NPS: scoring and evaluating the statistical significance of peptidic natural product–spectrum matches

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NPS: scoring and evaluating the statistical significance of peptidic natural product–spectrum matches. / Tagirdzhanov, A.M.; Shlemov, A.; Gurevich, A.

в: Bioinformatics, Том 35, № 14, btz374, 15.07.2019, стр. I315-I323.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

BibTeX

@article{f7709060d76748ef8ceddb9e6fd5a838,

title = "NPS: scoring and evaluating the statistical significance of peptidic natural product–spectrum matches",

abstract = "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%.",

keywords = "TANDEM MASS-SPECTRA, DATABASE SEARCH, SPECTROMETRY, IDENTIFICATION, DEREPLICATION, DISCOVERY",

author = "A.M. Tagirdzhanov and A. Shlemov and A. Gurevich",

note = "Publisher Copyright: {\textcopyright} 2019 The Author(s) 2019. Published by Oxford University Press.",

year = "2019",

month = jul,

day = "15",

doi = "10.1093/bioinformatics/btz374",

language = "English",

volume = "35",

pages = "I315--I323",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "14",

}

RIS

TY - JOUR

T1 - NPS: scoring and evaluating the statistical significance of peptidic natural product–spectrum matches

AU - Tagirdzhanov, A.M.

AU - Shlemov, A.

AU - Gurevich, A.

PY - 2019/7/15

Y1 - 2019/7/15

N2 - 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%.

AB - 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%.

KW - TANDEM MASS-SPECTRA

KW - DATABASE SEARCH

KW - SPECTROMETRY

KW - IDENTIFICATION

KW - DEREPLICATION

KW - DISCOVERY

UR - http://www.scopus.com/inward/record.url?scp=85068914175&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btz374

DO - 10.1093/bioinformatics/btz374

M3 - Article

VL - 35

SP - I315-I323

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 14

M1 - btz374

ER -

ID: 43669243