Probability profiles-novel approach in tandem mass spectrometry De Novo sequencing
peer-reviewed · Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 · 2004
| Date | 2004-03-30 |
| Type | peer-reviewed |
| Venue | Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003 |
| Publisher | IEEE Comput. Soc |
| Contribution | algorithm |
| DOI | 10.1109/csb.2003.1227351 |
| Citations (OpenAlex) | 4 |
Abstract
A novel method is proposed for deciphering experimental tandem mass spectra. A large database of previously resolved peptide spectra was used to determine “neighborhood patterns” for each peak category: C- or N-terminus ions, their dehydrated fragments, etc. The established patterns are applied to assign probabilities for new spectra peaks to fit into these categories. A few peaks often could be identified with a fair confidence creating strong “anchor points” for De Novo algorithm assembling sequence subgraphs. Our approach is utilizing all informational content of a given MS experimental data set, including peak intensities, weak and noisy peaks, and unusual fragments. We also discuss ways to provide learning features in our method: adjustments for a specific MS device and user initiated changes in the list of considered peak identities.
Methods and tools
- Probability profile method (ORNL): Learns neighbourhood patterns of b, y and related peaks from resolved spectra to assign each peak a category probability, giving anchor points for de novo sequence assembly.