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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

Venn Diagram. Venn diagram visualizing the number of significantly identified protein accessions using the three different approaches: Mascot, XTandem/OpenMS and PPINGUIN. We refer to protein accessions identified in all three experimental replications of the diabetes dataset (see Methods).
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Figure 3: Venn Diagram. Venn diagram visualizing the number of significantly identified protein accessions using the three different approaches: Mascot, XTandem/OpenMS and PPINGUIN. We refer to protein accessions identified in all three experimental replications of the diabetes dataset (see Methods).

Mentions: The numbers of protein accessions identified with the same FDR (see Methods) differ for each method: 225 for MASCOT, 177 for X!Tandem and OpenMS based approach and 176 for PPINGUIN. Ambiguous protein groups (e.g. H2B1B, H2B1C, H2B1F,...) identified with exclusively non-unique peptides, were not counted here. Therefore, the actual number of proteins and the overlaps of the three methods may be underestimated. Most of the representative accessions received from PPINGUIN analysis were also detected using X!Tandem (83%). Both methods have their set of unique proteins: 32 for PPINGUIN and 33 for X!Tandem. The overlap between MASCOT and the other two approaches is good: 70% of the X!Tandem IDs and 62% of PPINGUIN IDs were found with MASCOT (see Venn diagram in Figure 3). Explanations for these differences are provided in the discussion below.


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)

Venn Diagram. Venn diagram visualizing the number of significantly identified protein accessions using the three different approaches: Mascot, XTandem/OpenMS and PPINGUIN. We refer to protein accessions identified in all three experimental replications of the diabetes dataset (see Methods).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Venn Diagram. Venn diagram visualizing the number of significantly identified protein accessions using the three different approaches: Mascot, XTandem/OpenMS and PPINGUIN. We refer to protein accessions identified in all three experimental replications of the diabetes dataset (see Methods).
Mentions: The numbers of protein accessions identified with the same FDR (see Methods) differ for each method: 225 for MASCOT, 177 for X!Tandem and OpenMS based approach and 176 for PPINGUIN. Ambiguous protein groups (e.g. H2B1B, H2B1C, H2B1F,...) identified with exclusively non-unique peptides, were not counted here. Therefore, the actual number of proteins and the overlaps of the three methods may be underestimated. Most of the representative accessions received from PPINGUIN analysis were also detected using X!Tandem (83%). Both methods have their set of unique proteins: 32 for PPINGUIN and 33 for X!Tandem. The overlap between MASCOT and the other two approaches is good: 70% of the X!Tandem IDs and 62% of PPINGUIN IDs were found with MASCOT (see Venn diagram in Figure 3). Explanations for these differences are provided in the discussion below.

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