Abstract
BACKGROUND: Mass spectrometry based quantification of peptides can
be performed using the iTRAQ reagent in conjunction with mass spectrometry.
This technology yields information about the relative abundance of
single peptides. A method for the calculation of reliable quantification
information is required in order to obtain biologically relevant
data at the protein expression level. RESULTS: A method comprising
sound error estimation and statistical methods is presented that
allows precise abundance analysis plus error calculation at the peptide
as well as at the protein level. This yields the relevant information
that is required for quantitative proteomics. Comparing the performance
of our method named Quant with existing approaches the error estimation
is reliable and offers information for precise bioinformatic models.
Quant is shown to generate results that are consistent with those
produced by ProQuant, thus validating both systems. Moreover, the
results are consistent with that of Mascot 2.2. The MATLAB scripts
of Quant are freely available via http://www.protein-ms.de and http://sourceforge.net/projects/protms/,
each under the GNU Lesser General Public License. CONCLUSION: The
software Quant demonstrates improvements in protein quantification
using iTRAQ. Precise quantification data can be obtained at the protein
level when using error propagation and adequate visualization. Quant
integrates both and additionally provides the possibility to obtain
more reliable results by calculation of wise quality measures. Peak
area integration has been replaced by sum of intensities, yielding
more reliable quantification results. Additionally, Quant allows
the combination of quantitative information obtained by iTRAQ with
peptide and protein identifications from popular tandem MS identification
tools. Hence Quant is a useful tool for the proteomics community
and may help improving analysis of proteomic experimental data. In
addition, we have shown that a lognormal distribution fits the data
of mass spectrometry based relative peptide quantification.
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