Circall: fast and accurate methodology for discovery of circular RNAs from paired-end RNA-sequencing data

Dat Thanh Nguyen,Trung Nghia Vu,Ha-Nam Nguyen,Nir Ohad,Thi-Hau Nguyen,Quang Thinh Trac,Yudi Pawitan

doi:10.1186/s12859-021-04418-8

Dat Thanh Nguyen, Trung Nghia Vu + Show 5 more

Open Access

https://doi.org/10.1186/s12859-021-04418-8

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Abstract

BackgroundCircular RNA (circRNA) is an emerging class of RNA molecules attracting researchers due to its potential for serving as markers for diagnosis, prognosis, or therapeutic targets of cancer, cardiovascular, and autoimmune diseases. Current methods for detection of circRNA from RNA sequencing (RNA-seq) focus mostly on improving mapping quality of reads supporting the back-splicing junction (BSJ) of a circRNA to eliminate false positives (FPs). We show that mapping information alone often cannot predict if a BSJ-supporting read is derived from a true circRNA or not, thus increasing the rate of FP circRNAs.ResultsWe have developed Circall, a novel circRNA detection method from RNA-seq. Circall controls the FPs using a robust multidimensional local false discovery rate method based on the length and expression of circRNAs. It is computationally highly efficient by using a quasi-mapping algorithm for fast and accurate RNA read alignments. We applied Circall on two simulated datasets and three experimental datasets of human cell-lines. The results show that Circall achieves high sensitivity and precision in the simulated data. In the experimental datasets it performs well against current leading methods. Circall is also substantially faster than the other methods, particularly for large datasets.ConclusionsWith those better performances in the detection of circRNAs and in computational time, Circall facilitates the analyses of circRNAs in large numbers of samples. Circall is implemented in C++ and R, and available for use at https://www.meb.ki.se/sites/biostatwiki/circall and https://github.com/datngu/Circall.

Highlights

Circular RNA is an emerging class of RNA molecules attracting researchers due to its potential for serving as markers for diagnosis, prognosis, or thera‐ peutic targets of cancer, cardiovascular, and autoimmune diseases
The Circular RNA (circRNA) has been gaining attention in cancer research recently since researchers discovered its potential role as an ‘miRNA sponge’ that suppresses the activities of oncogenic miRNAs such as miR-21 and miR-221 [4]
Whole transcriptome sequencing by RNA sequencing (RNA-seq) technologies allows efficient discovery of circRNAs. It is mainly based on the detection of reads containing a back-splicing junction (BSJ), where the end of an exon joins to the start of itself or of another exon from the same gene

Summary

Introduction

Circular RNA (circRNA) is an emerging class of RNA molecules attracting researchers due to its potential for serving as markers for diagnosis, prognosis, or thera‐ peutic targets of cancer, cardiovascular, and autoimmune diseases. We show that mapping information alone often cannot predict if a BSJ-supporting read is derived from a true circRNA or not, increasing the rate of FP circRNAs. Circular RNA (circRNA) molecule identified in recent years is characterized by covalently closed-loop structures with neither a 5′ cap nor a 3′ poly (A) tail [1]. There are two main approaches to detect BSJ supporting-reads for circRNA candidates from RNA-seq data, including split-alignment-based and pseudoreference-based strategies [14]. The former splits a read into small fragments to map against a reference genome. The latter directly maps RNA-seq reads to the prebuilt BSJ pseudo sequences which are constructed based on an assumed genome annotation [16, 17]

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