A computational framework for analyzing single-cell chromatin data is presented in Nature Methods this week. Called Signac, the resource enables end-to-end analysis of chromatin data and includes functionality for a range of analysis tasks including identifying cells from background non-cell-containing barcodes, calling peaks, quantifying counts in genomic regions, and quality control filtering of cells. It also enables integration with single-cell gene expression data, interactive genome browser-style data visualization, finding differentially accessible peaks, transcription factor footprinting, and linking peaks to potential regulatory target genes, and more. Its developers at the New York Genome Center also designed Signac to work seamlessly with the Seurat package to enable the analysis of multimodal single-cell chromatin datasets including datasets that co-assay DNA accessibility with gene expression, protein abundance, and mitochondrial genotype.
By analyzing the genome of Aegilops tauschii, the diploid progenitor of the D subgenome of hexaploid bread wheat, a team led by scientists from the John Innes Centre have uncovered new gene targets that could be exploited to improve modern bread wheat. The creation of hexaploid bread wheat resulted in a crop better adapted to a wider range of environments and end uses, but at the cost of a pronounced genetic bottleneck. Only an estimated 25 percent of the genetic diversity of Ae. tauschii contributed to the initial gene flow into hexaploid wheat. To explore this diversity, the researchers sequenced 242 Ae. tauschii accessions and compared them to the wheat D subgenome. As reported in Nature Biotechnology, they find that a rare lineage of Ae. tauschii also contributed to the extant wheat genome, while association mapping revealed new gene candidates for disease and pest resistance and agromorphological traits underpinning abiotic stress tolerance and yield. "Exploiting the genomic diversity of the Ae. tauschii ancestral diploid genome permits rapid trait discovery and functional genetic validation in a hexaploid background amenable to breeding," the scientists write.
A haplotype-aware genotyping pipeline for producing state-of-the-art variant calling results with nanopore data is described in Nature Methods this week. Third-generation nanopore sequencing technologies produce long-read sequences that can map more confidently in the repetitive regions of the genome, overcoming the fundamental limitations of short-read data. However, current interpretation methods for their novel pore-based signal have unique error profiles, making accurate analysis challenging. To address this, collaborators from the University of California, Santa Cruz, Genomics Institute and Google built upon a previously developed universal small-variant calling method — called DeepVariant — to create PEPPER-Margin-DeepVariant, which outperforms other existing nanopore-based variant callers. The scientists show that nanopore-based single-nucleotide polymorphism identification with PEPPER-Margin-DeepVariant also outperforms short-read-based SNP identification with DeepVariant at whole-genome scale. They further extend PEPPER-Margin-DeepVariant to PacBio HiFi data, providing an efficient solution with superior performance over the current WhatsHap-DeepVariant standard, and demonstrate de novo assembly polishing methods that use nanopore and PacBio HiFi reads to produce diploid assemblies with high accuracy.