Detecting differentially expressed circular RNAs from multiple quantification methods using a generalized linear mixed model

Computational approaches and challenges in the analysis of circRNA data

Circular RNAs (circRNA) are a class of non-coding RNA, forming a single-stranded covalently closed loop structure generated via back-splicing. Advancements in sequencing methods and technologies in conjunction with algorithmic developments of bioinformatics tools have enabled researchers to characterise the origin and function of circRNAs, with practical applications as a biomarker of diseases becoming increasingly relevant. Computational methods developed for circRNA analysis are predicated on detecting the chimeric back-splice junction of circRNAs whilst mitigating false-positive sequencing artefacts. In this review, we discuss in detail the computational strategies developed for circRNA identification, highlighting a selection of tool strengths, weaknesses and assumptions. In addition to circRNA identification tools, we describe methods for characterising the role of circRNAs within the competing endogenous RNA (ceRNA) network, their interactions with RNA-binding proteins, and publicly available databases for rich circRNA annotation.

Systematic benchmarking of statistical methods to assess differential expression of circular RNAs

Circular RNAs (circRNAs) are covalently closed transcripts involved in critical regulatory axes, cancer pathways and disease mechanisms. CircRNA expression measured with RNA-seq has particular characteristics that might hamper the performance of standard biostatistical differential expression assessment methods (DEMs). We compared 38 DEM pipelines configured to fit circRNA expression data's statistical properties, including bulk RNA-seq, single-cell RNA-seq (scRNA-seq) and metagenomics DEMs. The DEMs performed poorly on data sets of typical size. Widely used DEMs, such as DESeq2, edgeR and Limma-Voom, gave scarce results, unreliable predictions or even contravened the expected behaviour with some parameter configurations. Limma-Voom achieved the most consistent performance throughout different benchmark data sets and, as well as SAMseq, reasonably balanced false discovery rate (FDR) and recall rate. Interestingly, a few scRNA-seq DEMs obtained results comparable with the best-performing bulk RNA-seq tools. Almost all DEMs' performance improved when increasing the number of replicates. CircRNA expression studies require careful design, choice of DEM and DEM configuration. This analysis can guide scientists in selecting the appropriate tools to investigate circRNA differential expression with RNA-seq experiments.

Systematic identification of aberrant non-coding RNAs and their mediated modules in rotator cuff tears

Background: Rotator cuff tears (RCT) is the most common cause of shoulder dysfunction, however, its molecular mechanisms remain unclear. Non-coding RNAs(ncRNAs), such as long ncRNA (lncRNA), microRNA (miRNA) and circular RNA (circRNA), are involved in a variety of diseases, but little is known about their roles in RCT. Therefore, the purpose of this study is to identify dysregulated ncRNAs and understand how they influence RCT.

Methods: We performed RNA sequencing and miRNA sequencing on five pairs of torn supraspinatus muscles and matched unharmed subscapularis muscles to identify RNAs dysregulated in RCT patients. To better comprehend the fundamental biological processes, we carried out enrichment analysis of these dysregulated mRNAs or the co-expressed genes of dysregulated ncRNAs. According to the competing endogenous RNA (ceRNA) theory, we finally established ceRNA networks to explore the relationship among dysregulated RNAs in RCT.

Results: A total of 151 mRNAs, 38 miRNAs, 20 lncRNAs and 90 circRNAs were differentially expressed between torn supraspinatus muscles and matched unharmed subscapularis muscles, respectively. We found that these dysregulated mRNAs, the target mRNAs of these dysregulated miRNAs or the co-expressed mRNAs of these dysregulated ncRNAs were enriched in muscle structure development, actin-mediated cell contraction and actin binding. Then we constructed and analyzed the ceRNA network and found that the largest module in the ceRNA network was associated with vasculature development. Based on the topological properties of the largest module, we identified several important ncRNAs including hsa_circ_0000722, hsa-miR-129-5p and hsa-miR-30c-5p, whose interacting mRNAs related to muscle diseases, fat and inflammation.

Conclusion: This study presented a systematic dissection of the expression profile of mRNAs and ncRNAs in RCT patients and revealed some important ncRNAs which may contribute to the development of RCT. Such results could provide new insights for further research on RCT.