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Supplemental Methods
Sodium di-hydrogen phosphate (NaH2PO4), di-sodium hydrogen phosphate (Na2HPO4),
sodium chloride and calcium chloride were purchased from Merck. Dithiothretiol (DTT) and
Complete Mini protease inhibitor were acquired from Roche Diagnostics, bicichinonic acid
(BCA) assay from Pierce, DryStrip cover fluid and Spec C18AR tips from Agilent. iTRAQ was
obtained from Applied Biosystems, TiO2 beads/tips from GL Science, Immobiline DryStrips
(pH 3-10) from GE Healthcare and HPLC solvents from Biosolve. The leukocount kit was
obtained from BD Biosciences, all other chemicals from Sigma Aldrich.
1. Lysis, sample preparation and digestion
Platelets were resuspended and lysed in 450 µl Gu-HCl, 50 mM Tris, pH 7.8 containing
Complete mini, protein concentrations were determined by BCA. With exception of cysteine
COFRADIC samples, disulfide bonds were reduced by addition of 10 mM DTT for 30 min at
56°C and free sulfhydryl groups were carbamidomethylated by adding 30 mM IAA for 30 min
in the dark at room temperature. Cys-COFRADIC samples were prepared as described1. GuHCl was diluted to a concentration of 0.2 M using 50 mM NH 4HCO3 and samples were
incubated with trypsin (protease:protein 1:20) overnight at 37°C. Samples were desalted by
C18 solid phase extraction.
2. iTRAQ samples and fractionation
To reduce sample complexity and increase proteome coverage and the number of
quantitative data points, iTRAQ-labeled samples were subjected to a variety of
prefractionation strategies, namely Met-COFRADIC2, Cys-COFRADIC1, strong cation exchange
chromatography (SCX), hydrophilic interaction liquid chromatography (HILIC) and in-solution
isoelectric focussing (IEF). Samples for label free (LF) estimation of copy numbers were
fractionated by IEF, whereas samples for assessing intra-subject variation as well as TiO2
enriched samples were directly analyzed via LC-MS without prior fractionation.
Prior to iTRAQ labeling samples were desalted, lyophilized and resuspended in 0.5 M TEAB
according to the manufacturer’s instructions. To avoid label-biases, labels were switched
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between different experiments. For label-free estimation of protein copy numbers (LF) and
for TiO2 enrichment, 50 µg and 250 µg of digest per donor were pooled, respectively, and
desalted. Finally, all samples were resuspended in the respective buffers for downstream
processing.
2.1. COFRADIC reversed phase chromatography
COFRADIC primary and secondary separations were performed on an Ultimate 3000 liquid
chromatography system consisting of a LPG 3000 quaternary micro pump, a TCC 3400
column compartment, an UVD 3400 variable wavelength detector and a WPS-T 3000 well
plate sampler equipped with 8-port valve, 0.15 mm bore for fractionation option, with a 125
µl loop (all Dionex, Germany). Labeled peptides were separated on a Zorbax 300SB-C18
column (5 µm particle size, 2.1 x 150 mm, Agilent) using a binary gradient (solvent A: 0.1%
TFA; solvent B 0.08% TFA, 84% ACN) ranging from 5-80% B in 80min at a flow rate of 80
µL/min.
2.2. Methionine COFRADIC
Upon desalting the iTRAQ labeled sample was resuspended in 0.1% TFA and separated by a
primary RP-HPLC run. In total 48 fractions were collected minute by minute during the
gradient, starting at 10% B. Fractions were pooled to 9 samples and methionine residues
were oxidized prior to the secondary run for 30 min at 30°C by adding H 2O2 to a final
concentration of 0.5% (v/v). Subsequently, each pooled sample was separated by a
secondary RP-HPLC run, using identical chromatographic conditions. Due to the hydrophilic
shift introduced by oxidation, Met containing peptides elute roughly 1-8 min earlier than in
the primary run. The collected fractions were dried and prepared for LC-MS/MS analysis.
2.3. Cysteine COFRADIC
The iTRAQ labeled sample was reconstituted in 0.1% TFA, H2O2 was added to a final
concentration of 0.5% (v/v) followed by incubation at 30°C for 30 min. The sample was then
separated in a primary RP-HPLC run. In total 48 fractions were collected during the gradient,
starting at 10% B. Fractions were pooled to a final number of 16 fractions and subsequently
dried under vacuum. Samples were resuspended in 10 mM Tris and incubated with 50 mM
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DTT at 56°C for 30 min in order to remove DNTB. The reaction was stopped by adding 0.1%
TFA (pH 2). All fractions were applied to a secondary RP-HPLC run, using identical
chromatographic conditions. Fractions were recollected in a time interval 3 to 10 min before
the respective elution time in the primary run. Collected fractions were dried under vacuum
and prepared for LC-MS/MS analysis.
2.4. Strong cation exchange chromatography
SCX was performed using a 1mmx15cm PolySULFOETHYL column (200 Å pore size, 5µm
particle size, PolyLC, USA) in combination with an inert Ultimate HPLC system. The iTRAQ
sample was resuspended in SCX buffer A and separated using a binary gradient (SCX buffer
A: 5 mM NaH2PO4, pH 2.7; SCX buffer B: 5 mM NaH2PO4, 350 mM NaCl, 15% ACN, pH 2.7)
ranging from 0 to 60% B in 45 min at a flow rate of 100 µL/min. Fractions were collected
minute-by-minute, dried under vacuum and prepared for LC-MS/MS analysis.
2.5. Hydrophilic Interaction Liquid chromatography
HILIC was performed using an inert Ultimate HPLC system (Dionex, Germering, Germany)
with a zwitterionic ZIC® -HILIC stationary phase (SeQuant, Umeå, Sweden). For
chromatographic separation a binary system (solvent A: 90% ACN, 10% ammonium acetate;
solvent B: 40% ACN, 60% ammonium acetate) conducting a gradient elution from 0-100% B
in 90 min. The iTRAQ sample was resuspended in solvent A and initially preconcentrated on
a ZIC® -HILIC trapping column (240 µm x 4 cm) and subsequently separated on a ZIC-HILIC
separating column (240 µm x 15 cm). Fractions were collected every two minutes.
3. Isoelectric focussing
IEF was accomplished using an Agilent 3100 OFFGEL Fractionator (Agilent, Germany). For
separation Immobiline DryStrips (GE Healthcare), pH 3-10, 24 cm length were used as
described previously3. Briefly, IPG strips and samples were treated with rehydration solution
containing 12% glycerol and ampholyte, pH 3-10 for 15 min. 150 µL of peptide solution were
loaded into each well. For IEF, voltage was increased from 300 V to 8000 V within 12 h and
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kept at 8000 V until a total of 64 kVh was reached. Finally fractions were desalted,
evaporated and prepared for LC-MS/MS analysis.
4. TiO2 enrichment
250 µg platelet digest per donor were pooled, followed by a two-step TiO2 enrichment
comprising a first TiO2 step with low and a second step with high phosphopeptide specificity,
in order to improve the general proteome coverage. Therefore, self-packed (described
previously by Beck et al.4) and commercially available TiO2 tips (GL Science) were used.
Following C18-based desalting the sample was resuspended in 100 µL loading buffer (80%
ACN, 6% TFA, saturated with phthalic acid) and incubated with TiO2 beads in a sample:bead
ratio of 1:4 at RT for 30 min while gently shaking. After several washing steps using 100 µl of
loading buffer and wash solution (80% ACN, 0.1% TFA), respectively, peptides were eluted
successively using three buffers: (a) 0.5% NH4OH, 250 mM NH4HCO3, (b) 4.5% NH4OH, 0.3%
H3PO4, 125 mM NH4HCO3, (c) 1.7% NH4OH and immediately acidified to pH 3 using TFA.
Peptides which did not bind during the first loading step were enriched a second time with
fresh beads. Eluates were combined, desalted using Omix C18 tips, an aliquot was measured
by LC-MS/MS and the rest subjected to a second TiO2 step in order to yield a high
phosphopeptide-specificity.
5. Mass spectrometry
iTRAQ Samples were analyzed on a QstarElite (Applied Biosystems) and an LTQ-Orbitrap XL
(Thermo Scientific) online coupled to U3000 nano-HPLC systems (Dionex). Peptides were
preconcentrated on an in-house packed 100 µm inner diameter C18 trapping column
(Synergi HydroRP, Phenomenex, 4 µm particle size, 80 Å pore size, 2 cm length) in 0.1%
trifluoroacetic acid and separated on an in-house packed 75 μm inner diameter C18 maincolumn (Synergi HydroRP, Phenomenex, 2 µm particle size, 80 Å pore size, 30 cm length)
applying a binary gradient from 4-42% acetonitrile in 0.1% formic acid. On the Orbitrap XL,
MS survey scans were acquired from 300-2,000 m/z at a resolution of 60,000 using the
polysiloxane m/z 445.120030 as lock mass5 and the three most intense ions (Top3) were first
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analyzed by collision induced dissociation (CID)-based MS/MS in the LTQ, followed by higher
energy collision induced dissociation (HCD)-based MS/MS in the Orbitrap (resolution 7,500).
AGC values were set to 104 for MS/MS and to 106 for MS scans. On the Qstar Elite, survey
scans were analyzed from 350-2,000 m/z and the Top3 were selected for MS/MS with the
iTRAQ reagent option, the fragment intensity multiplier set to 20 and a maximum
accumulation time of 2 s. LF IEF fractions were analyzed on an LTQ Orbitrap Velos,
essentially as described above, analyzing the Top20 by CID-MS/MS in the ion trap. TiO2
enriched samples were analyzed on a q-Exactive as described above, Top15. AGC values
were set to 2·105 for MS and 105 for MS/MS scans. Intra-variation samples were analyzed on
a q-Exactive mass spectrometer coupled to an Ultimate 3000 RSLC system (Thermo
Scientific), using a 75 µm x 2 cm C18 trapping column and a 75 µm x 50 cm C18 main column
(both Pepmap, Thermo Scientific) and a 3 h LC gradient ranging from 3-40%B at 60°C.
6. Peak list generation
All raw file were converted into mzML6 using msconvert as part of the Proteowizard 1.6.0
package7. Peak lists in mgf format were subsequently obtained using OpenMS version 1.8 8.
(A) Orbitrap Velos files were converted using the FileConverter; (B) Orbitrap XL files were
separated according to the fragmentation method, OpenMS high resolution peak-picker was
then applied on HCD spectra, for each LC-MS analysis, resulting peak lists (CID and HCD)
were then grouped in one mgf file; (C) Qstar Elite spectra were first processed with a
BaselineFilter in order to correct for baseline deviations, subsequently peak-picked using
OpenMS high resolution peak-picker and then converted into mgf format; (D) q-Exactive
spectra were peak-picked using OpenMS high resolution peak-picker and transformed into
mgf format.
7. Database searches
Spectra were searched using multiple search engines in order to improve proteome
coverage9, namely Mascot10 v2.3, OMSSA11 v2.1.9 and X!Tandem12 v2010.01.01 using
SearchGUI13 and the settings listed in supplemental table 1. All searches were searched
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against a concatenated target/decoy version of the UniprotKB/SwissProt human database
(4th of November 2010; 20,260 target sequences), generated using dbtoolkit14, allowing to
estimate the amount of retained false positive identifcations15. Parameters used for
searching are listed in supplemental table 1.
Identification results were extracted from search engine result files using MascotDatfile
parser16, OMSSA parser17 and X!Tandem parser18. Spectra matching peptide sequences that
were shared between target and decoy databases were omitted. To reduce the amount of
false positive identifications, PSMs meeting the following criteria were removed19,20: (1)
sequences <8 and >20 amino acids, (2) e-values >10, (3) precursor mass deviations >10 ppm
(Orbitrap) and >0.05 Da (Qstar).
8. Identification results post-processing
Results obtained from Mascot, OMSSA and X!Tandem were imported into PeptideShaker
(http://peptide-shaker.googlecode.com) excluding hits with an e-value over 10, peptide
length under 8 or over 20 amino acids and precursor mass deviations over 10 ppm (Orbitrap,
q-Exactive, Velos) or 0.05 Da (QStar Elite). Note that PeptideShaker automatically excludes
peptides deriving from both the target and the decoy database. The following steps were
subsequently conducted: (1) normalization of Peptide to Spectrum Matches (PSMs) using
Posterior Error Probabilities (PEPs), (2) merging of search engine results, (3) Peptide
inference and scoring, (4) Protein inference and scoring and (5) validation at 1% False
Discovery Rate (FDR).
9. Estimation of Posterior Error Probabilities
A list of all first hits was generated for each search engine and sorted by e-value. PEPs were
estimated for each PSM by comparing the decoy distribution to the target distribution with a
binning of Nmax, the maximal amount of target hits comprised between two subsequent
decoy hits20. The amount of decoy hits Ndecoy was used as an estimate for the amount of
false positives thus allowing the straightforward estimation of the Posterior Error Probability
(PEP) for each match by:
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̂=
𝑃𝐸𝑃
𝑁𝑑𝑒𝑐𝑜𝑦
𝑁𝑚𝑎𝑥
In the results presented in this study a confidence is estimated for each match
straightforwardly defined as 𝑐𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 = 1 − 𝑃𝐸𝑃.
10. Search engine results unification
For each MS/MS spectrum, the probability that all search engines p spectrum were wrong was
estimated as a product of all search engine PEPs:
̂
̂
̂
𝑝𝑠𝑝𝑒𝑐𝑡𝑟𝑢𝑚
̂ = 𝑃𝐸𝑃
𝑀𝑎𝑠𝑐𝑜𝑡 . 𝑃𝐸𝑃𝑂𝑀𝑆𝑆𝐴 . 𝑃𝐸𝑃𝑋!𝑇𝑎𝑛𝑑𝑒𝑚
A low pspectrum thus suggests that at least one search engine assigned the right peptide to the
spectrum confidently. In case of search engine disagreement, the most likely peptide (i.e.
the lowest PSM PEP) was retained. The spectra were sorted according to 𝑝𝑠𝑝𝑒𝑐𝑡𝑟𝑢𝑚
̂ and the
PEP of the retained PSM was estimated as described above. Note that PSMs were grouped
according to the identified charge when statistical significance was ensured. We considered
that statistical significance was ensured for a charge state when its Nmax was higher than 100
and more than 100 PSMs scored better than the first decoy hit.
11. Establishment of a peptide list
PSMs were combined into all possible peptides. It is important to note here that
differentially modified peptides were distinguished yet the modification site-localization was
not taken into account as its assessment is not possible for all spectra. As it is known that the
peptide PEP grows with the product of the PSM PEPs 9, peptides were scored using this
metric:
𝑁
̂
𝑠𝑐𝑜𝑟𝑒𝑝𝑒𝑝𝑡𝑖𝑑𝑒 = ∏ 𝑃𝐸𝑃
𝑃𝑆𝑀𝑖
𝑖=1
̂
where N represents the amounts of PSMs identifying the peptide of interest and 𝑃𝐸𝑃
𝑃𝑆𝑀𝑖
the estimated PEP of the ith PSM. The PEP of each peptide is yet not equal to this score as
already demonstrated21 and was thus re-estimated using the above method. Note that
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THE QUANTITATIVE HUMAN PLATELET PROTEOME, SUPPLEMENT
peptides with different modifications were processed separately if statistical significance
was ensured.
12. Establishment of a protein list
Following the same rationale proteins were scored using the product of peptide PEPs:
𝑁
𝑠𝑐𝑜𝑟𝑒𝑝𝑟𝑜𝑡𝑒𝑖𝑛 = ∏ 𝑃𝐸𝑃̂
𝑝𝑒𝑝𝑡𝑖𝑑𝑒𝑖
𝑖=1
th
Where 𝑃𝐸𝑃̂
𝑝𝑒𝑝𝑡𝑖𝑑𝑒𝑖 represents the estimated PEP of the i peptide among the N identifying
this protein. Here it is crucial to note the presence of shared peptides between various
proteins. In order to prevent the statistics from being impaired by these so-called
degenerated peptides, groups of proteins were generated as prescribed by Nesvizhskii 9:
Protein A
Protein B
Peptide 1
Peptide 2 Peptide 3
Peptide 4
As illustrated in figure 1, in case that 4 peptides are shared between two proteins, three
protein matches will be generated with three scores: protein A from peptide 1 with
𝑠𝑐𝑜𝑟𝑒𝑝𝑟𝑜𝑡𝑒𝑖𝑛 𝐴 = 𝑃𝐸𝑃̂
𝑝𝑒𝑝𝑡𝑖𝑑𝑒 1 ; protein AB from peptide 2 with 𝑠𝑐𝑜𝑟𝑒𝑝𝑟𝑜𝑡𝑒𝑖𝑛 𝐴𝐵 =
𝑃𝐸𝑃̂
𝑝𝑒𝑝𝑡𝑖𝑑𝑒 2 ;
protein
B
from
peptides
3
and
4
with
𝑠𝑐𝑜𝑟𝑒𝑝𝑟𝑜𝑡𝑒𝑖𝑛 𝐵 =
𝑃𝐸𝑃𝑝𝑒𝑝𝑡𝑖𝑑𝑒̂
3 . 𝑃𝐸𝑃𝑝𝑒𝑝𝑡𝑖𝑑𝑒 4 . In this model peptides are thus unique to protein groups allowing
the estimation of the PEP of every protein group. In the following, a protein can thus
implicitly denominate a group of proteins.
Based on the previous example, when sorting protein matches based on their confidence,
three cases can occur: (1) both matches A and B are scoring better than match AB; (2) A or B
is scoring better than AB; (3) neither A neither B is scoring better than AB. In cases (1) and
(2) the match AB is removed as sufficient peptide evidence allows the validation of a single
protein. In case (3) all cases are kept in the dataset potentially generating protein lists
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THE QUANTITATIVE HUMAN PLATELET PROTEOME, SUPPLEMENT
including A and AB. Indeed, in this case we cannot discriminate A from B. A clear example in
the presented results is the presence of various forms of Actin with extensive sequence
similarities. Yet, sufficient unique peptides confidently identified allowed us to discard the
intersection groups.
13. FDR validation
The final PSM, peptide and protein sets were validated at 1% FDR, indicating that the
expected result set contains one percent of false positives. For this, hits are sorted according
to their respective score and the amount of false positives is estimated using the number of
decoy hits. The highest score validating less decoy hits than 1% of the resulting dataset size
is retained for hit validation.
14. Phosphorylation site scoring
Although instruments allowed tremendous progress in PTM analysis, the location of
phosphorylation remains often uncertain. In order to assess the validity of the
phosphorylation site returned by the identification process, we combined the use of two
well-known PTM scoring approaches: the A-score19 and the scoring through secondary hit
scores4. For the later, the search engine independent score attached to the phosphorylation
site is the difference between the next hit with the first secondary hit with the same
sequence yet another phosphorylation site and the best hit PEP. Concretely, it corresponds
to the confidence increase when going from the second best location to the best according
to the search engines result. The A-score was estimated for singly modified peptides only. A
phosphorylation site was marked confident when both scores did agree on the
phosphorylation site or when the PEP difference was above 0.50.
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THE QUANTITATIVE HUMAN PLATELET PROTEOME, SUPPLEMENT
15. Absolute quantification
Proteins were quantified using the Normalized Spectrum Abundance Factor 22 (NSAF). Briefly,
here the number of validated spectra which can be ascribed to a protein match is divided by
the length of the protein.
16. Relative quantification
After discarding spectra containing null intensities, iTRAQ intensities were extracted from
mgf files and deisotoped using the isotope matrix provided by the manufacturer
23.
PSM
iTRAQ ratios were calculated by dividing reporter intensites 115, 116 and 117 m/z by the 114
reporter intensity. PSM ratios were used to compute peptide and finally protein ratios.
When less than six PSM (respectively peptide) ratios were available for peptide (respectively
protein) ratio estimation, the median of ratios was taken. Otherwise the peptide
(respectively protein) ratio was estimated using a redescending M-estimator chosen for its
robustness and accuracy24. In order to compensate for systematic errors, the log of each
reporter ratio was normalized to the median of all the respective ratios. For every protein,
the four ratios (114/114, 115/114, 116/114 and 117/114) were subsequently normalized by
the median of the ratios.
17. Comparison of proteome and transcriptome data
Protein
assignment
was
conducted
(http://www.uniprot.org/?tab=mapping)
using
and
PICR
the
Uniprot
mapping
tool
(http://www.ebi.ac.uk/Tools/picr/).
Identifiers which could not be assigned to a protein were searched in the NCBI database
(http://www.ncbi.nlm.nih.gov/sites/batchentrez). Obsolete/deleted entries were blasted
(http://blast.ncbi.nlm.nih.gov/Blast.cgi) and manually assigned. Entries referring to pseudogenes or non-coding RNA were removed, except for non-coding transcripts of proteins which
were taken as indicators for protein expression. Protein assignments for proteome and
transcriptome data were matched, merged, and entries unique to either dataset were
matched to ambiguous entries in the other one. To improve significance of transcriptome
data, a cutoff of one reads per kilobase of exon model per million mapped reads (RPKM) was
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THE QUANTITATIVE HUMAN PLATELET PROTEOME, SUPPLEMENT
applied. Frequency analysis was conducted by classifying copy numbers in classes with
logarithmic distances. A normal distribution function was approximated to the data. For
comparison of frequencies the data were stratified into percentiles according to the
frequency of copy numbers. As a normal distribution of the data cannot be assumed
normalization of the data was abandoned. The U2OS cell data provided are curtailed by low
and high thresholds (500 and 20,000,000 copies), thus only limited statistical analysis was
possible with these data.
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References
1.
Gevaert K, Ghesquiere B, Staes A, et al. Reversible labeling of cysteine-containing
peptides allows their specific chromatographic isolation for non-gel proteome studies.
Proteomics. 2004;4(4):897-908.
2.
Gevaert K, Van Damme J, Goethals M, et al. Chromatographic isolation of
methionine-containing peptides for gel-free proteome analysis: identification of more than
800 Escherichia coli proteins. Mol Cell Proteomics. 2002;1(11):896-903.
3.
Keidel EM, Dosch D, Brunner A, Kellermann J, Lottspeich F. Evaluation of protein
loading techniques and improved separation in OFFGEL isoelectric focusing. Electrophoresis.
2011;32(13):1659-1666.
4.
Beck F, Lewandrowski U, Wiltfang M, et al. The good, the bad, the ugly: validating the
mass spectrometric analysis of modified peptides. Proteomics. 2011;11(6):1099-1109.
5.
Olsen JV, de Godoy LM, Li G, et al. Parts per million mass accuracy on an Orbitrap
mass spectrometer via lock mass injection into a C-trap. Mol Cell Proteomics.
2005;4(12):2010-2021.
6.
Martens L, Chambers M, Sturm M, et al. mzML--a community standard for mass
spectrometry data. Mol Cell Proteomics. 2011;10(1):R110 000133.
7.
Kessner D, Chambers M, Burke R, Agus D, Mallick P. ProteoWizard: open source
software for rapid proteomics tools development. Bioinformatics. 2008;24(21):2534-2536.
8.
Andreotti S, Klau GW, Reinert K. Antilope &#x2013; A Lagrangian Relaxation
Approach to the de novo Peptide Sequencing Problem. IEEE/ACM Trans Comput Biol
Bioinform. 2011.
9.
Nesvizhskii AI. A survey of computational methods and error rate estimation
procedures for peptide and protein identification in shotgun proteomics. J Proteomics.
2010;73(11):2092-2123.
10.
Perkins DN, Pappin DJ, Creasy DM, Cottrell JS. Probability-based protein identification
by searching sequence databases using mass spectrometry data. Electrophoresis.
1999;20(18):3551-3567.
11.
Geer LY, Markey SP, Kowalak JA, et al. Open mass spectrometry search algorithm. J
Proteome Res. 2004;3(5):958-964.
12
BURKHART et al
12.
THE QUANTITATIVE HUMAN PLATELET PROTEOME, SUPPLEMENT
Fenyo D, Beavis RC. A method for assessing the statistical significance of mass
spectrometry-based protein identifications using general scoring schemes. Anal Chem.
2003;75(4):768-774.
13.
Vaudel M, Barsnes H, Berven FS, Sickmann A, Martens L. SearchGUI: An open-source
graphical user interface for simultaneous OMSSA and X!Tandem searches. Proteomics.
2011;11(5):996-999.
14.
Martens L, Vandekerckhove J, Gevaert K. DBToolkit: processing protein databases for
peptide-centric proteomics. Bioinformatics. 2005;21(17):3584-3585.
15.
Elias JE, Gygi SP. Target-decoy search strategy for increased confidence in large-scale
protein identifications by mass spectrometry. Nat Methods. 2007;4(3):207-214.
16.
Helsens K, Martens L, Vandekerckhove J, Gevaert K. MascotDatfile: an open-source
library to fully parse and analyse MASCOT MS/MS search results. Proteomics. 2007;7(3):364366.
17.
Barsnes H, Huber S, Sickmann A, Eidhammer I, Martens L. OMSSA Parser: an open-
source library to parse and extract data from OMSSA MS/MS search results. Proteomics.
2009;9(14):3772-3774.
18.
Muth T, Vaudel M, Barsnes H, Martens L, Sickmann A. XTandem Parser: an open-
source library to parse and analyse X!Tandem MS/MS search results. Proteomics.
2010;10(7):1522-1524.
19.
Beausoleil SA, Villen J, Gerber SA, Rush J, Gygi SP. A probability-based approach for
high-throughput protein phosphorylation analysis and site localization. Nat Biotechnol.
2006;24(10):1285-1292.
20.
Vaudel M, Burkhart JM, Sickmann A, Martens L, Zahedi RP. Peptide identification
quality control. Proteomics. 2011;11(10):2105-2114.
21.
Nesvizhskii AI, Keller A, Kolker E, Aebersold R. A statistical model for identifying
proteins by tandem mass spectrometry. Anal Chem. 2003;75(17):4646-4658.
22.
Paoletti AC, Parmely TJ, Tomomori-Sato C, et al. Quantitative proteomic analysis of
distinct mammalian Mediator complexes using normalized spectral abundance factors. Proc
Natl Acad Sci U S A. 2006;103(50):18928-18933.
23.
Vaudel M, Sickmann A, Martens L. Peptide and protein quantification: a map of the
minefield. Proteomics. 2010;10(4):650-670.
13
BURKHART et al
24.
THE QUANTITATIVE HUMAN PLATELET PROTEOME, SUPPLEMENT
Burkhart JM, Vaudel M, Zahedi RP, Martens L, Sickmann A. iTRAQ protein
quantification: a quality-controlled workflow. Proteomics. 2011;11(6):1125-1134.
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