National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
University of Chinese Academy of Sciences, Beijing, 100101, China.
Adv Sci (Weinh). 2024 Apr;11(14):e2308115. doi: 10.1002/advs.202308115. Epub 2024 Feb 2.
Circular RNAs (circRNAs) are a crucial yet relatively unexplored class of transcripts known for their tissue- and cell-type-specific expression patterns. Despite the advances in single-cell and spatial transcriptomics, these technologies face difficulties in effectively profiling circRNAs due to inherent limitations in circRNA sequencing efficiency. To address this gap, a deep learning model, CIRI-deep, is presented for comprehensive prediction of circRNA regulation on diverse types of RNA-seq data. CIRI-deep is trained on an extensive dataset of 25 million high-confidence circRNA regulation events and achieved high performances on both test and leave-out data, ensuring its accuracy in inferring differential events from RNA-seq data. It is demonstrated that CIRI-deep and its adapted version enable various circRNA analyses, including cluster- or region-specific circRNA detection, BSJ ratio map visualization, and trans and cis feature importance evaluation. Collectively, CIRI-deep's adaptability extends to all major types of RNA-seq datasets including single-cell and spatial transcriptomic data, which will undoubtedly broaden the horizons of circRNA research.
环状 RNA（circRNAs）是一类重要但研究相对较少的转录本，其表达模式具有组织和细胞类型特异性。尽管单细胞和空间转录组学技术取得了进展，但由于环状 RNA 测序效率的固有限制，这些技术在有效分析环状 RNA 方面仍面临困难。为了解决这一差距，提出了一种深度学习模型 CIRI-deep，用于全面预测各种 RNA-seq 数据中环状 RNA 的调控作用。CIRI-deep 是在一个包含 2500 万个高可信度环状 RNA 调控事件的大型数据集上进行训练的，在测试和外推数据上均取得了优异的性能，确保了其从 RNA-seq 数据中推断差异事件的准确性。结果表明，CIRI-deep 及其改编版本能够进行各种环状 RNA 分析，包括簇或区域特异性环状 RNA 检测、BSJ 比率图谱可视化以及顺式和反式特征重要性评估。总的来说，CIRI-deep 的适应性扩展到所有主要类型的 RNA-seq 数据集，包括单细胞和空间转录组学数据，这无疑将拓宽环状 RNA 研究的视野。
