A Data Set of 255,000 Randomly Selected and Manually Classified Extracted Ion Chromatograms for Evaluation of Peak Detection Methods

Abstract

Non-targeted mass spectrometry (MS) has become an important method over recent years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of non-targeted data sets, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection and refinement of it is arguably the most important step for non-targeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap-filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of the converted mass spectrometry files, intermediate processing files and the central file containing a table with all important information for the classified peaks.

Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics—Standardization, Coverage, and Throughput

“…While this also offers several other apparent advantages for the benchmarking of different DPP tools, 135 benchmark datasets in metabolomics come with significant problems. First, their curation process requires extensive manual work and is hugely time intensive (although some do exist; e.g., ref ( 136 )). This, in turn, implies that it is impractical to create benchmarks for different types of datasets (e.g.…”

Non-target screening for detecting the occurrence of plant metabolites in river waters

“…For data processing, the Thermo raw files acquired were converted to mzML format and centroids with Prote-oWizard (version 2.1.0) [25] and imported into MZmine 2.38 [53]. MZmine parameters such as mass detection, chromatogram building smoothing, peak alignment and gap filling were adjusted to get optimal peak detection (for more information see Additional file 2: Table S4) [29,30,43]. The transformed peak list was exported as csv file for further processing in MS Excel 2013.…”

Non-target screening for detecting the occurrence of plant metabolites in river waters

“…For data processing, the Thermo raw les acquired were converted to mzML format and centroids with ProteoWizard (version 2.1.0) (Holman et al 2014) and imported into MZmine 2.38 (Pluskal et al 2010). MZmine parameters such as mass detection, chromatogram building smoothing, peak alignment and gap lling were adjusted to get optimal peak detection (for more information see Table S4 in SI) (Müller et al 2020;Katajamaa et al 2006;Katajamaa and Oresic 2005). The transformed peak list was exported as csv le for further processing in MS Excel 2013.…”