Affiliation:
1. 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
2. University of Chinese Academy of Sciences Beijing 100101 China
3. Beijing Institutes of Life Science Chinese Academy of Sciences Beijing 100101 China
4. Center for Computational Biology Flatiron Institute New York 10010 USA
Abstract
AbstractCircular 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.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Subject
General Physics and Astronomy,General Engineering,Biochemistry, Genetics and Molecular Biology (miscellaneous),General Materials Science,General Chemical Engineering,Medicine (miscellaneous)