, Chen L, Collins B, Gillet LC, Wollscheid B, Zhang H, Aebersold R (2014) Glycoproteomic analysis of prostate cancer tissues by SWATH mass spectrometry discovers N‐acylethanolamine acid amidase and protein tyrosine kinase 7 as signatures for tumor aggressiveness. Mol Cell Proteomics 13: 1753–1768 · DOI · Paper page
  40. Ludwig C, Claassen M, Schmidt A, Aebersold R (2012) Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry. Mol Cell Proteomics 11: M111.013987 · DOI
  41. Mallick P, Schirle M, Chen SS, Flory MR, Lee H, Martin D, Ranish J, Raught B, Schmitt R, Werner T, Kuster B, Aebersold R (2007) Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol 25: 125–131 · DOI · Paper page
  42. Martens L, Hermjakob H, Jones P, Adamski M, Taylor C, States D, Gevaert K, Vandekerckhove J, Apweiler R (2005) PRIDE: the proteomics identifications database. Proteomics 5: 3537–3545 · DOI · Paper page
  43. Masselon C, Anderson GA, Harkewicz R, Bruce JE, Pasa‐Tolic L, Smith RD (2000) Accurate mass multiplexed tandem mass spectrometry for high‐throughput polypeptide identification from mixtures. Anal Chem 72: 1918–1924 · DOI
  44. Matsumoto M, Matsuzaki F, Oshikawa K, Goshima N, Mori M, Kawamura Y, Ogawa K, Fukuda E, Nakatsumi H, Natsume T, Fukui K, Horimoto K, Nagashima T, Funayama R, Nakayama K, Nakayama KI (2017) A large‐scale targeted proteomics assay resource based on an in vitro human proteome. Nat Methods 14: 251–258 · DOI
  45. Matthews DE, Hayes JM (1976) Systematic errors in gas chromatography‐mass spectrometry isotope ratio measurements. Anal Chem 48: 1375–1382 · DOI
  46. Moseley MA, Hughes CJ, Juvvadi PR, Soderblom EJ, Lennon S, Perkins SR, Thompson JW, Steinbach WJ, Geromanos SJ, Wildgoose J, Langridge JI, Richardson K, Vissers JPC (2018) Scanning quadrupole data‐independent acquisition, part A: qualitative and quantitative characterization. J Proteome Res 17: 770–779 · DOI
  47. Mueller LN, Rinner O, Schmidt A, Letarte S, Bodenmiller B, Brusniak MY, Vitek O, Aebersold R, Muller M (2007) SuperHirn ‐ a novel tool for high resolution LC‐MS‐based peptide/protein profiling. Proteomics 7: 3470–3480 · DOI · Paper page
  48. Muller DB, Schubert OT, Rost H, Aebersold R, Vorholt JA (2016) Systems‐level proteomics of two ubiquitous leaf commensals reveals complementary adaptive traits for phyllosphere colonization. Mol Cell Proteomics 15: 3256–3269 · DOI
  49. Muntel J, Xuan Y, Berger ST, Reiter L, Bachur R, Kentsis A, Steen H (2015) Advancing urinary protein biomarker discovery by data‐independent acquisition on a quadrupole‐orbitrap mass spectrometer. J Proteome Res 14: 4752–4762 · DOI
  50. Na S, Paek E (2015) Software eyes for protein post‐translational modifications. Mass Spectrom Rev 34: 133–147 · DOI
  51. Norbeck AD, Monroe ME, Adkins JN, Anderson KK, Daly DS, Smith RD (2005) The utility of accurate mass and LC elution time information in the analysis of complex proteomes. J Am Soc Mass Spectrom 16: 1239–1249 · DOI
  52. O'Brien JJ, O'Connell JD, Paulo JA, Thakurta S, Rose CM, Weekes MP, Huttlin EL, Gygi SP (2018) Compositional proteomics: effects of spatial constraints on protein quantification utilizing isobaric tags. J Proteome Res 17: 590–599 · DOI
  53. Okada H, Ebhardt HA, Vonesch SC, Aebersold R, Hafen E (2016) Proteome‐wide association studies identify biochemical modules associated with a wing‐size phenotype in Drosophila melanogaster. Nat Commun 7: 12649 · DOI
  54. Ortea I, Rodriguez‐Ariza A, Chicano‐Galvez E, Arenas Vacas MS, Jurado Gamez B (2016) Discovery of potential protein biomarkers of lung adenocarcinoma in bronchoalveolar lavage fluid by SWATH MS data‐independent acquisition and targeted data extraction. J Proteomics 138: 106–114 · DOI
  55. Panchaud A, Scherl A, Shaffer SA, von Haller PD, Kulasekara HD, Miller SI, Goodlett DR (2009) Precursor acquisition independent from ion count: how to dive deeper into the proteomics ocean. Anal Chem 81: 6481–6488 · DOI · Paper page
  56. Panse C, Trachsel C, Grossmann J, Schlapbach R (2015) specL–an R/Bioconductor package to prepare peptide spectrum matches for use in targeted proteomics. Bioinformatics 31: 2228–2231 · DOI
  57. Parker BL, Yang G, Humphrey SJ, Chaudhuri R, Ma X, Peterman S, James DE (2015a) Targeted phosphoproteomics of insulin signaling using data‐independent acquisition mass spectrometry. Sci Signal 8: rs6 · DOI
  58. Parker SJ, Rost H, Rosenberger G, Collins BC, Malmstrom L, Amodei D, Venkatraman V, Raedschelders K, Van Eyk JE, Aebersold R (2015b) Identification of a set of conserved eukaryotic internal retention time standards for data‐independent acquisition mass spectrometry. Mol Cell Proteomics 14: 2800–2813 · DOI
  59. Picotti P, Lam H, Campbell D, Deutsch EW, Mirzaei H, Ranish J, Domon B, Aebersold R (2008) A database of mass spectrometric assays for the yeast proteome. Nat Methods 5: 913–914 · DOI · Paper page
  60. Picotti P, Rinner O, Stallmach R, Dautel F, Farrah T, Domon B, Wenschuh H, Aebersold R (2010) High‐throughput generation of selected reaction‐monitoring assays for proteins and proteomes. Nat Methods 7: 43–46 · DOI · Paper page
  61. Prakash A, Mallick P, Whiteaker J, Zhang H, Paulovich A, Flory M, Lee H, Aebersold R, Schwikowski B (2006) Signal maps for mass spectrometry‐based comparative proteomics. Mol Cell Proteomics 5: 423–432 · DOI
  62. Purvine S, Eppel JT, Yi EC, Goodlett DR (2003) Shotgun collision‐induced dissociation of peptides using a time of flight mass analyzer. Proteomics 3: 847–850 · DOI · Paper page
  63. Rardin MJ, Schilling B, Cheng LY, MacLean BX, Sorensen DJ, Sahu AK, MacCoss MJ, Vitek O, Gibson BW (2015) MS1 peptide ion intensity chromatograms in MS2 (SWATH) data independent acquisitions. Improving post acquisition analysis of proteomic experiments. Mol Cell Proteomics 14: 2405–2419 · DOI
  64. Rosenberger G, Koh CC, Guo T, Rost HL, Kouvonen P, Collins BC, Heusel M, Liu Y, Caron E, Vichalkovski A, Faini M, Schubert OT, Faridi P, Ebhardt HA, Matondo M, Lam H, Bader SL, Campbell DS, Deutsch EW, Moritz RL et al (2014) A repository of assays to quantify 10,000 human proteins by SWATH‐MS. Sci Data 1: 140031 · DOI · Paper page
  65. Rosenberger G, Liu Y, Rost HL, Ludwig C, Buil A, Bensimon A, Soste M, Spector TD, Dermitzakis ET, Collins BC, Malmstrom L, Aebersold R (2017b) Inference and quantification of peptidoforms in large sample cohorts by SWATH‐MS. Nat Biotechnol 35: 781–788 · DOI
  66. Rost H, Malmstrom L, Aebersold R (2012) A computational tool to detect and avoid redundancy in selected reaction monitoring. Mol Cell Proteomics 11: 540–549 · DOI
  67. Rost HL, Rosenberger G, Navarro P, Gillet L, Miladinovic SM, Schubert OT, Wolski W, Collins BC, Malmstrom J, Malmstrom L, Aebersold R (2014) OpenSWATH enables automated, targeted analysis of data‐independent acquisition MS data. Nat Biotechnol 32: 219–223 · DOI · Paper page
  68. Rost HL, Liu Y, D'Agostino G, Zanella M, Navarro P, Rosenberger G, Collins BC, Gillet L, Testa G, Malmstrom L, Aebersold R (2016a) TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics. Nat Methods 13: 777–783 · DOI · Paper page
  69. Rost HL, Sachsenberg T, Aiche S, Bielow C, Weisser H, Aicheler F, Andreotti S, Ehrlich HC, Gutenbrunner P, Kenar E, Liang X, Nahnsen S, Nilse L, Pfeuffer J, Rosenberger G, Rurik M, Schmitt U, Veit J, Walzer M, Wojnar D et al (2016b) OpenMS: a flexible open‐source software platform for mass spectrometry data analysis. Nat Methods 13: 741–748 · DOI · Paper page
  70. Roumeliotis TI, Williams SP, Gonçalves E, Alsinet C, Del Castillo Velasco‐Herrera M, Aben N, Ghavidel FZ, Michaut M, Schubert M, Price S, Wright JC, Yu L, Yang M, Dienstmann R, Guinney J, Beltrao P, Brazma A, Pardo M, Stegle O, Adams DJ et al (2017) Genomic determinants of protein abundance variation in colorectal cancer cells. Cell Rep 20: 2201–2214 · DOI
  71. Savitski MM, Mathieson T, Zinn N, Sweetman G, Doce C, Becher I, Pachl F, Kuster B, Bantscheff M (2013) Measuring and managing ratio compression for accurate iTRAQ/TMT quantification. J Proteome Res 12: 3586–3598 · DOI · Paper page
  72. Schmidlin T, Garrigues L, Lane CS, Mulder TC, van Doorn S, Post H, de Graaf EL, Lemeer S, Heck AJ, Altelaar AF (2016) Assessment of SRM, MRM and DIA for the targeted analysis of phosphorylation dynamics in non‐small cell lung cancer. Proteomics 16: 2193–2205 · DOI
  73. Schubert OT, Mouritsen J, Ludwig C, Rost HL, Rosenberger G, Arthur PK, Claassen M, Campbell DS, Sun Z, Farrah T, Gengenbacher M, Maiolica A, Kaufmann SH, Moritz RL, Aebersold R (2013) The Mtb proteome library: a resource of assays to quantify the complete proteome of Mycobacterium tuberculosis. Cell Host Microbe 13: 602–612 · DOI · Paper page
  74. Schubert OT, Gillet LC, Collins BC, Navarro P, Rosenberger G, Wolski WE, Lam H, Amodei D, Mallick P, MacLean B, Aebersold R (2015a) Building high‐quality assay libraries for targeted analysis of SWATH MS data. Nat Protoc 10: 426–441 · DOI · Paper page
  75. Schubert OT, Ludwig C, Kogadeeva M, Zimmermann M, Rosenberger G, Gengenbacher M, Gillet LC, Collins BC, Rost HL, Kaufmann SH, Sauer U, Aebersold R (2015b) Absolute proteome composition and dynamics during dormancy and resuscitation of Mycobacterium tuberculosis. Cell Host Microbe 18: 96–108 · DOI · Paper page
  76. Searle BC, Egertson JD, Bollinger JG, Stergachis AB, MacCoss MJ (2015) Using data independent acquisition (DIA) to model high‐responding peptides for targeted proteomics experiments. Mol Cell Proteomics 14: 2331–2340 · DOI
  77. Selevsek N, Chang CY, Gillet LC, Navarro P, Bernhardt OM, Reiter L, Cheng LY, Vitek O, Aebersold R (2015) Reproducible and consistent quantification of the Saccharomyces cerevisiae proteome by SWATH‐mass spectrometry. Mol Cell Proteomics 14: 739–749 · DOI · Paper page
  78. Sharma V, Eckels J, Taylor GK, Shulman NJ, Stergachis AB, Joyner SA, Yan P, Whiteaker JR, Halusa GN, Schilling B, Gibson BW, Colangelo CM, Paulovich AG, Carr SA, Jaffe JD, MacCoss MJ, MacLean B (2014) Panorama: a targeted proteomics knowledge base. J Proteome Res 13: 4205–4210 · DOI · Paper page
  79. Sidoli S, Lin S, Xiong L, Bhanu NV, Karch KR, Johansen E, Hunter C, Mollah S, Garcia BA (2015) Sequential window acquisition of all theoretical mass spectra (SWATH) analysis for characterization and quantification of histone post‐translational modifications. Mol Cell Proteomics 14: 2420–2428 · DOI
  80. Sonnett M, Yeung E, Wuhr M (2018) Accurate, sensitive, and precise multiplexed proteomics using the complement reporter ion cluster. Anal Chem 90: 5032–5039 · DOI
  81. Sturm M, Bertsch A, Gropl C, Hildebrandt A, Hussong R, Lange E, Pfeifer N, Schulz‐Trieglaff O, Zerck A, Reinert K, Kohlbacher O (2008) OpenMS ‐ an open‐source software framework for mass spectrometry. BMC Bioinformatics 9: 163 · DOI · Paper page
  82. Tan SLW, Chadha S, Liu Y, Gabasova E, Perera D, Ahmed K, Constantinou S, Renaudin X, Lee M, Aebersold R, Venkitaraman AR (2017) A class of environmental and endogenous toxins induces BRCA2 haploinsufficiency and genome instability. Cell 169: 1105–1118.e15 · DOI · Paper page
  83. Teleman J, Rost HL, Rosenberger G, Schmitt U, Malmstrom L, Malmstrom J, Levander F (2015) DIANA–algorithmic improvements for analysis of data‐independent acquisition MS data. Bioinformatics 31: 555–562 · DOI
  84. Teleman J, Hauri S, Malmstrom J (2017) Improvements in mass spectrometry assay library generation for targeted proteomics. J Proteome Res 16: 2384–2392 · DOI
  85. Teo G, Kim S, Tsou CC, Collins B, Gingras AC, Nesvizhskii AI, Choi H (2015) mapDIA: preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry. J Proteomics 129: 108–120 · DOI
  86. Thompson A, Schafer J, Kuhn K, Kienle S, Schwarz J, Schmidt G, Neumann T, Johnstone R, Mohammed AK, Hamon C (2003) Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75: 1895–1904 · DOI · Paper page
  87. Ting YS, Egertson JD, Bollinger JG, Searle BC, Payne SH, Noble WS, MacCoss MJ (2017) PECAN: library‐free peptide detection for data‐independent acquisition tandem mass spectrometry data. Nat Methods 14: 903–908 · DOI · Paper page
  88. Toprak UH, Gillet LC, Maiolica A, Navarro P, Leitner A, Aebersold R (2014) Conserved peptide fragmentation as a benchmarking tool for mass spectrometers and a discriminating feature for targeted proteomics. Mol Cell Proteomics 13: 2056–2071 · DOI
  89. Tsou CC, Avtonomov D, Larsen B, Tucholska M, Choi H, Gingras AC, Nesvizhskii AI (2015) DIA‐Umpire: comprehensive computational framework for data‐independent acquisition proteomics. Nat Methods 12: 258–264 · DOI · Paper page
  90. Unwin RD, Pierce A, Watson RB, Sternberg DW, Whetton AD (2005) Quantitative proteomic analysis using isobaric protein tags enables rapid comparison of changes in transcript and protein levels in transformed cells. Mol Cell Proteomics 4: 924–935 · DOI
  91. Venable JD, Dong MQ, Wohlschlegel J, Dillin A, Yates JR (2004) Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat Methods 1: 39–45 · DOI · Paper page
  92. Wang X, Chambers MC, Vega‐Montoto LJ, Bunk DM, Stein SE, Tabb DL (2014) QC metrics from CPTAC raw LC‐MS/MS data interpreted through multivariate statistics. Anal Chem 86: 2497–2509 · DOI
  93. Wang J, Tucholska M, Knight JD, Lambert JP, Tate S, Larsen B, Gingras AC, Bandeira N (2015) MSPLIT‐DIA: sensitive peptide identification for data‐independent acquisition. Nat Methods 12: 1106–1108 · DOI
  94. Weisbrod CR, Eng JK, Hoopmann MR, Baker T, Bruce JE (2012) Accurate peptide fragment mass analysis: multiplexed peptide identification and quantification. J Proteome Res 11: 1621–1632 · DOI
  95. Whiteaker JR, Halusa GN, Hoofnagle AN, Sharma V, MacLean B, Yan P, Wrobel JA, Kennedy J, Mani DR, Zimmerman LJ, Meyer MR, Mesri M, Rodriguez H, Paulovich AG (2014) CPTAC assay portal: a repository of targeted proteomic assays. Nat Methods 11: 703–704 · DOI · Paper page
  96. Wu JX, Song X, Pascovici D, Zaw T, Care N, Krisp C, Molloy MP (2016) SWATH mass spectrometry performance using extended peptide MS/MS assay libraries. Mol Cell Proteomics 15: 2501–2514 · DOI
  97. Zhang Y, Bilbao A, Bruderer T, Luban J, Strambio‐De‐Castillia C, Lisacek F, Hopfgartner G, Varesio E (2015) The use of variable Q1 isolation windows improves selectivity in LC‐SWATH‐MS acquisition. J Proteome Res 14: 4359–4371 · DOI · Paper page
  98. Zi J, Zhang S, Zhou R, Zhou B, Xu S, Hou G, Tan F, Wen B, Wang Q, Lin L, Liu S (2014) Expansion of the ion library for mining SWATH‐MS data through fractionation proteomics. Anal Chem 86: 7242–7246 · DOI
  99. Zolg DP, Wilhelm M, Schnatbaum K, Zerweck J, Knaute T, Delanghe B, Bailey DJ, Gessulat S, Ehrlich HC, Weininger M, Yu P, Schlegl J, Kramer K, Schmidt T, Kusebauch U, Deutsch EW, Aebersold R, Moritz RL, Wenschuh H, Moehring T et al (2017) Building ProteomeTools based on a complete synthetic human proteome. Nat Methods 14: 259–262 · DOI · Paper page
  100. Zybailov B, Mosley AL, Sardiu ME, Coleman MK, Florens L, Washburn MP (2006) Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. J Proteome Res 5: 2339–2347 · DOI · Paper page

Cited by

See all 1,002

Metadata

Authors
Ludwig, C., Gillet, L., Rosenberger, G., Amon, S., Collins, B. C., & Aebersold, R.
Venue
Molecular Systems Biology
Publisher
Springer Science and Business Media LLC
Published
2018-08
Volume
14
Issue
8
Pages
e8126-e8126
ISSN
1744-4292 (ISSN-L)
DOI
10.15252/msb.20178126
OpenAlex
W2887294942
Cited by
1,002
References
31
FWCI
37.72
Type
journal article
Language
en
Open access
gold
Licence
Funders
  • Swiss National Science Foundation Ambizione Grant (PZ00P3_161435)
  • ERC Proteomics v3.0 (AdG‐233226)
  • Proteomics 4D (AdG‐670821)
  • PhosphoNetX Project of SystemsX.ch
  • Swiss National Science Foundation (31003A_166435)
  • H2020 – PrECISE (668858)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Data‐independent acquisition‐based SWATH ‐ MS for quantitative proteomics: a tutorial. https://app.cochat.ai/papers/10.15252/msb.20178126
The paper
Ludwig, C., Gillet, L., Rosenberger, G., Amon, S., Collins, B. C., & Aebersold, R. (2018). Data‐independent acquisition‐based SWATH ‐ MS for quantitative proteomics: a tutorial. Molecular Systems Biology. https://doi.org/10.15252/msb.20178126

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.