SemiBin: metagenomics binning with self-supervised deep learning - BigDataBiology/SemiBin

I am very excited to share our preprint on NFixPlanet, a resource and planet wide analysis of diazotrophy. Releasing this preprint feels bittersweet, as it is my first manuscript since Peer’s passing...

Reported gut microbiome associations with diet and disease are frequently inconsistent between studies and interventions. Ruple et al. show that a high-fiber diet and time-restricted feeding induce si...

Cellular metabolism is widely understood as an integrated network of redox reactions, energy conservation, and biosynthetic pathways. Here we show that across diverse prokaryotic lineages, loss of red...

Eriksson et al. conduct a global marine microbiome diversity analysis and demonstrate how the surface ocean latitudinal diversity gradient is disproportionately shaped by a few species-rich taxa. Mask...

AbstractMotivation. Data-enabled studies of microbial ecology and evolution depend on high-quality descriptions of microbial habitats, based on curated and

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