De Novo Analysis of Peptide Tandem Mass Spectra by Spectral Graph Partitioning

peer-reviewed · Journal of Computational Biology · 2006

peer-reviewed · Journal of Computational Biology · 2006. Marshall Bern et al. We report on a new de novo peptide sequencing algorithm that uses spectral graph partitioning. In this…
Date 2006-03-01
Type peer-reviewed
Venue Journal of Computational Biology
Publisher SAGE Publications
Contribution algorithm
DOI 10.1089/cmb.2006.13.364
Citations (OpenAlex) 58
Venue 2-year citedness 1.45

Abstract

We report on a new de novo peptide sequencing algorithm that uses spectral graph partitioning. In this approach, relationships between m/z peaks are represented by attractive and repulsive springs, and the vibrational modes of the spring system are used to infer information about the peaks (such as “likely b-ion” or “likely y-ion”). We demonstrate the effectiveness of this approach by comparison with other de novo sequencers on test sets of ion-trap and QTOF spectra, including spectra of mixtures of peptides. On all datasets, we outperform the other sequencers. Along with spectral graph theory techniques, the new de novo sequencer EigenMS incorporates another improvement of independent interest: robust statistical methods for recalibration of time-of-flight mass measurements. Robust recalibration greatly outperforms simple least-squares recalibration, achieving about three times the accuracy for one QTOF dataset.

Authors

  1. Marshall Bern · Palo Alto Research Center, Protein Metrics Inc.
  2. David Theo Goldberg · Palo Alto Research Center

Methods and tools

  • EigenMS: Spectral graph partitioning de novo sequencer with robust TOF mass recalibration.

Cites (10)

Cited by (11)

Seen in the charts

Back to the full map

Back to top