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

被引：5

作者：

Tagirdzhanov, Azat M. ^{[1
,2
]}

Shlemov, Alexander ^{[1
]}

Gurevich, Alexey ^{[1
]}

机构：

[1] St Petersburg State Univ, Ctr Algorithm Biotechnol, St Petersburg, Russia

[2] St Petersburg Electrotech Univ LETI, Dept Higher Math, St Petersburg, Russia

来源：

BIOINFORMATICS | 2019年 / 35卷 / 14期

基金：

俄罗斯科学基金会;

关键词：

TANDEM MASS-SPECTRA; DATABASE SEARCH; SPECTROMETRY; IDENTIFICATION; DEREPLICATION; DISCOVERY;

D O I：

10.1093/bioinformatics/btz374

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：I315 / I323

页数：9