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SAMPI: protein identification with mass spectra alignments.

Kaltenbach HM, Wilke A, Böcker S - BMC Bioinformatics (2007)

Bottom Line: A protein is digested (usually by trypsin) and its mass spectrum is compared to simulated spectra for protein sequences in a database.We prove the applicability of our approach using biological mass spectrometry data and compare our results to the standard software Mascot.Introducing more noise peaks, we are able to keep identification rates at a similar level by using the flexibility introduced by scoring schemes.

View Article: PubMed Central - HTML - PubMed

Affiliation: AG Genominformatik, Technische Fakultät, Universität Bielefeld, Bielefeld, Germany. michael@cebitec.uni-bielefeld.de

ABSTRACT

Background: Mass spectrometry based peptide mass fingerprints (PMFs) offer a fast, efficient, and robust method for protein identification. A protein is digested (usually by trypsin) and its mass spectrum is compared to simulated spectra for protein sequences in a database. However, existing tools for analyzing PMFs often suffer from missing or heuristic analysis of the significance of search results and insufficient handling of missing and additional peaks.

Results: We present an unified framework for analyzing Peptide Mass Fingerprints that offers a number of advantages over existing methods: First, comparison of mass spectra is based on a scoring function that can be custom-designed for certain applications and explicitly takes missing and additional peaks into account. The method is able to simulate almost every additive scoring scheme. Second, we present an efficient deterministic method for assessing the significance of a protein hit, independent of the underlying scoring function and sequence database. We prove the applicability of our approach using biological mass spectrometry data and compare our results to the standard software Mascot.

Conclusion: The proposed framework for analyzing Peptide Mass Fingerprints shows performance comparable to Mascot on small peak lists. Introducing more noise peaks, we are able to keep identification rates at a similar level by using the flexibility introduced by scoring schemes.

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Related in: MedlinePlus

Score distributions. SAMPI: Distribution of SAMPI scores of correct (solid line) and incorrect (dashed line) identifications using the Cg+SwissProt database, parameter set B, no missed cleavages, and 316 spectra.
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Figure 3: Score distributions. SAMPI: Distribution of SAMPI scores of correct (solid line) and incorrect (dashed line) identifications using the Cg+SwissProt database, parameter set B, no missed cleavages, and 316 spectra.

Mentions: We found the score separation of correct and incorrect identifications to be comparable to Mascot (Figures 2 and 3).


SAMPI: protein identification with mass spectra alignments.

Kaltenbach HM, Wilke A, Böcker S - BMC Bioinformatics (2007)

Score distributions. SAMPI: Distribution of SAMPI scores of correct (solid line) and incorrect (dashed line) identifications using the Cg+SwissProt database, parameter set B, no missed cleavages, and 316 spectra.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1851022&req=5

Figure 3: Score distributions. SAMPI: Distribution of SAMPI scores of correct (solid line) and incorrect (dashed line) identifications using the Cg+SwissProt database, parameter set B, no missed cleavages, and 316 spectra.
Mentions: We found the score separation of correct and incorrect identifications to be comparable to Mascot (Figures 2 and 3).

Bottom Line: A protein is digested (usually by trypsin) and its mass spectrum is compared to simulated spectra for protein sequences in a database.We prove the applicability of our approach using biological mass spectrometry data and compare our results to the standard software Mascot.Introducing more noise peaks, we are able to keep identification rates at a similar level by using the flexibility introduced by scoring schemes.

View Article: PubMed Central - HTML - PubMed

Affiliation: AG Genominformatik, Technische Fakultät, Universität Bielefeld, Bielefeld, Germany. michael@cebitec.uni-bielefeld.de

ABSTRACT

Background: Mass spectrometry based peptide mass fingerprints (PMFs) offer a fast, efficient, and robust method for protein identification. A protein is digested (usually by trypsin) and its mass spectrum is compared to simulated spectra for protein sequences in a database. However, existing tools for analyzing PMFs often suffer from missing or heuristic analysis of the significance of search results and insufficient handling of missing and additional peaks.

Results: We present an unified framework for analyzing Peptide Mass Fingerprints that offers a number of advantages over existing methods: First, comparison of mass spectra is based on a scoring function that can be custom-designed for certain applications and explicitly takes missing and additional peaks into account. The method is able to simulate almost every additive scoring scheme. Second, we present an efficient deterministic method for assessing the significance of a protein hit, independent of the underlying scoring function and sequence database. We prove the applicability of our approach using biological mass spectrometry data and compare our results to the standard software Mascot.

Conclusion: The proposed framework for analyzing Peptide Mass Fingerprints shows performance comparable to Mascot on small peak lists. Introducing more noise peaks, we are able to keep identification rates at a similar level by using the flexibility introduced by scoring schemes.

Show MeSH
Related in: MedlinePlus