Deep learning-enhanced R-loop prediction provides mechanistic implications for repeat expansion diseases.

R-loops play diverse functional roles, but controversial genomic localization of R-loops have emerged from experimental approaches, posing significant challenges for R-loop research. The development and application of an accurate computational tool for studying human R-loops remains an unmet need. Here, we introduce DeepER, a deep learning-enhanced R-loop prediction tool. DeepER showcases outstanding performance compared to existing tools, facilitating accurate genome-wide annotation of R-loops and a deeper understanding of the position- and context-dependent effects of nucleotide composition on R-loop formation. DeepER also unveils a strong association between certain tandem repeats and R-loop formation, opening a new avenue for understanding the mechanisms underlying some repeat expansion diseases. To facilitate broader utilization, we have developed a user-friendly web server as an integral component of R-loopBase. We anticipate that DeepER will find extensive applications in the field of R-loop research.

RNase H1 facilitates recombinase recruitment by degrading DNA-RNA hybrids during meiosis.

DNA-RNA hybrids play various roles in many physiological progresses, but how this chromatin structure is dynamically regulated during spermatogenesis remains largely unknown. Here, we show that germ cell-specific knockout of Rnaseh1, a specialized enzyme that degrades the RNA within DNA-RNA hybrids, impairs spermatogenesis and causes male infertility. Notably, Rnaseh1 knockout results in incomplete DNA repair and meiotic prophase I arrest. These defects arise from the altered RAD51 and DMC1 recruitment in zygotene spermatocytes. Furthermore, single-molecule experiments show that RNase H1 promotes recombinase recruitment to DNA by degrading RNA within DNA-RNA hybrids and allows nucleoprotein filaments formation. Overall, we uncover a function of RNase H1 in meiotic recombination, during which it processes DNA-RNA hybrids and facilitates recombinase recruitment.

R-loopBase: a knowledgebase for genome-wide R-loop formation and regulation.

R-loops play versatile roles in many physiological and pathological processes, and are of great interest to scientists in multiple fields. However, controversy about their genomic localization and incomplete understanding of their regulatory network raise great challenges for R-loop research. Here, we present R-loopBase (https://rloopbase.nju.edu.cn) to tackle these pressing issues by systematic integration of genomics and literature data. First, based on 107 high-quality genome-wide R-loop mapping datasets generated by 11 different technologies, we present a reference set of human R-loop zones for high-confidence R-loop localization, and spot conservative genomic features associated with R-loop formation. Second, through literature mining and multi-omics analyses, we curate the most comprehensive list of R-loop regulatory proteins and their targeted R-loops in multiple species to date. These efforts help reveal a global regulatory network of R-loop dynamics and its potential links to the development of cancers and neurological diseases. Finally, we integrate billions of functional genomic annotations, and develop interactive interfaces to search, visualize, download and analyze R-loops and R-loop regulators in a well-annotated genomic context. R-loopBase allows all users, including those with little bioinformatics background to utilize these data for their own research. We anticipate R-loopBase will become a one-stop resource for the R-loop community.