New computational approaches for de novo peptide sequencing from MS/MS experiments

Olaf Lubeck, Christopher Sewell, Sheng Gu, Xian Chen, D. Michael Cai

Research output: Contribution to journalArticle

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Abstract

We describe computational methods to solve the problem of identifying novel proteins from tandem mass spectrometry (tandem MS or MS/MS) data and introduce new approaches that will give more accurate solutions. These new approaches integrate chemical information and knowledge into a graph-theoretic framework. Two sources of chemical information that we investigate are mass tagging and dissociation chemistry in the tandem MS process itself. We describe machine learning techniques that are used to classify peaks according to ion types based on known dissociation chemistry. We describe the algorithms that are implemented in a software code called PepSUMS. Using PepSUMS, we give results on the effectiveness of the new methods on the ultimate goal of improved protein identification.

Original languageEnglish (US)
Pages (from-to)1868-1874
Number of pages7
JournalProceedings of the IEEE
Volume90
Issue number12
DOIs
StatePublished - 2002
Externally publishedYes

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Proteins
Computational methods
Peptides
Mass spectrometry
Learning systems
Ions
Experiments

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

New computational approaches for de novo peptide sequencing from MS/MS experiments. / Lubeck, Olaf; Sewell, Christopher; Gu, Sheng; Chen, Xian; Cai, D. Michael.

In: Proceedings of the IEEE, Vol. 90, No. 12, 2002, p. 1868-1874.

Research output: Contribution to journalArticle

Lubeck, Olaf; Sewell, Christopher; Gu, Sheng; Chen, Xian; Cai, D. Michael / New computational approaches for de novo peptide sequencing from MS/MS experiments.

In: Proceedings of the IEEE, Vol. 90, No. 12, 2002, p. 1868-1874.

Research output: Contribution to journalArticle

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