Expensive software licences are slowing down research. NFDI4Chem and FID Chemistry are committed to digital sovereignty through an open-source solution for chemistry structure editors. Help shape the future of drawing now!

👉 To the survey: https://t1p.de/p5b9p

#OpenScience #Chemistry #NFDI4Chem

From @dessimoz.bsky.social and colleagues in @nature.com 's #Scientific #Data | The missing link in FAIR data policy: biodata resources in life sciences | #FAIR #Genomics #OpenScience #OpenSource #Proteomics 🧬 🖥️ 🧪 🔓
⬇️
www.nature.com/articles/s41...

new blog post: "Rescuing @wdscholia #2: getting closer" chem-bla-ics.linkedchemistry.info/2025/12/31/rescuing-scho... https://doi.org/10.59350/6t2qh-2f839

"But we are getting close. So, please give qlever.scholia.wiki a go, and let us know your observations. As […]

MIBiG 5.0 Annotathons Registration Form Planned for spring 2026

Speaking of MIBiG hackathons: iteration 5.0 it is planned for spring 2026 and registrations are open! Please sign up if you are interested and feel free to share with your colleagues! (7/8) forms.gle/JKsf3XL3d1XN...

Strategies for community-sourced biocuration in bioinformatics: a case study on MIBiG 4.0 Abstract. Biocuration is essential to transform molecular sequence data into standardized, machine-readable resources. Such curated datasets enable compara

Have you ever used a #bioinformatics #database and were frustrated by its lack of coverage? Did you ever think about starting your own resource? We just published a new strategy for community-driven #biocuration, based on our experiences with the #MIBiG database (1/8)! doi.org/10.1093/bib/...

👉 Point your friends, family and connections to our new video 📹 on the point of bioinformatics!

The 90-second video is available in:

🇬🇧 youtu.be/yjbv1WZcM2M
🇫🇷 youtu.be/BG-h_nxwSE4
🇩🇪 youtu.be/e4S4m1IhfiU
🇮🇹 youtu.be/V4QcZHjnsGo

A Perspective on Unintentional Fragments and Their Impact on the Dark Metabolome, Untargeted Profiling, Molecular Networking, Public Data, and Repository Scale Analysis In/postsource fragments (ISFs) arise during electrospray ionization or ion transfer in mass spectrometry when molecular bonds break, generating ions that can complicate data interpretation. Although ISFs have been recognized for decades, their contribution to untargeted metabolomics─particularly in the context of the so-called “dark matter” (unannotated MS or MS/MS spectra) and the “dark metabolome” (unannotated molecules)─remains unsettled. This ongoing debate reflects a central tension: while some caution against overinterpreting unidentified signals lacking biological evidence, others argue that dismissing them too quickly risks overlooking genuine molecular discoveries. These discussions also raise a deeper question: what exactly should be considered part of the metabolome? As metabolomics advances toward large-scale data mining and high-throughput computational analysis, resolving these conceptual and methodological ambiguities has become essential. In this perspective, we propose a refined definition of the “dark metabolome” and present a systematic overview of ISFs and related ion forms, including adducts and multimers. We examine their impact on metabolite annotation, experimental design, statistical analysis, computational workflows, and repository-scale data mining. Finally, we provide practical recommendations─including a set of dos and do nots for researchers and reviewers─and discuss the broader implications of ISFs for how the field explores unknown molecular space. By embracing a more nuanced understanding of ISFs, metabolomics can achieve greater rigor, reduce misinterpretation, and unlock new opportunities for discovery.

The existence of unintentional fragmentation (often referred to as in-source fragments) in untargeted #metabolomics data can cause uncertainty among newcomers to the field and skepticism among data consumers such as medical experts or biologists.

pubs.acs.org/doi/10.1021/...

Reliable data for better care Every medical revolution has been driven by a technological breakthrough: chemical synthesis paved the way for modern medicines, genetics for the first gene therapies. The next frontier will be determ...

💡 No AI in health without great data. In @BilanMagazine’s health supplement, Christophe Dessimoz @dessimoz.bsky.social makes the case for Switzerland as a leader in trusted, high-quality health data — and how turning data into public value starts with strong infrastructures.
👇 Read more

Available to read now via Open Access:
pubs.rsc.org/en/content/a...
Fast, general-purpose metabolome analysis by mixed-mode liquid chromatography–mass spectrometry
Nicola Zamboni et al @nzamboni.bsky.social @alaaothman.bsky.social @ethz.ch
#MassSpec

Ilse Falls in the Harz National Park, Germany. Text says: Wikidata is now the second Wikimedia project recognized as a digital public good.

Wikidata has been recognized as a digital public good by the Digital Public Goods Alliance (DPGA) 🎉

Learn how Wikidata, which houses over 1.6 billion facts, advances education, innovation, and public institutions ➡️ wikimediafoundation.org/news/2025/10...

Building an Internet for Everyone: Wikidata Recognized as a Digital Public Good The Wikimedia Movement contributes every day toward a better internet, one that is free – filled with reliable information that belongs to everyone and benefits everyone. This is the ideal that the…

Thank you to everyone who’s been along for the ride for the past 13 years and those who will be for many more to come! diff.wikimedia.org/2025/10/29/b...

Wikidataians celebrate Wikidata being recognised as a Digital Public Good

Every day, thousands of editors on #Wikidata provide open, reliable and trustworthy data to the world helping make technology more open and inclusive.

Today, we are thrilled to announce that Wikidata has been recognized by the @DPGAlliance as a #DigitalPublicGood.

Slides from my #livingdata2025 talk "Wikis as sources of data and signal for the Biodiversity Heritage Library" are on @figshare.com doi.org/10.6084/m9.f... @biodivlibrary.bsky.social @wikidatacommunity.bsky.social #wikispecies @wikipedia.org

Correspondence in Nature: to make FAIR a reality, fund the data resources and expertise—not just data-management plans. A call to action co-signed with @francesarnold.bsky.social, Rich Roberts, and Tim Hubbard. doi.org/10.1038/d415...

@skepteis.bsky.social brought us to a local place close to Arnhem! 🥞

Best part was the quadruple, and now also the Pannenkoeks!

Reminder 👇

Interested in improving our R tools for #MassSpectrometry data analysis and integrating them into Galaxy?

⏲️ 3 year position
📍 Bolzano, 🇮🇹

👉 apply if you like:

- #rstats SW development
- @bioconductor.bsky.social
- large-scale #metabolomics data analysis
- hiking ⛰️

🔗 bit.ly/46AMawx

I still love the @wdscholia DOI redirection tool. When the DOI is not listed in @wikidata, it will use @larsgw's @citationjs to create QuickStatements that can be used with @magnusmanske's tool to create a new Wikidata item.

And it supports ORCID to link to authors, "title in HTML", mul, and […]

Aaand it's out! Meet MITE - the natural product tailoring enzyme database, just published in @narjournal.bsky.social! MITE DB captures the substrate- and reaction-specificity of tailoring enzymes, allowing to capture this information in a human- and machine-readable way! doi.org/10.1093/nar/...

@mehdibeni.bsky.social drove it forward, and it was a pleasure to contribute with @robinschmid.bsky.social, @Wout Bittremieux, @Olivier Cailloux, and @jjjvanderhooft.bsky.social.
It places the scalability of MS-based metabolomics for natural extracts as a central challenge rather than a side issue.

Predicting input signals of transcription factors in Escherichia coli | Molecular Systems Biology imageimageThis study developed a systematic workflow leveraging transcriptomics and metabolomics to identify input signals for 41 transcription factors in E. coli. A systematic workflow was developed...

🥳 New paper out!
In this work, we identified small molecule effectors modulating the activity of transcription factors in E. coli.
Using metabolomics + transcriptomics, we could parse the entire regulatory network and predict signal molecules for 41 TFs!

www.embopress.org/doi/full/10....

A Perspective on Unintentional Fragments and their Impact on the Dark Metabolome, Untargeted Profiling, Molecular Networking, Public Data, and Repository Scale Analysis. In/post-source fragments (ISFs) arise during electrospray ionization or ion transfer in mass spectrometry when molecular bonds break, generating ions that can complicate data interpretation. Although ISFs have been recognized for decades, their contribution to untargeted metabolomics - particularly in the context of the so-called “dark matter” (unannotated MS or MS/MS spectra) and the “dark metabolome” (unannotated molecules) - remains unsettled. This ongoing debate reflects a central tension: while some caution against overinterpreting unidentified signals lacking biological evidence, others argue that dismissing them too quickly risks overlooking genuine molecular discoveries. These discussions also raise a deeper question: what exactly should be considered part of the metabolome? As metabolomics advances toward large-scale data mining and high-throughput computational analysis, resolving these conceptual and methodological ambiguities has become essential. In this perspective, we propose a refined definition of the “dark metabolome” and present a systematic overview of ISFs and related ion forms, including adducts and multimers. We examine their impact on metabolite annotation, experimental design, statistical analysis, computational workflows, and repository-scale data mining. Finally, we provide practical recommendations - including a set of dos and don’ts for researchers and reviewers - and discuss the broader implications of ISFs for how the field explores unknown molecular space. By embracing a more nuanced understanding of ISFs, metabolomics can achieve greater rigor, reduce misinterpretation, and unlock new opportunities for discovery.

If you’ve been following #metabolomics literature, you’ve probably seen a lot of debate on in-source fragmentation. We’ve put together a manuscript to clarify what it is, how to deal with it, and what it means for discovery in #metabolomics and #exposomics.
doi.org/10.26434/che...

Into natural product biosynthesis & tailoring enzymes? Frustrated by the lack of a dedicated resource to explore their functions? Tired of endless literature searches for reaction info? Meet the MITE database, freely available at mite.bioinformatics.nl. Preprint: doi.org/10.26434/che... (1/8)

Graphical illustration of how EPS is enzymatically degraded in multiple steps into smaller fragments, fueling the growth of non-degrading species: EPS degraders break down EPS into larger oligomers for oligomer consumers; further degradation into monomers and small oligomers supports non-degrading consumers.

What is the role of extracellular polymeric substances (EPS) in carbon exchange among microbial species? @sammy-pontrelli.bsky.social &co show that #EPS, formed via #chitin degradation, drives #MicrobialDiversity by acting as a sequentially degraded #CarbonSource @plosbiology.org 🧪 plos.io/3J9kbuu

heading tomorrow to the InChI Technical Exchange Meeting Summer 2025 in Aachen/DE

Looking forward to it, and particularly talking about the InChI for inorganics and trying that in @wikidata :) See https://doi.org/10.26434/chemrxiv-2025-53n0w

And also the nano InChI, see […]

@skepteis.bsky.social You're not alone 😉

Funny feed showing Daniel Probst and ChemDraw both turning 40

My feed just made it even better:

As old as ChemDraw 🥳

🚀 We’ve launched the new MassBank! Now live at massbank.eu & massbank.jp — redesigned with a faster backend, better search, and powerful tools for exploring & sharing mass spectral data. Enjoy the fresh experience! Feedback and ideas welcome, please post them on github.com/MassBank/Mas...

Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS - Nature Biotechnology A transformer model is used to construct the DreaMS Atlas—a molecular network of 201 million MS/MS spectra.

Mass spectrometry is a key method to discover and identify molecules in biological and environmental samples. Yet, >90% of mass spectra remain hard to interpret. In our recent paper, we present DreaMS — a foundation model to interpret mass spectra of small molecules.
www.nature.com/articles/s41...

Excited to welcome our new Agilent Revident Q-TOF! We’re developing new approaches combining mass spectrometry and enzyme assays to study microbial interactions and carbon sequestration. We're hiring postdocs — reach out if interested!

pontrellilab.sites.vib.be/en