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New approaches for processing and annotations of high-throughput metabolomic data obtained by mass spectrometry

Abstract : Metabolomics is a phenotyping approach with promising prospects for the diagnosis and monitoring of several diseases. The most widely used observation technique in metabolomics is mass spectrometry (MS). Recent technological developments have significantly increased the size and complexity of data. This thesis focused on two bottlenecks in the processing of these data, the extraction of peaks from raw data and the annotation of MS/MS spectra. The first part of the thesis focused on the development of a new peak detection algorithm for Flow Injection Analysis (FIA) data, a high-throughput metabolomics technique. A model derived from the physics of the mass spectrometer taking into account the saturation of the instrument has been proposed. This model includes a peak common to all metabolites and a specific saturation phenomenon for each ion. This model has made it possible to create a workflow that estimates the common peak on well-behaved signals, then uses it to perform matched filtration on all signals. Its effectiveness on real data has been studied and it has been shown that proFIA is superior to existing algorithms, has good reproducibility and is very close to manual measurements made by an expert on several types of devices. The second part of this thesis focused on the development of a tool for detecting the structural similarities of a set of fragmentation spectra. To do this, a new graphical representation has been proposed, which does not require the metabolite formula. The graphs are also a natural representation of MS/MS spectra. Some properties of these graphs have then made it possible to create an efficient algorithm for detecting frequent subgraphs (FSM) based on the generation of trees covering graphs. This tool has been tested on two different data sets and has proven its speed and interpretability compared to state-of-the-art algorithms. These two algorithms have been implemented in R, proFIA and mineMS2 packages available to the community.
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Submitted on : Thursday, December 6, 2018 - 2:48:08 PM
Last modification on : Thursday, February 17, 2022 - 10:08:04 AM
Long-term archiving on: : Thursday, March 7, 2019 - 1:55:04 PM


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  • HAL Id : tel-01946976, version 1


Alexis Delabrière. New approaches for processing and annotations of high-throughput metabolomic data obtained by mass spectrometry. Bioinformatics [q-bio.QM]. Université Paris Saclay (COmUE), 2018. English. ⟨NNT : 2018SACLS359⟩. ⟨tel-01946976⟩



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