This Perspective establishes a comprehensive and practical framework to guide intrinsically disordered protein (IDP) ensemble determination, benchmarking and interpretation, as well as proposes a roadmap for IDP ensemble determination, uncertainty quantification and actionable benchmarking strategies.

A pangenome can reveal the spectrum of genome variation within a species. The toolbox for working with pangenomes is filling up.

SeeDB-Live is a tissue-clearing approach for live samples such as tissue slices or the in vivo brain. It improves image quality while having minimal effects on electrophysiological properties of neurons.

DECODE is a universal deconvolution framework for both cell types and cell states that can be applied to transcriptomic, proteomic and metabolomic data.

CREsted is an efficient and user-friendly toolbox for analysis, modeling and design of cell-type-specific enhancers across diverse species.

A model-agnostic empirical framework is proposed to measure the uncertainty associated with protein embeddings and to assess the biological relevance of these embeddings in order to improve model reliability and performance on downstream tasks.

ROCKET improves experimental structure elucidation by integrating implicit structural knowledge from OpenFold, a trainable reimplementation of AlphaFold2, with X-ray crystallographic and cryo-electron microscopy experiments.

In this study, long-read RNA sequencing achieves accurate single-nucleotide polymorphism calling, haplotype phasing and allele-specific expression analysis.

To study myelination in vitro, a hydrogel-based micropillar array is used to mimic the stiffness and diameter of axons, with variations in these properties allowing evaluation of their influence. The platform can also be used to assess the effects of putative remyelination drugs.

A co-distillation framework is used to iteratively adapt sequence-only protein language models for high-accuracy variant effect prediction, without the need for additional structural or genetic data. Individual protein language models therefore self-improve by distilling the most confident predictions from multiple models, achieving state-of-the-art performance across multiple variant effect prediction benchmarks.

Plenty of scientists give their instruments nicknames, chosen with affection and for good reasons.

A nascent group of researchers sees planetary health as an urgent reason to bridge the molecular and planetary scales. This is shaping their strategies, methods, collaborations and outlook about their fields.

Predictive virtual embryo systems that integrate single-cell and spatial data with artificial intelligence (AI) techniques offer a promising avenue for modeling mammalian embryogenesis across scales and could advance our fundamental understanding of development and congenital disease.

A non-comprehensive look back on the happenings in the spatial proteomics field since our Method of the Year 2024.