High-Performance Computing (HPC) environments are integral to quantum chemistry and computationally intense research, yet their complexity poses challenges for non-HPC experts. Navigating these environments proves challenging for researchers lacking extensive computational knowledge, hindering efficient use of domain specific research software. The prediction of mass spectra for in silico annotation is therefore inaccessible for many wet lab scientists. Our main goal is to facilitate non-experts ...

Galaxy Workflow Documentation: MS Finder Pipeline

This document outlines a MSFinder Galaxy workflow designed for peak annotation. The workflow consists of several steps aimed at preprocessing MS data, filtering, enhancing, and running MSFinder.

Step 1: Data Collection and Preprocessing

Collect if the inchi and smiles are missing from the dataset, and subsequently filter out the spectra which are missing inchi and smiles.

1.1 MSMetaEnhancer: Collect InChi, Isomeric_smiles, and Nominal_mass

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This workflow is composed with the XCMS tool R package (Smith, C.A. 2006) able to extract and the metaMS R package (Wehrens, R 2014) for the field of untargeted metabolomics.

https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/gcms/tutorial.html

This workflow is composed with the XCMS tool R package (Smith, C.A. 2006) able to extract, filter, align and fill gapand the possibility to annotate isotopes, adducts and fragments using the CAMERA R package (Kuhl, C 2012).

https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/lcms-preprocessing/tutorial.html

This repository hosts Metabolome Annotation Workflow (MAW). The workflow takes MS2 .mzML format data files as an input in R. It performs spectral database dereplication using R Package Spectra and compound database dereplication using SIRIUS OR MetFrag . Final candidate selection is done in Python using RDKit and PubChemPy.

This workflow is designed to analyze to a multi-omics data set that comprises genome-wide DNA methylation profiles, targeted metabolomics, and behavioral data of two cohorts that participated in the ACTION Biomarker Study (ACTION, Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies. (Boomsma 2015, Bartels 2018, Hagenbeek 2020, van Dongen 2021, Hagenbeek 2022). The ACTION-NTR cohort consists of twins that are either longitudinally concordant ...

Pre-processing of mass spectrometry-based metabolomics data