Kozo Nishida | 西田孝三

Perspectives in computational mass spectrometry: recent developments and key challenges Abstract. Summary: Mass spectrometry (MS) is a cornerstone technology in modern molecular biology, powering diverse applications across proteomics, metabol

Perspectives in computational mass spectrometry: recent developments and key challenges url: academic.oup.com/bioinformati...

Hacking for Metabolomics & Mass Spectrometry format interoperability - NFDI4Chem The ELIXIR BioHackathon Europe took place 3–7 November 2025 in Bad Saarow near Berlin. Among the ~30 Projects was the “Metabolomics and proteomics file format interoperability fest”, co-organised by S...

Hacking for Metabolomics & Mass Spectrometry format interoperability
www.nfdi4chem.de/metabolomics...

xcms in Peak Form: Now Anchoring a Complete Metabolomics Data Preprocessing and Analysis Software Ecosystem High-quality data preprocessing is essential for untargeted metabolomics experiments, where increasing data set scale and complexity demand adaptable, robust, and reproducible software solutions. Modern preprocessing tools must evolve to integrate seamlessly with downstream analysis platforms, ensuring efficient and streamlined workflows. Since its introduction in 2005, the xcms R package has become one of the most widely used tools for LC-MS data preprocessing. Developed through an open-source, community-driven approach, xcms maintains long-term stability while continuously expanding its capabilities and accessibility. We present recent advancements that position xcms as a central component of a modular and interoperable software ecosystem for metabolomics data analysis. Key improvements include enhanced scalability, enabling the processing of large-scale experiments with thousands of samples on standard computing hardware. These developments empower users to build comprehensive, customizable, and reproducible workflows tailored to diverse experimental designs and analytical needs. An expanding collection of tutorials, documentation, and teaching materials further supports both new and experienced users in leveraging broader R and Bioconductor ecosystems. These resources facilitate the integration of statistical modeling, visualization tools, and domain-specific packages, extending the reach and impact of xcms workflows. Together, these enhancements solidify xcms as a cornerstone of modern metabolomics research.

Out now! xcms in Peak Form: Now Anchoring a Complete Metabolomics Data Preprocessing and Analysis Software Ecosystem doi.org/10.1021/acs....
with Phillipine and @jorainer.bsky.social (EURAC), @metabomichael.bsky.social, Hendrik and Norman from @ipbhalle.bsky.social, @janstanstrup.bsky.social, et al.

Great work from @philouail.bsky.social 🙌

#xcms now fully integrated into @bioconductor.bsky.social 💪

👉 #metabolomics #MassSpectrometry #rstats

Eduomics: a Nextflow pipeline to simulate -omics data for education www.biorxiv.org/content/10.1... 🧬🖥️🧪 github.com/lescai-teach...

Community Events There are many opportunities to join community meetings, subcommittees and debriefing sessions. Find links to them on this Etherpad, and subscribe to the Google calendar below or use this ics feed to…

Upcoming community sessions this week
carpentries.org/community/ev...
Events are in UTC Time :

📣 Workbench Transition Coworking
24 Nov 09:00 & 19:00

📣 👋 Welcome Session with The Carpentries
24 Nov 12:00

🇬🇧 UK Carpentries Community Call
24 Nov 16:00

🕸️ Creating a workshop website
25 Nov 09:00

Bioconductor in Africa: Ethiopia’s First In-Person Course – Bioconductor community blog A blog for the Bioconductor community!

🌍 We are proud to share highlights from Ethiopia’s first in-person Bioconductor workshop, held in Addis Ababa from 25-29 Aug 2025!

The course united 26 participants for five days of hands-on training in R/Bioconductor and bulk RNASeq analysis.

🔗 Read more: blog.bioconductor.org/posts/2025-1...

The Reactome Knowledgebase 2026 Abstract. The Reactome Knowledgebase (https://reactome.org) is a freely accessible, expert-curated, open-source, and open-data resource that describes huma

Reactome Knowledgebase 2026 url: academic.oup.com/nar/article/...

Open Enzyme Database: a community-wide repository for sharing enzyme data Abstract. Enzymes are the molecular machines of life and play an indispensable role in numerous biotechnological and biomedical applications. Despite the a

Open Enzyme Database: a community-wide repository for sharing enzyme data url: academic.oup.com/nar/article/...

MassBank: an open and FAIR mass spectral data resource Abstract. The open spectral library MassBank (https://massbank.jp/) started in 2006 in Japan, as one of the first open source and open access cross-vendor

MassBank: an open and FAIR mass spectral data resource url: academic.oup.com/nar/article/...

DancePartner: Python Package to Mine Multiomics Relationship Networks from Literature and Databases A goal of multiomics experiments is to understand how mechanistic molecular biology is altered between conditions, typically a control group and experimental groups. Oftentimes, this involves studying...

DancePartner: Python Package to Mine Multiomics Relationship Networks from Literature and Databases | Journal of Proteome Research pubs.acs.org/doi/abs/10.1...

MSOne: An AI-powered software suite enabling end-to-end analysis of high-resolution LCMS data for metabolomics data mining Untargeted LC–MS metabolomics generates large, noisy datasets that demand complex, parameter-intensive workflows across multiple software tools. While over 5000 metabolomics datasets are available in ...

MSOne: An AI-powered software suite enabling end-to-end analysis of high-resolution LCMS data for metabolomics data mining | ChemRxiv - doi.org/10.26434/che...

Practical Guidance for Training Machine Learning Models in Metabolomics and Mass Spectrometry Research This tutorial offers a step-by-step guide for analytical chemists to train machine learning models for MS-based metabolomics. It covers data preparation, feature engineering, model selection, evaluati...

Practical Guidance for Training Machine Learning Models in Metabolomics and Mass Spectrometry Research | Analytical Chemistry pubs.acs.org/doi/full/10....

RDMkit: A research data management toolkit for life sciences The rise of data-driven science has made research data management (RDM) essential, yet implementing it remains complex due to diverse disciplinary needs, limited tailored guidance, and gaps in trainin...

RDMkit: A research data management toolkit for life sciences: Patterns www.cell.com/patterns/ful...

Ten quick tips for developing a reproducible Shiny application

From @plos.org #Computational #Biology | Ten quick tips for developing a reproducible #Shiny application | #Bioinformatics #Education #PLOSCBQT #OpenScience #OpenSource 🧬 🖥️ 🧪🔓
⬇️
journals.plos.org/ploscompbiol...

📣 HPC Carpentry Community Call
16 Oct 11:00 & 21:00.

Bioconductor Code of Conduct Committee Elections 2025 The Bioconductor Code of Conduct Committee updates, disseminates, upholds and enforces the Code of Conduct (http://bioconductor.org/about/code-of-conduct/) across all platforms of the Bioconductor…

🛡️ Applications are now open to join the Bioconductor Code of Conduct Committee 2025!

Be part of a thoughtful, community-driven team that meets 3–5 times a year to ensure Bioconductor remains a respectful, inclusive, and thriving space 🌱

👉 Apply by October 31, 2025:
docs.google.com/forms/d/e/1F...

Did you miss this.... Introducing Partial Spectra DB 🧪—an open-access database for partially annotated lipids!
✨ Explore curated spectra - Browse, Search, and Download data for your workflows.
🌍 Got spectra? Contribute & help grow this community-driven resource!
🔗 www.lipidmaps.org/databases/pa...

FAIR-SMART expands access to supplementary materials for research transparency Supplementary materials that accompany scientific articles enhance transparency, reproducibility and scientific impact. This study presents FAIR-SMART, a tool that enables computational access to thes...

Checkout this article I found at PLOS: dx.plos.org/10.1371/jour...

Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data - Nature Communications Mass spectrometry-based lipidomics and metabolomics generate large, complex datasets requiring effective analysis. Here, authors review key statistical and visualization methods alongside widely used R and Python tools, and provide a GitBook with step-by-step code for accessible, reproducible data analysis.

Thank you for citing #tidyplots 🙏

Jakub Idkowiak et al. Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data. Nature Communications (2025).

doi.org/10.1038/s414...

#rstats #dataviz #phd

mzPeak: Designing a Scalable, Interoperable, and Future-Ready Mass Spectrometry Data Format Advances in mass spectrometry (MS) instrumentation, including higher resolution, faster scan speeds, and improved sensitivity, have dramatically increased the data volume and complexity. The adoption ...

mzPeak: Designing a Scalable, Interoperable, and Future-Ready Mass Spectrometry Data Format | Journal of Proteome Research pubs.acs.org/doi/full/10....

Release Notes for Cytoscape 3.10.4
cytoscape.org/release_note...

Supervised Contrastive Learning Leads to More Reasonable Spectral Embeddings Over the past decades, mass spectrometry has served as a fundamental technique for molecular identification in the field of metabolomics, widely applied to the analysis and characterization of biomole...

Supervised Contrastive Learning Leads to More Reasonable Spectral Embeddings | Analytical Chemistry pubs.acs.org/doi/10.1021/...

Intelligent Tool Orchestration for Rapid Mechanistic Model Prototyping: MCP Servers as AI-Biology Interfaces The construction of multicellular mechanistic models in systems biology typically requires months of literature research, programming expertise, and deep knowledge of specialized computational tools. ...

Intelligent Tool Orchestration for Rapid Mechanistic Model Prototyping: MCP Servers as AI-Biology Interfaces

www.biorxiv.org/content/10.1...

Koza and Koza-Hub for born-interoperable knowledge graph generation using KGX Knowledge graph construction has become an essential domain for the future of biomedical research. But current approaches demand a high amount of redundant labor. These redundancies are the result of ...

Koza and Koza-Hub for born-interoperable knowledge graph generation using KGX
arxiv.org/abs/2509.09096

Interoperability of Bioconductor packages within the ELIXIR Research Software Ecosystem using the EDAM ontology - CNRS - Centre national de la recherche scientifique cnrs.hal.science/hal-05250261v1

SBMLtoOdin and menelmacar: Interactive visualisation of systems biology models for expert and non-expert audiences AbstractSummary. Computational models in biology can increase our understanding of biological systems, be used to answer research questions, and make predi

SBMLtoOdin and menelmacar: Interactive visualisation of systems biology models for expert and non-expert audiences url: academic.oup.com/bioinformati...

Understanding Data Analysis Steps in Mass-Spectrometry-Based Proteomics Is Key to Transparent Reporting Mass spectrometry (MS)-based proteomics data analysis is composed of many stages from quality control, data cleaning, and normalization to statistical and functional analysis, without forgetting multiple visualization steps. All of these need to be reported next to published results to make them fully understandable and reusable for the community. Although this seems straightforward, exhaustively reporting all aspects of an analysis workflow can be tedious and error prone. This letter reports good practices when describing data analysis of MS-based proteomics data and discusses why and how the community should put efforts into more transparently reporting data analysis workflows.

Understanding Data Analysis Steps in Mass-Spectrometry-Based Proteomics Is Key to Transparent Reporting | Journal of Proteome Research pubs.acs.org/doi/full/10....

Reverse Spectral Search Reimagined: A Simple but Overlooked Solution for Chimeric Spectral Annotation The exponential growth of untargeted metabolomics data, now reaching billions of mass spectra in public repositories, benefits from reannotation strategies for data reuse. While tandem mass spectromet...

Reverse Spectral Search Reimagined: A Simple but Overlooked Solution for Chimeric Spectral Annotation | Analytical Chemistry pubs.acs.org/doi/abs/10.1...