Name
Abstract | ||
|---|---|---|
Mixture spectra occur quite frequently in a typical wet-lab mass spectrometry experiment, which result from the concurrent fragmentation of multiple precursors. The ability to efficiently and confidently identify mixture spectra is essential to alleviate the existent bottleneck of low mass spectra identification rate. However, most of the traditional computational methods are not suitable for interpreting mixture spectra, because they still take the assumption that the acquired spectra come from the fragmentation of a single precursor. In this manuscript, we formulate the mixture spectra de novo sequencing problem mathematically, and propose a dynamic programming algorithm for the problem. Additionally, we use both simulated and real mixture spectra datasets to verify the merits of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TCBB.2015.2407401 | IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) |
Keywords | Field | DocType |
Computational Proteomics,Mass Spectrometry,Mixture Spectra,Peptide de novo Sequencing | Bottleneck,Dynamic programming,Computer science,Spectral line,Mass spectrometry,Bioinformatics | Journal |
Volume | Issue | ISSN |
14 | 2 | 1545-5963 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| yongmei liu | 1 | 307 | 31.79 |
| Bin Ma | 2 | 1758 | 155.63 |
| Zhang, K. | 3 | 0 | 0.34 |
| Gilles Lajoie | 4 | 13 | 3.60 |