Precise protein quantification based on peptide quantification using iTRAQ™

BMC Bioinformatics

Table 3 Quantification results of the sample 1:1 mix.

Protein Id	Protein name	Quant						EE	Pro Quant™			Bias
								1.3451				1.1439
		Ratio	n	IQR	RMS	LSE	μ	σ	Ratio	n	pVal	EF
P00698	Lysozyme C precursor LYSC_CHICK	0.7028	23	0.1581	0.0599	0.7973	-0.3169	0.4005	0.9244	23	0.3050	1.1668
P00722	Beta-galactosidase BGAL_ECOLI	0.8128	47	0.1902	0.0227	0.8268	-0.2365	0.3160	0.9256	47	0.2604	1.1455
P02787	Serotransferrin precursor TRFE_HUMAN	1.0053	118	0.6406	0.0453	1.2809	0.1111	0.4776	1.2401	118	0.0007	1.1291
P02769	Serum albumin precursor ALBU_BOVIN	0.8441	93	0.3619	0.0590	1.0420	-0.1225	0.5938	1.1436	93	0.0247	1.1224
P02754	Beta-lactoglobulin precursor LACB_BOVIN	1.3504	57	1.3201	0.0114	1.3495	0.0851	0.7473	1.1864	57	0.3423	1.4382
P00711	Alpha-lactalbumin precursor LALBA_BOVIN	0.7280	13	0.2355	0.0049	0.9227	-0.1637	0.3698	1.0557	13	0.7563	1.4874

The quantification results of the sample 1:1 mix include all peptides of each identified protein. Listed are the Protein Id, the protein name, the protein ratio (ratio) with the experiment error (EE) and the bias estimate of Quant and ProQuant™, respectively. The number of MS/MS spectra is listed in column n. Furthermore, several quality factors calculated by Quant are shown: the least squares estimator (LSE), interquartile range (IQR), root-mean-square value (RMS), mean value (μ) and standard deviation (σ). μ and σ were calculated from the log-transformed data. The results of ProQuant™ are taken from the ProGroup™ output that yields a p-value (pVal) and an error factor (EF) as described previously [35]. The experiment error of Quant (bias of ProQuant™) indicates the overall protein mixing ratio. The protein ratio results are normalized by this factor.

ISSN: 1471-2105