Skip to main content
SpringerLink
Log in

Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics

  • Protocol
  • First Online:
Serum/Plasma Proteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2628))

Abstract

In targeted proteomics experiments, selecting the appropriate proteotypic peptides as surrogate for the target protein is a crucial pre-acquisition step. This step is largely a bioinformatics exercise that involves integrating information on the peptides and proteins and using various software tools and knowledgebases. We present here a few resources that automate and simplify the selection process to a great degree. These tools and knowledgebases were developed primarily to streamline targeted proteomics assay development and include PeptidePicker, PeptidePickerDB, MRMAssayDB, MouseQuaPro, and PeptideTracker. We have used these tools to develop and document thousands of targeted proteomics assays, many of them for plasma proteins with focus on human and mouse. An important aspect in all these resources is the integrative approach on which they are based. Using these tools in the first steps of designing a singleplexed or multiplexed targeted proteomic experiment can reduce the necessary experimental steps tremendously. All the tools and knowledgebases we describe here are Web-based and freely accessible so scientists can query the information conveniently from the browser. This chapter provides an overview of these software tools and knowledgebases, their content, and how to use them for targeted plasma proteomics. We further demonstrate how to use them with the results of the HUPO Human Plasma Proteome Project to produce a new database of 3.8 k targeted assays for known human plasma proteins. Upon experimental validation, these assays should help in the further quantitative characterizing of the plasma proteome.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Lange V, Picotti P, Domon B, Aebersold R (2008) Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol 4:222

    Article  Google Scholar 

  2. Kirkpatrick DS, Gerber SA, Gygi SP (2005) The absolute quantification strategy: a general procedure for the quantification of proteins and post-translational modifications. Methods 35(3):265–273

    Article  CAS  Google Scholar 

  3. Schiess R, Wollscheid B, Aebersold R (2009) Targeted proteomic strategy for clinical biomarker discovery. Mol Oncol 3(1):33–44

    Article  CAS  Google Scholar 

  4. Shi T, Song E, Nie S, Rodland KD, Liu T, Qian WJ, Smith RD (2016) Advances in targeted proteomics and applications to biomedical research. Proteomics 16(15–16):2160–2182

    Article  CAS  Google Scholar 

  5. Pappireddi N, Martin L, Wuhr M (2019) A review on quantitative multiplexed proteomics. Chembiochem 20(10):1210–1224

    Article  CAS  Google Scholar 

  6. Peterson AC, Russell JD, Bailey DJ, Westphall MS, Coon JJ (2012) Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol Cell Proteomics 11(11):1475–1488

    Article  Google Scholar 

  7. Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP (2003) Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc Natl Acad Sci U S A 100(12):6940–6945

    Article  CAS  Google Scholar 

  8. 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(8):1111–1121

    Article  CAS  Google Scholar 

  9. Aebersold R, Mann M (2003) Mass spectrometry-based proteomics. Nature 422(6928):198–207

    Article  CAS  Google Scholar 

  10. Keshishian H, Addona T, Burgess M, Mani DR, Shi X, Kuhn E, Sabatine MS, Gerszten RE, Carr SA (2009) Quantification of cardiovascular biomarkers in patient plasma by targeted mass spectrometry and stable isotope dilution. Mol Cell Proteomics 8:2339–2349

    Article  CAS  Google Scholar 

  11. Picotti P, Bodenmiller B, Aebersold R (2013) Proteomics meets the scientific method. Nat Methods 10(1):25–27

    Article  Google Scholar 

  12. Percy AJ, Chambers AG, Yang J, Hardie DB, Borchers CH (2014) Advances in multiplexed MRM-based protein biomarker quantitation toward clinical utility. Biochim Biophys Acta 1844(5):917–926

    Article  CAS  Google Scholar 

  13. Addona TA, Abbatiello SE, Schilling B, Skates SJ, Mani DR, Bunk DM, Spiegelman CH, Zimmerman LJ, Ham AJ, Keshishian H, Hall SC, Allen S, Blackman RK, Borchers CH, Buck C, Cardasis HL, Cusack MP, Dodder NG, Gibson BW, Held JM, Hiltke T, Jackson A, Johansen EB, Kinsinger CR, Li J, Mesri M, Neubert TA, Niles RK, Pulsipher TC, Ransohoff D, Rodriguez H, Rudnick PA, Smith D, Tabb DL, Tegeler TJ, Variyath AM, Vega-Montoto LJ, Wahlander A, Waldemarson S, Wang M, Whiteaker JR, Zhao L, Anderson NL, Fisher SJ, Liebler DC, Paulovich AG, Regnier FE, Tempst P, Carr SA (2009) Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat Biotechnol 27(7):633–641

    Article  CAS  Google Scholar 

  14. Tilburg J, Michaud SA, Maracle CX, Versteeg HH, Borchers CH, van Vlijmen BJM, Mohammed Y (2020) Plasma protein signatures of a murine venous thrombosis model and Slc44a2 knockout mice using quantitative-targeted proteomics. Thromb Haemost 120(3):423–436

    Article  Google Scholar 

  15. Mohammed Y, van Vlijmen BJ, Yang J, Percy AJ, Palmblad M, Borchers CH, Rosendaal FR (2017) Multiplexed targeted proteomic assay to assess coagulation factor concentrations and thrombosis-associated cancer. Blood Adv 1(15):1080–1087

    Article  CAS  Google Scholar 

  16. Wu L; Agilent Technologies Inc (2020) Peptide quantification in plasma using the Agilent 6495 Triple Quadrupole LC/MS coupled with the Agilent 1290 Infinity II LC system. In: Agilent technologies application note proteomics, Agilent Technologies Inc: Santa Clara, California, USA

    Google Scholar 

  17. Bache N, Geyer PE, Bekker-Jensen DB, Hoerning O, Falkenby L, Treit PV, Doll S, Paron I, Muller JB, Meier F, Olsen JV, Vorm O, Mann M (2018) A novel LC system embeds analytes in pre-formed gradients for rapid, ultra-robust proteomics. Mol Cell Proteomics 17(11):2284–2296

    Article  CAS  Google Scholar 

  18. Burnum-Johnson KE, Nie S, Casey CP, Monroe ME, Orton DJ, Ibrahim YM, Gritsenko MA, Clauss TR, Shukla AK, Moore RJ, Purvine SO, Shi T, Qian W, Liu T, Baker ES, Smith RD (2016) Simultaneous proteomic discovery and targeted monitoring using liquid chromatography, ion mobility spectrometry, and mass spectrometry. Mol Cell Proteomics 15(12):3694–3705

    Article  CAS  Google Scholar 

  19. Liebler DC, Zimmerman LJ (2013) Targeted quantitation of proteins by mass spectrometry. Biochemistry 52(22):3797–3806

    Article  CAS  Google Scholar 

  20. Mohammed Y, Domański D, Jackson AM, Smith DS, Deelder AM, Palmblad M, Borchers CH (2014) PeptidePicker: a scientific workflow with web interface for selecting appropriate peptides for targeted proteomics experiments. J Proteome 106:151–161

    Article  CAS  Google Scholar 

  21. Perez-Riverol Y, Alpi E, Wang R, Hermjakob H, Vizcaino JA (2015) Making proteomics data accessible and reusable: current state of proteomics databases and repositories. Proteomics 15(5–6):930–949

    Article  CAS  Google Scholar 

  22. Vizcaino JA, Foster JM, Martens L (2010) Proteomics data repositories: providing a safe haven for your data and acting as a springboard for further research. J Proteome 73(11):2136–2146

    Article  CAS  Google Scholar 

  23. Riffle M, Eng JK (2009) Proteomics data repositories. Proteomics 9(20):4653–4663

    Article  CAS  Google Scholar 

  24. Deutsch EW, Bandeira N, Sharma V, Perez-Riverol Y, Carver JJ, Kundu DJ, Garcia-Seisdedos D, Jarnuczak AF, Hewapathirana S, Pullman BS, Wertz J, Sun Z, Kawano S, Okuda S, Watanabe Y, Hermjakob H, MacLean B, MacCoss MJ, Zhu Y, Ishihama Y, Vizcaino JA (2020) The ProteomeXchange consortium in 2020: enabling ‘big data’ approaches in proteomics. Nucleic Acids Res 48(D1):D1145–D1152

    CAS  Google Scholar 

  25. Mohammed Y, Domanski D, Jackson AM, Smith DS, Deelder AM, Palmblad M, Borchers CH (2014) PeptidePicker: a scientific workflow with web interface for selecting appropriate peptides for targeted proteomics experiments. J Proteome 106:151–161

    Article  CAS  Google Scholar 

  26. Mohammed Y, Borchers CH (2015) An extensive library of surrogate peptides for all human proteins. J Proteome 129:93–97

    Article  CAS  Google Scholar 

  27. Bhowmick P, Roome S, Borchers CH, Goodlett DR, Mohammed Y (2021) An update on MRMAssayDB: a comprehensive resource for targeted proteomics assays in the community. J Proteome Res 20(4):2105–2115

    Article  CAS  Google Scholar 

  28. Mohammed Y, Bhowmick P, Smith DS, Domanski D, Jackson AM, Michaud SA, Malchow S, Percy AJ, Chambers AG, Palmer A, Zhang S, Sickmann A, Borchers CH (2017) PeptideTracker: a knowledge base for collecting and storing information on protein concentrations in biological tissues. Proteomics 17(7). https://doi.org/10.1002/pmic.201600210

  29. Mohammed Y, Bhowmick P, Michaud SA, Sickmann A, Borchers CH (2021) Mouse Quantitative Proteomics Knowledgebase: reference protein concentration ranges in 20 mouse tissues using 5000 quantitative proteomics assays. Bioinformatics 37:1900

    Article  CAS  Google Scholar 

  30. R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. www.R-project.org

    Google Scholar 

  31. Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Natale DA, O’Donovan C, Redaschi N, Yeh LS (2004) UniProt: the Universal Protein knowledgebase. Nucleic Acids Res 32(Database issue):D115–D119

    Article  CAS  Google Scholar 

  32. UniProt Consortium, T (2018) UniProt: the universal protein knowledgebase. Nucleic Acids Res 46(5):2699

    Article  Google Scholar 

  33. UniProt, C (2021) UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res 49(D1):D480–D489

    Article  Google Scholar 

  34. Sherry ST, Ward M, Sirotkin K (1999) dbSNP-database for single nucleotide polymorphisms and other classes of minor genetic variation. Genome Res 9(8):677–679

    Article  CAS  Google Scholar 

  35. Wilkins MR, Gasteiger E, Bairocj A, Sanchez JC, Williams KL, Appel RD, Hochstrasser DF (1999) Protein identification and analysis tools in the ExPASy server. In: Link AJ (ed) Methods in molecular biology, vol 112. Humana Press, Totowa, pp 531–552

    Google Scholar 

  36. Deutsch EW, Lam H, Aebersold R (2008) PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep 9:429–434

    Article  CAS  Google Scholar 

  37. Vizcaino JA, Cote RG, Csordas A, Dianes JA, Fabregat A, Foster JM, Griss J, Alpi E, Birim M, Contell J, O’Kelly G, Schoenegger A, Ovelleiro D, Perez-Riverol Y, Reisinger F, Ríos D, Wang R, Hermjakob H (2013) The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res 41(Database issue):D1063–D1069

    CAS  Google Scholar 

  38. Craig R, Cortens JP, Beavis RC (2004) An open source system for analyzing, validating and storing protein identification data. J Proteome Res 3:1234–1242

    Article  CAS  Google Scholar 

  39. Fenyo D, Eriksson J, Beavis R (2010) Mass spectrometric protein identification using the global proteome machine. Methods Mol Biol 673:189–202

    Article  CAS  Google Scholar 

  40. Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. In: Sixth symposium on operating systems design and implementation (OSDI ’04), San Francisco, California, USA, 6–8 Dec 2004. http://static.googleusercontent.com/media/research.google.com/es/us/archive/mapreduce-osdi04.pdf

  41. Perez-Riverol Y, Sánchez A, Ramos Y, Schmidt A, Müller M, Betancourt L, González LJ, Vera R, Padron G, Besada V (2011) In silico analysis of accurate proteomics, complemented by selective isolation of peptides. J Proteome 74(10):2071–2082

    Article  CAS  Google Scholar 

  42. Schaeffer M, Gateau A, Teixeira D, Michel PA, Zahn-Zabal M, Lane L (2017) The neXtProt peptide uniqueness checker: a tool for the proteomics community. Bioinformatics 33(21):3471–3472

    Article  CAS  Google Scholar 

  43. Magrane M, Consortium U (2011) UniProt Knowledgebase: a hub of integrated protein data. Database (Oxford) 2011:bar009

    Article  Google Scholar 

  44. UniProt Consortium (2009) The Universal Protein Resource (UniProt) 2009. Nucleic Acids Res 37:D169–D174

    Article  Google Scholar 

  45. Peptide Cutter, https://www.expasy.org/resources/peptidecutter (accessed December 01, 2022)

  46. Keil B (1992) Specificity of proteolysis. Springer-Verlag, Berlin/Heidelberg/New York, p 335

    Book  Google Scholar 

  47. Lai MC, Topp EM (1999) Solid-state chemical stability of proteins and peptides. J Pharm Sci 88(5):489–500

    Article  CAS  Google Scholar 

  48. Spouge J, Phan L, Sherry ST (2014) Computation of average heterozygosity and standard error for dbSNP RefSNP clusters. https://www.ncbi.nlm.nih.gov/projects/SNP/Hetfreq.html (Accessed December 01, 2022)

  49. Wheeler DL, Barrett T, Benson DA, Bryant SH, Canese K, Chetvernin V, Church DM, DiCuccio M, Edgar R, Federhen S, Geer LY, Kapustin Y, Khovayko O, Landsman D, Lipman DJ, Madden TL, Maglott DR, Ostell J, Miller V, Pruitt KD, Schuler GD, Sequeira E, Sherry ST, Sirotkin K, Souvorov A, Starchenko G, Tatusov RL, Tatusova TA, Wagner L, Yaschenko E (2007) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 35(Database issue):D5–D12

    Article  CAS  Google Scholar 

  50. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJ, Groth P, Goble C, Grethe JS, Heringa J, t Hoen PA, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone SA, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B (2016) The FAIR guiding principles for scientific data management and stewardship. Sci Data 3:160018

    Article  Google Scholar 

  51. Farrah T, Deutsch EW, Kreisberg R, Sun Z, Campbell DS, Mendoza L, Kusebauch U, Brusniak MY, Hüttenhain R, Schiess R, Selevsek N, Aebersold R, Moritz RL (2012) PASSEL: the PeptideAtlas SRM experiment library. Proteomics 12(8):1170–1175

    Article  CAS  Google Scholar 

  52. 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 A, Clinical Proteomic Tumor Analysis Consortium (CPTAC) (2014) CPTAC Assay Portal: a repository of targeted proteomic assays. Nat Methods 11(7):703–704

    Article  CAS  Google Scholar 

  53. 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(9):4205–4210

    Article  CAS  Google Scholar 

  54. 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, Tran T, Demir E, Aitchison JD, Sander C, Hood L, Aebersold R, Moritz RL (2016) Human SRMAtlas: a resource of targeted assays to quantify the complete human proteome. Cell 166(3):766–778

    Article  CAS  Google Scholar 

  55. 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 SHE, 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(5):602–612

    Article  CAS  Google Scholar 

  56. Picotti P, Clement-Ziza M, Lam H, Campbell DS, Schmidt A, Deutsch EW, Rost H, Sun Z, Rinner O, Reiter L, Shen Q, Michaelson JJ, Frei A, Alberti S, Kusebauch U, Wollscheid B, Moritz RL, Beyer A, Aebersold R (2013) A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature 494(7436):266–270

    Article  CAS  Google Scholar 

  57. Bhowmick P, Mohammed Y, Borchers CH (2018) MRMAssayDB: an integrated resource for validated targeted proteomics assays. Bioinformatics 34(20):3566–3571

    Article  CAS  Google Scholar 

  58. Hoksza D, Gawron P, Ostaszewski M, Schneider R (2018) MolArt: a molecular structure annotation and visualization tool. Bioinformatics 34(23):4127–4128

    Article  CAS  Google Scholar 

  59. Nehrt NL, Peterson TA, Park D, Kann MG (2012) Domain landscapes of somatic mutations in cancer. BMC Genomics 13(Suppl 4):S9

    Article  CAS  Google Scholar 

  60. Lam SD, Dawson NL, Das S, Sillitoe I, Ashford P, Lee D, Lehtinen S, Orengo CA, Lees JG (2016) Gene3D: expanding the utility of domain assignments. Nucleic Acids Res 44(D1):D404–D409

    Article  CAS  Google Scholar 

  61. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30

    Article  CAS  Google Scholar 

  62. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering CV (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47(D1):D607–D613

    Article  CAS  Google Scholar 

  63. Rodchenkov I, Babur O, Luna A, Aksoy BA, Wong JV, Fong D, Franz M, Siper MC, Cheung M, Wrana M, Mistry H, Mosier L, Dlin J, Wen Q, O’Callaghan C, Li W, Elder G, Smith PT, Dallago C, Cerami E, Gross B, Dogrusoz U, Demir E, Bader GD, Sander C (2020) Pathway Commons 2019 Update: integration, analysis and exploration of pathway data. Nucleic Acids Res 48(D1):D489–D497

    CAS  Google Scholar 

  64. Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O, Anwar N, Schultz N, Bader GD, Sander C (2011) Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res 39(Database issue):D685–D690

    Article  CAS  Google Scholar 

  65. Binns D, Dimmer E, Huntley R, Barrell D, O’Donovan C, Apweiler R (2009) QuickGO: a web-based tool for Gene Ontology searching. Bioinformatics 25(22):3045–3046

    Article  CAS  Google Scholar 

  66. Pinero J, Bravo A, Queralt-Rosinach N, Gutierrez-Sacristan A, Deu-Pons J, Centeno E, Garcia-Garcia J, Sanz F, Furlong LI (2017) DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res 45(D1):D833–D839

    Article  CAS  Google Scholar 

  67. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Maciejewski A, Gale N, Wilson A, Chin L, Cummings R, Le D, Pon A, Knox C, Wilson M (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46(D1):D1074–D1082

    Article  CAS  Google Scholar 

  68. Mouse Genome Sequencing C, Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, Agarwala R, Ainscough R, Alexandersson M, An P, Antonarakis SE, Attwood J, Baertsch R, Bailey J, Barlow K, Beck S, Berry E, Birren B, Bloom T, Bork P, Botcherby M, Bray N, Brent MR, Brown DG, Brown SD, Bult C, Burton J, Butler J, Campbell RD, Carninci P, Cawley S, Chiaromonte F, Chinwalla AT, Church DM, Clamp M, Clee C, Collins FS, Cook LL, Copley RR, Coulson A, Couronne O, Cuff J, Curwen V, Cutts T, Daly M, David R, Davies J, Delehaunty KD, Deri J, Dermitzakis ET, Dewey C, Dickens NJ, Diekhans M, Dodge S, Dubchak I, Dunn DM, Eddy SR, Elnitski L, Emes RD, Eswara P, Eyras E, Felsenfeld A, Fewell GA, Flicek P, Foley K, Frankel WN, Fulton LA, Fulton RS, Furey TS, Gage D, Gibbs RA, Glusman G, Gnerre S, Goldman N, Goodstadt L, Grafham D, Graves TA, Green ED, Gregory S, Guigo R, Guyer M, Hardison RC, Haussler D, Hayashizaki Y, Hillier LW, Hinrichs A, Hlavina W, Holzer T, Hsu F, Hua A, Hubbard T, Hunt A, Jackson I, Jaffe DB, Johnson LS, Jones M, Jones TA, Joy A, Kamal M, Karlsson EK, Karolchik D, Kasprzyk A, Kawai J, Keibler E, Kells C, Kent WJ, Kirby A, Kolbe DL, Korf I, Kucherlapati RS, Kulbokas EJ, Kulp D, Landers T, Leger JP, Leonard S, Letunic I, Levine R, Li J, Li M, Lloyd C, Lucas S, Ma B, Maglott DR, Mardis ER, Matthews L, Mauceli E, Mayer JH, McCarthy M, McCombie WR, McLaren S, McLay K, McPherson JD, Meldrim J, Meredith B, Mesirov JP, Miller W, Miner TL, Mongin E, Montgomery KT, Morgan M, Mott R, Mullikin JC, Muzny DM, Nash WE, Nelson JO, Nhan MN, Nicol R, Ning Z, Nusbaum C, O’Connor MJ, Okazaki Y, Oliver K, Overton-Larty E, Pachter L, Parra G, Pepin KH, Peterson J, Pevzner P, Plumb R, Pohl CS, Poliakov A, Ponce TC, Ponting CP, Potter S, Quail M, Reymond A, Roe BA, Roskin KM, Rubin EM, Rust AG, Santos R, Sapojnikov V, Schultz B, Schultz J, Schwartz MS, Schwartz S, Scott C, Seaman S, Searle S, Sharpe T, Sheridan A, Shownkeen R, Sims S, Singer JB, Slater G, Smit A, Smith DR, Spencer B, Stabenau A, Stange-Thomann N, Sugnet C, Suyama M, Tesler G, Thompson J, Torrents D, Trevaskis E, Tromp J, Ucla C, Ureta-Vidal A, Vinson JP, Von Niederhausern AC, Wade CM, Wall M, Weber RJ, Weiss RB, Wendl MC, West AP, Wetterstrand K, Wheeler R, Whelan S, Wierzbowski J, Willey D, Williams S, Wilson RK, Winter E, Worley KC, Wyman D, Yang S, Yang SP, Zdobnov EM, Zody MC, Lander ES (2002) Initial sequencing and comparative analysis of the mouse genome. Nature 420(6915):520–562

    Article  Google Scholar 

  69. Lloyd KCK, Adams DJ, Baynam G, Beaudet AL, Bosch F, Boycott KM, Braun RE, Caulfield M, Cohn R, Dickinson ME, Dobbie MS, Flenniken AM, Flicek P, Galande S, Gao X, Grobler A, Heaney JD, Herault Y, de Angelis MH, Lupski JR, Lyonnet S, Mallon AM, Mammano F, MacRae CA, McInnes R, McKerlie C, Meehan TF, Murray SA, Nutter LMJ, Obata Y, Parkinson H, Pepper MS, Sedlacek R, Seong JK, Shiroishi T, Smedley D, Tocchini-Valentini G, Valle D, Wang CL, Wells S, White J, Wurst W, Xu Y, Brown SDM (2020) The Deep Genome Project. Genome Biol 21(1):18

    Article  Google Scholar 

  70. Hmeljak J, Justice MJ (2019) From gene to treatment: supporting rare disease translational research through model systems. Dis Model Mech 12(2):dmm039271

    Article  CAS  Google Scholar 

  71. Guo S, Jiang X, Mao B, Li QX (2019) The design, analysis and application of mouse clinical trials in oncology drug development. BMC Cancer 19(1):718

    Article  Google Scholar 

  72. Zuberi A, Lutz C (2016) Mouse models for drug discovery. Can new tools and technology improve translational power? ILAR J 57(2):178–185

    Article  CAS  Google Scholar 

  73. Uhl EW, Warner NJ (2015) Mouse models as predictors of human responses: evolutionary medicine. Curr Pathobiol Rep 3(3):219–223

    Article  Google Scholar 

  74. Li Q, Qin Z, Wang Q, Xu T, Yang Y, He Z (2019) Applications of genome editing technology in animal disease modeling and gene therapy. Comput Struct Biotechnol J 17:689–698

    Article  CAS  Google Scholar 

  75. Birling MC, Herault Y, Pavlovic G (2017) Modeling human disease in rodents by CRISPR/Cas9 genome editing. Mamm Genome 28(7–8):291–301

    Article  CAS  Google Scholar 

  76. Pavkovic M, Pantano L, Gerlach CV, Brutus S, Boswell SA, Everley RA, Shah JV, Sui SH, Vaidya VS (2019) Multi omics analysis of fibrotic kidneys in two mouse models. Sci Data 6(1):92

    Article  Google Scholar 

  77. Hasin Y, Seldin M, Lusis A (2017) Multi-omics approaches to disease. Genome Biol 18(1):83

    Article  Google Scholar 

  78. Cisek K, Krochmal M, Klein J, Mischak H (2016) The application of multi-omics and systems biology to identify therapeutic targets in chronic kidney disease. Nephrol Dial Transplant 31(12):2003–2011

    Article  Google Scholar 

  79. Omenn GS, States DJ, Adamski M, Blackwell TW, Menon R, Hermjakob H, Apweiler R, Haab BB, Simpson RJ, Eddes JS, Kapp EA, Moritz RL, Chan DW, Rai AJ, Admon A, Aebersold R, Eng J, Hancock WS, Hefta SA, Meyer H, Paik YK, Yoo JS, Ping P, Pounds J, Adkins J, Qian X, Wang R, Wasinger V, Wu CY, Zhao X, Zeng R, Archakov A, Tsugita A, Beer I, Pandey A, Pisano M, Andrews P, Tammen H, Speicher DW, Hanash SM (2005) Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics 5(13):3226–3245

    Article  CAS  Google Scholar 

  80. Hanash S, Celis JE (2002) The Human Proteome Organization: a mission to advance proteome knowledge. Mol Cell Proteomics 1(6):413–414

    Article  CAS  Google Scholar 

  81. Deutsch EW, Mendoza L, Shteynberg D, Slagel J, Sun Z, Moritz RL (2015) Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics. Proteomics Clin Appl 9(7–8):745–754

    Article  CAS  Google Scholar 

  82. 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

    Article  CAS  Google Scholar 

  83. 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 12(1):125–131

    Article  Google Scholar 

  84. Sanders WS, Bridges SM, McCarthy FM, Nanduri B, Burgess SC (2007) Prediction of peptides observable by mass spectrometry applied at the experimental set level. BMC Bioinformatics 1(8 Suppl 7):S23

    Article  Google Scholar 

  85. Boja ES, Rodriguez H (2012) Mass spectrometry-based targeted quantitative proteomics: achieving sensitive and reproducible detection of proteins. Proteomics 12:1093–1110

    Article  CAS  Google Scholar 

  86. PeptideAtlas. http://www.peptideatlas.org/ (accessed December 01, 2022)

  87. PABST Peptide Atlas Best SRM Transition tool, http://tools.proteomecenter.org/wiki/index.php?title=PABST (accessed December 01, 2022)

  88. Brusniak M-YK, Kwok S-T, Christiansen M, Campbell D, Reiter L, Picotti P, Kusebauch U, Ramos H, Deutsch EW, Chen J, Moritz RL, Aebersold R (2011) ATAQS: a computational software tool for high throughput transition optimization and validation for selected reaction monitoring mass spectrometry. BMC Bioinformatics 12:78

    Article  Google Scholar 

  89. Mead JA, Bianco L, Ottone V, Barton C, Kay RG, Lilley KS, Bond NJ, Bessant C (2009) MRMaid, the web-based tool for designing multiple reaction monitoring (MRM) transitions. Mol Cell Proteomics 8(4):696–705

    Article  CAS  Google Scholar 

  90. Walsh GM, Lin S, Evans DM, Khosrovi-Eghbal A, Beavis RC, Kast J (2009) Implementation of a data repository-driven approach for targeted proteomics experiments by multiple reaction monitoring. J Proteome 72:838–852

    Article  CAS  Google Scholar 

  91. Picotti P, Clément-Ziza M, Lam H, Campbell DS, Schmidt A, Deutsch EW, Röst H, Sun Z, Rinner O, Reiter L, Shen Q, Michaelson JJ, Frei A, Alberti S, Kusebauch U, Wollscheid B, Moritz RL, Beyer A, Aebersold R (2013) A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature 494(7436):266–270

    Article  CAS  Google Scholar 

  92. SRMAtlas, http://www.srmatlas.org/ (accessed December 01, 2022)

  93. Lange V, Malmström JA, Didion J, King NL, Johansson BP, Schäfer J, Rameseder J, Wong CH, Deutsch EW, Brusniak MY, Bühlmann P, Björck L, Domon B, Aebersold R (2008) Targeted quantitative analysis of Streptococcus pyogenes virulence factors by multiple reaction monitoring. Mol Cell Proteomics 7(8):1489–1500

    Article  CAS  Google Scholar 

  94. Targeted Identification for Quantitative Analysis by MRM – TIQAM, http://tools.proteomecenter.org/TIQAM/TIQAM.html (accessed December 01, 2022)

  95. MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, Kern R, Tabb DL, Liebler DC, MacCoss MJ (2010) Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26(7):966–968

    Article  CAS  Google Scholar 

  96. Skyline, https://skyline.ms/ (accessed December 01, 2022)

  97. Sherwood C, Eastham A, Peterson A, Eng JK, Shteynberg D, Mendoza L, Deutsch E, Risler J, Lee LW, Tasman N, Aebersold R, Henry L, Martin DB (2009) MaRiMba: a software application for spectral library-based MRM transition list assembly. J Proteome Res 8(10):4396–4405

    Article  CAS  Google Scholar 

  98. Cham Mead JA, Bianco L, Bessant C (2010) Free computational resources for designing selected reaction monitoring transitions. Proteomics 10(6):1106–1126

    Article  Google Scholar 

  99. PeptideSieve, http://tools.proteomecenter.org/wiki/index.php?title=Software:PeptideSieve (accessed December 01, 2022)

  100. Webb-Robertson BJ, Cannon WR, Oehmen CS, Shah AR, Gurumoorthi V, Lipton MS, Waters K (2008) A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics. Bioinformatics 24(13). https://doi.org/10.1093/bioinformatics/btn218

  101. STEPP, https://cbb.pnnl.gov/portal/software/stepp.html (accessed February 15, 2020)

  102. GenePattern, https://www.genepattern.org/ (accessed December 01, 2022)

  103. Braisted JC, Kuntumalla S, Vogel C, Marcotte EM, Rodrigues AR, Wang R, Huang S-T, Ferlanti ES, Saeed AI, Fleischmann RD, Peterson SN, Pieper R (2008) The APEX Quantitative Proteomics Tool: generating protein quantitation estimates from LC-MS/MS proteomics results. BMC Bioinformatics 9:529

    Article  Google Scholar 

  104. The APEX Quantitative Proteomics Tool, http://sourceforge.net/projects/apexqpt/ (accessed December 01, 2022)

  105. Wedge DC, Gaskell SJ, Hubbard SJ, Kell DB, Lau KW, Eyers C (2007) Peptide detectability following ESI mass spectrometry: prediction using genetic programming. In: Thierens D, Beyer H-G, Bongard J, Branke J, Clark JA, Cliff D, Congdon CB, Deb K, Doerr B, Tim K, Kumar S, Miller JF, Moore J, Neumann F, Pelikan M, Poli R, Sastry K, Stanley KO, Stutzle T, Watson RA, Wegener I (eds) 9th annual conference on genetic and evolutionary computation (GECCO). ACM Press, London, pp 2219–2225

    Chapter  Google Scholar 

  106. Whiteaker JR, Halusa GN, Hoofnagle AN, Sharma V, MacLean B, Yan P, Wrobel JA, Kennedy J, Mani DR, Zimmerman LJ, Meyer MR, Mesri M, Boja E, Carr SA, Chan DW, Chen X, Chen J, Davies SR, Ellis MJ, Fenyo D, Hiltke T, Ketchum KA, Kinsinger C, Kuhn E, Liebler DC, Liu T, Loss M, MacCoss MJ, Qian WJ, Rivers R, Rodland KD, Ruggles KV, Scott MG, Smith RD, Thomas S, Townsend RR, Whiteley G, Wu C, Zhang H, Zhang Z, Rodriguez H, Paulovich AG (2016) Using the CPTAC assay portal to identify and implement highly characterized targeted proteomics assays. Methods Mol Biol 1410:223–236

    Article  CAS  Google Scholar 

  107. 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, Clinical Proteomic Tumor Analysis, C (2014) CPTAC Assay Portal: a repository of targeted proteomic assays. Nat Methods 11(7):703–704

    Article  CAS  Google Scholar 

  108. Carr SA, Abbatiello SE, Ackermann BL, Borchers C, Domon B, Deutsch EW, Grant RP, Hoofnagle AN, Huttenhain R, Koomen JM, Liebler DC, Liu T, MacLean B, Mani DR, Mansfield E, Neubert H, Paulovich AG, Reiter L, Vitek O, Aebersold R, Anderson L, Bethem R, Blonder J, Boja E, Botelho J, Boyne M, Bradshaw RA, Burlingame AL, Chan D, Keshishian H, Kuhn E, Kinsinger C, Lee JS, Lee SW, Moritz R, Oses-Prieto J, Rifai N, Ritchie J, Rodriguez H, Srinivas PR, Townsend RR, Van Eyk J, Whiteley G, Wiita A, Weintraub S (2014) Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach. Mol Cell Proteomics 13(3):907–917

    Article  CAS  Google Scholar 

  109. Krokhin OV (2006) Sequence-specific retention calculator. Algorithm for peptide retention prediction in ion-pair RP-HPLC: application to 300- and 100-A pore size C18 sorbents. Anal Chem 78(22):7785–7795

    Article  CAS  Google Scholar 

  110. Palmblad M, Ramström M, Bailey CG, McCutchen-Maloney SL, Bergquist J, Zeller LC (2004) Protein identification by liquid chromatography-mass spectrometry using retention time prediction. J Chromatogr B 803(1):131–135

    Article  CAS  Google Scholar 

Download references

Acknowledgments

We are grateful to Genome Canada and Genome British Columbia for their financial support (grants 282PQP, 204PRO, 214PRO, 264PRO, 181-MRM-GAPP).

Author information

Authors and Affiliations

  1. Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, ZA, Netherlands

    Yassene Mohammed

  2. University of Victoria − Genome BC Proteomics Centre, Victoria, BC, Canada

    Yassene Mohammed & David Goodlett

  3. Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada

    Yassene Mohammed & David Goodlett

  4. University of Gdansk, International Centre for Cancer Vaccine Science, Gdansk, Poland

    David Goodlett

  5. Proteomics Centre, Segal Cancer Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada

    Christoph H. Borchers

  6. Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, QC, Canada

    Christoph H. Borchers

  7. Division of Experimental Medicine, McGill University, Montreal, QC, Canada

    Christoph H. Borchers

  8. Department of Pathology, McGill University, Montreal, QC, Canada

    Christoph H. Borchers

Authors
  1. Yassene Mohammed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Goodlett
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christoph H. Borchers
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yassene Mohammed .

Editor information

Editors and Affiliations

  1. Molecular Proteomics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia

    David W. Greening

  2. Department of Biochemistry and Chemistry, School of Agriculture, Biomedicine and Environment, La Trobe Institute for Molecular Science, Melbourne, VIC, Australia

    Richard J. Simpson

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Mohammed, Y., Goodlett, D., Borchers, C.H. (2023). Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics. In: Greening, D.W., Simpson, R.J. (eds) Serum/Plasma Proteomics. Methods in Molecular Biology, vol 2628. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2978-9_32

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2978-9_32

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2977-2

  • Online ISBN: 978-1-0716-2978-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation