📢 Galaxy Advanced Training: Mastering Workflows, Automation, and Scalability: pre-registration open until June 5 2026!
I'm thrilled to co-organize and teach at the first-ever training event focused on advanced Galaxy features: workflows, API, and more!
Registration is now open until June 5, so don’t miss your chance to dive deep into these powerful tools.
How every layer of science's "self-correcting machinery" failed when Iva Veseli and I simply wanted to reproduce the findings of a high-profile study on gut microbiome and autism:
merenlab.org/2026/04/15/u...
✨ Know a woman making an impact in microbiology?
Nominate her for recognition on the FEMS website and socials this International Women’s Day.
🗓️ Submit by 4 March: buff.ly/AKyMbQD
Let’s elevate the female voices driving microbiology forward.
#IWD2026
For more information and to pre-register: training.galaxyproject.org/training-mat...
@galaxyproject.bsky.social @galaxytraining.bsky.social
A short piece about what "the pangenome" is and is not.
open.substack.com/pub/profmcin...
Explore the results interactively: arg-pipelines.big-data-biology.org
Project led by Juan Inda-Diaz with a great team across the whole SEARCHER Consortium!
How well do ARG detection pipelines agree when applied to the same data? Spoiler: not very well.
In our new preprint, we ran 10 pipelines on 270M microbial unigenes from GMGCv1. The same data can support conflicting biological conclusions! 🧵
www.biorxiv.org/content/10.6...
The myth of open data, reproducibility, responsibility, and accountability in science, and your role in it
Join us for an **intensive, week-long, in-person training** designed to elevate your Galaxy expertise to new heights. This workshop is tailored for **data scientists, advanced Galaxy users, and team l...
This section highlights the difference in number of Antimicrobial Resistance Genes (ARGs) reported by each pipeline.
arg-pipelines.big-data-biology.org
A. Murat Eren (Meren)
Identifying antibiotic resistance genes (ARGs) from metagenomic data is critical for studying antimicrobial resistance across microbial communities and pathogens. However, there is no standardized methodology for ARG annotation. Here, we compare ten commonly used ARG detection pipelines by analysing over 270 million prokaryotic genes from the Global Microbial Gene Catalogue across 13 distinct habitats. We observed up to a 45-fold difference in the number of reported ARGs, with a mean Jaccard index of only 16% between pipelines. Pipeline selection profoundly impacted downstream biological interpretations, with drastic changes to estimates of ARG relative abundance and richness, to the characterization of pan- and core-resistomes, and to the class-level composition of the inferred resistome. ARG detection pipelines make different, defensible trade-offs, and no single approach should be treated as authoritative. Therefore, users should justify and communicate choices carefully, as our analyses show that, taken uncritically, the same data can support conflicting biological and ecological interpretations. ### Competing Interest Statement The authors have declared no competing interest. National Health and Medical Research Council of Australia (NHMRC), 2031902 Australian Research Council (ARC), FT230100724 International Development Research Centre (IDRC), 109304-001 Deutsche Forschungsgemeinschaft (DFG), FO1279/6-1 Bundesministerium für Bildung und Forschung (BMBF), F01KI1909A, 01KI2404B Swedish Research Council (VR), 2024-06123, 2019-00299, 2023-01721 Knut and Alice Wallenberg Foundation, KAW 2020.0239 Swedish Foundation for Strategic Research, FFL21-0174
Join us for an **intensive, week-long, in-person training** designed to elevate your Galaxy expertise to new heights. This workshop is tailored for **data scientists, advanced Galaxy users, and team l...
