Histone analysis articles from across Nature Portfolio

Here, the authors present droplet-based Paired-Tag, an improved method to concomitantly map histone modifications and gene expression at single-cell resolution. They functionally benchmark the method and demonstrate its advantages in mammalian cells and primary brain tissues.

Analysis of two histone marks in single cells reveals interplay between modifications.

Existing deep learning-based approaches for the prediction of gene expression by histone modifications (HMs) can only focus on narrow and linear genomic regions around promoters. Here, the authors address these problems by developing a transformer-based deep learning architecture named Chromoformer.

The authors describe procedures for genome-wide quantification of histone modifications and chromatin-associated proteins after replication (chromatin occupancy after replication-seq) and for profiling their relative occupancy in nascent sister chromatids (sister chromatids after replication-seq).

Transcription activator–like effectors (TALEs) fused to enzymes that change chromatin signatures interrogate the role of enhancers in gene expression.