Focus: A Robust Workflow for One-Dimensional NMR Spectral Analysis

详细信息    查看全文

• 作者：Arnald Alonso ; Miguel A. Rodr铆guez ; Maria Vinaixa ; Ra眉l Tortosa ; Xavier Correig ; Antonio Juli脿 ; Sara Marsal
• 刊名：Analytical Chemistry
• 出版年：2014
• 出版时间：January 21, 2014
• 年：2014
• 卷：86
• 期：2
• 页码：1160-1169
• 全文大小：528K
• ISSN：1520-6882

文摘

One-dimensional ¹H NMR represents one of the most commonly used analytical techniques in metabolomic studies. The increase in the number of samples analyzed as well as the technical improvements involving instrumentation and spectral acquisition demand increasingly accurate and efficient high-throughput data processing workflows. We present FOCUS, an integrated and innovative methodology that provides a complete data analysis workflow for one-dimensional NMR-based metabolomics. This tool will allow users to easily obtain a NMR peak feature matrix ready for chemometric analysis as well as metabolite identification scores for each peak that greatly simplify the biological interpretation of the results. The algorithm development has been focused on solving the critical difficulties that appear at each data processing step and that can dramatically affect the quality of the results. As well as method integration, simplicity has been one of the main objectives in FOCUS development, requiring very little user input to perform accurate peak alignment, peak picking, and metabolite identification. The new spectral alignment algorithm, RUNAS, allows peak alignment with no need of a reference spectrum, and therefore, it reduces the bias introduced by other alignment approaches. Spectral alignment has been tested against previous methodologies obtaining substantial improvements in the case of moderate or highly unaligned spectra. Metabolite identification has also been significantly improved, using the positional and correlation peak patterns in contrast to a reference metabolite panel. Furthermore, the complete workflow has been tested using NMR data sets from 60 human urine samples and 120 aqueous liver extracts, reaching a successful identification of 42 metabolites from the two data sets. The open-source software implementation of this methodology is available at http://www.urr.cat/FOCUS.