speaq 2.0: A complete workflow for high-throughput 1D NMR spectra processing and quantification

• Published in: PLoS computational biology 2018-03, Vol.14 (3), p.e1006018-e1006018
• Main Authors: Beirnaert, Charlie, Meysman, Pieter, Vu, Trung Nghia, Hermans, Nina, Apers, Sandra, Pieters, Luc, Covaci, Adrian, Laukens, Kris
• Format: Article
• Language: English
• Subjects: Analytical chemistry; Automation; Bioinformatics; Bioinformatik (beräkningsbiologi) (tillämpningar under 10610); Biology and Life Sciences; Biomarkers; Case studies; Computer and Information Sciences; Computer science; Data analysis; Data processing; Data- och informationsvetenskap (Datateknik); Datasets; Funding; Informatics; Liquid chromatography; Mass spectrometry; Mass spectroscopy; Mathematics; Metabolism; Metabolites; Metabolomics; Methods; Natural products; Naturvetenskap; NMR; Nuclear magnetic resonance; Nuclear magnetic resonance spectroscopy; Pharmaceutical sciences; Physical Sciences; Quantitation; Research and analysis methods; Scientific imaging; Software; Spectra; Statistical analysis; Statistical methods; Supervision; Technology application; Wavelet analysis; Wavelet transforms; Workflow; Workflow software
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006018

Nuclear Magnetic Resonance (NMR) spectroscopy is, together with liquid chromatography-mass spectrometry (LC-MS), the most established platform to perform metabolomics. In contrast to LC-MS however, NMR data is predominantly being processed with commercial software. Meanwhile its data processing remains tedious and dependent on user interventions. As a follow-up to speaq, a previously released workflow for NMR spectral alignment and quantitation, we present speaq 2.0. This completely revised framework to automatically analyze 1D NMR spectra uses wavelets to efficiently summarize the raw spectra with minimal information loss or user interaction. The tool offers a fast and easy workflow that starts with the common approach of peak-picking, followed by grouping, thus avoiding the binning step. This yields a matrix consisting of features, samples and peak values that can be conveniently processed either by using included multivariate statistical functions or by using many other recently developed methods for NMR data analysis. speaq 2.0 facilitates robust and high-throughput metabolomics based on 1D NMR but is also compatible with other NMR frameworks or complementary LC-MS workflows. The methods are benchmarked using a simulated dataset and two publicly available datasets. speaq 2.0 is distributed through the existing speaq R package to provide a complete solution for NMR data processing. The package and the code for the presented case studies are freely available on CRAN (https://cran.r-project.org/package=speaq) and GitHub (https://github.com/beirnaert/speaq).