Current status of retention time prediction in metabolite identification.
Michael WittingSebastian BöckerPublished in: Journal of separation science (2020)
Metabolite identification is a crucial step in nontargeted metabolomics, but also represents one of its current bottlenecks. Accurate identifications are required for correct biological interpretation. To date, annotation and identification are usually based on the use of accurate mass search or tandem mass spectrometry analysis, but neglect orthogonal information such as retention times obtained by chromatographic separation. While several tools are available for the analysis and prediction of tandem mass spectrometry data, prediction of retention times for metabolite identification are not widespread. Here, we review the current state of retention time prediction in liquid chromatography-mass spectrometry-based metabolomics, with a focus on publications published after 2010.
Keyphrases
- liquid chromatography
- mass spectrometry
- tandem mass spectrometry
- high resolution mass spectrometry
- ultra high performance liquid chromatography
- simultaneous determination
- high performance liquid chromatography
- gas chromatography
- high resolution
- solid phase extraction
- bioinformatics analysis
- capillary electrophoresis
- current status
- artificial intelligence
- randomized controlled trial
- big data
- single cell