R loops form when transcripts hybridize to homologous DNA on chromosomes, yielding a DNA: RNA hybrid and a displaced DNA single strand. R loops impact the genome of many organisms, regulating chromosome stability, gene expression, and DNA repair. Understanding the parameters dictating R-loop formation in vivo has been hampered by the limited quantitative and spatial resolution of current genomic strategies for mapping R loops. We report a novel whole-genome method, S1-DRIP-seq (S1 nuclease DNA: RNA immunoprecipitation with deep sequencing), for mapping hybrid-prone regions in budding yeast Saccharomyces cerevisiae. Using this methodology, we identified similar to 800 hybrid-prone regions covering 8% of the genome. Given the pervasive transcription of the yeast genome, this result suggests that R-loop formation is dictated by characteristics of the DNA, RNA, and/or chromatin. We successfully identified two features highly predictive of hybrid formation: high transcription and long homopolymeric dA:dT tracts. These accounted for >60% of the hybrid regions found in the genome. We demonstrated that these two factors play a causal role in hybrid formation by genetic manipulation. Thus, the hybrid map generated by S1-DRIP-seq led to the identification of the first global genomic features causal for R-loop formation in yeast.

Genome rearrangements often result from non-allelic homologous recombination (NAHR) between repetitive DNA elements dispersed throughout the genome. Here we systematically analyze NAHR between Ty retrotransposons using a genome-wide approach that exploits unique features of Saccharomyces cerevisiae purebred and Saccharomyces cerevisiae/Saccharomyces bayanus hybrid diploids. We find that DNA double-strand breaks (DSBs) induce NAHR-dependent rearrangements using Ty elements located 12 to 48 kilobases distal to the break site. This break-distal recombination (BDR) occurs frequently, even when allelic recombination can repair the break using the homolog. Robust BDR-dependent NAHR demonstrates that sequences very distal to DSBs can effectively compete with proximal sequences for repair of the break. In addition, our analysis of NAHR partner choice between Ty repeats shows that intrachromosomal Ty partners are preferred despite the abundance of potential interchromosomal Ty partners that share higher sequence identity. This competitive advantage of intrachromosomal Tys results from the relative efficiencies of different NAHR repair pathways. Finally, NAHR generates deleterious rearrangements more frequently when DSBs occur outside rather than within a Ty repeat. These findings yield insights into mechanisms of repeat-mediated genome rearrangements associated with evolution and cancer.

DNA double-strand breaks impact genome stability by triggering many of the large-scale genome rearrangements associated with evolution and cancer. One of the first steps in repairing this damage is 5'-> 3' resection beginning at the break site. Recently, tools have become available to study the consequences of not extensively resecting double-strand breaks. Here we examine the role of Sgs1- and Exo1-dependent resection on genome stability using a non-selective assay that we previously developed using diploid yeast. We find that Saccharomyces cerevisiae lacking Sgs1 and Exo1 retains a very efficient repair process that is highly mutagenic to genome structure. Specifically, 51% of cells lacking Sgs1 and Exo1 repair a double-strand break using repetitive sequences 12-48 kb distal from the initial break site, thereby generating a genome rearrangement. These Sgs1- and Exo1-independent rearrangements depend partially upon a Rad51-mediated homologous recombination pathway. Furthermore, without resection a robust cell cycle arrest is not activated, allowing a cell with a single double-strand break to divide before repair, potentially yielding multiple progeny each with a different rearrangement. This profusion of rearranged genomes suggests that cells tolerate any dangers associated with extensive resection to inhibit mutagenic pathways such as break-distal recombination. The activation of break-distal recipient repeats and amplification of broken chromosomes when resection is limited raise the possibility that genome regions that are difficult to resect may be hotspots for rearrangements. These results may also explain why mutations in resection machinery are associated with cancer.

Although animals have evolved multiple mechanisms to suppress transposons, "leaky'' mobilizations that cause mutations and diseases still occur. This suggests that transposons employ specific tactics to accomplish robust propagation. By directly tracking mobilization, we show that, during a short and specific time window of oogenesis, retrotransposons achieve massive amplification via a cell-type-specific targeting strategy. Retrotransposons rarely mobilize in undifferentiated germline stem cells. However, as oogenesis proceeds, they utilize supporting nurse cells-which are highly polyploid and eventually undergo apoptosis-as factories to massively manufacture invading products. Moreover, retrotransposons rarely integrate into nurse cells themselves but, instead, via microtubule-mediated transport, they preferentially target the DNA of the interconnected oocytes. Blocking microtubule-dependent intercellular transport from nurse cells significantly alleviates damage to the oocyte genome. Our data reveal that parasitic genomic elements can efficiently hijack a host developmental process to propagate robustly, thereby driving evolutionary change and causing disease.

The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) was developed to address a widespread community need for a unified computing environment for genomics data storage, management, and analysis. In this perspective, we present AnVIL, describe its ecosystem and interoperability with other platforms, and highlight how this platform and associated initiatives contribute to improved genomic data sharing efforts. The AnVIL is a federated cloud platform designed to manage and store genomics and related data, enable population-scale analysis, and facilitate collaboration through the sharing of data, code, and analysis results. By inverting the traditional model of data sharing, the AnVIL eliminates the need for data movement while also adding security measures for active threat detection and monitoring and provides scalable, shared computing resources for any researcher. We describe the core data management and analysis components of the AnVIL, which currently consists of Terra, Gen3, Galaxy, RStudio/Bioconductor, Dockstore, and Jupyter, and describe several flagship genomics datasets available within the AnVIL. We continue to extend and innovate the AnVIL ecosystem by implementing new capabilities, including mechanisms for interoperability and responsible data sharing, while streamlining access management. The AnVIL opens many new opportunities for analysis, collaboration, and data sharing that are needed to drive research and to make discoveries through the joint analysis of hundreds of thousands to millions of genomes along with associated clinical and molecular data types.

Over the past 20 years, the explosion of genomic data collection and the cloud computing revolution have made computational and data science research accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support these programs in local education and research at underserved institutions (UIs). These include community colleges, historically Black colleges and universities, Hispanic-serving institutions, and tribal colleges and universities that support ethnically, racially, and socioeconomically underrepresented students in the United States. We have formed the Genomic Data Science Community Network to support students, faculty, and their networks to identify opportunities and broaden access to genomic data science. These opportunities include expanding access to infrastructure and data, providing UI faculty development opportunities, strengthening collaborations among faculty, recognizing UI teaching and research excellence, fostering student awareness, developing modular and open-source resources, expanding course-based undergraduate research experiences (CUREs), building curriculum, supporting student professional development and research, and removing financial barriers through funding programs and collaborator support.