Classification
1.
2.
2.
Definition
General features — Global descriptors of the experiment
1.1. Experiment identifier or name
Yeast P
1.2. Responsible person or role
Mª Luisa Hernáez Technical manager.UCM-PCM.
mlhernae@farm.ucm.es
1.3. Quantitative approach
SILAC 2-plex
Experimental design and sample description —2.1 Experimental design
2.1.1.
Groups
Light: Wild-type
Heavy: Mutant
2.1.2.
Biological and technical replicates
N/A
Experimental design and sample description —2.2 Sample / Assay description
Labelling protocol (if applicable)
Wild-type (Light) : Lyysine and arginine auxotrophic S. cerevisiae strain
YMJ38 (his31 leu20 ura30 arg4::kanMX4 trp1::kanMx4 lys2
grown in SD medium supplemented with [12C6]l-lysine(100 mg/L) and [12C6]larginine (100 mg/L) and the other aminoacids (20 mg/l)
Mutant (Heavy): Lyysine and arginine auxotrophic S. cerevisiae strain
YMJ38a (his31 leu20 ura30 arg4::kanMX4 trp1::kanMx4 lys2
13
pkc1
C6]l-lysine(100
13
mg/L) and [ C6]l-arginine (100 mg/L) and the other aminoacids (20 mg/l)
Sample description
Sample name
YeastP:
Wild type( Wt) and mutant( Mut)
2.2.2.2 Sample amount
300 ug of each sample. 600 ug were loaded onto a 12% polyacrilamide gel
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
Wt : (Light )
Mut: (Heavy) was labelled with 13C6-Lysine and 13C6-Arginine
2.2.2.3 Replicates and/or groups
N/A
2.2.3 Isotopic correction coefficients
N/A
2.2.4 Internal references
N/A
3.
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
3.2. Input data format
4.
Full MS scan
Raw Thermo files (*.RAW)
3.3. Input data merging
Sample was fractionated in a 12% polyacrilamide gel. 15 slices were excised and
trypsin digested.
Raw data were combined and analysed using Thermo Proteome Discoverer
1.2
3.4. Availability of the input data
Provide a Pride repository.
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
Thermo Proteome Discoverer 1.2
4.2. Description of the selection and/or matching method of features, together with the
Precursor ion quantification: peptide abundance is determined from the relative
MS signal intensities of multiple isotopically labeled peptides and unlabeled
peptides. Peptide Ratio:Heavy/Light.
description of the method of the primary extracted quantification values
determination for each feature and/or peptide
Mass Precision: 4 ppm
S/N Threshold: 1
RT Tolerance of Isotope Pattern Multiplets [min]: 0.2
Single-Peak/Missing Channels Allowed: 0
4.3. Confidence filter of features or peptides prior to quantification
Peptide Confidence-Value: High Confidence
Protein Mascot score > 22
4.4. Description of data calculation and transformation methods
4.4.1.
4.4.2.
Missing values imputation and outliers removal
Quantification values calculation and / or ratio determination from the
primary extracted quantification values
Reject All Quan Values If Not All Quan Channels Are Present.
Quantification was taken from the spectrum with the highest ID score
Use Only Unique Peptides
Peptide Ratio:Heavy/Light as a log2-fold change
4.4.3.
Replicate aggregation
N/A
4.4.4.
Normalization
Normalization was done performing the median of all the ratios obtained at
peptide level.
4.4.5.
Protein quantification values calculation and / or ratio determination from
the peptide quantification values
4.4.6.
Protocol specific corrections
4.5. Description of methods for (statistical) estimation of correctness
4.6. Calibration curves of standards
Protein ratios are calculated as the median of all quantified peptide hits
belonging to a protein.
N/A
Heavy/Light Variability (the variability of the peptide ratios that are used to
calculate a particular protein ratio. They are similar to a coefficient of variation.)
Heavy/Light Variability <25 and Heavy/Light Count(the number of peptide
ratios that were used to calculate a particular protein ratio) >2
N/A
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, 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
http://proteo.cnb.csic.es/downloads/miape-quant/5.1.2.SILAC_MLHS.xls
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
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/5.2.2.SILAC_MLHS.xls