Why AI-generated event posters all look alike — and how naming a design style and era, from Bauhaus to punk fanzine, gets far better results.

Microbial species disappearing from industrialized populations have evolved with humans over millennia and migrated worldwide with them—this may have consequences for human health.

Many animals benefit from microbial symbionts that aid digestion, supply essential nutrients, or provide defense. When this dependence becomes obligate, as in gutless marine oligochaetes that have los...

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Nature Biotechnology - Ten thousand interactions for the molecular biologist

I was recently asked to review a paper that piqued my curiosity. From the abstract it sounded as if it was a secondary analysis of a datas...

an ice breaker game

antiSMASH is widely used for biosynthetic gene cluster (BGC) detection and annotation, but its standard workflow is poorly suited to large metagenomic assemblies, where massive contig counts create severe runtime bottlenecks and complicate downstream result exploration. We present metaSMASH, a re-engineered fork of antiSMASH for metagenome-scale BGC analysis. metaSMASH preserves the original antiSMASH detection and annotation logic while introducing streaming, memory-bounded execution, record-level parallelisation, optional output filtering, and an interactive dashboard for large result sets. Across 25 benchmark metagenome datasets, metaSMASH reproduced identical BGC detection results while dramatically reducing computational cost. Relative to the default antiSMASH configuration, metaSMASH was a geometric-mean 38x faster. It also outperformed an ad hoc chunked antiSMASH workflow: in the default configuration it achieved a geometric-mean 2.9x speed-up and 1.7x lower peak memory, and with extended-analysis modules enabled it was 2.7x faster and used 3.1x less memory while completing all datasets, whereas the ad hoc workflow ran out of memory on the two largest assemblies. By substantially reducing the computational burden of large-scale metagenome analysis without sacrificing result equivalence, metaSMASH makes routine mining of assembled metagenomes more practical and provides a scalable foundation for natural product discovery from complex microbial communities. ### Competing Interest Statement The authors have declared no competing interest. German Center for Infection Research, TTU Novel Antibiotics 09.716 Volkswagen Foundation, 0072511-00

Biologically inspired neural networks (BINNs) embed pathway, ontology, or protein-interaction structure directly into neural networks, promising interpretable disease prediction where hidden nodes map...

For almost a decade now, a team of molecular evolutionists has produced a plethora of seemingly precise molecular clock estimates for divergence events ranging from the speciation of cats and dogs to ...

This comprehensive implementation guide accompanies the framework described in "Build expertise first: why PhD training must sequence AI use after foundational skill development" (Krishnan, 2026, DOI:...

Claude Science is a customizable app that integrates the tools and packages researchers most often use, produces auditable artifacts, and provides flexible access to computing resources.

This comprehensive implementation guide accompanies the framework described in "Build expertise first: why PhD training must sequence AI use after foundational skill development" (Krishnan, 2026, DOI:...

Pekel, Karcher, et al. present the largest gut microbiome meta-analysis of colorectal cancer to date, integrating 6,779 amplicon and shotgun sequencing profiles. They establish a robust microbiome sig...