A new hybrid de novo sequencing method for protein identification
peer-reviewed · ANU Open Research (Australian National University) · 2012
| Date | 2012-01-01 |
| Type | peer-reviewed |
| Venue | ANU Open Research (Australian National University) |
| Contribution | algorithm |
| Link | http://hdl.handle.net/1885/23182 |
| Citations (OpenAlex) | 1 |
Abstract
Tandem mass spectrometry is a powerful tool for studying proteins. However, an open problem for proteomics research is how to accurately identify proteins from the experimental mass spectra. De novo sequencing based protein identification is the only feasible approach for finding new proteins and studying protein post-translational modifications. In this paper, we describe our novel hybrid de novo sequencing based protein identification method. It differs from existing methods which rely on finding one maximum path from a spectrum graph. Instead, to identify peptides, our method applies a novel Bayesian network and dynamic programming hybrid algorithm to explore the sub-optimal space. Thus our method can better accommodate various interferences and artefacts present in the mass spectra. Evaluated on a large number of spectra, our method outperforms the most popular de novo sequencing methods and can significantly improve the accuracy of de novo sequencing based protein identification. Keywords-Protein identification, de novo sequencing, Bayesian network, dynamic programming, proteomics.
Methods and tools
- Bayesian network + DP hybrid de novo (Wang, Zomaya): Combines a Bayesian network with dynamic programming to explore sub-optimal spectrum-graph paths for de novo peptide sequencing.