Publications by authors named "Tim Reska"
While the air microbiome and its diversity are essential for human health and ecosystem resilience, comprehensive air microbial diversity monitoring has remained rare, so that little is known about the air microbiome's composition, distribution, or functionality. Here we show that nanopore sequencing-based metagenomics can robustly assess the air microbiome in combination with active air sampling through liquid impingement and tailored computational analysis. We provide fast and portable laboratory and computational approaches for air microbiome profiling, which we leverage to robustly assess the taxonomic composition of the core air microbiome of a controlled greenhouse environment and of a natural outdoor environment.
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- Real-time genomics using nanopore sequencing can quickly predict antibiotic resistance in clinical settings, which is crucial for timely treatment.
- Despite some accuracy concerns compared to traditional methods, this approach can accurately identify low-abundance resistance factors often missed by conventional diagnostics.
- The study highlights that real-time genomic analysis can greatly enhance clinical decision-making by revealing hidden resistance profiles, ultimately improving patient outcomes.
Article Synopsis
- The ongoing environmental degradation is challenging our understanding of the interconnectedness of human and environmental health, a concept known as One Health.
- Real-time genomic analyses, particularly nanopore sequencing, can enhance our ability to assess ecosystem health by providing quick and detailed insights into various environmental and health-related issues.
- The implementation of these genomic technologies raises important considerations regarding equitable access, as well as practical, legal, and ethical challenges that must be addressed.
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.
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