Details zur Publikation

Referenztyp Zeitschriften
DOI / URL Link
Titel (primär) A data set of 255,000 randomly selected and manually classified extracted ion chromatograms for evaluation of peak detection methods
Autor Müller, E.; Huber, C.; Beckers, L.-M.; Brack, W.; Krauss, M.; Schulze, T.;
Journal / Serie Metabolites
Erscheinungsjahr 2020
Department WANA;
Band/Volume 10
Heft 4
Sprache englisch;
POF III (gesamt) T32;
Keywords peak detection; peak picking; EIC; XIC; LC-MS
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.
ID 23026
dauerhafte UFZ-Verlinkung https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=23026
Müller, E., Huber, C., Beckers, L.-M., Brack, W., Krauss, M., Schulze, T. (2020):
A data set of 255,000 randomly selected and manually classified extracted ion chromatograms for evaluation of peak detection methods
Metabolites 10 (4), art. 162