Bioinformatics articles from across Nature Portfolio

Existing 3D Spatial Transcriptomics reconstruction approaches often overlook spatial information or experiment-induced distortions. Here, authors propose ST-GEARS to bridge the gap between in vivo cell locations and in vitro analysis, accurately recovering spatial profiles.

Single-cell analysis of the transcriptome, chromatin accessibility and methylome of adult neural stem cells and astrocytes demonstrates that stemness is driven by methylation profiles distinct from those of astrocytes.

Transcriptome mining, phylogenetic analysis, bioimaging and serology experiments reveal a widespread diverse virome present in ~70% of parasitic nematodes, such as Brugia malayi and Onchocerca volvulus, where some viruses are abundant in the reproductive tract and are associated with seropositivity in vertebrate hosts.

Single-cell RNA-seq experiments often aim to identify cell types that have significant transcriptional differences between two or more biological conditions. Here, authors introduce scDist, a statistical approach to identify perturbed cell types that controls for common confounders in these data.

To overcome key challenges in scaffolding chromosome-level haplotypes with Hi-C data, we developed HapHiC, a Hi-C scaffolding tool that exhibits superior performance in handling haplotype-resolved assemblies without the need for reference genomes. We used HapHiC to construct the complex genome of triploid Miscanthus × giganteus.

Armed with the right data, advances in machine learning could help oncologists to home in quickly on the best treatment strategies for their patients.