Analyzing Tandem Mass Spectra: A Graphical Models Perspective

John T. Halloran
; Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, PMLR 73:6-6, 2017.

Abstract

In the past two decades, the field of proteomics has seen explosive growth, largely due to the development of tandem mass spectrometry (MS/MS). With a complex biological sample as input, a typical MS/MS experiment quickly produces a large (often numbering in the hundreds-of-thousands) collection of spectra representative of the proteins present in the original complex sample. A majority of widely used methods to search and identify MS/MS spectra use scoring functions which rely on static, hand-selected parameters rather than affording the ability to learn parameters and adapt to the widely varying characteristics of MS/MS data. In this talk, we discuss recent work utilizing dynamic Bayesian networks (DBNs) to identify MS/MS spectra. In particular, we discuss a recently proposed DBN for Rapid Identification of Peptides (DRIP) which, in contrast to popular scoring functions, allows efficient generative and discriminative learning of parameters to achieve state-of-theart spectrum-identification accuracy. Furthermore, facilitated by DRIP’s generative nature, we present current innovations leveraging DBNs to significantly enhance many other aspects of MS/MS analysis, such as improving downstream discriminative classification via detailed feature extraction and speeding up identification runtime using trellises and approximate inference.

Cite this Paper


BibTeX
@InProceedings{pmlr-v73-halloran17a, title = {Analyzing Tandem Mass Spectra: A Graphical Models Perspective}, author = {John T. Halloran}, booktitle = {Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks}, pages = {6--6}, year = {2017}, editor = {Antti Hyttinen and Joe Suzuki and Brandon Malone}, volume = {73}, series = {Proceedings of Machine Learning Research}, month = {20--22 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v73/halloran17a/halloran17a.pdf}, url = {http://proceedings.mlr.press/v73/halloran17a.html}, abstract = {In the past two decades, the field of proteomics has seen explosive growth, largely due to the development of tandem mass spectrometry (MS/MS). With a complex biological sample as input, a typical MS/MS experiment quickly produces a large (often numbering in the hundreds-of-thousands) collection of spectra representative of the proteins present in the original complex sample. A majority of widely used methods to search and identify MS/MS spectra use scoring functions which rely on static, hand-selected parameters rather than affording the ability to learn parameters and adapt to the widely varying characteristics of MS/MS data. In this talk, we discuss recent work utilizing dynamic Bayesian networks (DBNs) to identify MS/MS spectra. In particular, we discuss a recently proposed DBN for Rapid Identification of Peptides (DRIP) which, in contrast to popular scoring functions, allows efficient generative and discriminative learning of parameters to achieve state-of-theart spectrum-identification accuracy. Furthermore, facilitated by DRIP’s generative nature, we present current innovations leveraging DBNs to significantly enhance many other aspects of MS/MS analysis, such as improving downstream discriminative classification via detailed feature extraction and speeding up identification runtime using trellises and approximate inference. } }
Endnote
%0 Conference Paper %T Analyzing Tandem Mass Spectra: A Graphical Models Perspective %A John T. Halloran %B Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks %C Proceedings of Machine Learning Research %D 2017 %E Antti Hyttinen %E Joe Suzuki %E Brandon Malone %F pmlr-v73-halloran17a %I PMLR %J Proceedings of Machine Learning Research %P 6--6 %U http://proceedings.mlr.press %V 73 %W PMLR %X In the past two decades, the field of proteomics has seen explosive growth, largely due to the development of tandem mass spectrometry (MS/MS). With a complex biological sample as input, a typical MS/MS experiment quickly produces a large (often numbering in the hundreds-of-thousands) collection of spectra representative of the proteins present in the original complex sample. A majority of widely used methods to search and identify MS/MS spectra use scoring functions which rely on static, hand-selected parameters rather than affording the ability to learn parameters and adapt to the widely varying characteristics of MS/MS data. In this talk, we discuss recent work utilizing dynamic Bayesian networks (DBNs) to identify MS/MS spectra. In particular, we discuss a recently proposed DBN for Rapid Identification of Peptides (DRIP) which, in contrast to popular scoring functions, allows efficient generative and discriminative learning of parameters to achieve state-of-theart spectrum-identification accuracy. Furthermore, facilitated by DRIP’s generative nature, we present current innovations leveraging DBNs to significantly enhance many other aspects of MS/MS analysis, such as improving downstream discriminative classification via detailed feature extraction and speeding up identification runtime using trellises and approximate inference.
APA
Halloran, J.T.. (2017). Analyzing Tandem Mass Spectra: A Graphical Models Perspective. Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, in PMLR 73:6-6

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