Closing the circle : current state and perspectives of circular RNA databases

Circular RNAs (circRNAs) are covalently closed RNA molecules that have been linked to various diseases, including cancer. However, a precise function and working mechanism are lacking for the larger majority. Following many different experimental and computational approaches to identify circRNAs, multiple circRNA databases were developed as well. Unfortunately, there are several major issues with the current circRNA databases, which substantially hamper progression in the field. First, as the overlap in content is limited, a true reference set of circRNAs is lacking. This results from the low abundance and highly specific expression of circRNAs, and varying sequencing methods, data-analysis pipelines, and circRNA detection tools. A second major issue is the use of ambiguous nomenclature. Thus, redundant or even conflicting names for circRNAs across different databases contribute to the reproducibility crisis. Third, circRNA databases, in essence, rely on the position of the circRNA back-splice junction, whereas alternative splicing could result in circRNAs with different length and sequence. To uniquely identify a circRNA molecule, the full circular sequence is required. Fourth, circRNA databases annotate circRNAs' microRNA binding and protein-coding potential, but these annotations are generally based on presumed circRNA sequences. Finally, several databases are not regularly updated, contain incomplete data or suffer from connectivity issues. In this review, we present a comprehensive overview of the current circRNA databases and their content, features, and usability. In addition to discussing the current issues regarding circRNA databases, we come with important suggestions to streamline further research in this growing field

Large-scale benchmarking of circular RNA detection tools

Over the last ten years, many computational pipelines have been developed to identify circular RNA (circRNA) back-splice junctions (BSJs) in RNA sequencing datasets. These circRNA detection pipelines differ in their general approach (segmented-read versus candidate-based approach), alignment tool, and filtering steps. This leads to varying circRNA predictions, despite using the same RNA sequencing dataset as input. To date, a gold standard for circRNA detection in RNA sequencing data is missing. To tackle this issue, we set up a collaborative benchmarking study involving the developers of 15 circRNA detection tools with as primary goal the identification of the most accurate circRNA detection strategy. In the first phase, deep RNA sequencing datasets from RNase R treated, and untreated samples were shared with all collaborating tool developers. Comparing their circRNA prediction results in silico, substantial differences in the number of detected circRNAs were observed, ranging from less than 100 to more than 25,000 predicted circRNAs per sample. Furthermore, some tools have similar overall predictions, while others predict vastly different sets of circRNAs. In the second phase, the accuracy of the predictions of individual tools will be assessed. For this, RT-qPCR on RNase R treated versus untreated RNA is used as a gold standard. In addition, the exact sequence surrounding the BSJ position will be confirmed by sequencing the amplicons. To optimize the workflow and estimate the detectability of circRNAs with a low BSJ count, a first set of 96 circRNAs was selected and validated. Next, 1,500 circRNAs were selected from the extensive circRNA dataset (corresponding to 100 circRNAs per tool) to assess the performance of each tool. Lastly, a multiplex RT-qPCR and amplicon sequencing strategy will be performed to assess the false-negative rate of circRNA detection tools. At The Non-Coding Genome 2021 meeting, we will present data comparing each circRNA detection tool based on the estimated false-discovery rate and accuracy. In addition, we will provide valuable guidelines for accurate circRNA detection in RNA sequencing datasets

CIRCprimerXL : convenient and high-throughput PCR primer design for circular RNA quantification

Circular RNA (circRNA) is a class of endogenous non-coding RNA characterized by a back-splice junction (BSJ). In general, large-scale circRNA BSJ detection is performed based on RNA sequencing data, followed by the selection and validation of circRNAs of interest using RT-qPCR with circRNA-specific PCR primers. Such a primer pair is convergent and functional on the circRNA template but divergent and non-functional on the linear host gene. Although a few circRNA primer design pipelines have been published, none of them offer large-scale, easy-to-use circRNA primer design. Other limitations are that these tools generally do not take into account assay specificity, secondary structures, and SNPs in the primer annealing regions. Furthermore, these tools are limited to circRNA primer design for humans (no other organisms possible), and no wet-lab validation is demonstrated. Here, we present CIRCprimerXL, a circRNA RT-qPCR assay design pipeline based on the primer design framework primerXL. CIRCprimerXL takes a circRNA BSJ position as input, and designs BSJ-spanning primers using Primer3. The user can choose to use the unspliced or spliced circRNA sequence as template. Prior to primer design, sequence regions with secondary structures and common SNPs are flagged. Next, the primers are filtered based on predicted specificity and the absence of secondary structures of the amplicon to select a suitable primer pair. Our tool is both available as a user-friendly web tool and as a stand-alone pipeline based on Docker and Nextflow, allowing users to run the pipeline on a wide range of computer infrastructures. The CIRCprimerXL Nextflow pipeline can be used to design circRNA primers for any species by providing the appropriate reference genome. The CIRCprimerXL web tool supports circRNA primer design for human, mouse, rat, zebrafish, Xenopus tropicalis, and C. elegans. The design process can easily be scaled up for the qPCR assay design of tens of thousands of circRNAs within a couple of hours. We show how CIRCprimerXL has been successfully used to design qPCR assays for over 15,000 human circRNAs of which 20 were empirically validated. CIRCprimerXL software, documentation, and test data can be found at: https://github.com/OncoRNALab/CIRCprimerXL. CIRCprimerXL is also implemented as a webtool at: https://circprimerxl.cmgg.be

Validation of circular RNAs using RT‐qPCR after effective removal of linear RNAs by ribonuclease R

Circular RNAs (circRNAs) are a class of endogenous noncoding RNAs that have been shown to play a role in normal development, homeostasis, and dis- ease, including cancer. CircRNAs are formed through a process called back- splicing, which results in a covalently closed loop with a nonlinear back-spliced junction (BSJ). In general, circRNA BSJs are predicted in RNA sequencing data using one of numerous circRNA detection algorithms. Selected circRNAs are then typically validated using an orthogonal method such as reverse tran- scription quantitative PCR (RT-qPCR) with circRNA-specific primers. How- ever, linear transcripts originating from endogenous trans-splicing can lead to false-positive signals both in RNA sequencing and in RT-qPCR experiments. Therefore, it is essential to perform the RT-qPCR validation step only after linear RNAs have been degraded using an exonuclease such as ribonuclease R (RNase R). Several RNase R protocols are available for circRNA detec- tion using RNA sequencing or RT-qPCR. These protocols—which vary in en- zyme concentration, RNA input amount, incubation times, and cleanup steps— typically lack a detailed validated standard protocol and fail to provide a range of conditions that deliver accurate results. As such, some protocols use RNase R concentrations that are too high, resulting in partial degradation of the target circRNAs. Here, we describe an optimized workflow for circRNA validation, combining RNase R treatment and RT-qPCR. First, we outline the steps for cir- cRNA primer design and qPCR assay validation. Then, we describe RNase R treatment of total RNA and, importantly, a subsequent essential buffer cleanup step. Lastly, we outline the steps to perform the RT-qPCR and discuss the down- stream data analyses

CiLiQuant : quantification of RNA junction reads based on their circular or linear transcript origin

Distinguishing circular RNA reads from reads derived from the linear host transcript is a challenging task because of sequence overlap. We developed a computational approach, CiLiQuant, that determines the relative circular and linear abundance of transcripts and gene loci using back-splice and unambiguous forward-splice junction reads generated by existing mapping and circular RNA discovery tools

Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

: The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed using computational tools. Numerous such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools detected more than 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were validated using three orthogonal methods. Generally, tool-specific precision is high and similar (median of 98.8%, 96.3% and 95.5% for qPCR, RNase R and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant differentiators. Of note, precision values are lower when evaluating low-abundance circRNAs. We also show that the tools can be used complementarily to increase detection sensitivity. Finally, we offer recommendations for future circRNA detection and validation

The RNA Atlas expands the catalog of human non-coding RNAs

Existing compendia of non-coding RNA (ncRNA) are incomplete, in part because they are derived almost exclusively from small and polyadenylated RNAs. Here we present a more comprehensive atlas of the human transcriptome, which includes small and polyA RNA as well as total RNA from 300 human tissues and cell lines. We report thousands of previously uncharacterized RNAs, increasing the number of documented ncRNAs by approximately 8%. To infer functional regulation by known and newly characterized ncRNAs, we exploited pre-mRNA abundance estimates from total RNA sequencing, revealing 316 microRNAs and 3,310 long non-coding RNAs with multiple lines of evidence for roles in regulating protein-coding genes and pathways. Our study both refines and expands the current catalog of human ncRNAs and their regulatory interactions. All data, analyses and results are available for download and interrogation in the R2 web portal, serving as a basis for future exploration of RNA biology and function

Publisher Correction: The RNA Atlas expands the catalog of human non-coding RNAs (Nature Biotechnology, (2021), 39, 11, (1453-1465), 10.1038/s41587-021-00936-1)

In the version of this article initially published online, Pavel Sumazin’s affilation was given as Cancer Research Institute Ghent (CRIG), Ghent, Belgium. The correct affiliation is Texas Children’s Cancer Center, Baylor College of Medicine, Houston, TX, USA. The error has been corrected in the print, PDF and HTML versions of the article

The RNA Atlas expands the catalog of human non-coding RNAs

Existing compendia of non-coding RNA (ncRNA) are incomplete, in part because they are derived almost exclusively from small and polyadenylated RNAs. Here we present a more comprehensive atlas of the human transcriptome, which includes small and polyA RNA as well as total RNA from 300 human tissues and cell lines. We report thousands of previously uncharacterized RNAs, increasing the number of documented ncRNAs by approximately 8%. To infer functional regulation by known and newly characterized ncRNAs, we exploited pre-mRNA abundance estimates from total RNA sequencing, revealing 316 microRNAs and 3,310 long non-coding RNAs with multiple lines of evidence for roles in regulating protein-coding genes and pathways. Our study both refines and expands the current catalog of human ncRNAs and their regulatory interactions. All data, analyses and results are available for download and interrogation in the R2 web portal, serving as a basis for future exploration of RNA biology and function