Peptide refinement using a stochastic search
Identifying a peptide based on a scan from a mass spectrometer is an important yet highly challenging problem. To identify peptides, we present a Bayesian approach which uses prior information about the average relative abundances of bond cleavages and the prior probability of any particular amino a...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/51283/ |
| _version_ | 1848798459890499584 |
|---|---|
| author | Lewis, Nicole H. Hitchcock, David B. Dryden, Ian L. Rose, John R. |
| author_facet | Lewis, Nicole H. Hitchcock, David B. Dryden, Ian L. Rose, John R. |
| author_sort | Lewis, Nicole H. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Identifying a peptide based on a scan from a mass spectrometer is an important yet highly challenging problem. To identify peptides, we present a Bayesian approach which uses prior information about the average relative abundances of bond cleavages and the prior probability of any particular amino acid sequence. The proposed scoring function is composed of two overall distance measures, which measure how close an observed spectrum is to a theoretical scan for a peptide. Our use of our scoring function, which approximates a likelihood, has connections to the generalization presented by Bissiri et al. (2016) of the Bayesian framework. A Markov chain Monte Carlo algorithm is employed to simulate candidate choices from the posterior distribution of the peptide sequence. The true peptide is estimated as the peptide with the largest posterior density. |
| first_indexed | 2025-11-14T20:20:07Z |
| format | Article |
| id | nottingham-51283 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:20:07Z |
| publishDate | 2018 |
| publisher | Wiley |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-512832019-04-18T04:30:23Z https://eprints.nottingham.ac.uk/51283/ Peptide refinement using a stochastic search Lewis, Nicole H. Hitchcock, David B. Dryden, Ian L. Rose, John R. Identifying a peptide based on a scan from a mass spectrometer is an important yet highly challenging problem. To identify peptides, we present a Bayesian approach which uses prior information about the average relative abundances of bond cleavages and the prior probability of any particular amino acid sequence. The proposed scoring function is composed of two overall distance measures, which measure how close an observed spectrum is to a theoretical scan for a peptide. Our use of our scoring function, which approximates a likelihood, has connections to the generalization presented by Bissiri et al. (2016) of the Bayesian framework. A Markov chain Monte Carlo algorithm is employed to simulate candidate choices from the posterior distribution of the peptide sequence. The true peptide is estimated as the peptide with the largest posterior density. Wiley 2018-04-18 Article PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/51283/1/Journal-Jan29-submitted.pdf Lewis, Nicole H., Hitchcock, David B., Dryden, Ian L. and Rose, John R. (2018) Peptide refinement using a stochastic search. Journal of the Royal Statistical Society: Series C . ISSN 0035-9254 Stochastic Search Bayesian Methods Markov Chain Monte Carlo Peptide Identification Tandem Mass Spectrometry. https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssc.12280 doi:10.1111/rssc.12280 doi:10.1111/rssc.12280 |
| spellingShingle | Stochastic Search Bayesian Methods Markov Chain Monte Carlo Peptide Identification Tandem Mass Spectrometry. Lewis, Nicole H. Hitchcock, David B. Dryden, Ian L. Rose, John R. Peptide refinement using a stochastic search |
| title | Peptide refinement using a stochastic search |
| title_full | Peptide refinement using a stochastic search |
| title_fullStr | Peptide refinement using a stochastic search |
| title_full_unstemmed | Peptide refinement using a stochastic search |
| title_short | Peptide refinement using a stochastic search |
| title_sort | peptide refinement using a stochastic search |
| topic | Stochastic Search Bayesian Methods Markov Chain Monte Carlo Peptide Identification Tandem Mass Spectrometry. |
| url | https://eprints.nottingham.ac.uk/51283/ https://eprints.nottingham.ac.uk/51283/ https://eprints.nottingham.ac.uk/51283/ |