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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

Sample peptide profiles. Visualization of peptide quantitation profiles of the three different approaches employed (rows) demonstrated for 3 exemplary chosen proteins (columns). The three rows correspond to the applied method: first row = MASCOT, second row = X!Tandem and OpenMS, last row = PPINGUIN. Each individual plot shows ratio profiles of peptides uniquely assigned to the corresponding protein. For every ratio a box plot giving the lower quartile, median and upper quartile is drawn.
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Figure 4: Sample peptide profiles. Visualization of peptide quantitation profiles of the three different approaches employed (rows) demonstrated for 3 exemplary chosen proteins (columns). The three rows correspond to the applied method: first row = MASCOT, second row = X!Tandem and OpenMS, last row = PPINGUIN. Each individual plot shows ratio profiles of peptides uniquely assigned to the corresponding protein. For every ratio a box plot giving the lower quartile, median and upper quartile is drawn.

Mentions: As described above, a protein represented by multiple unique peptides should result in strictly correlated quantitation ratios for the peptides. But often heterogeneous ratio profiles are observed using MASCOT as well as X!Tandem, naturally leading to difficulties in quantitative interpretation. This situation is illustrated in the first and second row of Figure 4 for three example proteins. An obvious reason for heterogeneous quantitation values are non-unique peptides shared by different proteins. For avoiding this fact non-unique peptides are left out for all plots and statistical assessments. Using our approach, peptide profiles are more homogeneous supporting a consistent quantitative interpretation (see bottom row of Figure 4). A distinctive feature of PPINGUIN is demonstrated by the ribosomal protein RS_30: inconsistent quantitation profiles are resolved by splitting up in two groups each with homogeneous profiles. This effect is illustrated in more detail in Figure 5 (and as addition examples in Additional File 3). The protein is identified in two different clusters (1 and 4) with different peptide profiles. The peptides in cluster 1 show low relative concentration for NZO_SD (114) and high relative concentration for NZO_HF (117) while peptides in cluster 4 show the opposite behavior. The peptides belonging to each cluster are located in different sites of the protein. As discussed below, this finding is a hint towards two variants of the RS_30 protein.


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)

Sample peptide profiles. Visualization of peptide quantitation profiles of the three different approaches employed (rows) demonstrated for 3 exemplary chosen proteins (columns). The three rows correspond to the applied method: first row = MASCOT, second row = X!Tandem and OpenMS, last row = PPINGUIN. Each individual plot shows ratio profiles of peptides uniquely assigned to the corresponding protein. For every ratio a box plot giving the lower quartile, median and upper quartile is drawn.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Sample peptide profiles. Visualization of peptide quantitation profiles of the three different approaches employed (rows) demonstrated for 3 exemplary chosen proteins (columns). The three rows correspond to the applied method: first row = MASCOT, second row = X!Tandem and OpenMS, last row = PPINGUIN. Each individual plot shows ratio profiles of peptides uniquely assigned to the corresponding protein. For every ratio a box plot giving the lower quartile, median and upper quartile is drawn.
Mentions: As described above, a protein represented by multiple unique peptides should result in strictly correlated quantitation ratios for the peptides. But often heterogeneous ratio profiles are observed using MASCOT as well as X!Tandem, naturally leading to difficulties in quantitative interpretation. This situation is illustrated in the first and second row of Figure 4 for three example proteins. An obvious reason for heterogeneous quantitation values are non-unique peptides shared by different proteins. For avoiding this fact non-unique peptides are left out for all plots and statistical assessments. Using our approach, peptide profiles are more homogeneous supporting a consistent quantitative interpretation (see bottom row of Figure 4). A distinctive feature of PPINGUIN is demonstrated by the ribosomal protein RS_30: inconsistent quantitation profiles are resolved by splitting up in two groups each with homogeneous profiles. This effect is illustrated in more detail in Figure 5 (and as addition examples in Additional File 3). The protein is identified in two different clusters (1 and 4) with different peptide profiles. The peptides in cluster 1 show low relative concentration for NZO_SD (114) and high relative concentration for NZO_HF (117) while peptides in cluster 4 show the opposite behavior. The peptides belonging to each cluster are located in different sites of the protein. As discussed below, this finding is a hint towards two variants of the RS_30 protein.

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