Classification
1.
2.
Definition
General features — Global descriptors of the experiment
1.1. Experiment identifier or name
01_Cvs4h_iTRAQ, 02_Cvs4h_iTRAQ
1.2. Responsible person or role
Dra. Montserrat Carrascal. CSIC/UAB Proteomics Facility, IIBB-CSIC, Building M, Campus UAB,
08193 Bellaterra, Spain
1.3. Quantitative approach
Isotopic labeling, iTRAQ-4plex
Experimental design and sample description —2.1 Experimental design
Control vs 4h activation with PMA ionomicine.
2.1.1 Groups
2 biological Cvs4h_iTRAQ replicates for each condition
2.1.2 Biological and technical replicates
2.
Experimental design and sample description —2.2 Sample / Assay description
Labelling protocol (if applicable)
iTRAQ from AB Sciex at the peptide level.
2.2.2 Sample description
Sample name
Control and activated samples, each one correspond to a pool of five donors..
2.2.2.2 Sample amount
1 mg before SCX and phosphopeptide enrichment.
2.2.2.3 Sample labelling with assay definition, i.e. MS run / data set together
with reporting ion mass, reagent or isotope labelled amino acid
C1(114) and C2(115) group resting T-lymphocytes, A1(116) and A2(117) group activated Tlymphocytes.
2.2.2.3 Replicates and/or groups
C1 and C2 are technical replicates in resting T-lymphocytes group, A1 and A2 are technical
replicates in activated T-lymphocytes group
2.2.3 Isotopic correction coefficients
Label reagent (-2,-1,1,2) iTRAQ114 (0.0,1.0,5.9,0.2) iTRAQ115 (0.0,2.0,5.6,0.1) iTRAQ116
(0.0,3.0,4.5,0.1) iTRAQ117 (0.1,4.0,3.5,0.1)
2.2.4 Internal references
3.
4.
Input data — Description and reference of the dataset used for quantitative analysis: type, format and availability of the data. No actual values are requested here.
3.1. Input data type
MS/MS data
3.2. Input data format
mgf converted from raw file with ReAdW.
3.3. Input data merging
16 SCX fractions
3.4. Availability of the input data
32 mgf files
Protocol —Description of the software and methods applied in the quantitative analysis (including transformation functions, aggregation functions and statistical calculations).
4.1. Quantification software name, version and manufacturer
Homemade python script
4.2. Description of the selection and/or matching method of features,
together with the description of the method of the primary
extracted quantification values determination for each feature
and/or peptide
4.3. Confidence filter of features or peptides prior to quantification
4.4. Description of data calculation and transformation methods
Average of reporter ions of replicates in the same experiment.Log2(A/C) of no phosphopeptides
were adjuted
those with a µThree search engines for peptide identification were used. Only peptides found at least in two
search engines were selected.
4.4.1.
Missing values imputation and outliers removal
4.4.2.
Quantification values calculation and / or ratio
determination from the primary extracted quantification
Peptide replicated missing an iTRAQ reported ion were removed together with peptides without
labeling.
Average of sample replicates and log2(A/C)
values
4.4.3.
Replicate aggregation
n.a.
4.4.4.
Normalization
n.a.
Protein quantification values calculation and / or ratio
n.a.
4.4.5.
determination from the peptide quantification values
4.4.6.
5.
Protocol specific corrections
n.a.
4.5. Description of methods for (statistical) estimation of correctness
Activated phosphopeptides were those wiht a µ-
4.6. Calibration curves of standards
n.a.
-
Resulting data —Provide the actual quantification values resulting from your quantification software together with their estimated confidence. Depending of the quantification technique
or even of the quantification software, only some of the following items could be satisfied (e.g., for spectral counting, only quantification values at protein level can be provided)
5.1 Quantification values at peptide and/or feature level: Actual quantification values achieved for each peptide and/or, in case of feature-based quantification, for the corresponding features
(mapped back from each peptide), together with their estimated confidence.
5.1.1 Primary extracted quantification values for each feature (e.g. area,
height, etc.), with their statistical estimation of correctness
http://proteo.cnb.csic.es/downloads/miape-quant/Results_peptides_LPCSICUAB.xlsx
5.1.2 Quantification values for each peptide as a result of the aggregation of
the values of the previous section (5.1.1), with their statistical estimation of
correctness
Output data of phosphopeptides are stored in relational database accessible in
www.lymphos.org
5.2 Quantification values at protein level: Actual quantification values achieved for each protein and for each protein ambiguity group, together with the confidence in the quantification
value.
Basic / raw quantification values with statistical estimation of correctness
n.a.
5.2.2 Transformed / aggregated / combined quantification values of the
proteins at group level, with their statistical estimation of correctness
http://proteo.cnb.csic.es/downloads/miape-quant/Results_protein_LPCSICUAB.xlsx