Chen, LiWang, FengBruggeman, Emily C.Li, ChaoYao, Bing2022-08-242022-08-242020-01-15Chen L, Wang F, Bruggeman EC, Li C, Yao B. circMeta: a unified computational framework for genomic feature annotation and differential expression analysis of circular RNAs. Bioinformatics. 2020;36(2):539-545. doi:10.1093/bioinformatics/btz606https://hdl.handle.net/1805/29866Motivation: Circular RNAs (circRNAs), a class of non-coding RNAs generated from non-canonical back-splicing events, have emerged to play key roles in many biological processes. Though numerous tools have been developed to detect circRNAs from rRNA-depleted RNA-seq data based on back-splicing junction-spanning reads, computational tools to identify critical genomic features regulating circRNA biogenesis are still lacking. In addition, rigorous statistical methods to perform differential expression (DE) analysis of circRNAs remain under-developed. Results: We present circMeta, a unified computational framework for circRNA analyses. circMeta has three primary functional modules: (i) a pipeline for comprehensive genomic feature annotation related to circRNA biogenesis, including length of introns flanking circularized exons, repetitive elements such as Alu elements and SINEs, competition score for forming circulation and RNA editing in back-splicing flanking introns; (ii) a two-stage DE approach of circRNAs based on circular junction reads to quantitatively compare circRNA levels and (iii) a Bayesian hierarchical model for DE analysis of circRNAs based on the ratio of circular reads to linear reads in back-splicing sites to study spatial and temporal regulation of circRNA production. Both proposed DE methods without and with considering host genes outperform existing methods by obtaining better control of false discovery rate and comparable statistical power. Moreover, the identified DE circRNAs by the proposed two-stage DE approach display potential biological functions in Gene Ontology and circRNA-miRNA-mRNA networks that are not able to be detected using existing mRNA DE methods. Furthermore, top DE circRNAs have been further validated by RT-qPCR using divergent primers spanning back-splicing junctions.en-USPublisher PolicyBayes theoremComputational biologyRNA splicingArticle