Computational approaches for circRNAs prediction and in silico characterization

Abstract

Circular RNAs (circRNAs) are single-stranded and covalently closed non-coding RNA molecules originated from RNA splicing. Their functions include regulatory potential over other RNA species, such as microRNAs, messenger RNAs and RNA binding proteins. For circRNA identification, several algorithms are available and can be classified in two major types: pseudo-reference-based and split-alignment-based approaches. In general, the data generated from circRNA transcriptome initiatives is deposited on public specific databases, which provide a large amount of information on different species and functional annotations. In this review, we describe the main computational resources for the identification and characterization of circRNAs, covering the algorithms and predictive tools to evaluate its potential role in a particular transcriptomics project, including the public repositories containing relevant data and information for circRNAs, recapitulating their characteristics, reliability and amount of data reported.

Keywords: bioinformatics; circRNA; circRNA regulation; circRNA-miRNA prediction.

Figure 1

General back-splicing mechanism of circRNAs. (A) Transcription mediated by RNA polymerase II gives rise to a pre-mRNA that is subjected to canonical or alternative splicing, becoming mature mRNA accessible to protein translation and detectable as Forward splice junction reads (FSJ reads). (B) Intron-free mature mRNA have special well-defined characteristics, including defined 5′ - and 3′ sites, capping and poly(A) tail. (C) In circRNAs, alternative splicing is replaced by back-splicing reaction generating a diversity of circRNAs through different mechanisms of circularization such as: Intron pairing-driven circularization facilitated by proximity of donor (5′) and acceptor (3′) sites by inverse complementary sequences near to BSJ splice site, RNA-binding-protein-driven circularization, mediated by proteins that forms dimers allowing to circularize RNA sequence, and lariat-driven circularization facilitated by specific motifs (GU-AG; GC rich zone and others) on flanking sequences. In this manner, different factors bring about a diversity of circRNAs.

Figure 2

General strategies for circRNA detection. In the split-alignment-based approach, circRNA sequencing reads are split and then reverse aligned to reference genome. In the pseudo reference-based approach, pseudo-sequences are constructed from annotated exons and sequence reads are aligned against the pseudo-sequences.

Figure 3

Main circRNA tools in the circRNA research along the last decade. (A) Representation of the number of publications on circRNA investigation containing the term ‘circRNA’ in the title or abstract searched in PubMed by year. (B) Summary of circRNA identification tools, multi-tools software and sequence reconstruction tools; (C) circRNA publicly available databases; and (D) other useful tools for characterization, visualization and functional exploration of circRNAs developed in the last decade.