Bridging the Gap: A Systematic Workflow to Convert Low-Resolution GC-MS Libraries to High-Resolution in Silico for Enhanced Chemical Profiling
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Zargham A, Helge H, Wudmir R, Elliot P, Jana K
helge.hecht@recetox.muni.cz
Problem Statement: Limited availability of high-resolution gas chromatography - mass spectrometry (GC-HR-MS) libraries hinders accurate chemical profiling in metabolomics and chemical exposomics.�
Impact: Need to match vs. low-resolution – false positives & not leveraging high-resolution peak information leads to ambiguity in compound identification.
Objective: Enhance existing GC libraries to enable high-resolution (HR) annotation capabilities.
Approach: Develop and implement a systematic workflow using computational tools in Galaxy to convert LR libraries into HR libraries in silico.
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Background
Theory: Peaks must be substructures of the parent structure. Significant peaks should be unambiguously identifiable substructures. Substructures can be identified via hydrogen rearrangements rules1.
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Problems: Libraries often lack metadata (i.e. structure annotations) and unique identifiers. Metadata can concern derivatized or non-derivatized compound.
Workflow Overview
Key Steps:
Metadata collection
Library cleaning
Substructure annotation��
Key Tools:
MSMetaEnhancer1 for metadata integration.
matchms2 for metadata preparation and filtering.
recetox-msfinder for peak annotation.
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Limitations
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Fragmentation pathways differ across instruments��Spectra are cleaned �🡪 contaminant removal
Not all peaks are annotated correctly – formula and sub-structure annotations
Summary
Contribution: Built comprehensive workflow for converting LR GC-MS libraries to HR that is available on WorkflowHub1. Addressed some of the limitations of current LR libraries by providing a systematic approach to enhance chemical profiling.
Future Work: Method validation using large comprehensive libraries (NIST and Wiley) acquired on different instruments. Generalizing the workflow for different libraries and metadata formats. Handling discrepancies due to derivatization.
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Acknowledgements
Human Exposome Research Group
Jana Klánová –Head
Kapil Mandrah – MSCA fellow
Žiga Tkalec – ERA fellow
Helge Hecht – PhD candidate
Akrem Jbebli – PhD candidate
Hana Seličová – PhD candidate
Thomas Contini – PhD candidate
Biomarker Analytical Laboratories RI
Eliška Benešová - Researcher
Kateřina Coufaliková – Senior researcher
Gabriela Přibyl Dovrtělová - Researcher
Štěpán Koudelka – Senior researcher
Veronika Vidová – Senior researcher
Helge Hecht – Senior researcher
Wudmir Yudy Rojas Verastegui – Senior researcher
Zargham Ahmad – Researcher
Kristína Gömöryová – Senior Researcher
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Trace Analytical Laboratories RI
Petra Přibylová & team
Data services
Richard Hůlek & team
EIRENE RI coordination
Jan Ostřížek
Shachar Dvir
RECETOX RI coordination
Petra Růžičková
Operations and management
Martin Životek, Šárka Palátová & team
Partners
Institute of computer science at MU
CERIT-SC
CESNET
ELIXIR-CZ
Galaxy Project
EIRENE consortia