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      A comprehensive overview and evaluation of circular RNA detection tools

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          Abstract

          Circular RNA (circRNA) is mainly generated by the splice donor of a downstream exon joining to an upstream splice acceptor, a phenomenon known as backsplicing. It has been reported that circRNA can function as microRNA (miRNA) sponges, transcriptional regulators, or potential biomarkers. The availability of massive non-polyadenylated transcriptomes data has facilitated the genome-wide identification of thousands of circRNAs. Several circRNA detection tools or pipelines have recently been developed, and it is essential to provide useful guidelines on these pipelines for users, including a comprehensive and unbiased comparison. Here, we provide an improved and easy-to-use circRNA read simulator that can produce mimicking backsplicing reads supporting circRNAs deposited in CircBase. Moreover, we compared the performance of 11 circRNA detection tools on both simulated and real datasets. We assessed their performance regarding metrics such as precision, sensitivity, F1 score, and Area under Curve. It is concluded that no single method dominated on all of these metrics. Among all of the state-of-the-art tools, CIRI, CIRCexplorer, and KNIFE, which achieved better balanced performance between their precision and sensitivity, compared favorably to the other methods.

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          Most cited references15

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          ART: a next-generation sequencing read simulator.

          ART is a set of simulation tools that generate synthetic next-generation sequencing reads. This functionality is essential for testing and benchmarking tools for next-generation sequencing data analysis including read alignment, de novo assembly and genetic variation discovery. ART generates simulated sequencing reads by emulating the sequencing process with built-in, technology-specific read error models and base quality value profiles parameterized empirically in large sequencing datasets. We currently support all three major commercial next-generation sequencing platforms: Roche's 454, Illumina's Solexa and Applied Biosystems' SOLiD. ART also allows the flexibility to use customized read error model parameters and quality profiles. Both source and binary software packages are available at http://www.niehs.nih.gov/research/resources/software/art.
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            CIRI: an efficient and unbiased algorithm for de novo circular RNA identification

            Recent studies reveal that circular RNAs (circRNAs) are a novel class of abundant, stable and ubiquitous noncoding RNA molecules in animals. Comprehensive detection of circRNAs from high-throughput transcriptome data is an initial and crucial step to study their biogenesis and function. Here, we present a novel chiastic clipping signal-based algorithm, CIRI, to unbiasedly and accurately detect circRNAs from transcriptome data by employing multiple filtration strategies. By applying CIRI to ENCODE RNA-seq data, we for the first time identify and experimentally validate the prevalence of intronic/intergenic circRNAs as well as fragments specific to them in the human transcriptome. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0571-3) contains supplementary material, which is available to authorized users.
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              Genome-wide analysis of drosophila circular RNAs reveals their structural and sequence properties and age-dependent neural accumulation.

              Circularization was recently recognized to broadly expand transcriptome complexity. Here, we exploit massive Drosophila total RNA-sequencing data, >5 billion paired-end reads from >100 libraries covering diverse developmental stages, tissues, and cultured cells, to rigorously annotate >2,500 fruit fly circular RNAs. These mostly derive from back-splicing of protein-coding genes and lack poly(A) tails, and the circularization of hundreds of genes is conserved across multiple Drosophila species. We elucidate structural and sequence properties of Drosophila circular RNAs, which exhibit commonalities and distinctions from mammalian circles. Notably, Drosophila circular RNAs harbor >1,000 well-conserved canonical miRNA seed matches, especially within coding regions, and coding conserved miRNA sites reside preferentially within circularized exons. Finally, we analyze the developmental and tissue specificity of circular RNAs and note their preferred derivation from neural genes and enhanced accumulation in neural tissues. Interestingly, circular isoforms increase substantially relative to linear isoforms during CNS aging and constitute an aging biomarker. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                8 June 2017
                June 2017
                : 13
                : 6
                : e1005420
                Affiliations
                [1 ]School of Information Science and Engineering, Xiamen University, Xiamen, China
                [2 ]School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
                [3 ]School of Computer Science and Technology, Tianjin University, Tianjin, China
                University of Canterbury, NEW ZEALAND
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-7208-3488
                http://orcid.org/0000-0001-6406-1142
                Article
                PCOMPBIOL-D-16-01219
                10.1371/journal.pcbi.1005420
                5466358
                28594838
                31a05b5e-81ae-456b-8298-d624ef13f419
                © 2017 Zeng et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Page count
                Figures: 6, Tables: 5, Pages: 21
                Funding
                This work has been supported by the Natural Science Foundation of China (No. 61370010, 61571163, 61532014). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
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                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Research and analysis methods
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Biology and life sciences
                Biochemistry
                Proteins
                DNA-binding proteins
                Nucleases
                Ribonucleases
                Biology and Life Sciences
                Biochemistry
                Enzymology
                Enzymes
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                Nucleases
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                Biology and Life Sciences
                Biochemistry
                Proteins
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                Biological Databases
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                Sequence Analysis
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                Bioinformatics
                Sequence Analysis
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                Signal Filtering
                Biology and Life Sciences
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                Biology and Life Sciences
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                Quantitative & Systems biology
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