Выдержка
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%.
| Язык оригинала | английский |
|---|---|
| Номер статьи | btz374 |
| Страницы (с-по) | i315–i323 |
| Число страниц | 9 |
| Журнал | Bioinformatics |
| Том | 35 |
| Номер выпуска | 14 |
| DOI | |
| Состояние | Опубликовано - 15 июл 2019 |
Отпечаток
Предметные области Scopus
- Теория вероятности и статистика
- Биохимия
- Молекулярная биология
- Прикладные компьютерные науки
- Математика и теория расчета
- Вычислительная математика
Цитировать
}
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.Результат исследований: Научные публикации в периодических изданиях › статья
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%.
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 -