Sharing our đ Metabonaut resource:
A collection of comprehensive tutorials for LC-MS/MS #metabolomics data analysis in 
by @phili et al.
Learn raw data processing, annotation & stats with #xcms, #RforMassSpectrometry & @bioconductor - all reproducible & community-driven! #rstats
Our proposed hackathon to improve integration of #rstats and #Python 
packages for #MassSpectrometry was selected for the #EuBIC2025 @EuBIC_MS developer meeting! đ„ł
Looking forward to expand and improve our SpectryPy package https://bit.ly/4hQfhj1
The SpectriPy package allows integration of Python-based MS analysis code with the Spectra package. Spectra objects can be converted into Python's matchms Spectrum objects. In addition, SpectriPy integrates and wraps the similarity scoring functions from the matchms package into R.
@bioconductor 3.20 is out with two of our new #RforMassSpectrometry packages:
- SpectraQL
- MsBackendMetaboLights
big thanks to @phili
#Metabolomics #MassSpectrometry
And @phili with her poster on a complete end-to-end workflow for untargeted #metabolomics data analysis in #rstats with @bioconductor and #RforMassSpectrometry #xcms etc
#MetSoc2024 poster # 1008
đ https://doi.org/10.5281/zenodo.11370612 đ
Despite untargeted LC-MS/MS data being a powerful approach for large-scale metabolomics analysis, a significant challenge in the field lies in the reproducible and efficient analysis of such data. The power of R-based analysis workflows lies in their high customizability and adaptability to specific instrumental and experimental setups, but while various specialized packages exist for individual analysis steps, their seamless integration and application to large cohort datasets remains elusive. Addressing this gap, we present a comprehensible end-to-end R workflow that leverages xcms and packages of the RforMassSpectrometry environment to encompass all aspects of pre-processing and downstream analyses for LC-MS/MS datasets in a reproducible manner. This poster/presentation delineates a step-by-step analysis of an example untargeted metabolomics dataset tailored to quantify the small polar metabolome in human plasma samples and aimed to identify differences between individuals suffering from cardiovascular disease and healthy controls. The objective of the workflow is to meticulously detail each step, from the preprocessing of raw mzML files to the annotation of differentially abundant ions between the two groups. Our workflow seamlessly integrates Bioconductor packages, offering adaptability to diverse study designs and analysis requirements. This workflow facilitates preprocessing, feature detection, alignment, normalization, statistical analysis, and annotation within a unified framework, thereby enhancing the efficiency of metabolomic investigations. We also discuss alternative approaches to accommodate various datasets and goals, while emphasizing proper quality management for LC-MS data analysis.
Thrilled to be at #MetSoc2024 conference in Osaka đŻđ”!
I'll have poster # 1006 on our #RforMassSpectrometry effort and the related #rstats @bioconductor packages!
For those not at the conference: also available on zenodo: https://doi.org/10.5281/zenodo.11370345
Now @lgatto , beat this đâșïž
And btw - that's the new treat - pins (!) in addition to stickers for our #Spectra #RforMassSpectrometry @bioconductor package! đ
The startup message of the #MSnbase package for #MassSpectrometry and #Proteomics has been update in version 2.29.2, and now officially asks users to look into the #RforMassSpectrometry initiative packages:
> library(MSnbase)
This is MSnbase version 2.29.2
Visit https://lgatto.github.io/MSnbase/ to get started.
Consider switching to the 'R for Mass Spectrometry'
packages - see https://RforMassSpectrometry.org for details.
Got some new stuff to share at conferences âșïž - #RforMassSpectrometry pins!
for users of our #rstats #MassSpectrometry #metabolomics #proteomics @bioconductor packages đ„ł
Recent extension to our #MsExperiment @bioconductor package:
Store sample annotations and phenodata along with whole #MassSpectrometry data into SQL databases.
Details: https://bit.ly/489SSaP
#TeamMassSpec #Metabolomics #Proteomics #RforMassSpectrometry
Johannes Rainer
Laurent Gatto