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

Research output

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%.

Original languageEnglish
Article numberbtz374
Pages (from-to)i315–i323
Number of pages9
JournalBioinformatics
Volume35
Issue number14
DOIs
Publication statusPublished - 15 Jul 2019

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Statistical Significance
Computational methods
Biological Products
Scoring
Medicine
Mass spectrometry
Pipelines
Throughput
Statistical Learning
Mass Spectrometry
Computational Methods
High Throughput
Learning
Databases
Technology
Demonstrate

Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

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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{\%}.",
author = "A.M. Tagirdzhanov and A. Shlemov and A. Gurevich",
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