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  • 1. Ce—M—M— Research Center for Molecular Medicine of the Austrian Academy of Sciences The isobar R package:Analysis of quantitative proteomics data F. Breitwieser J. Colinge Bioinformatics Open Source Conference, 2011 1 / 10
  • 2. isobar for Analysis of Quantitative Proteomics DataCe—M—M— F. Breitwieser & J. Colinge Journal of Proteome Research | 3b2 | ver.9 | 6/5/011 | 12:56 | Msc: pr-2010-012784 | TEID: sbh00 | BATID: 00000 | Pages: 8.99 ARTICLE pubs.acs.org/jpr 1 General Statistical Modeling of Data from Protein Relative 2 Expression Isobaric Tags 3 Florian P. Breitwieser,† Andr M€ller,† Loïc Dayon,‡ Thomas K€cher,z Alexandre Hainard,‡ Peter Pichler,§ e u o 4 Ursula Schmidt-Erfurth,|| Giulio Superti-Furga,† Jean-Charles Sanchez,‡ Karl Mechtler,z Keiryn L. Bennett,† 5 and Jacques Colinge*,† † 6 CeMM, Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria ‡ 7 Biomedical Proteomics Group, Department of Structural Biology and Bioinformatics, Faculty of Medicine, University of Geneva, 8 Geneva, Switzerland ■ 9 Mass Spectrometers to identify and quantify proteins z Institute of Molecular Pathology, Vienna, Austria § 10 CD Laboratory for Proteome Analysis, University of Vienna, 1030 Vienna, Austria ■ isobar: R package for handling isobarically tagged data ) 11 Department of Ophtalmology, Medical University of Vienna, Vienna, Austria 12 □b Supporting Information S analyze and visualize protein expression changes 13 interactive within R □ ABSTRACT: Quantitative comparison of the protein content of biological 14 samples is a fundamental tool of research. The TMT and iTRAQ isobaric □ labeling technologies allow the comparison of 2, 4, 6, or LT X) in one Excel reports scripts to generate PDF (via Asamples and mass spectrometric analysis. Sound statistical models that E with the 15 8 16 scale most advanced mass spectrometry (MS) instruments are essential for their ■ 17 18 http://bioinformatics.cemm.oeaw.ac.at/isobar efficient use. Through the application of robust statistical methods, we 19 developed models that capture variability from individual spectra to 20 biological samples. Classical experimental designs with a distinct sample 21 in each channel as well as the use of replicates in multiple channels are 22 integrated into a single statistical framework. We have prepared complex 23 test samples including controlled ratios ranging from 100:1 to 1:100 to 2 / 10
  • 3. Quantitative Proteomics via Mass SpectrometryCe—M—M— F. Breitwieser & J. Colinge ■ peptide fragmentation spectrum for identification ■ isobaric peptide tags for quantification □ up to 8 different samples ■ isobar package □ extracts identification from Mascot/Phenyx results □ extracts quantitative information from spectrum □ groups proteins to have reporters with specific peptides 3 / 10
  • 4. Modelling Technical Variability on a Spectrum LevelCe—M—M— F. Breitwieser & J. Colinge ■ correct for isotope impurities ■ normalize ■ handle technical variability □ depends on signal intensity □ using noise model ib <- correctIsotopeImpurities (ib) ib <- normalize (ib) nm <- NoiseModel (ib) maplot (ib , channel1 ="114",channel2 ="115",noise.model =nm) 4 / 10
  • 5. Modelling Technical Variability on a Spectrum LevelCe—M—M— F. Breitwieser & J. Colinge ■ correct for isotope impurities ✓ ■ normalize ■ handle technical variability □ depends on signal intensity □ using noise model ib <- correctIsotopeImpurities (ib) ib <- normalize (ib) nm <- NoiseModel (ib) maplot (ib , channel1 ="114",channel2 ="115",noise.model =nm) 4 / 10
  • 6. Modelling Technical Variability on a Spectrum LevelCe—M—M— F. Breitwieser & J. Colinge ■ correct for isotope impurities ✓ ■ normalize ✓ ■ handle technical variability □ depends on signal intensity □ using noise model ib <- correctIsotopeImpurities (ib) ib <- normalize (ib) nm <- NoiseModel (ib) maplot (ib , channel1 ="114",channel2 ="115",noise.model =nm) 4 / 10
  • 7. Modelling Technical Variability on a Spectrum LevelCe—M—M— F. Breitwieser & J. Colinge ■ correct for isotope impurities ✓ ■ normalize ✓ ■ handle technical variability □ depends on signal intensity □ using noise model ✓ ib <- correctIsotopeImpurities (ib) ib <- normalize (ib) nm <- NoiseModel (ib) maplot (ib , channel1 ="114",channel2 ="115",noise.model =nm) 4 / 10
  • 8. Differential Protein ExpressionCe—M—M— F. Breitwieser & J. Colinge CERU_RAT ■ spectra → peptides → protein 20 q 115/114 116/114 117/114 ■ summarize ratio with a weighted 10 mean q □ relative to spectrum intensity ratio 5 q qq q q q q qq q q q qq q q q qq q q q q q q q q q qq ■ calculate significance after qq qq q q q q q qqq qqq q q q q q q q q qq qq qqqq q qq q q q qq q q q qq qq q q q q qqqq qq qqq q q q q q assessing biological variability 2 q qq q q q q q q q q q q qq q q q q q q qq q qqq q q q q qq q q q q q q q qq qq q qq qq q q q q qqqq q qq q q qq qq q q q qq q qqq qqq q qqq q qq q q qq q q qq q q qq q q q q q qq q q q q q qq qq q qqqq q qq q qq qq q qq q ■ compute ratios between classes 1 q q q q q 5e+02 q q qq 5e+03 5e+04 5e+05 5e+06 □ Healthy versus Diseased average intensity estimateRatio (ib , noise . model .hcd ,"114","116",ceru.rat) proteinRatios (ib ,cl=c("H","H","D","D"), summarize =TRUE , method =" interclass ") maplot2 (ib , relative .to="114",ceru.rat ,main="CERU_RAT") 5 / 10
  • 9. Deciding for significant regulationCe—M—M— F. Breitwieser & J. Colinge ■ ’Volcanoe plot’ □ fold change versus p-value ■ Biological variability □ can be learned from replicates 60 50 − log10 signal p−value 40 30 −1 0 1 20 q qqq q q q qq 10 qq q q q q qq q q q q q q qq qqq q qqq q qq q qqqqqq q qq qqq q qqq qq qq q q qqq qq qqq qqqqqq q qqqqqqq qq qqqqqqqq q q qqqqqqq qqqq qq q qqq q q qqqqqqq q qq qq qqq q qqqqqq qq q qqqqqqq qqqqqqq qqq q qqqqqqq qqqqqqq qq q qqqq q qqqq q qqqq qq q q qq q q qqqqq qqqq q q q qqqqqqq qq qqqq h1 h2 d1 d2 q qq qqq qqq qqqq qq q qqqqqqqqq q q qq qqq q q q qq q qqqqqqqqq qqqqqqq q q qqqqqqqqqqqq qqqqqqqqqqqqqq q qq q q qq qqqqq q q q qqqqqqqqqqq qqqqqqqqqq qq q q qqqqqqqqqqqqq qq qq qqqqq q qqqqqqqq qqqqqqqqqq q q qqqqqqqqqqq q qq q 0 −4 −2 0 2 4 − log10 sample p−value 6 / 10
  • 10. Automating the Analysis - PDF ReportCe—M—M— F. Breitwieser & J. Colinge -5 1 5 ch1 ch2 protein group peptides spectra ratio . 1 C T Serpina1e: Q00898 1/1 7 1 0.22 < . > 2 C T Acaca: Q5SWU91,2 2/2 5 4 0.40 < . > 3 C T Atp5j: P97450 1/1 4 19 0.49 < . > . . . . . . . . . . . . . . . Hist1h3a: P68433, 130 C T 2/3 8 2 2.42 < . > Hist1h3c: P84228 131 C T Postn: Q620091−5 5/5 1 3 3.05 < . > 132 C T Myh7: Q91Z83 1/1 128 62 3.66 < . > ■ via Sweave: R code within LTEX A □ reproducible research Proteins pos accession gene name protein name ■ sections 1 P68433 Hist1h3a Histone H3.1 1 P84228 Hist1h3c Histone H3.2 □ Significantly regulated proteins 2 P84244 H3f3b Histone H3.3 □ All protein ratios Peptides □ Protein grouping rs gs us peptides 1 1 7 0 ■ not shown: QC report, Excel report 2 0 7 0 Sweave ("isobar - analysis .Rnw") # generate report using Sweave 7 / 10
  • 11. AcknowledgmentsCe—M—M— F. Breitwieser & J. Colinge ■ Research Center for Molecular Medicine, Vienna □ Jacques Colinge □ Keiryn Bennett’s Masspec group □ Giulio Superti-Furga □ Bioinformatics group ■ Alexey Stukalov ■ Gerhard Duernberger ■ Patrick Meidl ■ .. isobar Collaborators □ University of Geneva: Jean-Charles Sanchez □ IMP, Vienna: Peter Pichler and Karl Mechtler ■ Open Source Software Developers □ Richard Stallman, Linus Torvalds, Robert Gentleman, . . . □ Donald Knuth, Hadley Wickham, Till Tantau, . . . 8 / 10
  • 12. Appendix: Quality Control ReportCe—M—M— F. Breitwieser & J. Colinge tag 116: m/z 116.11 tag 117: m/z 117.11 500 400 count 300 200 100 0 −1 −5 0e 5e 1e −1 −5 0e 5e 1e e− e− +0 −0 −0 e− e− +0 −0 −0 03 04 0 4 3 03 04 0 4 3 mass ■ shows reporter mass precision and biological variability reporterMassPrecision (ib) Sweave ("isobar -qc.Rnw") 9 / 10
  • 13. Appendix: Protein Identification using Mass SpectrometerCe—M—M— F. Breitwieser & J. Colinge 10 / 10