Circular RNAs (circRNAs) own unique capabilities to communicate with nucleic acids and ribonucleoproteins and are emerging as indispensable compositions of the regulatory messages encoded in the genome. Due to lack of 3 termini, circRNAs are more resistant to degradation by exonuclease RNase R and possess greater stability than linear RNAs. Moreover, circRNAs can act as microRNA (miRNA) sponge and affect messenger RNA (mRNA) splicing and transcription. By virtue of their great stability and elaborate regulatory mechanisms of gene expression, circRNAs play important roles in certain physiological activities. The development, homeostasis and stress response of the central nervous system (CNS) depend upon precise temporal and spatial regulation of gene networks. Moreover, emerging evidence has revealed that circRNAs are spatiotemporally regulated and dynamically expressed during brain development; therefore, they can exert significant influences on CNS development and diseases. In this review, we highlight the biogenesis of circRNAs and their central roles in regulation of CNS development and diseases.

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.