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Reconstruction of full-length circular RNAs enables isoform-level quantification.

Yi ZhengPeifeng JiShuai ChenLingling HouFangqing Zhao
Published in: Genome medicine (2019)
Currently, circRNA studies are shifting from the identification of circular transcripts to understanding their biological functions. However, such endeavors have been limited by large-scale determination of their full-length sequences and also by the inability of accurate quantification at the isoform level. Here, we propose a new feature, reverse overlap (RO), for circRNA detection, which outperforms back-splice junction (BSJ)-based methods in identifying low-abundance circRNAs. By combining RO and BSJ features, we present a novel approach for effective reconstruction of full-length circRNAs and isoform-level quantification from the transcriptome. We systematically compared the difference between the BSJ-level and isoform-level differential expression analyses using human liver tumor and normal tissues and highlight the necessity of deepening circRNA studies to the isoform-level resolution. The CIRI-full software can be accessed at https://sourceforge.net/projects/ciri .
Keyphrases
  • machine learning
  • dna methylation
  • deep learning
  • single molecule
  • rna seq
  • sensitive detection
  • case control
  • molecularly imprinted
  • data analysis
  • simultaneous determination