The document describes an R package called isobar that provides tools for analyzing quantitative proteomics data from isobaric labeling experiments. The package extracts identification and quantitative information from mass spectrometry data. It models technical variability, normalizes data, and handles biological variability to determine differential protein expression accurately. Statistical tests and data visualization help decide significant regulation. Automated reporting of results in PDF format via Sweave is demonstrated.

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)
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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)
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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)
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8. Differential Protein Expression
Ce—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
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■ 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)
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9. Deciding for significant regulation
Ce—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
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q q
q qq
10
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0
−4 −2 0 2 4
− log10 sample p−value
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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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13. Appendix: Protein Identification using Mass Spectrometer
Ce—M—M— F. Breitwieser J. Colinge
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