RNA

Nanopore direct RNA sequencing is redefining how we detect RNA modifications, enabling long-read, transcriptome-wide views with minimal RNA handling. This perspective highlights both the power and current limitations of DRS in probing the epitranscriptome bit.ly/4dx1lec

How well can machine learning predict RNA secondary structure? This review surveys current approaches—from deep learning to hybrid models—highlighting both impressive gains and key limitations in generalizability and interpretability bit.ly/4spSj6s

What does it mean to study RNA at system scale? This Special Issue perspective lays out a roadmap—from quantitative transcriptomics to integrative modeling—defining how we measure, model, and ultimately manipulate RNA across biological systems bit.ly/3POaGo9
#RNAbiology

Excited to highlight the new Special Issue of RNA showcasing advances in system-level RNA biology, from epitranscriptomics and RNA damage to deep learning–driven structure prediction and beyond. A strong snapshot of where the field is heading bit.ly/4tEQL9I

Two amino acids in each homologous PUF repeat make molecular contacts that drive base recognition. These molecular contacts cannot generally be functionally interchanged between repeats, as shown bit.ly/3OrhTtq