Cargando…
NPS: scoring and evaluating the statistical significance of peptidic natural product–spectrum matches
MOTIVATION: Peptidic natural products (PNPs) are considered a promising compound class that has many applications in medicine. Recently developed mass spectrometry-based pipelines are transforming PNP discovery into a high-throughput technology. However, the current computational methods for PNP ide...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612854/ https://www.ncbi.nlm.nih.gov/pubmed/31510666 http://dx.doi.org/10.1093/bioinformatics/btz374 |
_version_ | 1783432952244338688 |
---|---|
author | Tagirdzhanov, Azat M Shlemov, Alexander Gurevich, Alexey |
author_facet | Tagirdzhanov, Azat M Shlemov, Alexander Gurevich, Alexey |
author_sort | Tagirdzhanov, Azat M |
collection | PubMed |
description | MOTIVATION: Peptidic natural products (PNPs) are considered a promising compound class that has many applications in medicine. Recently developed mass spectrometry-based pipelines are transforming PNP discovery into a high-throughput technology. However, the current computational methods for PNP identification via database search of mass spectra are still in their infancy and could be substantially improved. RESULTS: Here we present NPS, a statistical learning-based approach for scoring PNP–spectrum matches. We incorporated NPS into two leading PNP discovery tools and benchmarked them on millions of natural product mass spectra. The results demonstrate more than 45% increase in the number of identified spectra and 20% more found PNPs at a false discovery rate of 1%. AVAILABILITY AND IMPLEMENTATION: NPS is available as a command line tool and as a web application at http://cab.spbu.ru/software/NPS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-6612854 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-66128542019-07-12 NPS: scoring and evaluating the statistical significance of peptidic natural product–spectrum matches Tagirdzhanov, Azat M Shlemov, Alexander Gurevich, Alexey Bioinformatics Ismb/Eccb 2019 Conference Proceedings MOTIVATION: Peptidic natural products (PNPs) are considered a promising compound class that has many applications in medicine. Recently developed mass spectrometry-based pipelines are transforming PNP discovery into a high-throughput technology. However, the current computational methods for PNP identification via database search of mass spectra are still in their infancy and could be substantially improved. RESULTS: Here we present NPS, a statistical learning-based approach for scoring PNP–spectrum matches. We incorporated NPS into two leading PNP discovery tools and benchmarked them on millions of natural product mass spectra. The results demonstrate more than 45% increase in the number of identified spectra and 20% more found PNPs at a false discovery rate of 1%. AVAILABILITY AND IMPLEMENTATION: NPS is available as a command line tool and as a web application at http://cab.spbu.ru/software/NPS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2019-07 2019-07-05 /pmc/articles/PMC6612854/ /pubmed/31510666 http://dx.doi.org/10.1093/bioinformatics/btz374 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), 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 | Ismb/Eccb 2019 Conference Proceedings Tagirdzhanov, Azat M Shlemov, Alexander Gurevich, Alexey NPS: scoring and evaluating the statistical significance of peptidic natural product–spectrum matches |
title | NPS: scoring and evaluating the statistical significance of peptidic natural product–spectrum matches |
title_full | NPS: scoring and evaluating the statistical significance of peptidic natural product–spectrum matches |
title_fullStr | NPS: scoring and evaluating the statistical significance of peptidic natural product–spectrum matches |
title_full_unstemmed | NPS: scoring and evaluating the statistical significance of peptidic natural product–spectrum matches |
title_short | NPS: scoring and evaluating the statistical significance of peptidic natural product–spectrum matches |
title_sort | nps: scoring and evaluating the statistical significance of peptidic natural product–spectrum matches |
topic | Ismb/Eccb 2019 Conference Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612854/ https://www.ncbi.nlm.nih.gov/pubmed/31510666 http://dx.doi.org/10.1093/bioinformatics/btz374 |
work_keys_str_mv | AT tagirdzhanovazatm npsscoringandevaluatingthestatisticalsignificanceofpeptidicnaturalproductspectrummatches AT shlemovalexander npsscoringandevaluatingthestatisticalsignificanceofpeptidicnaturalproductspectrummatches AT gurevichalexey npsscoringandevaluatingthestatisticalsignificanceofpeptidicnaturalproductspectrummatches |