Kozo Nishida | 西田孝三

Webinar at EMBL-EBI: Automatic annotation systems in UniProt. 12 March 2026, 15:00-16:00 GMT

Webinar speakers (with headshots): David CJ Carpentier, Scientific Database Curator; Pedro Raposo, Senior Curator/Bioinformatician. EMBL-EBI

If you are interested in knowing how #UniProt uses automatic annotation tools to enrich its data, join our #webinar on 12 March: we will discuss the two main tools currently in use in the database, ARBA and UniRule.

Registration is free but essential: www.ebi.ac.uk/training/eve...

🖥️🧬

Scientific Community Engagement Fundamentals (CEF26W) Course DescriptionThis eight-week course is for new or existing community managers who are looking to learn about core frameworks and vocabulary to describe their community and their role within it. W...

If you’re interested in growing as a community professional in STEM, we encourage you to sign up for our next CEF cohort, which begins on Thursday. 19 March (reg deadline is this Thursday, 5 March): www.zeffy.com/en-US/ticket...

Please Update Your AMY Profile! We have recently released some updates on AMY, our community data management software. We are calling on our Instructors, Instructor Trainers, Maintainers, and all other Carpentries community members ...

🔊 We recently released updates on AMY, our community data management software, and are calling on our community members with a profile on AMY to please take a few minutes to update your airport location (now a required field) and any other information on your profile: carpentries.org/blog/2026/03...

A Few Claude Skills for R Users – R Works The community has come together to create some great Claude Skills that you can try out today.

I rounded up a few Claude Skills for #RStats users.

Huge thanks to the creators who developed them. They share Skills for everything from tidyverse code to brand.yml files to learning while using AI.

Hope the list is useful, and please let me know what I missed! 🧡

rworks.dev/posts/claude...

The future of #DataScience is at #positconf 2026. Check out our keynote lineup:
Wes McKinney: Future of high-performance computing
Christine Zhang: Data storytelling at NY Times
Emily Riederer: Scaling open-source at Capital One
Sara Altman & Simon Couch: AI-driven workflows
Register: pos.it/conf

BioAIrepo: EMBL-EBI’s hub for life science AI models New EMBL-EBI repository helps researchers share and reuse machine learning models trained on life science data.

BioAIrepo: EMBL-EBI’s hub for life science AI models
www.ebi.ac.uk/about/news/t...

A Milestone for Global Image Data Interoperability - FoundingGIDE The FBbi ontology has been migrated to a dedicated repository at https://github.com/foundingGIDE/fbbi, where new releases will be made available. To ensure the ontology remains community-driven, GerBI...

A significant step forward in standardizing biological imaging data.
Our project partners, @gerbi-gmb.de and @bioimagearchive.bsky.social, will assure the continued development of the #FBbi ontology. Discuss this milestone and many other at our Community Event 4-6 May! tinyurl.com/26rbksyx

Important: WikiPathways Technical Transition - Action Required for Some Users

Dear WikiPathways Community,

Effective April 1st, 2026, we will begin taking major steps toward retiring the classic WikiPathways backend as we complete our migration to our GitHub-based infrastructure.

1/ […]

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....