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Tandem mass spectrometry data quality assessment by self-convolution.

Choo KW, Tham WM - BMC Bioinformatics (2007)

Bottom Line: The quality score was defined as the ratio of the intensity at the mid point to the remaining peaks of the convolution result.The results were encouraging, revealing a high percentage of positive prediction rates for spectra with good quality scores.By pre-determining the quality of tandem MS data before subjecting them to protein identification algorithms, spurious protein predictions due to poor tandem MS data are avoided, giving scientists greater confidence in the predicted results.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Group, Nanyang Polytechnic, 569830 Singapore, Republic Of Singapore. choo_keng_wah@nyp.edu.sg

ABSTRACT

Background: Many algorithms have been developed for deciphering the tandem mass spectrometry (MS) data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on de novo sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified.

Results: The proposed method measures the qualities of MS data sets based on the symmetric property of b- and y-ion peaks present in a MS spectrum. Self-convolution on MS data and its time-reversal copy was employed. Due to the symmetric nature of b-ions and y-ions peaks, the self-convolution result of a good spectrum would produce a highest mid point intensity peak. To reduce processing time, self-convolution was achieved using Fast Fourier Transform and its inverse transform, followed by the removal of the "DC" (Direct Current) component and the normalisation of the data set. The quality score was defined as the ratio of the intensity at the mid point to the remaining peaks of the convolution result. The method was validated using both theoretical mass spectra, with various permutations, and several real MS data sets. The results were encouraging, revealing a high percentage of positive prediction rates for spectra with good quality scores.

Conclusion: We have demonstrated in this work a method for determining the quality of tandem MS data set. By pre-determining the quality of tandem MS data before subjecting them to protein identification algorithms, spurious protein predictions due to poor tandem MS data are avoided, giving scientists greater confidence in the predicted results. We conclude that the algorithm performs well and could potentially be used as a pre-processing for all mass spectrometry based protein identification tools.

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Plot of self-convolution of experimental mass spectrum. This figure shows the actual mass spectrum (left) and its respective self-convolution result (right). A high mid-point intensity might not indicate a good quality spectrum as a product of two high intensity peaks could generate it by chance.
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Figure 4: Plot of self-convolution of experimental mass spectrum. This figure shows the actual mass spectrum (left) and its respective self-convolution result (right). A high mid-point intensity might not indicate a good quality spectrum as a product of two high intensity peaks could generate it by chance.

Mentions: We started the quality assessment by simply performing a self-convolution on some of the experimental MS spectra. Fig. 4 shows a plot of the result of self-convolution of one of the raw tandem MS spectra. Although the plot does show a high peak at the mid-point window of the result, we found out that the product of two high intensity peaks happened incidentally to be at the mid-point. This could cause misinterpretation and therefore erroneous for us to consider this result as an indication of good quality spectrum. We have thus further improved on the approach by considering side peaks and normalisation process.


Tandem mass spectrometry data quality assessment by self-convolution.

Choo KW, Tham WM - BMC Bioinformatics (2007)

Plot of self-convolution of experimental mass spectrum. This figure shows the actual mass spectrum (left) and its respective self-convolution result (right). A high mid-point intensity might not indicate a good quality spectrum as a product of two high intensity peaks could generate it by chance.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2164967&req=5

Figure 4: Plot of self-convolution of experimental mass spectrum. This figure shows the actual mass spectrum (left) and its respective self-convolution result (right). A high mid-point intensity might not indicate a good quality spectrum as a product of two high intensity peaks could generate it by chance.
Mentions: We started the quality assessment by simply performing a self-convolution on some of the experimental MS spectra. Fig. 4 shows a plot of the result of self-convolution of one of the raw tandem MS spectra. Although the plot does show a high peak at the mid-point window of the result, we found out that the product of two high intensity peaks happened incidentally to be at the mid-point. This could cause misinterpretation and therefore erroneous for us to consider this result as an indication of good quality spectrum. We have thus further improved on the approach by considering side peaks and normalisation process.

Bottom Line: The quality score was defined as the ratio of the intensity at the mid point to the remaining peaks of the convolution result.The results were encouraging, revealing a high percentage of positive prediction rates for spectra with good quality scores.By pre-determining the quality of tandem MS data before subjecting them to protein identification algorithms, spurious protein predictions due to poor tandem MS data are avoided, giving scientists greater confidence in the predicted results.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Group, Nanyang Polytechnic, 569830 Singapore, Republic Of Singapore. choo_keng_wah@nyp.edu.sg

ABSTRACT

Background: Many algorithms have been developed for deciphering the tandem mass spectrometry (MS) data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on de novo sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified.

Results: The proposed method measures the qualities of MS data sets based on the symmetric property of b- and y-ion peaks present in a MS spectrum. Self-convolution on MS data and its time-reversal copy was employed. Due to the symmetric nature of b-ions and y-ions peaks, the self-convolution result of a good spectrum would produce a highest mid point intensity peak. To reduce processing time, self-convolution was achieved using Fast Fourier Transform and its inverse transform, followed by the removal of the "DC" (Direct Current) component and the normalisation of the data set. The quality score was defined as the ratio of the intensity at the mid point to the remaining peaks of the convolution result. The method was validated using both theoretical mass spectra, with various permutations, and several real MS data sets. The results were encouraging, revealing a high percentage of positive prediction rates for spectra with good quality scores.

Conclusion: We have demonstrated in this work a method for determining the quality of tandem MS data set. By pre-determining the quality of tandem MS data before subjecting them to protein identification algorithms, spurious protein predictions due to poor tandem MS data are avoided, giving scientists greater confidence in the predicted results. We conclude that the algorithm performs well and could potentially be used as a pre-processing for all mass spectrometry based protein identification tools.

Show MeSH