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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

Peptide Heterogeneity. Exemplary chosen protein accession 40S ribosomal protein S30 (RS_30) for demonstration of peptide heterogeneity. Every line represents a unique peptide profile (peptide-spectrum-match) identified as originating from the RS_30 protein. iTRAQ ratios are calculated using 116 channel (SJL mouse with standard diet) as reference. For every ratio a box plot giving the lower quartile, median and upper quartile is drawn. Especially for the 117/116 ratio (NZO mouse with high fat diet) the quantitation ratios are very heterogeneous ranging from -0.5 to +1 (corresponding to a 1.4 fold down-regulation or 2 fold up-regulation).
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Figure 1: Peptide Heterogeneity. Exemplary chosen protein accession 40S ribosomal protein S30 (RS_30) for demonstration of peptide heterogeneity. Every line represents a unique peptide profile (peptide-spectrum-match) identified as originating from the RS_30 protein. iTRAQ ratios are calculated using 116 channel (SJL mouse with standard diet) as reference. For every ratio a box plot giving the lower quartile, median and upper quartile is drawn. Especially for the 117/116 ratio (NZO mouse with high fat diet) the quantitation ratios are very heterogeneous ranging from -0.5 to +1 (corresponding to a 1.4 fold down-regulation or 2 fold up-regulation).

Mentions: The continuing popularity of iTRAQ makes an evaluation of the technique in terms of accuracy and precision a valuable task [19]. Accuracy assesses the closeness to the real quantification value. Precision in this context refers to reproducibility of experiments. Since accuracy is difficult to evaluate, precision is the most frequently applied measure for experimental quality [20,21]. Gan et al. [22] tried to assess the precision of iTRAQ data by analyzing technical (different channels of the same MS run), experimental (same channel but different runs) and biological variations (different biological samples). They designed different iTRAQ experiments covering the different types of replications and they found technical variation to be small (11%) whereas experimental and biological variations where more than twice as high. For iTRAQ - like for the majority of MS based quantitation approaches - quantitation measurements are performed at the peptide level. Since often multiple peptides potentially with different modifications are measured for the same protein, the need for some kind of summarizing strategy is obvious. Different ideas regarding the calculation of protein quantitation from multiple peptides have been applied including mean or median calculation [23,24] and error weighted means [25]. Because of the fixed stoichiometric ratio, quantitation measurements for peptides uniquely assigned to the same protein should be strictly correlated [26]. But often this presumption is not fulfilled and the quantitation values exhibit a substantial heterogeneity. The heterogeneity is also observed for quantitation ratios and z-transformed values and is not due to different ionization or fragmentation efficiency. This is illustrated in Figure 1 presenting the quantitation ratios of unique peptides for an exemplary chosen protein: 40S ribosomal protein S30. Especially the 117/116 ratio (rightmost bar in Figure 1) varies from 1.4 fold down-regulation to 2 fold up-regulation. An obvious reason for heterogeneous quantitation values are non-unique peptides shared by different proteins.


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)

Peptide Heterogeneity. Exemplary chosen protein accession 40S ribosomal protein S30 (RS_30) for demonstration of peptide heterogeneity. Every line represents a unique peptide profile (peptide-spectrum-match) identified as originating from the RS_30 protein. iTRAQ ratios are calculated using 116 channel (SJL mouse with standard diet) as reference. For every ratio a box plot giving the lower quartile, median and upper quartile is drawn. Especially for the 117/116 ratio (NZO mouse with high fat diet) the quantitation ratios are very heterogeneous ranging from -0.5 to +1 (corresponding to a 1.4 fold down-regulation or 2 fold up-regulation).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Peptide Heterogeneity. Exemplary chosen protein accession 40S ribosomal protein S30 (RS_30) for demonstration of peptide heterogeneity. Every line represents a unique peptide profile (peptide-spectrum-match) identified as originating from the RS_30 protein. iTRAQ ratios are calculated using 116 channel (SJL mouse with standard diet) as reference. For every ratio a box plot giving the lower quartile, median and upper quartile is drawn. Especially for the 117/116 ratio (NZO mouse with high fat diet) the quantitation ratios are very heterogeneous ranging from -0.5 to +1 (corresponding to a 1.4 fold down-regulation or 2 fold up-regulation).
Mentions: The continuing popularity of iTRAQ makes an evaluation of the technique in terms of accuracy and precision a valuable task [19]. Accuracy assesses the closeness to the real quantification value. Precision in this context refers to reproducibility of experiments. Since accuracy is difficult to evaluate, precision is the most frequently applied measure for experimental quality [20,21]. Gan et al. [22] tried to assess the precision of iTRAQ data by analyzing technical (different channels of the same MS run), experimental (same channel but different runs) and biological variations (different biological samples). They designed different iTRAQ experiments covering the different types of replications and they found technical variation to be small (11%) whereas experimental and biological variations where more than twice as high. For iTRAQ - like for the majority of MS based quantitation approaches - quantitation measurements are performed at the peptide level. Since often multiple peptides potentially with different modifications are measured for the same protein, the need for some kind of summarizing strategy is obvious. Different ideas regarding the calculation of protein quantitation from multiple peptides have been applied including mean or median calculation [23,24] and error weighted means [25]. Because of the fixed stoichiometric ratio, quantitation measurements for peptides uniquely assigned to the same protein should be strictly correlated [26]. But often this presumption is not fulfilled and the quantitation values exhibit a substantial heterogeneity. The heterogeneity is also observed for quantitation ratios and z-transformed values and is not due to different ionization or fragmentation efficiency. This is illustrated in Figure 1 presenting the quantitation ratios of unique peptides for an exemplary chosen protein: 40S ribosomal protein S30. Especially the 117/116 ratio (rightmost bar in Figure 1) varies from 1.4 fold down-regulation to 2 fold up-regulation. An obvious reason for heterogeneous quantitation values are non-unique peptides shared by different proteins.

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