Sign in

bigdatabiology.bsky.social

@bigdatabiology.bsky.social
336 followers 989 following 101 posts
PostsRepliesMedia
Reposted by @bigdatabiology.bsky.social
Svetlana Ugarcina Perovic @svetlanaup.bsky.social · 05/10/2026
Juan Inda (@bigdatabiology.bsky.social) will discuss how the choice of ARG annotation tool influences resistome profiles derived from metagenomic data. (More context in the thread below) See you at #MVIF 52! cassyni.com/s/mvif-52
024
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 21/09/2026
Jug 2.6.0 is out Robustness and QoL fixes Heads-up: Py 3.14+ and lambda Tasklets will recompute once., but this does ensure backwards compatibility with Python 3.13 and earlier jug.readthedocs.io/en/latest/hi...
jug.readthedocs.io
History — Jug 2.6.0 documentation
001
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 16/09/2026
SemiBin 2.5.0 is out 🎉 concatenate_fasta() can now preserve full FASTA headers (e.g. circularity flags) when merging samples /ht Jim Downie (github.com/prototaxites) for the suggestion Agents make releases easy now: expect more frequent ones in the future github.com/BigDataBiolo...
github.com
GitHub - BigDataBiology/SemiBin: SemiBin: metagenomics binning with self-supervised deep learning
SemiBin: metagenomics binning with self-supervised deep learning - BigDataBiology/SemiBin
083
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 16/08/2026
AI/ML day at #ISME20 Our group is well represented today, with @luispedrocoelho.bsky.social chairing and presenting a session! Come say Hi!
011
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 04/08/2026
New paper led by Daniel Howard & Simon Phipps: why breastfeeding protects infants against severe lung infections. Milk osteopontin reshapes the infant gut microbiome → Lactobacillaceae → phenyllactic acid → dendritic cell development in liver & lung → disease tolerance doi.org/10.1016/j.ce...
doi.org
Redirecting
030
Reposted by @bigdatabiology.bsky.social
Steven Robbins @stevenjrobbins.bsky.social · 22/07/2026
It's out! Excited to present the Great Barrier Reef Microbial Genomes Database (GBR-MGD), a comprehensive DB of 1000s of high-quality prokaryote, virus, plasmid, and chromosome-level eukaryote MAGs using Nanopore long reads. Subthreads incoming. Please share widely. 🙂 www.nature.com/articles/s41...
nature.com
The planktonic microbiome of the Great Barrier Reef - Nature
The Great Barrier Reef Microbial Genomes Database compiles prokaryotic, viral and eukaryotic genomes from seawater collected from the Great Barrier Reef, providing a rich resource for the study of mar...
88443
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 17/07/2026
This is a support website for the preprint "The elusive resistome: a global comparison reveals large discrepancies among detection pipelines" by Inda-Diaz et al. doi.org/10.64898/202...
doi.org
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 met...
020
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 17/07/2026
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
arg-pipelines.big-data-biology.org
ARG Pipeline Explorer — The Elusive Resistome
Interactive companion to "The elusive resistome": compare 10 antibiotic-resistance-gene detection pipelines across 13 habitats and 278M genes.
110
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 24/06/2026
New newsletter with updates from the group! Focus is on Juan's preprint on the elusive resistome bigdatabiology.substack.com/p/updates-fo...
bigdatabiology.substack.com
BDB-Lab June 2026 Updates
The elusive resistome and upcoming travel/conferences
011
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 07/06/2026
Basic functionality is still as described in the papers: academic.oup.com/bioinformati...
academic.oup.com
SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing
AbstractMotivation. Metagenomic binning methods to reconstruct metagenome-assembled genomes (MAGs) from environmental samples have been widely used in larg
000
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 07/06/2026
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...
github.com
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...
123
Reposted by @bigdatabiology.bsky.social
Mike Neugent PhD @mikeneugent.bsky.social · 19/05/2026
Happy Tuesday, Svetlana!
212
Reposted by @bigdatabiology.bsky.social
Willem van Schaik @wvschaik.bsky.social · 13/05/2026
This is an incredibly relevant preprint. Your favourite pipeline and/or AMR gene database cannot be considered authoritative, but neither can others.
0145
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 13/05/2026
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!
arg-pipelines.big-data-biology.org
This section highlights the difference in number of Antimicrobial Resistance Genes (ARGs) reported by each pipeline.
032
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 13/05/2026
Our conclusions: no single pipeline should be treated as authoritative. A constrained methodological pluralism, whereby multiple approaches are seen as valid, each with limitations communicated carefully is the most honest reflection of the state-of-the-art.
110
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 13/05/2026
Which pipeline is right? Fundamentally, they are not answering the same question! They make different trade-offs between precision and recall, and disagree on even the basic question of "what is an ARG?"
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 13/05/2026
Which means that we can draw very different conclusions about the resistome's size and composition: ★ Relative abundance estimates differ by 2–5× across habitats ★ RGI uniquely estimates marine resistance higher than soil ★ Pan-resistomes from DeepARG/RGI are 2.5–32× larger than other pipelines
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 13/05/2026
The original authors of DeepARG to caution against using results marked as "multidrug" (such as *rpoB*) without experimental validation, but this is rarely done.
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 13/05/2026
DeepARG reported 6,808 *rpoB* genes, 96% with <80% identity to the reference, with no obvious filtering for resistance-conferring mutations. *rpoB* is a universal gene, this likely reflects bacterial diversity, not resistance.
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 13/05/2026
Two gene classes dominate most pipelines' outputs: efflux pumps (36%) and *van* genes (30%). But many of these calls are based on low identity to references. For RGI, 74% of ARGs had <50% identity to the reference. For DeepARG, 74% had <70%.
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 13/05/2026
Differences aren't only about databases. RGI and ABRicate both use CARD, but ABRicate-CARD captured only 8.2% of RGI's predictions, and RGI reported just 53.8% of ABRicate-CARD's hits. Same database, very different answers!
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 13/05/2026
The number of reported ARGs varied up to 45-fold between pipelines. ABRicate-ResFinder: 2,213 ARGs DeepARG: 100,075 ARGs Mean Jaccard index across pipelines? Just 16%.
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 13/05/2026
We compared RGI, DeepARG, fARGene, ResFinder, AMRFinderPlus, and ABRicate with 5 different databases on the Global Microbial Gene Catalogue (GMGCv1). 178,107 unigenes were called as ARGs by at least one pipeline. Mean Jaccard index across pipelines? Just 16%.
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 13/05/2026
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...
biorxiv.org
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
12313
Reposted by @bigdatabiology.bsky.social
bioRxiv Bioinfo @biorxiv-bioinfo.bsky.social · 13/05/2026
The elusive resistome: a global comparison reveals large discrepancies among detection pipelines www.biorxiv.org/content/10.64898/20…
053
Reposted by @bigdatabiology.bsky.social
Sebastian Schmidt @tsbschm.bsky.social · 03/04/2026
Our work on 'hidden diversity' in unbinned contigs is now published in @natmicrobiol.nature.com : www.nature.com/articles/s41... See the linked threads for more details!
nature.com
Unbinned contigs expand known diversity in the global microbiome - Nature Microbiology
Re-analysis of over 92,000 metagenomes reveals hundreds of thousands of previously undescribed Bacterial and Archaeal clades hidden in plain sight.
36942
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 25/03/2026
We built an app to explore the data too at: urban-soil-mags.big-data-biology.org
urban-soil-mags.big-data-biology.org
MAGs view
000
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 25/03/2026
Preprint + data are fully available. Urban microbiomes are still underexplored www.biorxiv.org/content/10.6...
biorxiv.org
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 25/03/2026
Antibiotic resistance landscape: many latent ARGs, but relatively few established ones. Suggests a large reservoir of uncharacterized resistance potential, but also nuance is needed when interpreting these results!
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 25/03/2026
Also: >2 million small protein families linked to defense systems and mobile elements. These remain systematically overlooked
110
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 25/03/2026
We identified >30,000 biosynthetic gene clusters, highly contiguous thanks to long reads
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 25/03/2026
From 58 urban soil samples (Shanghai & Nanjing), deep sequencing (≥40 Gbp/sample) enabled recovery of 7,949 MAGs (incl. 1,060 near-finished genomes) 97% of species-level genome bins do not match a reference genome. Urban soil is largely uncharted microbial territory
110
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 25/03/2026
Urban soils are a small part of the world and lack the glamour of the wilderness or the economic importance of agriculture, but as most people live in cities, they are very important Our latest preprint using long-read metagenomics reveals massive hidden diversity and function in city soils 🧵
154
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 25/03/2026
We put these out 4x/year (never more than that!), so if you sign up, we won't be spamming your mailbox with every little update!
000
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 25/03/2026
We also have a lot of tool updates: 1. GSMC-mapper had a lot of polish put on with more checking and faster downloads 2. SPIREpy was updated to work with the newer SPIRE API (in anticipation of SPIRE2) 3. Jug now ships with skills to help use it with AI coding assistants
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 25/03/2026
We had described collecting the data all the way back in 2023. Now, the analysis is finally out as a preprint www.biorxiv.org/content/10.6...
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 25/03/2026
Quarterly updates from the group! Focus is our latest work on urban soil microbiomes. They are far richer than might be expected. Our latest work using long-read metagenomics reveals massive hidden diversity and function in city soils bigdatabiology.substack.com/p/bdb-lab-ma...
bigdatabiology.substack.com
BDB-Lab March 2026 Updates
What lurks in urban soil microbiomes
112
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 23/03/2026
The original paper describing the resource is Duan et al., 2024 www.nature.com/articles/s41...
nature.com
A catalog of small proteins from the global microbiome - Nature Communications
Here, the authors built a non-redundant catalogue of nearly 1 billion putative small proteins from the global microbiome as a publicly-available resource, and highlight how some highly prevalent and e...
010
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 23/03/2026
Also, the full database is available online at gmsc.big-data-biology.org
gmsc.big-data-biology.org
GMSC
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 23/03/2026
Several bug fixes: corrected contig length check, duplicate sequence filtering, and a filename typo (filterd → filtered) Also, DIAMOND and MMseqs2 must now be pre-installed. GMSC-mapper no longer auto-downloads them. github.com/BigDataBiolo...
github.com
GMSC-mapper/ChangeLog at main · BigDataBiology/GMSC-mapper
Contribute to BigDataBiology/GMSC-mapper development by creating an account on GitHub.
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 23/03/2026
What's new: 1. database downloads are now much smaller (compressed indexes decompressed on the fly) 2. outputs are version-stamped for reproducibility 3. there's a new `gmsc-mapper citation` subcommand 4. better error messages
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 23/03/2026
We released GMSC-mapper v0.2.0! GMSC-mapper queries the Global Microbial smORFs Catalog to find and annotate small proteins from metagenomic data github.com/BigDataBiolo...
github.com
GitHub - BigDataBiology/GMSC-mapper
Contribute to BigDataBiology/GMSC-mapper development by creating an account on GitHub.
144
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 20/03/2026
If you use Jug in your research, please cite: Coelho, L.P., (2017). Jug: Software for Parallel Reproducible Computation in Python. Journal of Open Research Software. 5(1), p.30. doi.org/10.5334/jors...
doi.org
Jug: Software for Parallel Reproducible Computation in Python | Journal of Open Research Software
000
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 20/03/2026
Bugfixes: support for Python 3.14, fixes for dict_store on Python 3, and a fix for task describe. Full changelog: jug.readthedocs.io/en/latest/hi...
jug.readthedocs.io
History — Jug 2.5.0 documentation
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 20/03/2026
Also new: Project-local config files: Jug now discovers .jugrc files by walking up the directory tree to the git root. Keep project-specific configuration alongside your code Faster polars DataFrame saving. The file store now special-cases polars DataFrames for significantly faster serialization
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 20/03/2026
New in 2.5.0: AI assistant integration. Jug now ships a built-in skill for Claude Code and Codex. Install it with one command: jug install-skills --output ~/.claude/skills Then ask your AI assistant to write jugfiles, debug stuck tasks, or explain dependencies
100
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 20/03/2026
Jug 2.5.0 is out! Jug is a Python framework for parallel & reproducible computation. Write plain Python, run it across many processes or machines with no message-passing code. pip install jug --upgrade (Or use the conda-forge packages with conda/pixi)
123
Reposted by @bigdatabiology.bsky.social
Luis Pedro Coelho @luispedrocoelho.bsky.social · 17/02/2026
I will be speaking at BrisJAMS next week!
233
bigdatabiology.bsky.social @bigdatabiology.bsky.social · 21/01/2026
New Year updates from the group with a focus on Anil Pokhrel who is working on metagenomics for food security! bigdatabiology.substack.com/p/bdb-lab-ja...
bigdatabiology.substack.com
BDB-Lab January 2026 Updates
New Year!
032
Reposted by @bigdatabiology.bsky.social
Sean Gibbons 🦠💩 @gibbological.bsky.social · 19/12/2025
It's time for the 2025 Gibbons Lab Research Roundup! Yeehaw! 🤠 @isbscience.org In these dark times, let the joy of scientific discovery and trainee success be your balm ❤️‍🩹 🧵...
4185