Evaluation of CircRNA Sequence Assembly Methods Using Long Reads.

Jingjing Zhang,Yanjie Wei,Yi Pan,Yin Peng,Weiguo Liu,Md Tofazzal Hossain

doi:10.3389/fgene.2022.816825

Abstract

The functional study on circRNAs has been increasing in the past decade due to its important roles in micro RNA sponge, protein coding, the initiation, and progression of diseases. The study of circRNA functions depends on the full-length sequences of circRNA, and current sequence assembly methods based on short reads face challenges due to the existence of linear transcript. Long reads produced by long-read sequencing techniques such as Nanopore technology can cover full-length sequences of circRNA and therefore can be used to evaluate the correctness and completeness of circRNA full sequences assembled from short reads of the same sample. Using long reads of the same samples, one from human and the other from mouse, we have comprehensively evaluated the performance of several well-known circRNA sequence assembly algorithms based on short reads, including circseq_cup, CIRI_full, and CircAST. Based on the F1 score, the performance of CIRI-full was better in human datasets, whereas in mouse datasets CircAST was better. In general, each algorithm was developed to handle special situations or circumstances. Our results indicated that no single assembly algorithm generated better performance in all cases. Therefore, these assembly algorithms should be used together for reliable full-length circRNA sequence reconstruction. After analyzing the results, we have introduced a screening protocol that selects out exonic circRNAs with full-length sequences consisting of all exons between back splice sites as the final result. After screening, CIRI-full showed better performance for both human and mouse datasets. The average F1 score of CIRI-full over four circRNA identification algorithms increased from 0.4788 to 0.5069 in human datasets, and it increased from 0.2995 to 0.4223 in mouse datasets.

Highlights

Recently has circular RNA appeared as a hot research topic since it was first discovered in the 1970s (Sanger et al, 1976; Arnberg et al, 1980; Kos et al, 1986)
A total of 25,634 distinct circRNAs candidates were identified by CIRI, 23,763 (92.70%) of which were generated from exons, and the remaining were generated from introns or intergenic regions
For circRNA_finder and find_circ, 25,925 and 29,828 circRNAs were identified, respectively. Most of these circRNAs were derived from exons; only less than 10% were derived from introns and Evaluation of circRNA Assembly Tools intergenic regions

Summary

Introduction

Recently has circular RNA (circRNA) appeared as a hot research topic since it was first discovered in the 1970s (Sanger et al, 1976; Arnberg et al, 1980; Kos et al, 1986). Different from linear RNAs, the special covalent circular structure of circRNA is formed by back splicing (Jeck et al, 2013). Identifying the back splice sites is the most important factor for circRNA identification from the sequencing reads (Kristensen et al, 2019). Various identification algorithms were developed, such as find_circ (Memczak et al, 2013), KNIFE (Szabo et al, 2015), Evaluation of circRNA Assembly Tools. The gene muscleblind (MBL) of Drosophila can encode MBL protein as a transcript factor, and MBL regulates the dynamic balance of circular transcript (circRNA circMbl) and linear transcript (Ashwal-Fluss et al, 2014). Another circRNA, circE7, derived from oncogenic human papilloma viruses (HPVs), is found to produce E7 oncoprotein with modified N6methyladenosine (m6A) (Zhao et al, 2019)

Methods

Results

Conclusion