UPS1 & UPS2 Proteomic Standards

We now offer both the Universal Proteomics Standard and the Proteomics Dynamic Range Standard as complex, well-defined, well characterized reference standards for mass spectrometry. Both standards contain the same 48 human proteins ranging in molecular mass from 6,000 to 83,000 Daltons. Each constituent protein has been HPLC purified and AAA quantitated prior to formulation.

• Troubleshoot and optimize your analytical protocol
• Confirm system suitability before analyzing critical samples
• Normalize analytical results day-to-day or lab-to-lab
• Determine your limit of detection

Universal Proteomics Standard, UPS1

Developed in collaboration with the Association of Biomolecular Resource Facilities Proteomics Standards Research Group (sPRG), the Universal Proteomics Standard (UPS1) contains 48 human proteins (5 pmols of each) ranging in molecular weight from 6,000 to 83,000 daltons.

Proteomics Dynamic Range Standard, UPS2

This standard is an enhancement of our original Universal Proteomics Standard (UPS2). The same complex mixture of 48 human proteins has been formulated into a dynamic range of concentrations, ranging from 500 amoles to 50 pmoles.

Each set contains one vial of Universal Proteomics Standard and one vial (20 µg) of Proteomics Grade Trypsin (T6567)

The ABRF sPRG 2006 Study

In the Fall and Winter of 2005/2006, the ABRF sPRG (Proteomics Standards Research Group) conducted a study to assess the analytical capabilities of proteomics laboratories. Approximately 125 labs from across the world volunteered to participate. Each lab received a complex mixture of 49 unknown proteins and were asked to identify as many of these proteins as possible using their best analytical strategies. The results, presented in February 2006, were quite impressive and in some cases, surprising. Learn more about the sPRG’s 2006 study.

List of UPS Proteins

UniProt Accession Number	UPS1 Amount (fmol)	UPS2 Amount (fmol)	UniProt Protein Name [Synonym]	MW (Da) (calculated)	Source or recombinant	Host	Tag	Potential PTMs
P00915	5,000	50,000	Carbonic anhydrase 1	28,738	Erythrocytes			Acetylation
P00918	5,000	50,000	Carbonic anhydrase 2	29,115	Erythrocytes			Acetylation
P01031	5,000	50,000	Complement C5 [Complement C5a]	8,536	Recombinant	E. coli
P69905	5,000	50,000	Hemoglobin alpha chain	15,126	Erythrocytes
P68871	5,000	50,000	Hemoglobin beta chain	15,867	Erythrocytes			Acetylation, nitrosylation, glycosylation
P41159	5,000	50,000	Leptin	16,158	Recombinant	E. coli
P02768	5,000	50,000	Serum Albumin	66,357	Recombinant	Pichia pastoris
P62988	5,000	50,000	Ubiquitin	10,597	Recombinant	E. coli	6-His
P04040	5,000	5,000	Catalase	59,625	Erythrocytes
P00167	5,000	5,000	Cytochrome b5	16,022	Recombinant	E. coli	6-His
P01133	5,000	5,000	Epidermal Growth Factor	6,353	Recombinant	E. coli
P02144	5,000	5,000	Myoglobin C	17,053	Heart
P15559	5,000	5,000	NAD(P)H dehydrogenase [quinone] 1 [DT Diaphorase] C	30,736	Recombinant	E. coli
P62937	5,000	5,000	Peptidyl-prolyl cis-trans isomerase A [Cyclophilin A]	20,176	Recombinant	E. coli
Q06830	5,000	5,000	Peroxiredoxin 1	21,979	Recombinant	E. coli
P63165	5,000	5,000	Small ubiquitin-related modifier 1 [SUMO-1]	38,815	Recombinant	E. coli	GST
P00709	5,000	500	Alpha-lactalbumin	14,078	Milk			Glycosylation
P06732	5,000	500	Creatine kinase M-type [CK-MM]	43,101	Heart
P12081	5,000	500	Histidyl-tRNA synthetase [Jo-1]	58,233	Recombinant	E. coli
P61626	5,000	500	Lysozyme C	14,701	Milk
Q15843	5,000	500	Neddylin [Nedd8]	9,072	Recombinant	E. coli
P02753	5,000	500	Retinol-binding protein	21,071	Urine
P16083	5,000	500	Ribosyldihydronicotinamide dehydrogenase [quinone] [Quinone oxidoreductase 2] [NQO2]	25,821	Recombinant	E. coli
P63279	5,000	500	Ubiquitin-conjugating enzyme E2 I [UbcH9]	18,007	Recombinant	E. coli
P01008	5,000	50	Antithrombin-III	49,039	Plasma			Glycosylation
P61769	5,000	50	Beta-2-microglobulin	11,731	Urine
P55957	5,000	50	BH3 Interacting domain death agonist [BID]	21,995	Recombinant	E. coli
O76070	5,000	50	Gamma-synuclein	15,363	Recombinant	E. coli
P08263	5,000	50	Glutathione S-transferase A1 [GST A1-1]	25,500	Recombinant	E. coli
P01344	5,000	50	Insulin-like growth factor II	7,475	Recombinant	E. coli
P01127	5,000	50	Platelet-derived growth factor B chain	12,294	Recombinant	E. coli
P10599	5,000	50	Thioredoxin	12,429	Recombinant	E. coli	6-His
P99999	5,000	5	Cytochrome c[Apocytochrome c]	11,618	Recombinant	E. coli
P06396	5,000	5	Gelsolin	82,959	Plasma			Phosphorylation
P09211	5,000	5	Glutathione S-transferase P [GST]	23,225	Placenta
P01112	5,000	5	GTPase HRas [Ras protein]	21,298	Recombinant	E. coli
P01579	5,000	5	Interferon gamma (IFN-gamma)	16,879	Recombinant	E. coli
P02787	5,000	5	Serotransferrin [Apotransferrin]	75,181	Plasma			Glycosylation
O00762	5,000	5	Ubiquitin-conjugating enzyme E2 C [UbcH10]	20,475	Recombinant	E. coli	6-His
P51965	5,000	5	Ubiquitin-conjugating enzyme E2 E1 [UbcH6]	22,227	Recombinant	E. coli	6-His
P08758	5,000	0.5	Annexin A 5	35,806	Placenta			Acetylation
P02741	5,000	0.5	C-reactive protein	23,047	Plasma
P05413	5,000	0.5	Fatty acid-binding protein	14,727	Plasma			Acetylation, phosphorylation
P10145	5,000	0.5	Interleukin-8	8,386	Recombinant	E. coli
P02788	5,000	0.5	Lactotransferrin	76,165	Milk			Glycosylation
P10636	5,000	0.5	Microtubule-associated protein tau [Tau protein]	45,719	Recombinant	E. coli	6-His
P00441	5,000	0.5	Superoxide dismutase [Cu-Zn]	15,805	Erythrocytes			Acetylation
P01375	5,000	0.5	Tumor necrosis factor [TNF-alpha]	17,353	Recombinant	E. coli

References for UPS1

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2. Uwaje NC, Mueller NS, Maccarrone G, Turck CW. 2007. Interrogation of MS/MS search data with anpI Filter algorithm to increase protein identification success. Electrophoresis. 28(12):1867-1874. https://doi.org/10.1002/elps.200700022

3. Brosch M, Swamy S, Hubbard T, Choudhary J. 2008. Comparison of Mascot and X!Tandem Performance for Low and High Accuracy Mass Spectrometry and the Development of an Adjusted Mascot Threshold. Molecular & Cellular Proteomics. 7(5):962-970. https://doi.org/10.1074/mcp.m700293-mcp200

4. Molina H, Matthiesen R, Kandasamy K, Pandey A. 2008. Comprehensive Comparison of Collision Induced Dissociation and Electron Transfer Dissociation. Anal. Chem.. 80(13):4825-4835. https://doi.org/10.1021/ac8007785

5. Fusaro VA, Mani DR, Mesirov JP, Carr SA. 2009. Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat Biotechnol. 27(2):190-198. https://doi.org/10.1038/nbt.1524

6. Davidson WS, Silva RGD, Chantepie S, Lagor WR, Chapman MJ, Kontush A. 2009. Proteomic Analysis of Defined HDL Subpopulations Reveals Particle-Specific Protein Clusters. ATVB. 29(6):870-876. https://doi.org/10.1161/atvbaha.109.186031

7. Paulovich AG, Billheimer D, Ham AL, Vega-Montoto L, Rudnick PA, Tabb DL, Wang P, Blackman RK, Bunk DM, Cardasis HL, et al. 2010. Interlaboratory Study Characterizing a Yeast Performance Standard for Benchmarking LC-MS Platform Performance*. Molecular & Cellular Proteomics. 9(2):242-254. https://doi.org/10.1074/mcp.m900222-mcp200

8. Tabb DL, Vega-Montoto L, Rudnick PA, Variyath AM, Ham AL, Bunk DM, Kilpatrick LE, Billheimer DD, Blackman RK, Cardasis HL, et al. 2010. Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography?Tandem Mass Spectrometry. J. Proteome Res.. 9(2):761-776. https://doi.org/10.1021/pr9006365

9. Kim M, Kandasamy K, Chaerkady R, Pandey A. 2010. Assessment of resolution parameters for CID-based shotgun proteomic experiments on the LTQ-Orbitrap mass spectrometer. J Am Soc Mass Spectrom. 21(9):1606-1611. https://doi.org/10.1016/j.jasms.2010.04.011

10. Li B, Held JM, Schilling B, Danielson SR, Gibson BW. 2011. Confident identification of 3-nitrotyrosine modifications in mass spectral data across multiple mass spectrometry platforms. Journal of Proteomics. 74(11):2510-2521. https://doi.org/10.1016/j.jprot.2011.04.007

11. Yen C, Houel S, Ahn NG, Old WM. 2011. Spectrum-to-Spectrum Searching Using a Proteome-wide Spectral Library. Molecular & Cellular Proteomics. 10(7):M111.007666. https://doi.org/10.1074/mcp.m111.007666

12. Ma X, Cui J, Zhang J. 2011. Processing methods for signal suppression of FTMS data. Proteome Sci. 9(Suppl 1):S2. https://doi.org/10.1186/1477-5956-9-s1-s2

13. Wu Q, Zhao Q, Liang Z, Qu Y, Zhang L, Zhang Y. 2012. NSI and NSMT: usages of MS/MS fragment ion intensity for sensitive differential proteome detection and accurate protein fold change calculation in relative label-free proteome quantification. Analyst. 137(13):3146. https://doi.org/10.1039/c2an35173k

14. Wright JC, Collins MO, Yu L, Käll L, Brosch M, Choudhary JS. 2012. Enhanced Peptide Identification by Electron Transfer Dissociation Using an Improved Mascot Percolator*. Molecular & Cellular Proteomics. 11(8):478-491. https://doi.org/10.1074/mcp.o111.014522

15. Nahnsen S, Kohlbacher O. 2012. In silico design of targeted SRM-based experiments. BMC Bioinformatics. 13(S16): https://doi.org/10.1186/1471-2105-13-s16-s8

16. Reiz B, Kertész-Farkas A, Pongor S, Myers MP. 2013. Chemical rule-based filtering of MS/MS spectra. 29(7):925-932. https://doi.org/10.1093/bioinformatics/btt061

17. Augustsson P, Malm J, Ekström S. 2012. Acoustophoretic microfluidic chip for sequential elution of surface bound molecules from beads or cells. Biomicrofluidics. 6(3):034115. https://doi.org/10.1063/1.4749289

18. Milac TI, Randolph TW, Wang P. 2012. Analyzing LC-MS/MS data by spectral count and ion abundance: two case studies. 5(1):75-87. https://doi.org/10.4310/sii.2012.v5.n1.a7

19. Jian L, Niu X, Xia Z, Samir P, Sumanasekera C, Mu Z, Jennings JL, Hoek KL, Allos T, Howard LM, et al. 2013. A Novel Algorithm for Validating Peptide Identification from a Shotgun Proteomics Search Engine. J. Proteome Res.. 12(3):1108-1119. https://doi.org/10.1021/pr300631t

20. Zerck A, Nordhoff E, Lehrach H, Reinert K. 2013. Optimal precursor ion selection for LC-MALDI MS/MS. BMC Bioinformatics. 14(1): https://doi.org/10.1186/1471-2105-14-56

21. Higgs RE, Butler JP, Han B, Knierman MD. 2013. Quantitative Proteomics via High Resolution MS Quantification: Capabilities and Limitations. International Journal of Proteomics. 20131-10. https://doi.org/10.1155/2013/674282

22. Kertész-Farkas A, Reiz B, Vera R, Myers MP, Pongor S. 2014. PTMTreeSearch: a novel two-stage tree-search algorithm with pruning rules for the identification of post-translational modification of proteins in MS/MS spectra. 30(2):234-241. https://doi.org/10.1093/bioinformatics/btt642

23. Gatto L, Christoforou A. 2014. Using R and Bioconductor for proteomics data analysis. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics. 1844(1):42-51. https://doi.org/10.1016/j.bbapap.2013.04.032

24. Van Riper SK, de Jong EP, Higgins L, Carlis JV, Griffin TJ. 2014. Improved Intensity-Based Label-Free Quantification via Proximity-Based Intensity Normalization (PIN). J. Proteome Res.. 13(3):1281-1292. https://doi.org/10.1021/pr400866r

25. Rudnick PA, Wang X, Yan X, Sedransk N, Stein SE. 2014. Improved Normalization of Systematic Biases Affecting Ion Current Measurements in Label-free Proteomics Data. Molecular & Cellular Proteomics. 13(5):1341-1351. https://doi.org/10.1074/mcp.m113.030593

26. Ivanov MV, Levitsky LI, Lobas AA, Panic T, Laskay ÜA, Mitulovic G, Schmid R, Pridatchenko ML, Tsybin YO, Gorshkov MV. 2014. Empirical Multidimensional Space for Scoring Peptide Spectrum Matches in Shotgun Proteomics. J. Proteome Res.. 13(4):1911-1920. https://doi.org/10.1021/pr401026y

27. Baba T, Kashiwagi Y, Arimitsu N, Kogure T, Edo A, Maruyama T, Nakao K, Nakanishi H, Kinoshita M, Frohman MA, et al. 2014. Phosphatidic Acid (PA)-preferring Phospholipase A1 Regulates Mitochondrial Dynamics. Journal of Biological Chemistry. 289(16):11497-11511. https://doi.org/10.1074/jbc.m113.531921

28. Kannaste O, Suomi T, Salmi J, Uusipaikka E, Nevalainen O, Corthals GL. 2014. Cross-Correlation of Spectral Count Ranking to Validate Quantitative Proteome Measurements. J. Proteome Res.. 13(4):1957-1968. https://doi.org/10.1021/pr401096z

29. Tu C, Li J, Sheng Q, Zhang M, Qu J. 2014. Systematic Assessment of Survey Scan and MS2-Based Abundance Strategies for Label-Free Quantitative Proteomics Using High-Resolution MS Data. J. Proteome Res.. 13(4):2069-2079. https://doi.org/10.1021/pr401206m

30. Prokai L, Guo J, Prokai-Tatrai K. 2014. Selective chemoprecipitation to enrich nitropeptides from complex proteomes for mass-spectrometric analysis. Nat Protoc. 9(4):882-895. https://doi.org/10.1038/nprot.2014.052

31. Chawade A, Alexandersson E, Levander F. 2014. Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets. J. Proteome Res.. 13(6):3114-3120. https://doi.org/10.1021/pr401264n

32. Ebhardt HA, Nan J, Chaulk SG, Fahlman RP, Aebersold R. 2014. Enzymatic generation of peptides flanked by basic amino acids to obtain MS/MS spectra with 2× sequence coverage. Rapid Commun. Mass Spectrom.. 28(24):2735-2743. https://doi.org/10.1002/rcm.7069

33. Koh HWL, Swa HLF, Fermin D, Ler SG, Gunaratne J, Choi H. 2015. EBprot: Statistical analysis of labeling-based quantitative proteomics data. Proteomics. 15(15):2580-2591. https://doi.org/10.1002/pmic.201400620

34. Qi D, Zhang H, Fan J, Perkins S, Pisconti A, Simpson DM, Bessant C, Hubbard S, Jones AR. 2015. The mzqLibrary - An open source Java library supporting the HUPO-PSI quantitative proteomics standard. Proteomics. 15(18):3152-3162. https://doi.org/10.1002/pmic.201400535

35. Pursiheimo A, Vehmas AP, Afzal S, Suomi T, Chand T, Strauss L, Poutanen M, Rokka A, Corthals GL, Elo LL. 2015. Optimization of Statistical Methods Impact on Quantitative Proteomics Data. J. Proteome Res.. 14(10):4118-4126. https://doi.org/10.1021/acs.jproteome.5b00183

36. Suomi T, Corthals GL, Nevalainen OS, Elo LL. 2015. Using Peptide-Level Proteomics Data for Detecting Differentially Expressed Proteins. J. Proteome Res.. 14(11):4564-4570. https://doi.org/10.1021/acs.jproteome.5b00363

37. Liang X, Xia Z, Jian L, Niu X, Link A. 2015. An adaptive classification model for peptide identification. BMC Genomics. 16(Suppl 11):S1. https://doi.org/10.1186/1471-2164-16-s11-s1

38. Ramus C, Hovasse A, Marcellin M, Hesse A, Mouton-Barbosa E, Bouyssié D, Vaca S, Carapito C, Chaoui K, Bruley C, et al. 2016. Benchmarking quantitative label-free LC?MS data processing workflows using a complex spiked proteomic standard dataset. Journal of Proteomics. 13251-62. https://doi.org/10.1016/j.jprot.2015.11.011

39. Ramus C, Hovasse A, Marcellin M, Hesse A, Mouton-Barbosa E, Bouyssié D, Vaca S, Carapito C, Chaoui K, Bruley C, et al. 2016. Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods. Data in Brief. 6286-294. https://doi.org/10.1016/j.dib.2015.11.063

References for UPS2

1. Dicker L, Lin X, Ivanov AR. 2010. Increased Power for the Analysis of Label-free LC-MS/MS Proteomics Data by Combining Spectral Counts and Peptide Peak Attributes. Molecular & Cellular Proteomics. 9(12):2704-2718. https://doi.org/10.1074/mcp.m110.002774

2. Zhang J, Haskins W. 2010. ICPD-A New Peak Detection Algorithm for LC/MS. BMC Genomics. 11(Suppl 3):S8. https://doi.org/10.1186/1471-2164-11-s3-s8

3. Kwon T, Choi H, Vogel C, Nesvizhskii AI, Marcotte EM. 2011. MSblender: A Probabilistic Approach for Integrating Peptide Identifications from Multiple Database Search Engines. J. Proteome Res.. 10(7):2949-2958. https://doi.org/10.1021/pr2002116

4. Schwanhäusser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W, Selbach M. 2011. Global quantification of mammalian gene expression control. Nature. 473(7347):337-342. https://doi.org/10.1038/nature10098

5. Forshed J, Johansson HJ, Pernemalm M, Branca RM, Sandberg A, Lehtiö J. 2011. Enhanced Information Output From Shotgun Proteomics Data by Protein Quantification and Peptide Quality Control (PQPQ). Molecular & Cellular Proteomics. 10(10):M111.010264. https://doi.org/10.1074/mcp.m111.010264

6. Arike L, Valgepea K, Peil L, Nahku R, Adamberg K, Vilu R. 2012. Comparison and applications of label-free absolute proteome quantification methods on Escherichia coli. Journal of Proteomics. 75(17):5437-5448. https://doi.org/10.1016/j.jprot.2012.06.020

7. Shin J, Krey JF, Hassan A, Metlagel Z, Tauscher AN, Pagana JM, Sherman NE, Jeffery ED, Spinelli KJ, Zhao H, et al. 2013. Molecular architecture of the chick vestibular hair bundle. Nat Neurosci. 16(3):365-374. https://doi.org/10.1038/nn.3312

8. Wu Q, Shan Y, Qu Y, Jiang H, Yuan H, Liu J, Zhang S, Liang Z, Zhang L, Zhang Y. Improved accuracy for label-free absolute quantification of proteome by combining the absolute protein expression profiling algorithm and summed tandem mass spectrometric total ion current. Analyst. 139(1):138-146. https://doi.org/10.1039/c3an01738a

9. Brendel J, Stoll B, Lange SJ, Sharma K, Lenz C, Stachler A, Maier L, Richter H, Nickel L, Schmitz RA, et al. 2014. A Complex of Cas Proteins 5, 6, and 7 Is Required for the Biogenesis and Stability of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-derived RNAs (crRNAs) in Haloferax volcanii. Journal of Biological Chemistry. 289(10):7164-7177. https://doi.org/10.1074/jbc.m113.508184

10. Sandberg A, Branca RM, Lehtiö J, Forshed J. 2014. Quantitative accuracy in mass spectrometry based proteomics of complex samples: The impact of labeling and precursor interference. Journal of Proteomics. 96133-144. https://doi.org/10.1016/j.jprot.2013.10.035

11. Smits AH, Lindeboom RG, Perino M, van Heeringen SJ, Veenstra G, Vermeulen M. 2014. Global absolute quantification reveals tight regulation of protein expression in single Xenopus eggs. 42(15):9880-9891. https://doi.org/10.1093/nar/gku661

12. Hutchinson EC, Charles PD, Hester SS, Thomas B, Trudgian D, Martínez-Alonso M, Fodor E. 2014. Conserved and host-specific features of influenza virion architecture. Nat Commun. 5(1): https://doi.org/10.1038/ncomms5816

13. Böhm, Juila Wiebke,. 2015. “A comprehensive C/EBPβ interactome”. Dr. rer. nat. dissertation, Humboldt University Berlin,. [dissertation].

14. Tsou C, Avtonomov D, Larsen B, Tucholska M, Choi H, Gingras A, Nesvizhskii AI. 2015. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat Methods. 12(3):258-264. https://doi.org/10.1038/nmeth.3255

15. Soufi B, Krug K, Harst A, Macek B. Characterization of the E. coli proteome and its modifications during growth and ethanol stress. Front. Microbiol.. 6 https://doi.org/10.3389/fmicb.2015.00103

16. Ma B. 2015. Novor: Real-Time Peptide de Novo Sequencing Software. J. Am. Soc. Mass Spectrom.. 26(11):1885-1894. https://doi.org/10.1007/s13361-015-1204-0

References for UPS1 and UPS2

1. Geiger T, Cox J, Mann M. 2010. Proteomics on an Orbitrap Benchtop Mass Spectrometer Using All-ion Fragmentation. Molecular & Cellular Proteomics. 9(10):2252-2261. https://doi.org/10.1074/mcp.m110.001537

2. Wi?niewski JR, Ostasiewicz P, Du? K, Zieli?ska DF, Gnad F, Mann M. 2012. Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma. Mol Syst Biol. 8(1):611. https://doi.org/10.1038/msb.2012.44

3. Ivanov AR, Colangelo CM, Dufresne CP, Friedman DB, Lilley KS, Mechtler K, Phinney BS, Rose KL, Rudnick PA, Searle BC, et al. 2013. Interlaboratory studies and initiatives developing standards for proteomics. Proteomics. 13(6):904-909. https://doi.org/10.1002/pmic.201200532

4. Krey JF, Wilmarth PA, Shin J, Klimek J, Sherman NE, Jeffery ED, Choi D, David LL, Barr-Gillespie PG. 2014. Accurate Label-Free Protein Quantitation with High- and Low-Resolution Mass Spectrometers. J. Proteome Res.. 13(2):1034-1044. https://doi.org/10.1021/pr401017h

5. Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M. 2014. Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ. Molecular & Cellular Proteomics. 13(9):2513-2526. https://doi.org/10.1074/mcp.m113.031591

6. Laskay ÜA, Srzenti? K, Monod M, Tsybin YO. 2014. Extended bottom-up proteomics with secreted aspartic protease Sap9. Journal of Proteomics. 11020-31. https://doi.org/10.1016/j.jprot.2014.07.035

7. Beck S, Michalski A, Raether O, Lubeck M, Kaspar S, Goedecke N, Baessmann C, Hornburg D, Meier F, Paron I, et al. 2015. The Impact II, a Very High-Resolution Quadrupole Time-of-Flight Instrument (QTOF) for Deep Shotgun Proteomics *. Molecular & Cellular Proteomics. 14(7):2014-2029. https://doi.org/10.1074/mcp.m114.047407

8. K?rl? K, Karaca S, Dehne HJ, Samwer M, Pan KT, Lenz C, Urlaub H, Görlich D. A deep proteomics perspective on CRM1-mediated nuclear export and nucleocytoplasmic partitioning. 4 https://doi.org/10.7554/elife.11466