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
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2. isobar for Analysis of Quantitative Proteomics Data
Ce—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 Spectrometry
Ce—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
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4. Modelling Technical Variability on a Spectrum Level
Ce—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)
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5. Modelling Technical Variability on a Spectrum Level
Ce—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 Level
Ce—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 Level
Ce—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
10. Automating the Analysis - PDF Report
Ce—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
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11. Acknowledgments
Ce—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, . . .
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12. Appendix: Quality Control Report
Ce—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)
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