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PPINGUIN: Peptide Profiling Guided Identification of Proteins improves quantitation of iTRAQ ratios.

Bauer C, Kleinjung F, Rutishauser D, Panse C, Chadt A, Dreja T, Al-Hasani H, Reinert K, Schlapbach R, Schuchhardt J - BMC Bioinformatics (2012)

Bottom Line: Our method increases experimental reproducibility and decreases variability of quantitations of peptides assigned to the same protein.Regarding the increasing importance of quantitative proteomics we think that this method will be useful in practical applications like model fitting or functional enrichment analysis.We recommend to use this method if quantitation is a major objective of research.

View Article: PubMed Central - HTML - PubMed

Affiliation: MicroDiscovery GmbH, Marienburger Str, 1, 10405 Berlin, Germany. chris.bauer@microdiscovery.de

ABSTRACT

Background: Recent development of novel technologies paved the way for quantitative proteomics. One of the most important among them is iTRAQ, employing isobaric tags for relative or absolute quantitation. Despite large progress in technology development, still many challenges remain for derivation and interpretation of quantitative results. One of these challenges is the consistent assignment of peptides to proteins.

Results: We have developed Peptide Profiling Guided Identification of Proteins (PPINGUIN), a statistical analysis workflow for iTRAQ data addressing the problem of ambiguous peptide quantitations. Motivated by the assumption that peptides uniquely derived from the same protein are correlated, our method employs clustering as a very early step in data processing prior to protein inference. Our method increases experimental reproducibility and decreases variability of quantitations of peptides assigned to the same protein. Giving further support to our method, application to a type 2 diabetes dataset identifies a list of protein candidates that is in very good agreement with previously performed transcriptomics meta analysis. Making use of quantitative properties of signal patterns identified, PPINGUIN can reveal new isoform candidates.

Conclusions: Regarding the increasing importance of quantitative proteomics we think that this method will be useful in practical applications like model fitting or functional enrichment analysis. We recommend to use this method if quantitation is a major objective of research.

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Related in: MedlinePlus

Workflow. Standard workflow of proteomics data evaluation (left hand side) compared to the PPINGUIN workflow presented in our manuscript (right hand side). Starting point for both workflows is the mzML [48] file containing the spectral peak data. In contrast to the standard workflow we employ clustering as a very early step prior to protein inference. This leads to splitting of spectra into different groups. Quantitation and identification is performed independently for each group. The result is a list of identified and quantified proteins ready for downstream analysis.
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Figure 2: Workflow. Standard workflow of proteomics data evaluation (left hand side) compared to the PPINGUIN workflow presented in our manuscript (right hand side). Starting point for both workflows is the mzML [48] file containing the spectral peak data. In contrast to the standard workflow we employ clustering as a very early step prior to protein inference. This leads to splitting of spectra into different groups. Quantitation and identification is performed independently for each group. The result is a list of identified and quantified proteins ready for downstream analysis.

Mentions: Here we present a statistical analysis workflow for iTRAQ data employing clustering prior to protein inference with the aim to reduce peptide heterogeneity (see Figure 2).


PPINGUIN: Peptide Profiling Guided Identification of Proteins improves quantitation of iTRAQ ratios.

Bauer C, Kleinjung F, Rutishauser D, Panse C, Chadt A, Dreja T, Al-Hasani H, Reinert K, Schlapbach R, Schuchhardt J - BMC Bioinformatics (2012)

Workflow. Standard workflow of proteomics data evaluation (left hand side) compared to the PPINGUIN workflow presented in our manuscript (right hand side). Starting point for both workflows is the mzML [48] file containing the spectral peak data. In contrast to the standard workflow we employ clustering as a very early step prior to protein inference. This leads to splitting of spectra into different groups. Quantitation and identification is performed independently for each group. The result is a list of identified and quantified proteins ready for downstream analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3368728&req=5

Figure 2: Workflow. Standard workflow of proteomics data evaluation (left hand side) compared to the PPINGUIN workflow presented in our manuscript (right hand side). Starting point for both workflows is the mzML [48] file containing the spectral peak data. In contrast to the standard workflow we employ clustering as a very early step prior to protein inference. This leads to splitting of spectra into different groups. Quantitation and identification is performed independently for each group. The result is a list of identified and quantified proteins ready for downstream analysis.
Mentions: Here we present a statistical analysis workflow for iTRAQ data employing clustering prior to protein inference with the aim to reduce peptide heterogeneity (see Figure 2).

Bottom Line: Our method increases experimental reproducibility and decreases variability of quantitations of peptides assigned to the same protein.Regarding the increasing importance of quantitative proteomics we think that this method will be useful in practical applications like model fitting or functional enrichment analysis.We recommend to use this method if quantitation is a major objective of research.

View Article: PubMed Central - HTML - PubMed

Affiliation: MicroDiscovery GmbH, Marienburger Str, 1, 10405 Berlin, Germany. chris.bauer@microdiscovery.de

ABSTRACT

Background: Recent development of novel technologies paved the way for quantitative proteomics. One of the most important among them is iTRAQ, employing isobaric tags for relative or absolute quantitation. Despite large progress in technology development, still many challenges remain for derivation and interpretation of quantitative results. One of these challenges is the consistent assignment of peptides to proteins.

Results: We have developed Peptide Profiling Guided Identification of Proteins (PPINGUIN), a statistical analysis workflow for iTRAQ data addressing the problem of ambiguous peptide quantitations. Motivated by the assumption that peptides uniquely derived from the same protein are correlated, our method employs clustering as a very early step in data processing prior to protein inference. Our method increases experimental reproducibility and decreases variability of quantitations of peptides assigned to the same protein. Giving further support to our method, application to a type 2 diabetes dataset identifies a list of protein candidates that is in very good agreement with previously performed transcriptomics meta analysis. Making use of quantitative properties of signal patterns identified, PPINGUIN can reveal new isoform candidates.

Conclusions: Regarding the increasing importance of quantitative proteomics we think that this method will be useful in practical applications like model fitting or functional enrichment analysis. We recommend to use this method if quantitation is a major objective of research.

Show MeSH
Related in: MedlinePlus