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UniNovo: a universal tool for de novo peptide sequencing

Motivation: Mass spectrometry (MS) instruments and experimental protocols are rapidly advancing, but de novo peptide sequencing algorithms to analyze tandem mass (MS/MS) spectra are lagging behind. Although existing de novo sequencing tools perform well on certain types of spectra [e.g. Collision In...

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Autores principales: Jeong, Kyowon, Kim, Sangtae, Pevzner, Pavel A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3722526/
https://www.ncbi.nlm.nih.gov/pubmed/23766417
http://dx.doi.org/10.1093/bioinformatics/btt338
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author Jeong, Kyowon
Kim, Sangtae
Pevzner, Pavel A.
author_facet Jeong, Kyowon
Kim, Sangtae
Pevzner, Pavel A.
author_sort Jeong, Kyowon
collection PubMed
description Motivation: Mass spectrometry (MS) instruments and experimental protocols are rapidly advancing, but de novo peptide sequencing algorithms to analyze tandem mass (MS/MS) spectra are lagging behind. Although existing de novo sequencing tools perform well on certain types of spectra [e.g. Collision Induced Dissociation (CID) spectra of tryptic peptides], their performance often deteriorates on other types of spectra, such as Electron Transfer Dissociation (ETD), Higher-energy Collisional Dissociation (HCD) spectra or spectra of non-tryptic digests. Thus, rather than developing a new algorithm for each type of spectra, we develop a universal de novo sequencing algorithm called UniNovo that works well for all types of spectra or even for spectral pairs (e.g. CID/ETD spectral pairs). UniNovo uses an improved scoring function that captures the dependences between different ion types, where such dependencies are learned automatically using a modified offset frequency function. Results: The performance of UniNovo is compared with PepNovo+, PEAKS and pNovo using various types of spectra. The results show that the performance of UniNovo is superior to other tools for ETD spectra and superior or comparable with others for CID and HCD spectra. UniNovo also estimates the probability that each reported reconstruction is correct, using simple statistics that are readily obtained from a small training dataset. We demonstrate that the estimation is accurate for all tested types of spectra (including CID, HCD, ETD, CID/ETD and HCD/ETD spectra of trypsin, LysC or AspN digested peptides). Availability: UniNovo is implemented in JAVA and tested on Windows, Ubuntu and OS X machines. UniNovo is available at http://proteomics.ucsd.edu/Software/UniNovo.html along with the manual. Contact: kwj@ucsd.edu or ppevzner@ucsd.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-37225262013-07-25 UniNovo: a universal tool for de novo peptide sequencing Jeong, Kyowon Kim, Sangtae Pevzner, Pavel A. Bioinformatics Original Papers Motivation: Mass spectrometry (MS) instruments and experimental protocols are rapidly advancing, but de novo peptide sequencing algorithms to analyze tandem mass (MS/MS) spectra are lagging behind. Although existing de novo sequencing tools perform well on certain types of spectra [e.g. Collision Induced Dissociation (CID) spectra of tryptic peptides], their performance often deteriorates on other types of spectra, such as Electron Transfer Dissociation (ETD), Higher-energy Collisional Dissociation (HCD) spectra or spectra of non-tryptic digests. Thus, rather than developing a new algorithm for each type of spectra, we develop a universal de novo sequencing algorithm called UniNovo that works well for all types of spectra or even for spectral pairs (e.g. CID/ETD spectral pairs). UniNovo uses an improved scoring function that captures the dependences between different ion types, where such dependencies are learned automatically using a modified offset frequency function. Results: The performance of UniNovo is compared with PepNovo+, PEAKS and pNovo using various types of spectra. The results show that the performance of UniNovo is superior to other tools for ETD spectra and superior or comparable with others for CID and HCD spectra. UniNovo also estimates the probability that each reported reconstruction is correct, using simple statistics that are readily obtained from a small training dataset. We demonstrate that the estimation is accurate for all tested types of spectra (including CID, HCD, ETD, CID/ETD and HCD/ETD spectra of trypsin, LysC or AspN digested peptides). Availability: UniNovo is implemented in JAVA and tested on Windows, Ubuntu and OS X machines. UniNovo is available at http://proteomics.ucsd.edu/Software/UniNovo.html along with the manual. Contact: kwj@ucsd.edu or ppevzner@ucsd.edu Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2013-08-15 2013-06-12 /pmc/articles/PMC3722526/ /pubmed/23766417 http://dx.doi.org/10.1093/bioinformatics/btt338 Text en © The Author 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Papers
Jeong, Kyowon
Kim, Sangtae
Pevzner, Pavel A.
UniNovo: a universal tool for de novo peptide sequencing
title UniNovo: a universal tool for de novo peptide sequencing
title_full UniNovo: a universal tool for de novo peptide sequencing
title_fullStr UniNovo: a universal tool for de novo peptide sequencing
title_full_unstemmed UniNovo: a universal tool for de novo peptide sequencing
title_short UniNovo: a universal tool for de novo peptide sequencing
title_sort uninovo: a universal tool for de novo peptide sequencing
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3722526/
https://www.ncbi.nlm.nih.gov/pubmed/23766417
http://dx.doi.org/10.1093/bioinformatics/btt338
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