Luis Pedro Coelho

@luispedrocoelho.bsky.social

Content creator for Elsevier, NPG, and others Microbiome researcher Based in Brisbane

I would like to do more of these as time goes on. Claude is very good at this (even between when we started drafting the paper and now, the models are so much better!) and I think this can be a great addition to any paper

@bigdatabiology.bsky.social · 3w ago

We updated our companion website to the elusive resistome paper to be more responsive (it's 100% client side now) You can generate all the alternative plots that we could not get into the paper: arg-pipelines.big-data-biology.org

I've been thinking of how to make tools "AI-ready" so it's not just an empty buzzword I'd like to hear other opinions, but this is what I'm trying with SemiBin: 1) bundle skills with the tool SemiBin2 install-skills will (in the next release) install a skill for claude/codex/...

A bit delayed announcement, but we recently released SemiBin 2.30! No big new features, but it contains a large number of small fixes and improvements (include having had all major LLMs audit and improve the code) bioconda & pypi packages have been updated too github.com/BigDataBiolo...

Release Version 2.3.0 · BigDataBiology/SemiBin

This release bundles a large number of bug fixes (several of which fix silent correctness issues in training and clustering), removes the deprecated SemiBin1 command, and includes many documentatio...

github.com

We started with a simple question: which tool and db should we use for resistome profiling in the EMBARK project? That question sent us down a rabbit hole. Different ARG annotation pipelines don't just give different ARG numbers, they give you different resistome profiles. 1/3

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 elusive resistome: a global comparison reveals large discrepancies among detection pipelines

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

biorxiv.org

BrisJAMS is back this April and it’s shaping up to be a seriously fun night of science, stories, and good vibes 🧪🧫✨ 📅 When: Wednesday, 29 April 2026 | 6:00–7:30pm 📍 Where: The Burrow (Beer Garden Room), West End. 👉 Register via the QR code on the poster!

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