@bigdatabiology.bsky.social

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...

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...

nature.com

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

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

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 🧵

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)

New version of macrel released (v1.6.0) The biggest change is internal, using a much better approach to saving and loading models (thus removing the dependency on particular versions of scikit-learn) A few other bugfixes were included github.com/BigDataBiolo...

GitHub - BigDataBiology/macrel: Predict AMPs in (meta)genomes and peptides

Predict AMPs in (meta)genomes and peptides. Contribute to BigDataBiology/macrel development by creating an account on GitHub.

github.com