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Gewählte Publikation:

Bueschl, C; Doppler, M; Varga, E; Seidl, B; Flasch, M; Warth, B; Zanghellini, J.
(2022): PeakBot: machine-learning-based chromatographic peak picking
BIOINFORMATICS. 2022; 38(13): 3422-3428. FullText FullText_BOKU

Abstract:
Motivation: Chromatographic peak picking is among the first steps in data processing workflows of raw LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Random noise, nonbaseline separated compounds and unspecific background signals complicate this task. Results: A machine-learning-based approach entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention-time versus m/z). These are subsequently inspected by a customtrained convolutional neural network that forms the basis of PeakBot's architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box. In training and independent validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (accuracy of 0.99). For training the machine-learning model a minimum of 100 reference features are needed to learn their characteristics to achieve high-quality peak-picking results for detecting such chromatographic peaks in an untargeted fashion. PeakBot is implemented in python (3.8) and uses the TensorFlow (2.5.0) package for machine-learning related tasks. It has been tested on Linux and Windows OSs.
Autor*innen der BOKU Wien:
Büschl Christoph
Doppler Maria
Seidl Bernhard Klaus
Varga Elisabeth
Warth Benedikt
Zanghellini Hans Jürgen
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