A high-quality reference genome of the cultivated oat (Avena sativa) is presented in this week's Nature, offering insights into this agriculturally important cereal grain. Oat is an allohexaploid believed to have been domesticated more than 3,000 years ago as a weed growing in wheat, emmer, and barley fields in Western Asia. Despite oat's popularity as a food, efforts to explore its evolutionary history and functional gene dynamics have been held back by a lack of a fully annotated reference genome. To address this knowledge gap, scientists from Lund University and Helmholtz Munich produced a reference genome of A. sativa, as well as close relatives of its diploid and tetraploid progenitors. This has revealed the mosaic structure of the oat genome and uncovered a breeding barrier associated with the genome's architecture, and more. The researchers also performed mapping-by-sequencing of an agronomic trait related to water-use efficiency. "This fully annotated hexaploid oat reference genome lays the foundation for advances in oat breeding and basic oat biology and for [an] ongoing pan-genome project," the authors write.
A study benchmarking spatial and single-cell transcriptomic integration methods for the prediction of the spatial distribution of transcripts for cell type deconvolution is published in Nature Methods this week. Spatial transcriptomic technologies have enabled the detection of RNA transcripts in tissue. Ones based on in situ hybridization and fluorescence microscopy allow for the detection of these transcripts with high resolution and accuracy, but they are limited in the total number of RNA transcripts that they can detect. Conversely, spatial transcriptomics approaches based on next-generation sequencing can capture expressed RNAs at the whole-transcriptome scale from spots in space, but each spot may contain multiple cells, limiting spatial resolution. To overcome such issues, groups have developed integration methods to combine spatial transcriptomic data with single-cell RNA-seq data to predict the spatial distribution of undetected transcripts and/or perform cell type deconvolution of spots in histological sections. However, there has been no independent effort to compare the performance of such integration methods. In the study, investigators from the University of Science and Technology of China benchmarked 16 integration methods using 45 paired datasets and 32 simulated datasets. They find that some were better for predicting the spatial distribution of RNA transcripts, while others were best for the cell type deconvolution of spots. "Our study helps researchers to choose appropriate tools and to optimize data-analysis workflows to accurately and efficiently integrate spatial transcriptomics data with scRNA-seq data," they write.