Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry

John Halloran, Jeffrey Bilmes, William Noble
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:775-784, 2014.

Abstract

We present a peptide-spectrum alignment strategy that employs a dynamic Bayesian network (DBN) for the identification of spectra produced by tan- dem mass spectrometry (MS/MS). Our method is fundamentally generative in that it models peptide fragmentation in MS/MS as a physical process. The model traverses an observed MS/MS spec- trum and a peptide-based theoretical spectrum to calculate the best alignment between the two spectra. Unlike all existing state-of-the-art meth- ods for spectrum identification that we are aware of, our method can learn alignment probabilities given a dataset of high-quality peptide-spectrum pairs. The method, moreover, accounts for noise peaks and absent theoretical peaks in the observed spectrum. We demonstrate that our method out- performs, on a majority of datasets, several widely used, state-of-the-art database search tools for spectrum identification. Furthermore, the pro- posed approach provides an extensible framework for MS/MS analysis and provides useful informa- tion that is not produced by other methods, thanks to its generative structure.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-halloran14a, title = {Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry}, author = {Halloran, John and Bilmes, Jeffrey and Noble, William}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {775--784}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/halloran14a/halloran14a.pdf}, url = {https://proceedings.mlr.press/r12/halloran14a.html}, abstract = {We present a peptide-spectrum alignment strategy that employs a dynamic Bayesian network (DBN) for the identification of spectra produced by tan- dem mass spectrometry (MS/MS). Our method is fundamentally generative in that it models peptide fragmentation in MS/MS as a physical process. The model traverses an observed MS/MS spec- trum and a peptide-based theoretical spectrum to calculate the best alignment between the two spectra. Unlike all existing state-of-the-art meth- ods for spectrum identification that we are aware of, our method can learn alignment probabilities given a dataset of high-quality peptide-spectrum pairs. The method, moreover, accounts for noise peaks and absent theoretical peaks in the observed spectrum. We demonstrate that our method out- performs, on a majority of datasets, several widely used, state-of-the-art database search tools for spectrum identification. Furthermore, the pro- posed approach provides an extensible framework for MS/MS analysis and provides useful informa- tion that is not produced by other methods, thanks to its generative structure.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry %A John Halloran %A Jeffrey Bilmes %A William Noble %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-halloran14a %I PMLR %P 775--784 %U https://proceedings.mlr.press/r12/halloran14a.html %V R12 %X We present a peptide-spectrum alignment strategy that employs a dynamic Bayesian network (DBN) for the identification of spectra produced by tan- dem mass spectrometry (MS/MS). Our method is fundamentally generative in that it models peptide fragmentation in MS/MS as a physical process. The model traverses an observed MS/MS spec- trum and a peptide-based theoretical spectrum to calculate the best alignment between the two spectra. Unlike all existing state-of-the-art meth- ods for spectrum identification that we are aware of, our method can learn alignment probabilities given a dataset of high-quality peptide-spectrum pairs. The method, moreover, accounts for noise peaks and absent theoretical peaks in the observed spectrum. We demonstrate that our method out- performs, on a majority of datasets, several widely used, state-of-the-art database search tools for spectrum identification. Furthermore, the pro- posed approach provides an extensible framework for MS/MS analysis and provides useful informa- tion that is not produced by other methods, thanks to its generative structure. %Z Reissued by PMLR on 04 October 2026.
APA
Halloran, J., Bilmes, J. & Noble, W.. (2014). Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:775-784 Available from https://proceedings.mlr.press/r12/halloran14a.html. Reissued by PMLR on 04 October 2026.

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