Resolution of structural variation in diverse mouse genomes reveals chromatin remodeling due to transposable elements.

Diverse inbred mouse strains are important biomedical research models, yet genome characterization of many strains is fundamentally lacking in comparison with humans. In particular, catalogs of structural variants (SVs) (variants ≥ 50 bp) are incomplete, limiting the discovery of causative alleles for phenotypic variation. Here, we resolve genome-wide SVs in 20 genetically distinct inbred mice with long-read sequencing. We report 413,758 site-specific SVs affecting 13% (356 Mbp) of the mouse reference assembly, including 510 previously unannotated coding variants. We substantially improve the Mus musculus transposable element (TE) callset, and we find that TEs comprise 39% of SVs and account for 75% of altered bases. We further utilize this callset to investigate how TE heterogeneity affects mouse embryonic stem cells and find multiple TE classes that influence chromatin accessibility. Our work provides a comprehensive analysis of SVs found in diverse mouse genomes and illustrates the role of TEs in epigenetic differences.

Transposable element-mediated rearrangements are prevalent in human genomes.

Transposable elements constitute about half of human genomes, and their role in generating human variation through retrotransposition is broadly studied and appreciated. Structural variants mediated by transposons, which we call transposable element-mediated rearrangements (TEMRs), are less well studied, and the mechanisms leading to their formation as well as their broader impact on human diversity are poorly understood. Here, we identify 493 unique TEMRs across the genomes of three individuals. While homology directed repair is the dominant driver of TEMRs, our sequence-resolved TEMR resource allows us to identify complex inversion breakpoints, triplications or other high copy number polymorphisms, and additional complexities. TEMRs are enriched in genic loci and can create potentially important risk alleles such as a deletion in TRIM65, a known cancer biomarker and therapeutic target. These findings expand our understanding of this important class of structural variation, the mechanisms responsible for their formation, and establish them as an important driver of human diversity.

SVision: a deep learning approach to resolve complex structural variants.

Complex structural variants (CSVs) encompass multiple breakpoints and are often missed or misinterpreted. We developed SVision, a deep-learning-based multi-object-recognition framework, to automatically detect and haracterize CSVs from long-read sequencing data. SVision outperforms current callers at identifying the internal structure of complex events and has revealed 80 high-quality CSVs with 25 distinct structures from an individual genome. SVision directly detects CSVs without matching known structures, allowing sensitive detection of both common and previously uncharacterized complex rearrangements.