Cell-type deconvolution methods are often needed to analyse spatial transcriptomic data to recover cell-type distributions. In this Review, the authors describe the process of cell-type deconvolution, contrast the tools available and highlight important considerations for which tool to use.

Barbadilla-Martínez et al. review recent progress in deep-learning-based sequence-to-expression models, which predict gene expression levels solely from DNA sequence. These models are providing new insights into the complex combinatorial logic underlying cis-regulatory control of gene expression.

Differences in X chromosome sequence content can trigger competitive interactions between clones that may alter organismal development and skew the representation of X-linked sequence variants in a cell-type-specific manner. The authors review this recently described phenomenon of X-linked competition and map out the implications for X-linked human diseases.

Genomic advances have enhanced our understanding of schizophrenia, bipolar disorder and major depressive disorder, revealing genetic architectures and risk mechanisms through large-scale genome-wide association studies and sequencing, which could address limitations in current diagnostic frameworks and treatment strategies in the future.