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Score regularization for peptide identification.

He Z, Zhao H, Yu W - BMC Bioinformatics (2011)

Bottom Line: Thus, it is critical to develop new post-processing techniques that can distinguish true identifications from false identifications effectively.In this paper, we present a consistency-based PSM re-ranking method to improve the initial identification results.The score regularization method can be used as a general post-processing step for improving peptide identifications.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Software, Dalian University of Technology, Dalian, China. zyhe@dlut.edu.cn

ABSTRACT

Background: Peptide identification from tandem mass spectrometry (MS/MS) data is one of the most important problems in computational proteomics. This technique relies heavily on the accurate assessment of the quality of peptide-spectrum matches (PSMs). However, current MS technology and PSM scoring algorithm are far from perfect, leading to the generation of incorrect peptide-spectrum pairs. Thus, it is critical to develop new post-processing techniques that can distinguish true identifications from false identifications effectively.

Results: In this paper, we present a consistency-based PSM re-ranking method to improve the initial identification results. This method uses one additional assumption that two peptides belonging to the same protein should be correlated to each other. We formulate an optimization problem that embraces two objectives through regularization: the smoothing consistency among scores of correlated peptides and the fitting consistency between new scores and initial scores. This optimization problem can be solved analytically. The experimental study on several real MS/MS data sets shows that this re-ranking method improves the identification performance.

Conclusions: The score regularization method can be used as a general post-processing step for improving peptide identifications. Source codes and data sets are available at: http://bioinformatics.ust.hk/SRPI.rar.

Show MeSH
Identification performance of baseline ranker and our method. Here we use X!Tandem as the baseline ranker to rank PSMs according to their E-values. Our method outputs an optimal ranking that balances the score consistency among similar peptides and the score consistency between initial identification and updated identification.
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Figure 3: Identification performance of baseline ranker and our method. Here we use X!Tandem as the baseline ranker to rank PSMs according to their E-values. Our method outputs an optimal ranking that balances the score consistency among similar peptides and the score consistency between initial identification and updated identification.

Mentions: We plot the receiver operating characteristic (ROC) curves of the baseline method and our method in Fig.3. We also use the area under ROC curve (AUC) as a single numeric indicator of overall performance. Fig.3 shows that:


Score regularization for peptide identification.

He Z, Zhao H, Yu W - BMC Bioinformatics (2011)

Identification performance of baseline ranker and our method. Here we use X!Tandem as the baseline ranker to rank PSMs according to their E-values. Our method outputs an optimal ranking that balances the score consistency among similar peptides and the score consistency between initial identification and updated identification.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3044274&req=5

Figure 3: Identification performance of baseline ranker and our method. Here we use X!Tandem as the baseline ranker to rank PSMs according to their E-values. Our method outputs an optimal ranking that balances the score consistency among similar peptides and the score consistency between initial identification and updated identification.
Mentions: We plot the receiver operating characteristic (ROC) curves of the baseline method and our method in Fig.3. We also use the area under ROC curve (AUC) as a single numeric indicator of overall performance. Fig.3 shows that:

Bottom Line: Thus, it is critical to develop new post-processing techniques that can distinguish true identifications from false identifications effectively.In this paper, we present a consistency-based PSM re-ranking method to improve the initial identification results.The score regularization method can be used as a general post-processing step for improving peptide identifications.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Software, Dalian University of Technology, Dalian, China. zyhe@dlut.edu.cn

ABSTRACT

Background: Peptide identification from tandem mass spectrometry (MS/MS) data is one of the most important problems in computational proteomics. This technique relies heavily on the accurate assessment of the quality of peptide-spectrum matches (PSMs). However, current MS technology and PSM scoring algorithm are far from perfect, leading to the generation of incorrect peptide-spectrum pairs. Thus, it is critical to develop new post-processing techniques that can distinguish true identifications from false identifications effectively.

Results: In this paper, we present a consistency-based PSM re-ranking method to improve the initial identification results. This method uses one additional assumption that two peptides belonging to the same protein should be correlated to each other. We formulate an optimization problem that embraces two objectives through regularization: the smoothing consistency among scores of correlated peptides and the fitting consistency between new scores and initial scores. This optimization problem can be solved analytically. The experimental study on several real MS/MS data sets shows that this re-ranking method improves the identification performance.

Conclusions: The score regularization method can be used as a general post-processing step for improving peptide identifications. Source codes and data sets are available at: http://bioinformatics.ust.hk/SRPI.rar.

Show MeSH