Classification
1.
General features — Global descriptors of the experiment
1.1. Experiment identifier or name
1.2. Responsible person or role
1.3. Quantitative approach
2.
Definition
104330001_iTRAQ macrofagos
Lola Gutierrez / Felipe Clemente
iTRAQ-8plex
Experimental design and sample description —2.1 Experimental design
Control vs interaction
2.1.1 Groups
2.1.2 Biological and technical replicates
2.
3 biological replicates and 1 technical replicate (mix of controls and mix of interactions) in the
same iTRAQ experiment
Experimental design and sample description —2.2 Sample / Assay description
Labelling protocol (if applicable)
Labelling at peptide level.
Sample description
Sample name
2.2.2.2 Sample amount
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
View table 2.2.2.2.
50 ug/ sample
Sample name
iTRAQ
reagent
CONTROL 1
CONTROL 3
CONTROL 4
INTERACCION 1
INTERACCION 3
INTERACCION 4
CONTROL MIX
INTERACCION MIX
2.2.2.4 Replicates and/or groups
2.2.3 Isotopic correction coefficients
2.2.4 Internal references
3
113
114
115
116
117
118
119
121
View table 2.2.2.2.
Reagent
iTRAQ113
iTRAQ114
iTRAQ115
iTRAQ116
iTRAQ117
iTRAQ118
iTRAQ119
iTRAQ121
% of -2
0.00
0.00
0.00
0.00
0.06
0.09
0.14
0.27
% of -1
0.00
0.94
1.88
2.82
3.77
4.71
5.66
7.44
% of 0
92.87
93.00
93.12
93.21
93.29
93.29
93.33
92.11
% of +1 % of +2
6.89
0.24
5.90
0.16
4.90
0.10
3.90
0.07
2.88
0.00
1.91
0.00
0.87
0.00
0.18
0.00
List of internal references used and their amount. Also state their specific purpose such as
quantification, normalization or alignment.
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
tandem MS
3.2 Input data format
Import from oracle DB 4000 Series Explorer v 3.6
4800 MALDI TOF/TOF ABI Sciex ( .t2d).
3.3 Input data merging
Sample was fractionated using a 3100 off-gel fractionator (Agilent Technologies), collecting 24
fractions on pH range of 3-10. Each fraction was individually fractionated in a nano-HPLC
(LC Packings), coupled off line to a 4800 Plus MALDI-TOF/TOF (ABSciex), each off-gel
fraction yields 416 MALDI spots. Datasets corresponding to each fraction were merged and
combined by the Protein Pilot software v3.0 (ABSciex) for identification and quantitation
purposes.
3.4 Availability of the input data
4
http://proteo.cnb.csic.es/downloads/miape-quant/iTRAQ8plex_UCMPCM_peaklist.xml
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
Protein Pilot v.3.0 from AbSciex
4.2 Description of the selection and/or matching method of features,
Centroid, peak area and peak area error.
Ratio of peak reporter area, the protein ratio is calculated as average ratio:
10exp(weighted average of log ratios)/bias
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
Peptide confidence is based on the score, which is the number of matches
between the data and the theoretical fragment ions. In general,
a higher score leads to a higher confidence, but the factors listed below also influence
the confidence.
Hypothesized modifications – Rare, unexpected modifications tend to decrease confidence.
Delta mass – A larger delta mass tends to decrease confidence.
Peptide cleavage – Cleavages inconsistent with the digest agent specified in
the analysis method tend to decrease confidence.
Alternative hypotheses for the same spectrum – A peptide with a high score
could receive a low confidence because there is another peptide hypothesis
for this spectrum with an even higher score.
4.4 Description of data calculation and transformation methods
4.4.1
Missing values imputation and outliers removal
4.4.2
Quantification values calculation and / or ratio determination from
the primary extracted quantification values
Peptides with a combined feature probability < 30% are excluded . Features with
this low probability include semi-tryptic peptides, peptides missing an iTRAQ reagent
label, peptides with low probability modifications and peptides with large delta masses.
Peptide ratios 0 and 9999.99
Ratios of peak area iTRAQ reporters.
4.4.3
Replicate aggregation
Normalization: mean protein iTRAQ ratio in all replicates and all ratios in each replicate
Are divided by mean.
Average ratio for quantified protein in all replicates and p-value (<0.05) and/or ST < 0.2
4.4.4
Normalization
Auto bias correction: median average protein ratio..
4.4.5
Protein quantification values calculation and / or ratio
determination from the peptide quantification values
4.4.6
As stated in point 4.4. For shared peptides: A shared peptide is when the same
spectrum with the same peptide sequence is claimed by more than one protein.
These peptides are excluded when there is another protein which claims this peptide
but only if the Unused ProtScore for the other protein is 1.3 or higher.
Precursor overlap – The spectrum yielding the identified peptide is also claimed
by a different protein above the confidence cutoff, but with an unrelated
peptide sequence. This can happen when more than one peptide is fragmented at
the same time. These peptides are excluded.
Only proteins with at least two peptides identified are included in the quantification.
Protocol specific corrections
4.5 Description of methods for (statistical) estimation of correctness
p-value < 0.05; EF<2; FDR 1%
4.6 Calibration curves of standards
5
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,
http://proteo.cnb.csic.es/downloads/miape-quant/iTRAQ8plex_UCMPCM_peptide summary.xls
height, etc.), with their statistical estimation of correctness
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
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
http://proteo.cnb.csic.es/downloads/miape-quant/iTRAQ8plex_UCMPCM_protein summary.xls
5.2.2 Transformed / aggregated / combined quantification values of the
http://proteo.cnb.csic.es/downloads/miape-quant/iTRAQ8plex_UCMPCM_significant
changes_R1R3Rmix.xls
proteins at group level, with their statistical estimation of correctness