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High-throughput peptide quantification using mTRAQ reagent triplex.

Yoon JY, Yeom J, Lee H, Kim K, Na S, Park K, Paek E, Lee C - BMC Bioinformatics (2011)

Bottom Line: It improved the accuracy of quantification in two features.Our algorithm successfully identified the overlaps and found accurate elution areas.Its software implementation is compatible with Trans-Proteomic Pipeline (TPP), and thus enables high-throughput analysis of proteomics data.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea. jyyoon@theory.snu.ac.kr

ABSTRACT

Background: Protein quantification is an essential step in many proteomics experiments. A number of labeling approaches have been proposed and adopted in mass spectrometry (MS) based relative quantification. The mTRAQ, one of the stable isotope labeling methods, is amine-specific and available in triplex format, so that the sample throughput could be doubled when compared with duplex reagents.

Methods and results: Here we propose a novel data analysis algorithm for peptide quantification in triplex mTRAQ experiments. It improved the accuracy of quantification in two features. First, it identified and separated triplex isotopic clusters of a peptide in each full MS scan. We designed a schematic model of triplex overlapping isotopic clusters, and separated triplex isotopic clusters by solving cubic equations, which are deduced from the schematic model. Second, it automatically determined the elution areas of peptides. Some peptides have similar atomic masses and elution times, so their elution areas can have overlaps. Our algorithm successfully identified the overlaps and found accurate elution areas. We validated our algorithm using standard protein mixture experiments.

Conclusions: We showed that our algorithm was able to accurately quantify peptides in triplex mTRAQ experiments. Its software implementation is compatible with Trans-Proteomic Pipeline (TPP), and thus enables high-throughput analysis of proteomics data.

Show MeSH
Elution area approximation to normal distribution. Elution area approximation for ‘HPIKHQGLPQEVLNENLLR’. The blue line represents the sum of intensities of the peptide over the elution area and the red line is an approximated normal distribution. From the given elution time (951.423 s), where tandem MS scan was acquired, we first found the maximum point of elution area (957.93 s). Then we extended the area until the sum of intensities is below 50% of that of the maximum point and obtained μ = 960.63 and FWHM = 19.9. Finally, we used the area with higher intensities than 10% of the maximum intensity of the approximated normal distribution.
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Figure 2: Elution area approximation to normal distribution. Elution area approximation for ‘HPIKHQGLPQEVLNENLLR’. The blue line represents the sum of intensities of the peptide over the elution area and the red line is an approximated normal distribution. From the given elution time (951.423 s), where tandem MS scan was acquired, we first found the maximum point of elution area (957.93 s). Then we extended the area until the sum of intensities is below 50% of that of the maximum point and obtained μ = 960.63 and FWHM = 19.9. Finally, we used the area with higher intensities than 10% of the maximum intensity of the approximated normal distribution.

Mentions: When a peptide identification and the associated tandem MS scan is given, our algorithm first finds the maximum point of the peptide’s elution profile. For each MS scan within ±30s range from the given tandem scan, it identifies triplex isotopic clusters and calculates the sum of intensities. (Details are explained in the next section.) The MS scan whose sum of intensities is the highest is selected as the maximum point of the elution area. Then it extends the elution area while the sum of intensities of MS scan is above a half of that of the maximum point. The length of the extended area is used as FWHM and weighted average time of scans in the extended area is used as μ. The area with higher intensities than 10% of the maximum intensity in the normal distribution (from to ) is used as the elution area of a peptide. An example for approximation to normal distribution is shown in Figure 2.


High-throughput peptide quantification using mTRAQ reagent triplex.

Yoon JY, Yeom J, Lee H, Kim K, Na S, Park K, Paek E, Lee C - BMC Bioinformatics (2011)

Elution area approximation to normal distribution. Elution area approximation for ‘HPIKHQGLPQEVLNENLLR’. The blue line represents the sum of intensities of the peptide over the elution area and the red line is an approximated normal distribution. From the given elution time (951.423 s), where tandem MS scan was acquired, we first found the maximum point of elution area (957.93 s). Then we extended the area until the sum of intensities is below 50% of that of the maximum point and obtained μ = 960.63 and FWHM = 19.9. Finally, we used the area with higher intensities than 10% of the maximum intensity of the approximated normal distribution.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3044303&req=5

Figure 2: Elution area approximation to normal distribution. Elution area approximation for ‘HPIKHQGLPQEVLNENLLR’. The blue line represents the sum of intensities of the peptide over the elution area and the red line is an approximated normal distribution. From the given elution time (951.423 s), where tandem MS scan was acquired, we first found the maximum point of elution area (957.93 s). Then we extended the area until the sum of intensities is below 50% of that of the maximum point and obtained μ = 960.63 and FWHM = 19.9. Finally, we used the area with higher intensities than 10% of the maximum intensity of the approximated normal distribution.
Mentions: When a peptide identification and the associated tandem MS scan is given, our algorithm first finds the maximum point of the peptide’s elution profile. For each MS scan within ±30s range from the given tandem scan, it identifies triplex isotopic clusters and calculates the sum of intensities. (Details are explained in the next section.) The MS scan whose sum of intensities is the highest is selected as the maximum point of the elution area. Then it extends the elution area while the sum of intensities of MS scan is above a half of that of the maximum point. The length of the extended area is used as FWHM and weighted average time of scans in the extended area is used as μ. The area with higher intensities than 10% of the maximum intensity in the normal distribution (from to ) is used as the elution area of a peptide. An example for approximation to normal distribution is shown in Figure 2.

Bottom Line: It improved the accuracy of quantification in two features.Our algorithm successfully identified the overlaps and found accurate elution areas.Its software implementation is compatible with Trans-Proteomic Pipeline (TPP), and thus enables high-throughput analysis of proteomics data.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea. jyyoon@theory.snu.ac.kr

ABSTRACT

Background: Protein quantification is an essential step in many proteomics experiments. A number of labeling approaches have been proposed and adopted in mass spectrometry (MS) based relative quantification. The mTRAQ, one of the stable isotope labeling methods, is amine-specific and available in triplex format, so that the sample throughput could be doubled when compared with duplex reagents.

Methods and results: Here we propose a novel data analysis algorithm for peptide quantification in triplex mTRAQ experiments. It improved the accuracy of quantification in two features. First, it identified and separated triplex isotopic clusters of a peptide in each full MS scan. We designed a schematic model of triplex overlapping isotopic clusters, and separated triplex isotopic clusters by solving cubic equations, which are deduced from the schematic model. Second, it automatically determined the elution areas of peptides. Some peptides have similar atomic masses and elution times, so their elution areas can have overlaps. Our algorithm successfully identified the overlaps and found accurate elution areas. We validated our algorithm using standard protein mixture experiments.

Conclusions: We showed that our algorithm was able to accurately quantify peptides in triplex mTRAQ experiments. Its software implementation is compatible with Trans-Proteomic Pipeline (TPP), and thus enables high-throughput analysis of proteomics data.

Show MeSH