ABSTRACT
When performing chromatography-mass spectrometry-based nontargeted metabolomics, or exposomics, one of the key steps in the analysis is to obtain MS1-based feature tables. Inapt parameter settings in feature detection will result in missing or wrong quantitative values and might ultimately lead to downstream incorrect biological interpretations. However, until recently, no strategies to assess the completeness and abundance accuracy of feature tables were available. Here, we show that mzRAPP enables the generation of benchmark peak lists by using an internal set of known molecules in the analyzed data set. Using the benchmark, the completeness and abundance accuracy of feature tables can be assessed in an automated pipeline. We demonstrate that our approach adds to other commonly applied quality assurance methods such as manual or automatized parameter optimization techniques or removal of false-positive signals. Moreover, we show that as few as 10 benchmark molecules can already allow for representative performance metrics to further improve quantitative biological understanding.
Subject(s)
Metabolomics , Chromatography, Liquid/methods , Mass Spectrometry/methods , Metabolomics/methodsABSTRACT
SUMMARY: Reliability assessment of automated pre-processing of liquid chromatography-high resolution mass spectrometry data presents a significant challenge. Here, we present a tool named mzRAPP, which generates and validates a benchmark from user-supplied information and later utilizes it for reliability assessment of data pre-processing. As a result, mzRAPP produces several performance metrics for different steps of the pre-processing workflow, supporting five of the most commonly used pre-processing tools. AVAILABILITY AND IMPLEMENTATION: mzRAPP is implemented in R and can be downloaded from GitHub under GNU GPL v.3.0 licence. Extensive documentation, background and examples are available at (https://github.com/YasinEl/mzRAPP). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
