Mutation-tolerant protein identification by mass-spectrometry
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
An effective algorithm for peptide de novo sequencing from MS/MS spectra
Journal of Computer and System Sciences - Special issue on bioinformatics II
Partial digest is hard to solve for erroneous input data
Theoretical Computer Science
Table design in dynamic programming
Information and Computation
Solving peptide sequencing as satisfiability
Computers & Mathematics with Applications
Matching peptide sequences with mass spectra
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Antilope—A Lagrangian Relaxation Approach to the de novo Peptide Sequencing Problem
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Peptide sequence tags for fast database search in mass-spectrometry
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
EigenMS: de novo analysis of peptide tandem mass spectra by spectral graph partitioning
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
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The correct interpretation of tandem mass spectra is a difficult problem, even when it is limited to scoring peptides against a database. De novo sequencing is considerably harder, but critical when sequence databases are incomplete or not available. In this paper we build upon earlier work due to Dancik et al., and Chen et al. to provide a dynamic programming algorithm for interpreting de novo spectra. Our method can handle most of the commonly occurring ions, including a; b; y, and their neutral losses. Additionally, we shift the emphasis away from sequencing to assigning ion types to peaks. In particular, we introduce the notion of core interpretations, which allow us to give confidence values to individual peak assignments, even in the absence of a strong interpretation. Finally, we introduce a systematic approach to evaluating de novo algorithms as a function of spectral quality. We show that our algorithm, in particular the core-interpretation, is robust in the presence of measurement error, and low fragmentation probability.