Real-time genomics for One Health.

The ongoing degradation of natural systems and other environmental changes has put our society at a crossroad with respect to our future relationship with our planet. While the concept of One Health describes how human health is inextricably linked with environmental health, many of these complex interdependencies are still not well-understood. Here, we describe how the advent of real-time genomic analyses can benefit One Health and how it can enable timely, in-depth ecosystem health assessments. We introduce nanopore sequencing as the only disruptive technology that currently allows for real-time genomic analyses and that is already being used worldwide to improve the accessibility and versatility of genomic sequencing. We showcase real-time genomic studies on zoonotic disease, food security, environmental microbiome, emerging pathogens, and their antimicrobial resistances, and on environmental health itself - from genomic resource creation for wildlife conservation to the monitoring of biodiversity, invasive species, and wildlife trafficking. We stress why equitable access to real-time genomics in the context of One Health will be paramount and discuss related practical, legal, and ethical limitations.

Detection of hidden antibiotic resistance through real-time genomics.

Real-time genomics through nanopore sequencing holds the promise of fast antibiotic resistance prediction directly in the clinical setting. However, concerns about the accuracy of genomics-based resistance predictions persist, particularly when compared to traditional, clinically established diagnostic methods. Here, we leverage the case of a multi-drug resistant Klebsiella pneumoniae infection to demonstrate how real-time genomics can enhance the accuracy of antibiotic resistance profiling in complex infection scenarios. Our results show that unlike established diagnostics, nanopore sequencing data analysis can accurately detect low-abundance plasmid-mediated resistance, which often remains undetected by conventional methods. This capability has direct implications for clinical practice, where such "hidden" resistance profiles can critically influence treatment decisions. Consequently, the rapid, in situ application of real-time genomics holds significant promise for improving clinical decision-making and patient outcomes.

Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data.

The accuracy of methods for assembling transcripts from short-read RNA sequencing data is limited by the lack of long-range information. Here we introduce Ladder-seq, an approach that separates transcripts according to their lengths before sequencing and uses the additional information to improve the quantification and assembly of transcripts. Using simulated data, we show that a kallisto algorithm extended to process Ladder-seq data quantifies transcripts of complex genes with substantially higher accuracy than conventional kallisto. For reference-based assembly, a tailored scheme based on the StringTie2 algorithm reconstructs a single transcript with 30.8% higher precision than its conventional counterpart and is more than 30% more sensitive for complex genes. For de novo assembly, a similar scheme based on the Trinity algorithm correctly assembles 78% more transcripts than conventional Trinity while improving precision by 78%. In experimental data, Ladder-seq reveals 40% more genes harboring isoform switches compared to conventional RNA sequencing and unveils widespread changes in isoform usage upon m6A depletion by Mettl14 knockout.