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Identifier

000466048

Title

Σύγκριση λογισμικών για την ανάλυση δεδομένων μηστοχευμένης μεταβολομικής μέσω ανάλυσης με υγρή χρωματογραφία συζευγμένη με φασματομετρία μαζών – εφαρμογές μεγάλης κλίμακας

Alternative Title

Comparison of analysis tools for LC-MS untargeted metabolomics data – large-scale applications

Author

Περράκη, Μαριλένα Ν.

Thesis advisor

Χατζηιωάννου, Αναστασία-Χρυσοβαλάντου

Reviewer

Σπύρος, Απόστολος
Ελευθεριάδης, Νικόλαος

Abstract

In recent years, the field of metabolomics has contributed to various scientific fields, such as medicine, microbiology, biotechnology, toxicology and others. Through metabolomics studies, researchers investigate changes that occur in biological systems, usually caused by genetic factors, environmental influences, or even diseases. Metabolomics allows us to examine metabolic pathways, interactions between metabolites, and the roles they play in the proper or improper functioning of an organism. By using advanced analytical technologies, such as mass spectrometry, researchers can uncover new information about the functioning of various biological systems. To explore and evaluate metabolomic research data, we need software that facilitate the detection, quantification, and alignment of various peaks in complex systems. Additionally, the information extracted by such software allows us to detect unique characteristics and discover associations between metabolites and physiological or pathological conditions. In this thesis, three software were evaluated, namely MS-Dial, XCMS, and Agilent Profinder. The purpose of this study is to compare the aforementioned software by evaluating the performance of each in the analysis of untargeted metabolomic data. Through this process, valuable insights were sought to determine the differences of these software for similar types of metabolomic studies. In this study, we used data deriving from umbilical cord blood samples from 500 neonates, sourced from four hospital environments in four different countries. These samples were analyzed using high-performance liquid chromatography coupled with time-of-flight mass spectrometry (TOF-MS). The statistical analysis was conducted in two parts. In the first part, we attempted to reproduce the parameters of the original article from which the data originated with a different pre-processing software. Furthermore, using Spearman and Pearson correlations we compared our results with both the original and an article that had related observations on the same sample set. However, due to the lack of detailed anthropometric information required for the adjustments of the epidemiological models, we could not draw definitive conclusions from the first part of the analysis. Therefore, we proceeded to the second part, focusing on applying identical parameters among the three software programs, and comparing the number of obtained features. Applying unpaired t-test, we detected the statistically significant features as resulted per software and we evaluated the similarities and differences of the results, as well as their overall performance in the analysis of the same set of metabolomics data.

Language

Greek

Subject

Biological samples

Liquid chromatography coupled with mass spectrometry

Preprocessing

Untargeted metabolomic

Βιοδείγματα

Μη στοχευμένη μεταβολομική ανάλυση

Issue date

2024-07-19

Collection