24. Collins BC et al (2017) Multi-laboratory
assessment of reproducibility, qualitative and
quantitative performance of SWATH-mass
spectrometry. Nat Commun 8:291
25. Perez-Riverol Y et al (2019) The PRIDE database and related tools and resources in 2019:
improving support for quantification data.
Nucleic Acids Res 47(D1):D442–D450
26. Escher C et al (2012) Using iRT, a normalized
retention time for more targeted measurement
of peptides. Proteomics 12(8):1111–1121
27. Li X et al (2015) Proteomic analyses reveal
distinct chromatin-associated and soluble transcription factor complexes. Mol Syst Biol 11
(1):775
28. Rost HL et al (2014) OpenSWATH enables
automated, targeted analysis of dataindependent acquisition MS data. Nat Biotechnol 32(3):219–223
29. Rost HL et al (2016) TRIC: an automated
alignment strategy for reproducible protein
quantification in targeted proteomics. Nat
Methods 13(9):777–783
30. Rost HL, Aebersold R, Schubert OT (2017)
Automated SWATH data analysis using targeted extraction of ion chromatograms. Methods Mol Biol 1550:289–307
31. Ludwig C et al (2018) Data-independent
acquisition-based SWATH-MS for quantitative
proteomics: a tutorial. Mol Syst Biol 14(8):
e8126
32. Rosenberger G et al (2017) Statistical control
of peptide and protein error rates in large-scale
targeted data-independent acquisition analyses. Nat Methods 14(9):921
33. Mallam AL et al (2019) Systematic discovery of
endogenous human ribonucleoprotein complexes. Cell Rep 29(5):1351
34. Gilbert M, Schulze WX (2019) Global identification of protein complexes within the membrane proteome of Arabidopsis roots using a
SEC-MS approach. J Proteome Res 18
(1):107–119
35. Crozier TWM et al (2017) Prediction of protein complexes in Trypanosoma brucei by protein correlation profiling mass spectrometry
and machine learning. Mol Cell Proteomics
16(12):2254–2267
36. Bruderer R et al (2015) Extending the limits of
quantitative proteome profiling with dataindependent acquisition and application to
acetaminophen-treated
three-dimensional
liver microtissues. Mol Cell Proteomics 14
(5):1400–1410
37. Tsou CC et al (2015) DIA-umpire: comprehensive computational framework for dataindependent acquisition proteomics. Nat
Methods 12(3):258–264, 7 p following 264
38. Schubert OT et al (2015) Building highquality assay libraries for targeted analysis of
SWATH MS data. Nat Protoc 10(3):426–441
39. Rosenberger G et al (2014) A repository of
assays to quantify 10,000 human proteins by
SWATH-MS. Sci Data 1:140031
294
Andrea Fossati et al.
assessment of reproducibility, qualitative and
quantitative performance of SWATH-mass
spectrometry. Nat Commun 8:291
25. Perez-Riverol Y et al (2019) The PRIDE database and related tools and resources in 2019:
improving support for quantification data.
Nucleic Acids Res 47(D1):D442–D450
26. Escher C et al (2012) Using iRT, a normalized
retention time for more targeted measurement
of peptides. Proteomics 12(8):1111–1121
27. Li X et al (2015) Proteomic analyses reveal
distinct chromatin-associated and soluble transcription factor complexes. Mol Syst Biol 11
(1):775
28. Rost HL et al (2014) OpenSWATH enables
automated, targeted analysis of dataindependent acquisition MS data. Nat Biotechnol 32(3):219–223
29. Rost HL et al (2016) TRIC: an automated
alignment strategy for reproducible protein
quantification in targeted proteomics. Nat
Methods 13(9):777–783
30. Rost HL, Aebersold R, Schubert OT (2017)
Automated SWATH data analysis using targeted extraction of ion chromatograms. Methods Mol Biol 1550:289–307
31. Ludwig C et al (2018) Data-independent
acquisition-based SWATH-MS for quantitative
proteomics: a tutorial. Mol Syst Biol 14(8):
e8126
32. Rosenberger G et al (2017) Statistical control
of peptide and protein error rates in large-scale
targeted data-independent acquisition analyses. Nat Methods 14(9):921
33. Mallam AL et al (2019) Systematic discovery of
endogenous human ribonucleoprotein complexes. Cell Rep 29(5):1351
34. Gilbert M, Schulze WX (2019) Global identification of protein complexes within the membrane proteome of Arabidopsis roots using a
SEC-MS approach. J Proteome Res 18
(1):107–119
35. Crozier TWM et al (2017) Prediction of protein complexes in Trypanosoma brucei by protein correlation profiling mass spectrometry
and machine learning. Mol Cell Proteomics
16(12):2254–2267
36. Bruderer R et al (2015) Extending the limits of
quantitative proteome profiling with dataindependent acquisition and application to
acetaminophen-treated
three-dimensional
liver microtissues. Mol Cell Proteomics 14
(5):1400–1410
37. Tsou CC et al (2015) DIA-umpire: comprehensive computational framework for dataindependent acquisition proteomics. Nat
Methods 12(3):258–264, 7 p following 264
38. Schubert OT et al (2015) Building highquality assay libraries for targeted analysis of
SWATH MS data. Nat Protoc 10(3):426–441
39. Rosenberger G et al (2014) A repository of
assays to quantify 10,000 human proteins by
SWATH-MS. Sci Data 1:140031
294
Andrea Fossati et al.