Cheminformatics in MS-based environmental exposomics: Current achievements and future directions |
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Affiliation: | 1. School of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China;2. Guangxi Key Laboratory of Processing for Non-ferrous Metals and Featured Materials & MOE Key Laboratory of New Processing Technology for Non-ferrous Metals and Materials, School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China;3. Guangxi Key Laboratory of Clean Pulp & Papermaking and Pollution Control, College of Light Industry and Food Engineering, Guangxi University, Nanning 530004, China |
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Abstract: | Compound annotation using MS/MS data is the major bottleneck in interpretation of mass spectrometry data during non-targeted screening and suspect screening exposomics studies. Apart from compound identification using available databases or mass spectral libraries, the true challenge comes when completely new compounds have to be identified. Along with recent advances in MS instrumentation that set grounds to a new revolutionary age in environmental exposomics, a multitude of cheminformatics annotation approaches has been developed. Herein, we review the basic principles of the cutting-edge cheminformatics MS-based approaches employed in eco-exposome annotation.We give a solid background discussing the eco-exposome concept in relation to the advances in MS instrumentation, and define the three crucial cheminformatics tasks used in the eco-exposome annotation: molecular formula assignment, compound prioritization and compound annotation. The basic principles of compound annotation are discussed, which are based on three approaches of utilizing structural information inherent to MS data. These involve direct, indirect and joint annotation approaches. We assess their performance through the ability to annotate eco-exposome constituents. We discuss future perspectives and give directions to new annotation strategies and performance evaluation protocols aiming to solve current issues hampering the incorporation of cheminformatics annotation approaches in regular eco-exposome annotation workflows. |
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Keywords: | Eco-exposome annotation Mass spectrometry Molecular formula assignment Substructure prediction Structural elucidation Machine learning |
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