The Nubeam reference-free approach to analyze metagenomic sequencing reads

Divergence and gene flow history at two large chromosomal inversions underlying ecotype differentiation in the long-snouted seahorse

Chromosomal inversions can play an important role in divergence and reproductive isolation by building and maintaining distinct allelic combinations between evolutionary lineages. Alternatively, they can take the form of balanced polymorphisms that segregate within populations until one arrangement becomes fixed. Many questions remain about how inversion polymorphisms arise, how they are maintained over the long term, and ultimately, whether and how they contribute to speciation. The long-snouted seahorse (Hippocampus guttulatus) is genetically subdivided into geographic lineages and marine-lagoon ecotypes, with shared structural variation underlying lineage and ecotype divergence. Here, we aim to characterize structural variants and to reconstruct their history and suspected role in ecotype formation. We generated a near chromosome-level genome assembly and described genome-wide patterns of diversity and divergence through the analysis of 112 whole-genome sequences from Atlantic, Mediterranean, and Black Sea populations. By also analysing linked-read sequencing data, we found evidence for two chromosomal inversions that were several megabases in length and showed contrasting allele frequency patterns between lineages and ecotypes across the species range. We reveal that these inversions represent ancient intraspecific polymorphisms, one likely being maintained by divergent selection and the other by pseudo-overdominance. A possible selective coupling between the two inversions was further supported by the absence of specific haplotype combinations and a putative functional interaction between the two inversions in reproduction. Lastly, we detected gene flux eroding divergence between inverted alleles at varying levels for the two inversions, with a likely impact on their dynamics and contribution to divergence and speciation.

A haplotype resolved chromosome-scale assembly of North American wild apple Malus fusca and comparative genomics of the fire blight Mfu10 locus

The Pacific crabapple (Malus fusca) is a wild relative of the commercial apple (Malus × domestica). With a range extending from Alaska to Northern California, M. fusca is extremely hardy and disease resistant. The species represents an untapped genetic resource for the development of new apple cultivars with enhanced stress resistance. However, gene discovery and utilization of M. fusca have been hampered by the lack of genomic resources. Here, we present a high‐quality, haplotype‐resolved, chromosome‐scale genome assembly and annotation for M. fusca. The genome was assembled using high‐fidelity long‐reads and scaffolded using genetic maps and high‐throughput chromatin conformation capture sequencing, resulting in one of the most contiguous apple genomes to date. We annotated the genome using public transcriptomic data from the same species taken from diverse plant structures and developmental stages. Using this assembly, we explored haplotypic structural variation within the genome of M. fusca, identifying thousands of large variants. We further showed high sequence co‐linearity with other domesticated and wild Malus species. Finally, we resolve a known quantitative trait locus associated with resistance to fire blight (Erwinia amylovora). Insights gained from the assembly of a reference‐quality genome of this hardy wild apple relative will be invaluable as a tool to facilitate DNA‐informed introgression breeding.

Assessing single-cell transcriptomic variability through density-preserving data visualization

Nonlinear data visualization methods, such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), summarize the complex transcriptomic landscape of single cells in two dimensions or three dimensions, but they neglect the local density of data points in the original space, often resulting in misleading visualizations where densely populated subsets of cells are given more visual space than warranted by their transcriptional diversity in the dataset. Here we present den-SNE and densMAP, which are density-preserving visualization tools based on t-SNE and UMAP, respectively, and demonstrate their ability to accurately incorporate information about transcriptomic variability into the visual interpretation of single-cell RNA sequencing data. Applied to recently published datasets, our methods reveal significant changes in transcriptomic variability in a range of biological processes, including heterogeneity in transcriptomic variability of immune cells in blood and tumor, human immune cell specialization and the developmental trajectory of Caenorhabditis elegans. Our methods are readily applicable to visualizing high-dimensional data in other scientific domains.