http://www.cnr.it/ontology/cnr/individuo/prodotto/ID186265
The EIPeptiDi tool: Enhancing peptide discovery in ICAT-based LC MS/MS experiments (Articolo in rivista)
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- The EIPeptiDi tool: Enhancing peptide discovery in ICAT-based LC MS/MS experiments (Articolo in rivista) (literal)
- Anno
- 2007-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1186/1471-2105-8-255 (literal)
- Alternative label
Cannataro, Mario (1); Tradigo, Giuseppe (1); Veltri, Pierangelo (1); Cuda, Giovanni (2); Gaspari, Marco (2); Greco, Sergio (3) (2007)
The EIPeptiDi tool: Enhancing peptide discovery in ICAT-based LC MS/MS experiments
in BMC bioinformatics; Biomed Central Ltd., London (Regno Unito)
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Cannataro, Mario (1); Tradigo, Giuseppe (1); Veltri, Pierangelo (1); Cuda, Giovanni (2); Gaspari, Marco (2); Greco, Sergio (3) (literal)
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- http://www.biomedcentral.com/1471-2105/8/255 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
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- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Cannataro, Mario (1); Tradigo, Giuseppe (1); Veltri, Pierangelo (1); Cuda, Giovanni (2); Gaspari, Marco (2); Greco, Sergio (3)
(1) Bioinformatics Laboratory - Experimental and Clinical Medicine Department;
(2) Proteomics Laboratory - Experimental and Clinical Medicine Department;
(3) Department of Electronics - Computer and System Sciences (DEIS) (literal)
- Titolo
- The EIPeptiDi tool: Enhancing peptide discovery in ICAT-based LC MS/MS experiments (literal)
- Abstract
- Background: Isotope-coded affinity tags (ICAT) is a method for quantitative proteomics based on
differential isotopic labeling, sample digestion and mass spectrometry (MS). The method allows the
identification and relative quantification of proteins present in two samples and consists of the following
phases. First, cysteine residues are either labeled using the ICAT Light or ICAT Heavy reagent (having
identical chemical properties but different masses). Then, after whole sample digestion, the labeled
peptides are captured selectively using the biotin tag contained in both ICAT reagents. Finally, the
simplified peptide mixture is analyzed by nanoscale liquid chromatography-tandem mass spectrometry
(LC-MS/MS). Nevertheless, the ICAT LC-MS/MS method still suffers from insufficient sample-to-sample
reproducibility on peptide identification. In particular, the number and the type of peptides identified in
different experiments can vary considerably and, thus, the statistical (comparative) analysis of sample sets
is very challenging. Low information overlap at the peptide and, consequently, at the protein level, is very
detrimental in situations where the number of samples to be analyzed is high.
Results: We designed a method for improving the data processing and peptide identification in sample
sets subjected to ICAT labeling and LC-MS/MS analysis, based on cross validating MS/MS results. Such a
method has been implemented in a tool, called EIPeptiDi, which boosts the ICAT data analysis software
improving peptide identification throughout the input data set. Heavy/Light (H/L) pairs quantified but not
identified by the MS/MS routine, are assigned to peptide sequences identified in other samples, by using
similarity criteria based on chromatographic retention time and Heavy/Light mass attributes. EIPeptiDi
significantly improves the number of identified peptides per sample, proving that the proposed method has
a considerable impact on the protein identification process and, consequently, on the amount of potentially
critical information in clinical studies. The EIPeptiDi tool is available at http://bioingegneria.unicz.it/~veltri/
projects/eipeptidi/ with a demo data set.
Conclusion: EIPeptiDi significantly increases the number of peptides identified and quantified in analyzed
samples, thus reducing the number of unassigned H/L pairs and allowing a better comparative analysis of
sample data sets. (literal)
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