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This Week in Genome Biology: Mar 20, 2019

A Chinese team takes a look at genome sequences for hundreds of wild or cultivated peach accessions to explore domestication-related changes in the plant's traits and search for potential markers to further improve Prunus persica. After sequencing 215 improved peach cultivars, 213 landraces, and 52 wild relative plants, the researchers identified nearly 5 million high-quality SNPs, including new and known quantitative trait loci. Their results pointed to expanded peach range, fruit size, and taste over prior phases of P. persica, while subsequent genome-wide association analyses led to potential markers for other agriculturally important traits. "Our variation dataset provides a valuable resource for future peach improvement using novel breeding technologies and strategies such as genomic selection, molecular design breeding, and introgression of novel alleles from landraces and wild relatives," the authors write.

University of Technology Sydney researchers present a Hi-C-based pipeline for assembling microbial genomes from metagenomic data. Their open-source pipeline, known as bin3, relies on a prior clustering algorithm as well as an unsupervised method for putting together metagenome-assembled genomes based on metagenomic shotgun sequence data and hierarchical interactions gleaned from Hi-C sequence data. The team applied bin3C for binning and retrieving microbial genomes in simulated and real human fecal microbe collections, demonstrating that the approach compared favorably to a published metagenome genome binning method.

Finally, researchers from Germany and the UK present a "partition-based graph abstraction" (PAGA) approach for mapping single-cell RNA sequence data in a manner that provides both cell clustering and distinct cell type insights. Indeed, the team argues that its PAGA maps "preserve the global topology of data, allow analyzing data at different resolutions, and result in much higher computational efficiency of the typical exploratory data analysis workflow." The authors applied PAGA to several simulated and real single-cell RNA-seq datasets, including data for thousands of hematopoietic cells, cells from adult Schmidtea mediterranea flatworms, developing zebrafish embryos, and more than a million neuronal cells.