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Digby B, Finn S, Ó Broin P. Computational approaches and challenges in the analysis of circRNA data. BMC Genomics 2024; 25:527. [PMID: 38807085 PMCID: PMC11134749 DOI: 10.1186/s12864-024-10420-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 05/15/2024] [Indexed: 05/30/2024] Open
Abstract
Circular RNAs (circRNA) are a class of non-coding RNA, forming a single-stranded covalently closed loop structure generated via back-splicing. Advancements in sequencing methods and technologies in conjunction with algorithmic developments of bioinformatics tools have enabled researchers to characterise the origin and function of circRNAs, with practical applications as a biomarker of diseases becoming increasingly relevant. Computational methods developed for circRNA analysis are predicated on detecting the chimeric back-splice junction of circRNAs whilst mitigating false-positive sequencing artefacts. In this review, we discuss in detail the computational strategies developed for circRNA identification, highlighting a selection of tool strengths, weaknesses and assumptions. In addition to circRNA identification tools, we describe methods for characterising the role of circRNAs within the competing endogenous RNA (ceRNA) network, their interactions with RNA-binding proteins, and publicly available databases for rich circRNA annotation.
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Affiliation(s)
- Barry Digby
- School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland.
| | - Stephen Finn
- Discipline of Histopathology, School of Medicine, Trinity College Dublin and Cancer Molecular Diagnostic Laboratory, Dublin, Ireland
| | - Pilib Ó Broin
- School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland
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2
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Whittle BJ, Izuogu OG, Lowes H, Deen D, Pyle A, Coxhead J, Lawson RA, Yarnall AJ, Jackson MS, Santibanez-Koref M, Hudson G. Early-stage idiopathic Parkinson's disease is associated with reduced circular RNA expression. NPJ Parkinsons Dis 2024; 10:25. [PMID: 38245550 PMCID: PMC10799891 DOI: 10.1038/s41531-024-00636-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/08/2024] [Indexed: 01/22/2024] Open
Abstract
Neurodegeneration in Parkinson's disease (PD) precedes diagnosis by years. Early neurodegeneration may be reflected in RNA levels and measurable as a biomarker. Here, we present the largest quantification of whole blood linear and circular RNAs (circRNA) in early-stage idiopathic PD, using RNA sequencing data from two cohorts (PPMI = 259 PD, 161 Controls; ICICLE-PD = 48 PD, 48 Controls). We identified a replicable increase in TMEM252 and LMNB1 gene expression in PD. We identified novel differences in the expression of circRNAs from ESYT2, BMS1P1 and CCDC9, and replicated trends of previously reported circRNAs. Overall, using circRNA as a diagnostic biomarker in PD did not show any clear improvement over linear RNA, minimising its potential clinical utility. More interestingly, we observed a general reduction in circRNA expression in both PD cohorts, accompanied by an increase in RNASEL expression. This imbalance implicates the activation of an innate antiviral immune response and suggests a previously unknown aspect of circRNA regulation in PD.
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Affiliation(s)
- Benjamin J Whittle
- Wellcome Centre for Mitochondrial Research, Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Osagie G Izuogu
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Hannah Lowes
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Dasha Deen
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Angela Pyle
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Jon Coxhead
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Rachael A Lawson
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Alison J Yarnall
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Michael S Jackson
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | | | - Gavin Hudson
- Wellcome Centre for Mitochondrial Research, Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
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3
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Rebolledo C, Silva JP, Saavedra N, Maracaja-Coutinho V. Computational approaches for circRNAs prediction and in silico characterization. Brief Bioinform 2023; 24:7150741. [PMID: 37139555 DOI: 10.1093/bib/bbad154] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 03/20/2023] [Accepted: 03/30/2023] [Indexed: 05/05/2023] Open
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.
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Affiliation(s)
- Camilo Rebolledo
- Center of Molecular Biology & Pharmacogenetics, Department of Basic Sciences, Scientific and Technological Resources, Universidad de La Frontera, Temuco, Chile
- Advanced Center for Chronic Diseases - ACCDiS, Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile
- Centro de Modelamiento Molecular, Biofísica y Bioinformática - CM2B2, Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile
| | - Juan Pablo Silva
- Centro de Modelamiento Molecular, Biofísica y Bioinformática - CM2B2, Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile
- ANID Anillo ACT210004 SYSTEMIX, Rancagua, Chile
| | - Nicolás Saavedra
- Center of Molecular Biology & Pharmacogenetics, Department of Basic Sciences, Scientific and Technological Resources, Universidad de La Frontera, Temuco, Chile
| | - Vinicius Maracaja-Coutinho
- Advanced Center for Chronic Diseases - ACCDiS, Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile
- Centro de Modelamiento Molecular, Biofísica y Bioinformática - CM2B2, Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile
- ANID Anillo ACT210004 SYSTEMIX, Rancagua, Chile
- Anillo Inflammation in HIV/AIDS - InflammAIDS, Santiago, Chile
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4
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Ma L, He LN, Kang S, Gu B, Gao S, Zuo Z. Advances in detecting N6-methyladenosine modification in circRNAs. Methods 2022; 205:234-246. [PMID: 35878749 DOI: 10.1016/j.ymeth.2022.07.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/15/2022] [Accepted: 07/18/2022] [Indexed: 12/14/2022] Open
Abstract
Circular RNAs (circRNAs) are a class of noncoding RNAs with covalently single-stranded closed loop structures derived from back-splicing event of linear precursor mRNAs (pre-mRNAs). N6-methyladenosine (m6A), the most abundant epigenetic modification in eukaryotic RNAs, has been shown to play a crucial role in regulating the fate and biological function of circRNAs, and thus affecting various physiological and pathological processes. Accurate identification of m6A modification in circRNAs is an essential step to fully elucidate the crosstalk between m6A and circRNAs. In recent years, the rapid development of high-throughput sequencing technology and bioinformatic methodology has propelled the establishment of a multitude of approaches to detect circRNAs and m6A modification, including in vitro-based and in silico methods. Based on this, the research community has started on a new journey to develop methods for identification of m6A modification in circRNAs. In this review, we provide a comprehensive review and evaluation of the existing methods responsible for detecting circRNAs, m6A modification, and especially, m6A modification in circRNAs, which mainly focused on those developed based on high-throughput technologies and methodology of bioinformatics. This handy reference can help researchers figure out towards which direction this field will go.
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Affiliation(s)
- Lixia Ma
- State Key Laboratory of Esophageal Cancer Prevention & Treatment, Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, Henan Key Laboratory of Cancer Epigenetics, Cancer Hospital, The First Affiliated Hospital (College of Clinical Medical) of Henan University of Science and Technology, Luoyang, China
| | - Li-Na He
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Shiyang Kang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Bianli Gu
- State Key Laboratory of Esophageal Cancer Prevention & Treatment, Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, Henan Key Laboratory of Cancer Epigenetics, Cancer Hospital, The First Affiliated Hospital (College of Clinical Medical) of Henan University of Science and Technology, Luoyang, China
| | - Shegan Gao
- State Key Laboratory of Esophageal Cancer Prevention & Treatment, Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, Henan Key Laboratory of Cancer Epigenetics, Cancer Hospital, The First Affiliated Hospital (College of Clinical Medical) of Henan University of Science and Technology, Luoyang, China.
| | - Zhixiang Zuo
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
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5
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Liu H, Akhatayeva Z, Pan C, Liao M, Lan X. Comprehensive comparison of two types of algorithm for circRNA detection from short-read RNA-Seq. Bioinformatics 2022; 38:3037-3043. [PMID: 35482518 DOI: 10.1093/bioinformatics/btac302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/12/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Circular RNA is generally formed by the "back-splicing" process between the upstream splice acceptor and the downstream donor in/not in the regulation of the corresponding RNA-binding proteins or cis-elements. Therefore, more and more software packages have been developed and they are mostly based on the identification of the back-spliced junction reads. However, recent studies developed two software tools that can detect circRNA candidates by constructing k-mer table or/and de bruijn graph rather than reads mapping. RESULTS Here, we compared the precision, sensitivity and detection efficiency between software tools based on different algorithms. Eleven representative detection tools with two types of algorithm were selected for the overall pipeline analysis of RNA-seq datasets with/without RNase R treatment in two cell lines. Precision, sensitivity, AUC, F1 score and detection efficiency metrics were assessed to compare the prediction tools. Meanwhile, the sensitivity and distribution of highly expressed circRNAs before and after RNase R treatment were also revealed by their enrichment, unaffected and depleted candidate frequencies. Eventually, we found that compared to the k-mer based tools, CIRI2 and KNIFE based on reads mapping had relatively superior and more balanced detection performance regardless of the cell line or RNase R (-/+) datasets. AVAILABILITY AND IMPLEMENTATION All predicted results and source codes can be retrieved from https://github.com/luffy563/circRNA_tools_comparison. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hongfei Liu
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Zhanerke Akhatayeva
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Chuanying Pan
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Mingzhi Liao
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Xianyong Lan
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, 712100, China
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Nührenberg TG, Stöckle J, Marini F, Zurek M, Grüning BA, Benes V, Hein L, Neumann FJ, Stratz C, Cederqvist M, Hochholzer W. Impact of high platelet turnover on the platelet transcriptome: Results from platelet RNA-sequencing in patients with sepsis. PLoS One 2022; 17:e0260222. [PMID: 35085240 PMCID: PMC8794123 DOI: 10.1371/journal.pone.0260222] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 11/04/2021] [Indexed: 12/13/2022] Open
Abstract
Background
Sepsis is associated with high platelet turnover and elevated levels of immature platelets. Changes in the platelet transcriptome and the specific impact of immature platelets on the platelet transcriptome remain unclear. Thus, this study sought to address whether and how elevated levels of immature platelets affect the platelet transcriptome in patients with sepsis.
Methods
Blood samples were obtained from patients with sepsis requiring vasopressor therapy (n = 8) and from a control group of patients with stable coronary artery disease and otherwise similar demographic characteristics (n = 8). Immature platelet fraction (IPF) was determined on a Sysmex XE 2100 analyser and platelet function was tested by impedance aggregometry. RNA from leukocyte-depleted platelets was used for transcriptome analysis by Next Generation Sequencing integrating the use of unique molecular identifiers.
Results
IPF (median [interquartile range]) was significantly elevated in sepsis patients (6.4 [5.3–8.7] % vs. 3.6 [2.6–4.6] %, p = 0.005). Platelet function testing revealed no differences in adenosine diphosphate- or thrombin receptor activating peptide-induced platelet aggregation between control and sepsis patients. Putative circular RNA transcripts were decreased in platelets from septic patients. Leukocyte contamination defined by CD45 abundance levels in RNA-sequencing was absent in both groups. Principal component analysis of transcripts showed only partial overlap of clustering with IPF levels. RNA sequencing showed up-regulation of 524 and down-regulation of 118 genes in platelets from sepsis patients compared to controls. Upregulated genes were mostly related to catabolic processes and protein translation. Comparison to published platelet transcriptomes showed a large overlap of changes observed in sepsis and COVID-19 but not with reticulated platelets from healthy donors.
Conclusions
Patients with sepsis appear to have a less degraded platelet transcriptome as indicated by increased levels of immature platelets and decreased levels of putative circular RNA transcripts. The present data suggests that increased protein translation is a characteristic mechanism of systemic inflammation.
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Affiliation(s)
- Thomas G. Nührenberg
- Klinik für Kardiologie und Angiologie II, Universitätsklinik Freiburg, Universitäts-Herzzentrum Campus Bad Krozingen, Bad Krozingen, Germany
- Institut für experimentelle und klinische Pharmakologie und Toxikologie, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany
- * E-mail:
| | - Jasmin Stöckle
- Klinik für Kardiologie und Angiologie II, Universitätsklinik Freiburg, Universitäts-Herzzentrum Campus Bad Krozingen, Bad Krozingen, Germany
| | - Federico Marini
- Institut für Medizinische Biometrie, Epidemiologie und Informatik, Universitätsmedizin der Johannes-Gutenberg-Universität Mainz, Mainz, Germany
| | - Mark Zurek
- Klinik für Kardiologie und Angiologie II, Universitätsklinik Freiburg, Universitäts-Herzzentrum Campus Bad Krozingen, Bad Krozingen, Germany
| | - Björn A. Grüning
- Institut für Informatik, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany
| | - Vladimir Benes
- Genomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Lutz Hein
- Institut für experimentelle und klinische Pharmakologie und Toxikologie, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany
- BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany
| | - Franz-Josef Neumann
- Klinik für Kardiologie und Angiologie II, Universitätsklinik Freiburg, Universitäts-Herzzentrum Campus Bad Krozingen, Bad Krozingen, Germany
| | - Christian Stratz
- Klinik für Kardiologie und Angiologie II, Universitätsklinik Freiburg, Universitäts-Herzzentrum Campus Bad Krozingen, Bad Krozingen, Germany
| | - Marco Cederqvist
- Klinik für Kardiologie und Angiologie II, Universitätsklinik Freiburg, Universitäts-Herzzentrum Campus Bad Krozingen, Bad Krozingen, Germany
| | - Willibald Hochholzer
- Klinik für Kardiologie und Angiologie II, Universitätsklinik Freiburg, Universitäts-Herzzentrum Campus Bad Krozingen, Bad Krozingen, Germany
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7
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Abstract
Circular RNAs (circRNAs) are a vast class of covalently closed, noncoding RNAs expressed in specific tissues and developmental stages. The molecular, cellular, and pathophysiologic roles of circRNAs are not fully known, but their impact on gene expression programs is beginning to emerge, as circRNAs often associate with RNA-binding proteins and nucleic acids. With rising interest in identifying circRNAs associated with disease processes, it has become particularly important to identify circRNAs in RNA sequencing (RNA-seq) datasets, either generated by the investigator or reported in the literature. Here, we present a methodology to identify and analyze circRNAs in RNA-seq datasets, including those archived in repositories. We elaborate on the unique features of circRNAs that require specialized attention in RNA-seq datasets, the software packages designed for circRNA identification, the ongoing efforts to reconstruct the body of circRNAs starting from unique circularizing junctions, and the interacting factors that can be proposed from putative circRNA body sequences. We discuss the advantages and limitations of the current approaches for high-throughput circRNA analysis from RNA-sequencing datasets and identify areas that would benefit from the development of superior bioinformatic tools.
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Affiliation(s)
- Kyle R Cochran
- Laboratory of Genetics and Genomics and Computational Biology and Genomics Core, National Institute on Aging-Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | - Myriam Gorospe
- Laboratory of Genetics and Genomics and Computational Biology and Genomics Core, National Institute on Aging-Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | - Supriyo De
- Laboratory of Genetics and Genomics and Computational Biology and Genomics Core, National Institute on Aging-Intramural Research Program, National Institutes of Health, Baltimore, MD, USA.
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8
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Yu KHO, Shi CH, Wang B, Chow SHC, Chung GTY, Lung RWM, Tan KE, Lim YY, Tsang ACM, Lo KW, Yip KY. Quantifying full-length circular RNAs in cancer. Genome Res 2021; 31:2340-2353. [PMID: 34663689 PMCID: PMC8647826 DOI: 10.1101/gr.275348.121] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 10/12/2021] [Indexed: 01/22/2023]
Abstract
Circular RNAs (circRNAs) are abundantly expressed in cancer. Their resistance to exonucleases enables them to have potentially stable interactions with different types of biomolecules. Alternative splicing can create different circRNA isoforms that have different sequences and unequal interaction potentials. The study of circRNA function thus requires knowledge of complete circRNA sequences. Here we describe psirc, a method that can identify full-length circRNA isoforms and quantify their expression levels from RNA sequencing data. We confirm the effectiveness and computational efficiency of psirc using both simulated and actual experimental data. Applying psirc on transcriptome profiles from nasopharyngeal carcinoma and normal nasopharynx samples, we discover and validate circRNA isoforms differentially expressed between the two groups. Compared with the assumed circular isoforms derived from linear transcript annotations, some of the alternatively spliced circular isoforms have 100 times higher expression and contain substantially fewer microRNA response elements, showing the importance of quantifying full-length circRNA isoforms.
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Affiliation(s)
- Ken Hung-On Yu
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Christina Huan Shi
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Bo Wang
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Savio Ho-Chit Chow
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
- School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Grace Tin-Yun Chung
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Raymond Wai-Ming Lung
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Ke-En Tan
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Yat-Yuen Lim
- Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Anna Chi-Man Tsang
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Kwok-Wai Lo
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Kevin Y Yip
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
- Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
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9
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Filippenkov IB, Stavchansky VV, Denisova AE, Valieva LV, Remizova JA, Mozgovoy IV, Zaytceva EI, Gubsky LV, Limborska SA, Dergunova LV. Genome-Wide RNA-Sequencing Reveals Massive Circular RNA Expression Changes of the Neurotransmission Genes in the Rat Brain after Ischemia-Reperfusion. Genes (Basel) 2021; 12:genes12121870. [PMID: 34946819 PMCID: PMC8701796 DOI: 10.3390/genes12121870] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/20/2021] [Accepted: 11/22/2021] [Indexed: 12/12/2022] Open
Abstract
Ischemic brain stroke is one of the most serious and socially significant diseases. In addition to messenger RNAs (mRNAs), encoding protein, the study of regulatory RNAs in ischemic has exceptional importance for the development of new strategies for neuroprotection. Circular RNAs (circRNAs) have a closed structure, predominantly brain-specific expression, and remain highly promising targets of research. They can interact with microRNAs (miRNAs), diminish their activity and thereby inhibit miRNA-mediated repression of mRNA. Genome-wide RNA-Seq analysis of the subcortical structures of the rat brain containing an ischemic damage focus and penumbra area revealed 395 circRNAs changed their expression significantly at 24 h after transient middle cerebral artery occlusion model (tMCAO) conditions. Furthermore, functional annotation revealed their association with neuroactive signaling pathways. It was found that about a third of the differentially expressed circRNAs (DECs) originate from genes whose mRNA levels also changed at 24 h after tMCAO. The other DECs originate from genes encoding non-regulated mRNAs under tMCAO conditions. In addition, bioinformatic analysis predicted a circRNA–miRNA–mRNA network which was associated with the neurotransmission signaling regulation. Our results show that such circRNAs can persist as potential miRNA sponges for the protection of mRNAs of neurotransmitter genes. The results expanded our views about the neurotransmission regulation in the rat brain after ischemia–reperfusion with circRNA action.
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Affiliation(s)
- Ivan B. Filippenkov
- Institute of Molecular Genetics of National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia; (V.V.S.); (L.V.V.); (J.A.R.); (I.V.M.); (S.A.L.); (L.V.D.)
- Correspondence: ; Tel.: +7-499-196-1858
| | - Vasily V. Stavchansky
- Institute of Molecular Genetics of National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia; (V.V.S.); (L.V.V.); (J.A.R.); (I.V.M.); (S.A.L.); (L.V.D.)
| | - Alina E. Denisova
- Department of Neurology, Neurosurgery and Medical Genetics, Pirogov Russian National Research Medical University, Ostrovitianov str. 1, 117997 Moscow, Russia; (A.E.D.); (L.V.G.)
| | - Liya V. Valieva
- Institute of Molecular Genetics of National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia; (V.V.S.); (L.V.V.); (J.A.R.); (I.V.M.); (S.A.L.); (L.V.D.)
| | - Julia A. Remizova
- Institute of Molecular Genetics of National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia; (V.V.S.); (L.V.V.); (J.A.R.); (I.V.M.); (S.A.L.); (L.V.D.)
| | - Ivan V. Mozgovoy
- Institute of Molecular Genetics of National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia; (V.V.S.); (L.V.V.); (J.A.R.); (I.V.M.); (S.A.L.); (L.V.D.)
| | - Elizaveta I. Zaytceva
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskie Gory, 119991 Moscow, Russia;
| | - Leonid V. Gubsky
- Department of Neurology, Neurosurgery and Medical Genetics, Pirogov Russian National Research Medical University, Ostrovitianov str. 1, 117997 Moscow, Russia; (A.E.D.); (L.V.G.)
- Federal Center for the Brain and Neurotechnologies, Federal Biomedical Agency, Ostrovitianov str. 1, Building 10, 117997 Moscow, Russia
| | - Svetlana A. Limborska
- Institute of Molecular Genetics of National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia; (V.V.S.); (L.V.V.); (J.A.R.); (I.V.M.); (S.A.L.); (L.V.D.)
| | - Lyudmila V. Dergunova
- Institute of Molecular Genetics of National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia; (V.V.S.); (L.V.V.); (J.A.R.); (I.V.M.); (S.A.L.); (L.V.D.)
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10
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Sharma D, Sehgal P, Sivasubbu S, Scaria V. A genome-wide circular RNA transcriptome in rat. Biol Methods Protoc 2021; 6:bpab016. [PMID: 34527809 PMCID: PMC8435660 DOI: 10.1093/biomethods/bpab016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 11/26/2022] Open
Abstract
Circular RNAs (circRNAs) are a novel class of noncoding RNAs that back-splice from 5ʹ donor site and 3ʹ acceptor sites to form a circular structure. A number of circRNAs have been discovered in model organisms including human, mouse, Drosophila, among other organisms. There are a few candidate-based studies on circRNAs in rat, a well-studied model organism as well. A number of pipelines have been published to identify the back splice junctions for the discovery of circRNAs but studies comparing these tools have suggested that a combination of tools would be a better approach to identify high-confidence circRNAs. The availability of a recent dataset of transcriptomes encompassing 11 tissues, 4 developmental stages, and 2 genders motivated us to explore the landscape of circRNAs in the organism in this context. In order to understand the difference among different pipelines, we employed five different combinations of tools to identify circular RNAs from the dataset. We compared the results of the different combination of tools/pipelines with respect to alignment, total number of circRNAs identified and read-coverage. In addition, we identified tissue-specific, development-stage specific and gender-specific circRNAs and further independently validated 16 circRNA junctions out of 24 selected candidates in 5 tissue samples and estimated the quantitative expression of five circRNA candidates using real-time polymerase chain reaction and our analysis suggests three candidates as tissue-enriched. This study is one of the most comprehensive studies which provides a map of circRNAs transcriptome as well as to understand the difference among different computational pipelines in rat.
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Affiliation(s)
- Disha Sharma
- GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mathura Road, Delhi 110025, India.,Academy of Scientific & Innovative Research (AcSIR), CSIR-IGIB South Campus, Mathura Road, Delhi 110025, India
| | - Paras Sehgal
- Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology, Mathura Road (CSIR-IGIB), Delhi 110025, India.,Academy of Scientific & Innovative Research (AcSIR), CSIR-IGIB South Campus, Mathura Road, Delhi 110025, India
| | - Sridhar Sivasubbu
- Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology, Mathura Road (CSIR-IGIB), Delhi 110025, India.,Academy of Scientific & Innovative Research (AcSIR), CSIR-IGIB South Campus, Mathura Road, Delhi 110025, India
| | - Vinod Scaria
- GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mathura Road, Delhi 110025, India.,Academy of Scientific & Innovative Research (AcSIR), CSIR-IGIB South Campus, Mathura Road, Delhi 110025, India
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11
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Chen L, Wang C, Sun H, Wang J, Liang Y, Wang Y, Wong G. The bioinformatics toolbox for circRNA discovery and analysis. Brief Bioinform 2021; 22:1706-1728. [PMID: 32103237 PMCID: PMC7986655 DOI: 10.1093/bib/bbaa001] [Citation(s) in RCA: 178] [Impact Index Per Article: 59.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 12/16/2019] [Accepted: 01/02/2020] [Indexed: 12/21/2022] Open
Abstract
Circular RNAs (circRNAs) are a unique class of RNA molecule identified more than 40 years ago which are produced by a covalent linkage via back-splicing of linear RNA. Recent advances in sequencing technologies and bioinformatics tools have led directly to an ever-expanding field of types and biological functions of circRNAs. In parallel with technological developments, practical applications of circRNAs have arisen including their utilization as biomarkers of human disease. Currently, circRNA-associated bioinformatics tools can support projects including circRNA annotation, circRNA identification and network analysis of competing endogenous RNA (ceRNA). In this review, we collected about 100 circRNA-associated bioinformatics tools and summarized their current attributes and capabilities. We also performed network analysis and text mining on circRNA tool publications in order to reveal trends in their ongoing development.
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Affiliation(s)
- Liang Chen
- Department of Computer Science, Key Laboratory of Intelligent Manufacturing Technology of Ministry of Education, Shantou University
| | | | - Huiyan Sun
- School of Artificial Intelligence, Jilin University
| | - Juexin Wang
- Department of Electrical Engineering and Computer Science and Bond Life Science Center, University of Missouri
| | - Yanchun Liang
- College of Computer Science and Technology, Jilin University
| | - Yan Wang
- College of Computer Science and Technology, Jilin University
| | - Garry Wong
- Faculty of Health Sciences, University of Macau
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12
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Chen XT, Li ZW, Zhao X, Li ML, Hou PF, Chu SF, Zheng JN, Bai J. Role of Circular RNA in Kidney-Related Diseases. Front Pharmacol 2021; 12:615882. [PMID: 33776764 PMCID: PMC7990792 DOI: 10.3389/fphar.2021.615882] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 02/02/2021] [Indexed: 12/13/2022] Open
Abstract
The kidney is vital in maintaining fluid, electrolyte, and acid–base balance. Kidney-related diseases, which are an increasing public health issue, can happen to people of any age and at any time. Circular RNAs (circRNAs) are endogenous RNA that are produced by selective RNA splicing and are involved in progression of various diseases. Studies have shown that various kidney diseases, including renal cell carcinoma, acute kidney injury, and chronic kidney disease, are linked to circRNAs. This review outlines the characteristics and biological functions of circRNAs and discusses specific studies that provide insights into the function and potential of circRNAs for application in the diagnosis and treatment of kidney-related diseases.
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Affiliation(s)
- Xin-Tian Chen
- Cancer Institute, Xuzhou Medical University, Xuzhou, China
| | - Zhong-Wei Li
- Cancer Institute, Xuzhou Medical University, Xuzhou, China.,Center of Clinical Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Xue Zhao
- Cancer Institute, Xuzhou Medical University, Xuzhou, China
| | - Min-Le Li
- Cancer Institute, Xuzhou Medical University, Xuzhou, China.,Center of Clinical Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Ping-Fu Hou
- Cancer Institute, Xuzhou Medical University, Xuzhou, China.,Center of Clinical Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Su-Fang Chu
- Cancer Institute, Xuzhou Medical University, Xuzhou, China
| | - Jun-Nian Zheng
- Cancer Institute, Xuzhou Medical University, Xuzhou, China.,Center of Clinical Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Jin Bai
- Cancer Institute, Xuzhou Medical University, Xuzhou, China.,Center of Clinical Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
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13
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Nisar S, Bhat AA, Singh M, Karedath T, Rizwan A, Hashem S, Bagga P, Reddy R, Jamal F, Uddin S, Chand G, Bedognetti D, El-Rifai W, Frenneaux MP, Macha MA, Ahmed I, Haris M. Insights Into the Role of CircRNAs: Biogenesis, Characterization, Functional, and Clinical Impact in Human Malignancies. Front Cell Dev Biol 2021; 9:617281. [PMID: 33614648 PMCID: PMC7894079 DOI: 10.3389/fcell.2021.617281] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 01/04/2021] [Indexed: 01/17/2023] Open
Abstract
Circular RNAs (circRNAs) are an evolutionarily conserved novel class of non-coding endogenous RNAs (ncRNAs) found in the eukaryotic transcriptome, originally believed to be aberrant RNA splicing by-products with decreased functionality. However, recent advances in high-throughput genomic technology have allowed circRNAs to be characterized in detail and revealed their role in controlling various biological and molecular processes, the most essential being gene regulation. Because of the structural stability, high expression, availability of microRNA (miRNA) binding sites and tissue-specific expression, circRNAs have become hot topic of research in RNA biology. Compared to the linear RNA, circRNAs are produced differentially by backsplicing exons or lariat introns from a pre-messenger RNA (mRNA) forming a covalently closed loop structure missing 3′ poly-(A) tail or 5′ cap, rendering them immune to exonuclease-mediated degradation. Emerging research has identified multifaceted roles of circRNAs as miRNA and RNA binding protein (RBP) sponges and transcription, translation, and splicing event regulators. CircRNAs have been involved in many human illnesses, including cancer and neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease, due to their aberrant expression in different pathological conditions. The functional versatility exhibited by circRNAs enables them to serve as potential diagnostic or predictive biomarkers for various diseases. This review discusses the properties, characterization, profiling, and the diverse molecular mechanisms of circRNAs and their use as potential therapeutic targets in different human malignancies.
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Affiliation(s)
- Sabah Nisar
- Functional and Molecular Imaging Laboratory, Cancer Research Department, Sidra Medicine, Doha, Qatar
| | - Ajaz A Bhat
- Functional and Molecular Imaging Laboratory, Cancer Research Department, Sidra Medicine, Doha, Qatar
| | - Mayank Singh
- Dr. B. R. Ambedkar Institute Rotary Cancer Hospital (BRAIRCH), All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | | | - Arshi Rizwan
- Department of Nephrology, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Sheema Hashem
- Functional and Molecular Imaging Laboratory, Cancer Research Department, Sidra Medicine, Doha, Qatar
| | - Puneet Bagga
- Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Ravinder Reddy
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
| | - Farrukh Jamal
- Dr. Rammanohar Lohia Avadh University, Ayodhya, India
| | - Shahab Uddin
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Gyan Chand
- Department of Endocrine Surgery, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India
| | - Davide Bedognetti
- Laboratory of Cancer Immunogenomics, Cancer Research Department, Sidra Medicine, Doha, Qatar.,Department of Internal Medicine and Medical Specialties, University of Genoa, Genoa, Italy.,College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Wael El-Rifai
- Department of Surgery, University of Miami Miller School of Medicine, Miami, FL, United States
| | | | - Muzafar A Macha
- Watson-Crick Centre for Molecular Medicine, Islamic University of Science and Technology (IUST), Pulwama, India
| | - Ikhlak Ahmed
- Research Branch, Sidra Medicine, Doha, Qatar.,Research Branch, Sidra Medicine, Doha, Qatar
| | - Mohammad Haris
- Functional and Molecular Imaging Laboratory, Cancer Research Department, Sidra Medicine, Doha, Qatar.,Laboratory Animal Research Center, Qatar University, Doha, Qatar
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14
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van der Graaf K, Jindrich K, Mitchell R, White-Cooper H. Roles for RNA export factor, Nxt1, in ensuring muscle integrity and normal RNA expression in Drosophila. G3-GENES GENOMES GENETICS 2021; 11:6046993. [PMID: 33561245 PMCID: PMC8022728 DOI: 10.1093/g3journal/jkaa046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 12/10/2020] [Indexed: 11/15/2022]
Abstract
The mRNA export pathway is responsible for the transport of mRNAs from the nucleus to the cytoplasm, and thus is essential for protein production and normal cellular functions. A partial loss of function allele of the mRNA export factor Nxt1 in Drosophila shows reduced viability and sterility. A previous study has shown that the male fertility defect is due to a defect in transcription and RNA stability, indicating the potential for this pathway to be implicated in processes beyond the known mRNA transport function. Here we investigate the reduced viability of Nxt1 partial loss of function mutants, and describe a defect in growth and maintenance of the larval muscles, leading to muscle degeneration. RNA-seq revealed reduced expression of a set of mRNAs, particularly from genes with long introns in Nxt1 mutant carcass. We detected differential expression of circRNA, and significantly fewer distinct circRNAs expressed in the mutants. Despite the widespread defects in gene expression, muscle degeneration was rescued by increased expression of the costamere component tn (abba) in muscles. This is the first report of a role for the RNA export pathway gene Nxt1 in the maintenance of muscle integrity. Our data also links the mRNA export pathway to a specific role in the expression of mRNA and circRNA from common precursor genes, in vivo.
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Affiliation(s)
| | - Katia Jindrich
- School of Biosciences, Cardiff University, Cardiff CF10 3AT, UK
| | - Robert Mitchell
- School of Biosciences, Cardiff University, Cardiff CF10 3AT, UK
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15
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Kui L, Tang M. Overview of Computational Methods and Resources for Circular RNAs. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11638-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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16
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Disease-Associated Circular RNAs: From Biology to Computational Identification. BIOMED RESEARCH INTERNATIONAL 2020; 2020:6798590. [PMID: 32908906 PMCID: PMC7450300 DOI: 10.1155/2020/6798590] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 07/10/2020] [Indexed: 02/07/2023]
Abstract
Circular RNAs (circRNAs) are endogenous RNAs with a covalently closed continuous loop, generated through various backsplicing events of pre-mRNA. An accumulating number of studies have shown that circRNAs are potential biomarkers for major human diseases such as cancer and Alzheimer's disease. Thus, identification and prediction of human disease-associated circRNAs are of significant importance. To this end, a computational analysis-assisted strategy is indispensable to detect, verify, and quantify circRNAs for downstream applications. In this review, we briefly introduce the biology of circRNAs, including the biogenesis, characteristics, and biological functions. In addition, we outline about 30 recent bioinformatic analysis tools that are publicly available for circRNA study. Principles for applying these computational strategies and considerations will be briefly discussed. Lastly, we give a complete survey on more than 20 key computational databases that are frequently used. To our knowledge, this is the most complete and updated summary on publicly available circRNA resources. In conclusion, this review summarizes key aspects of circRNA biology and outlines key computational strategies that will facilitate the genome-wide identification and prediction of circRNAs.
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17
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Bravo JI, Nozownik S, Danthi PS, Benayoun BA. Transposable elements, circular RNAs and mitochondrial transcription in age-related genomic regulation. Development 2020; 147:dev175786. [PMID: 32527937 PMCID: PMC10680986 DOI: 10.1242/dev.175786] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Our understanding of the molecular regulation of aging and age-related diseases is still in its infancy, requiring in-depth characterization of the molecular landscape shaping these complex phenotypes. Emerging classes of molecules with promise as aging modulators include transposable elements, circRNAs and the mitochondrial transcriptome. Analytical complexity means that these molecules are often overlooked, even though they exhibit strong associations with aging and, in some cases, may directly contribute to its progress. Here, we review the links between these novel factors and age-related phenotypes, and we suggest tools that can be easily incorporated into existing pipelines to better understand the aging process.
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Affiliation(s)
- Juan I Bravo
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
- Graduate Program in the Biology of Aging, University of Southern California, Los Angeles, CA 90089, USA
| | - Séverine Nozownik
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
- Magistère européen de Génétique, Université Paris Diderot-Paris 7, Paris 75014, France
| | - Prakroothi S Danthi
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
| | - Bérénice A Benayoun
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90089, USA
- USC Norris Comprehensive Cancer Center, Epigenetics and Gene Regulation, Los Angeles, CA 90089, USA
- USC Stem Cell Initiative, Los Angeles, CA 90089, USA
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18
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Haque S, Ames RM, Moore K, Lee BP, Jeffery N, Harries LW. Islet-expressed circular RNAs are associated with type 2 diabetes status in human primary islets and in peripheral blood. BMC Med Genomics 2020; 13:64. [PMID: 32312268 PMCID: PMC7171860 DOI: 10.1186/s12920-020-0713-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Accepted: 04/14/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Circular RNAs are non-coding RNA molecules with gene regulatory potential that have been associated with several human diseases. They are stable and present in the circulation, making them excellent candidates for biomarkers of disease. Despite their promise as biomarkers or future therapeutic targets, information on their expression and functionality in human pancreatic islets is a relatively unexplored subject. METHODS Here we aimed to produce an enriched circRNAome profile for human pancreatic islets by CircleSeq, and to explore the relationship between circRNA expression, diabetes status, genotype at T2D risk loci and measures of glycaemia (insulin secretory index; SI and HbA1c) in human islet preparations from healthy control donors and donors with type 2 diabetes using ANOVA or linear regression as appropriate. We also assessed the effect of elevated glucose, cytokine and lipid and hypoxia on circRNA expression in the human beta cell line EndoC-βH1. RESULTS We identified over 2600 circRNAs present in human islets. Of the five most abundant circRNAs in human islets, four (circCIRBP, circZKSCAN, circRPH3AL and circCAMSAP1) demonstrated marked associations with diabetes status. CircCIRBP demonstrated an association with insulin secretory index in isolated human islets and circCIRBP and circRPH3AL displayed altered expression with elevated fatty acid in treated EndoC-βH1 cells. CircCAMSAP1 was also noted to be associated with T2D status in human peripheral blood. No associations between circRNA expression and genotype at T2D risk loci were identified in our samples. CONCLUSIONS Our data suggest that circRNAs are abundantly expressed in human islets, and that some are differentially regulated in the islets of donors with type 2 diabetes. Some islet circRNAs are also expressed in peripheral blood and the expression of one, circCAMSAP1, correlates with diabetes status. These findings highlight the potential of circRNAs as biomarkers for T2D.
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Affiliation(s)
- Shahnaz Haque
- RNA-Mediated Mechanisms of Disease Group, Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, University of Exeter, RILD South, Barrack Road, Exeter, EX2 5DW, UK
| | - Ryan M Ames
- Biosciences, University of Exeter, Exeter, UK
| | - Karen Moore
- College of Life and Environmental Sciences, University of Exeter, Exeter, UK
| | - Benjamin P Lee
- RNA-Mediated Mechanisms of Disease Group, Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, University of Exeter, RILD South, Barrack Road, Exeter, EX2 5DW, UK
| | - Nicola Jeffery
- RNA-Mediated Mechanisms of Disease Group, Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, University of Exeter, RILD South, Barrack Road, Exeter, EX2 5DW, UK
| | - Lorna W Harries
- RNA-Mediated Mechanisms of Disease Group, Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, University of Exeter, RILD South, Barrack Road, Exeter, EX2 5DW, UK.
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19
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Asghari H, Lin YY, Xu Y, Haghshenas E, Collins CC, Hach F. CircMiner: accurate and rapid detection of circular RNA through splice-aware pseudo-alignment scheme. Bioinformatics 2020; 36:3703-3711. [DOI: 10.1093/bioinformatics/btaa232] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 03/23/2020] [Accepted: 04/01/2020] [Indexed: 12/14/2022] Open
Abstract
Abstract
Motivation
The ubiquitous abundance of circular RNAs (circRNAs) has been revealed by performing high-throughput sequencing in a variety of eukaryotes. circRNAs are related to some diseases, such as cancer in which they act as oncogenes or tumor-suppressors and, therefore, have the potential to be used as biomarkers or therapeutic targets. Accurate and rapid detection of circRNAs from short reads remains computationally challenging. This is due to the fact that identifying chimeric reads, which is essential for finding back-splice junctions, is a complex process. The sensitivity of discovery methods, to a high degree, relies on the underlying mapper that is used for finding chimeric reads. Furthermore, all the available circRNA discovery pipelines are resource intensive.
Results
We introduce CircMiner, a novel stand-alone circRNA detection method that rapidly identifies and filters out linear RNA sequencing reads and detects back-splice junctions. CircMiner employs a rapid pseudo-alignment technique to identify linear reads that originate from transcripts, genes or the genome. CircMiner further processes the remaining reads to identify the back-splice junctions and detect circRNAs with single-nucleotide resolution. We evaluated the efficacy of CircMiner using simulated datasets generated from known back-splice junctions and showed that CircMiner has superior accuracy and speed compared to the existing circRNA detection tools. Additionally, on two RNase R treated cell line datasets, CircMiner was able to detect most of consistent, high confidence circRNAs compared to untreated samples of the same cell line.
Availability and implementation
CircMiner is implemented in C++ and is available online at https://github.com/vpc-ccg/circminer.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hossein Asghari
- School of Computing Science, Simon Fraser University, Burnaby, BC V5A1S6, Canada
- Vancouver Prostate Centre, Vancouver, BC V6H3Z6, Canada
| | - Yen-Yi Lin
- Vancouver Prostate Centre, Vancouver, BC V6H3Z6, Canada
| | - Yang Xu
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Ehsan Haghshenas
- School of Computing Science, Simon Fraser University, Burnaby, BC V5A1S6, Canada
- Vancouver Prostate Centre, Vancouver, BC V6H3Z6, Canada
| | - Colin C Collins
- Vancouver Prostate Centre, Vancouver, BC V6H3Z6, Canada
- Department of Urologic Sciences, University of British Columbia, Vancouver, BC V5Z1M9, Canada
| | - Faraz Hach
- Vancouver Prostate Centre, Vancouver, BC V6H3Z6, Canada
- Department of Urologic Sciences, University of British Columbia, Vancouver, BC V5Z1M9, Canada
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20
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CircAST: Full-length Assembly and Quantification of Alternatively Spliced Isoforms in Circular RNAs. GENOMICS PROTEOMICS & BIOINFORMATICS 2020; 17:522-534. [PMID: 32007626 PMCID: PMC7056934 DOI: 10.1016/j.gpb.2019.03.004] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 02/15/2019] [Accepted: 03/09/2019] [Indexed: 11/20/2022]
Abstract
Circular RNAs (circRNAs), covalently closed continuous RNA loops, are generated from cognate linear RNAs through back splicing events, and alternative splicing events may generate different circRNA isoforms at the same locus. However, the challenges of reconstruction and quantification of alternatively spliced full-length circRNAs remain unresolved. On the basis of the internal structural characteristics of circRNAs, we developed CircAST, a tool to assemble alternatively spliced circRNA transcripts and estimate their expression by using multiple splice graphs. Simulation studies showed that CircAST correctly assembled the full sequences of circRNAs with a sensitivity of 85.63%–94.32% and a precision of 81.96%–87.55%. By assigning reads to specific isoforms, CircAST quantified the expression of circRNA isoforms with correlation coefficients of 0.85–0.99 between theoretical and estimated values. We evaluated CircAST on an in-house mouse testis RNA-seq dataset with RNase R treatment for enriching circRNAs and identified 380 circRNAs with full-length sequences different from those of their corresponding cognate linear RNAs. RT-PCR and Sanger sequencing analyses validated 32 out of 37 randomly selected isoforms, thus further indicating the good performance of CircAST, especially for isoforms with low abundance. We also applied CircAST to published experimental data and observed substantial diversity in circular transcripts across samples, thus suggesting that circRNA expression is highly regulated. CircAST can be accessed freely at https://github.com/xiaofengsong/CircAST.
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21
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Haque S, Ames RM, Moore K, Pilling LC, Peters LL, Bandinelli S, Ferrucci L, Harries LW. circRNAs expressed in human peripheral blood are associated with human aging phenotypes, cellular senescence and mouse lifespan. GeroScience 2019; 42:183-199. [PMID: 31811527 PMCID: PMC7031184 DOI: 10.1007/s11357-019-00120-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 10/04/2019] [Indexed: 10/25/2022] Open
Abstract
Circular RNAs (circRNAs) are an emerging class of non-coding RNA molecules that are thought to regulate gene expression and human disease. Despite the observation that circRNAs are known to accumulate in older organisms and have been reported in cellular senescence, their role in aging remains relatively unexplored. Here, we have assessed circRNA expression in aging human blood and followed up age-associated circRNA in relation to human aging phenotypes, mammalian longevity as measured by mouse median strain lifespan and cellular senescence in four different primary human cell types. We found that circRNAs circDEF6, circEP300, circFOXO3 and circFNDC3B demonstrate associations with parental longevity or hand grip strength in 306 subjects from the InCHIANTI study of aging, and furthermore, circFOXO3 and circEP300 also demonstrate differential expression in one or more human senescent cell types. Finally, four circRNAs tested showed evidence of conservation in mouse. Expression levels of one of these, circPlekhm1, was nominally associated with lifespan. These data suggest that circRNA may represent a novel class of regulatory RNA involved in the determination of aging phenotypes, which may show future promise as both biomarkers and future therapeutic targets for age-related disease.
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Affiliation(s)
- Shahnaz Haque
- RNA-Mediated Mechanisms of Disease Group, Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, University of Exeter, RILD South, Barrack Road, Exeter, EX2 5DW, UK
| | - Ryan M Ames
- Biosciences, University of Exeter, Exeter, UK
| | - Karen Moore
- College of Life and Environmental Sciences, University of Exeter, Exeter, UK
| | - Luke C Pilling
- Epidemiology and Public Health, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Luanne L Peters
- The Jackson Laboratory Nathan Shock Centre of Excellence in the Basic Biology of Aging, Bar Harbor, ME, USA
| | | | - Luigi Ferrucci
- National Institute on Aging, Clinical Research Branch, Harbor Hospital, Baltimore, MD, 21225, USA
| | - Lorna W Harries
- RNA-Mediated Mechanisms of Disease Group, Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, University of Exeter, RILD South, Barrack Road, Exeter, EX2 5DW, UK.
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22
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Santer L, Bär C, Thum T. Circular RNAs: A Novel Class of Functional RNA Molecules with a Therapeutic Perspective. Mol Ther 2019; 27:1350-1363. [PMID: 31324392 DOI: 10.1016/j.ymthe.2019.07.001] [Citation(s) in RCA: 178] [Impact Index Per Article: 35.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 07/04/2019] [Accepted: 07/04/2019] [Indexed: 12/26/2022] Open
Abstract
Circular RNAs (circRNAs) are a subclass of non-coding RNAs that lack free 3' and 5' ends and, thus, exist as continuous loop RNAs. Such circular transcripts have been identified for thousands of genes, are regulated in developmental stages and pathophysiological conditions, and are often expressed in a tissue- or cell-type-specific manner. For a long time, circular transcripts were considered as aberrant splicing by-products. However, high-throughput transcriptome sequencing and focused molecular characterization of individual circRNAs uncovered their ubiquity. Evidence emerges suggesting circRNAs are functional molecules. In this review, we illustrate the current knowledge of circRNA formation and circRNA detection methods. We summarize different molecular mechanisms of action and highlight circRNAs with specific roles in cardiovascular disease. Finally, we describe a number of tools for circRNA manipulation, which may be exploited for circRNA-based therapeutic interventions in the future.
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Affiliation(s)
- Laura Santer
- Institute of Molecular and Translational Therapeutic Strategies, Hannover Medical School, Hannover, Germany
| | - Christian Bär
- Institute of Molecular and Translational Therapeutic Strategies, Hannover Medical School, Hannover, Germany; REBIRTH Cluster of Excellence, Hannover Medical School, Hannover, Germany.
| | - Thomas Thum
- Institute of Molecular and Translational Therapeutic Strategies, Hannover Medical School, Hannover, Germany; REBIRTH Cluster of Excellence, Hannover Medical School, Hannover, Germany; National Heart and Lung Institute, Imperial College London, London, UK.
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23
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Mellough CB, Bauer R, Collin J, Dorgau B, Zerti D, Dolan DWP, Jones CM, Izuogu OG, Yu M, Hallam D, Steyn JS, White K, Steel DH, Santibanez-Koref M, Elliott DJ, Jackson MS, Lindsay S, Grellscheid S, Lako M. An integrated transcriptional analysis of the developing human retina. Development 2019; 146:146/2/dev169474. [PMID: 30696714 PMCID: PMC6361134 DOI: 10.1242/dev.169474] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 12/24/2018] [Indexed: 12/11/2022]
Abstract
The scarcity of embryonic/foetal material as a resource for direct study means that there is still limited understanding of human retina development. Here, we present an integrated transcriptome analysis combined with immunohistochemistry in human eye and retinal samples from 4 to 19 post-conception weeks. This analysis reveals three developmental windows with specific gene expression patterns that informed the sequential emergence of retinal cell types and enabled identification of stage-specific cellular and biological processes, and transcriptional regulators. Each stage is characterised by a specific set of alternatively spliced transcripts that code for proteins involved in the formation of the photoreceptor connecting cilium, pre-mRNA splicing and epigenetic modifiers. Importantly, our data show that the transition from foetal to adult retina is characterised by a large increase in the percentage of mutually exclusive exons that code for proteins involved in photoreceptor maintenance. The circular RNA population is also defined and shown to increase during retinal development. Collectively, these data increase our understanding of human retinal development and the pre-mRNA splicing process, and help to identify new candidate disease genes.
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Affiliation(s)
- Carla B. Mellough
- Institute of Genetic Medicine, Newcastle University, Newcastle NE1 3BZ, UK,Lions Eye Institute, 2 Verdun Street, Nedlands, Perth, WA 6009, Australia
| | - Roman Bauer
- School of Computing, Newcastle University, Newcastle NE4 5TG, UK
| | - Joseph Collin
- Institute of Genetic Medicine, Newcastle University, Newcastle NE1 3BZ, UK
| | - Birthe Dorgau
- Institute of Genetic Medicine, Newcastle University, Newcastle NE1 3BZ, UK
| | - Darin Zerti
- Institute of Genetic Medicine, Newcastle University, Newcastle NE1 3BZ, UK
| | - David W. P. Dolan
- Department of Biosciences, Durham University, Stockton Road, Durham DH1 3LE, UK
| | - Carl M. Jones
- Department of Biosciences, Durham University, Stockton Road, Durham DH1 3LE, UK
| | - Osagie G. Izuogu
- Institute of Genetic Medicine, Newcastle University, Newcastle NE1 3BZ, UK,European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
| | - Min Yu
- Institute of Genetic Medicine, Newcastle University, Newcastle NE1 3BZ, UK
| | - Dean Hallam
- Institute of Genetic Medicine, Newcastle University, Newcastle NE1 3BZ, UK
| | - Jannetta S. Steyn
- Institute of Genetic Medicine, Newcastle University, Newcastle NE1 3BZ, UK
| | - Kathryn White
- EM Research Services, Newcastle University, Newcastle NE2 4HH, UK
| | - David H. Steel
- Institute of Genetic Medicine, Newcastle University, Newcastle NE1 3BZ, UK
| | | | - David J. Elliott
- Institute of Genetic Medicine, Newcastle University, Newcastle NE1 3BZ, UK
| | - Michael S. Jackson
- Institute of Genetic Medicine, Newcastle University, Newcastle NE1 3BZ, UK
| | - Susan Lindsay
- Institute of Genetic Medicine, Newcastle University, Newcastle NE1 3BZ, UK
| | - Sushma Grellscheid
- Department of Biosciences, Durham University, Stockton Road, Durham DH1 3LE, UK
| | - Majlinda Lako
- Institute of Genetic Medicine, Newcastle University, Newcastle NE1 3BZ, UK
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24
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Sharma D, Sehgal P, Hariprakash J, Sivasubbu S, Scaria V. Methods for Annotation and Validation of Circular RNAs from RNAseq Data. Methods Mol Biol 2019; 1912:55-76. [PMID: 30635890 DOI: 10.1007/978-1-4939-8982-9_3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Circular RNAs are an emerging class of transcript isoforms created by unique back splicing of exons to form a closed covalent circular structure. While initially considered as product of aberrant splicing, recent evidence suggests unique functions and conservation across evolution. While circular RNAs could be largely attributed to have little or no potential to encode for proteins, recent evidence points to at least a small subset of circular RNAs which encode for peptides. Circular RNAs are also increasingly shown to be biomarkers for a number of diseases including neurological disorders and cancer. The advent of deep sequencing has enabled large-scale identification of circular RNAs in human and other genomes. A number of computational approaches have come up in recent years to query circular RNAs on a genome-wide scale from RNA-seq data. In this chapter, we describe the application and methodology of identifying circular RNAs using three popular computational tools: FindCirc, Segemehl, and CIRI along with approaches for experimental validation of the unique splice junctions.
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Affiliation(s)
- Disha Sharma
- G.N. Ramachandran Knowledge Center for Bioinformatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Paras Sehgal
- Academy of Scientific and Innovative Research (AcSIR), CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Judith Hariprakash
- G.N. Ramachandran Knowledge Center for Bioinformatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Sridhar Sivasubbu
- Academy of Scientific and Innovative Research (AcSIR), CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
- Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Vinod Scaria
- G.N. Ramachandran Knowledge Center for Bioinformatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.
- Academy of Scientific and Innovative Research (AcSIR), CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.
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25
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Xiang Y, Ye Y, Zhang Z, Han L. Maximizing the Utility of Cancer Transcriptomic Data. Trends Cancer 2018; 4:823-837. [PMID: 30470304 DOI: 10.1016/j.trecan.2018.09.009] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 09/23/2018] [Accepted: 09/24/2018] [Indexed: 12/13/2022]
Abstract
Transcriptomic profiling has been applied to large numbers of cancer samples, by large-scale consortia, including The Cancer Genome Atlas, International Cancer Genome Consortium, and Cancer Cell Line Encyclopedia. Advances in mining cancer transcriptomic data enable us to understand the endless complexity of the cancer transcriptome and thereby to discover new biomarkers and therapeutic targets. In this paper, we review computational resources for deep mining of transcriptomic data to identify, quantify, and determine the functional effects and clinical utility of transcriptomic events, including noncoding RNAs, post-transcriptional regulation, exogenous RNAs, and transcribed genetic variants. These approaches can be applied to other complex diseases, thereby greatly leveraging the impact of this work.
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Affiliation(s)
- Yu Xiang
- Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; These authors contributed equally
| | - Youqiong Ye
- Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; These authors contributed equally
| | - Zhao Zhang
- Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Leng Han
- Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Center for Precision Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
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26
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Wilusz JE. A 360° view of circular RNAs: From biogenesis to functions. WILEY INTERDISCIPLINARY REVIEWS. RNA 2018; 9:e1478. [PMID: 29655315 PMCID: PMC6002912 DOI: 10.1002/wrna.1478] [Citation(s) in RCA: 328] [Impact Index Per Article: 54.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 03/06/2018] [Accepted: 03/07/2018] [Indexed: 12/14/2022]
Abstract
The first circular RNA (circRNA) was identified more than 40 years ago, but it was only recently appreciated that circRNAs are common outputs of many eukaryotic protein-coding genes. Some circRNAs accumulate to higher levels than their associated linear mRNAs, especially in the nervous system, and have clear regulatory functions that result in organismal phenotypes. The pre-mRNA splicing machinery generates circRNAs via backsplicing reactions, which are often facilitated by intronic repeat sequences that base pair to one another and bring the intervening splice sites into close proximity. When spliceosomal components are limiting, circRNAs can become the preferred gene output, and backsplicing reactions are further controlled by exon skipping events and the combinatorial action of RNA binding proteins. This allows circRNAs to be expressed in a tissue- and stage-specific manner. Once generated, circRNAs are highly stable transcripts that often accumulate in the cytoplasm. The functions of most circRNAs remain unknown, but some can regulate the activities of microRNAs or be translated to produce proteins. Circular RNAs can further interface with the immune system as well as control gene expression events in the nucleus, including alternative splicing decisions. Circular RNAs thus represent a large class of RNA molecules that are tightly regulated, and it is becoming increasingly clear that they likely impact many biological processes. This article is categorized under: RNA Processing > Splicing Mechanisms RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution RNA Evolution and Genomics > Computational Analyses of RNA.
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Affiliation(s)
- Jeremy E. Wilusz
- Department of Biochemistry and Biophysics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
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27
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Holdt LM, Kohlmaier A, Teupser D. Molecular functions and specific roles of circRNAs in the cardiovascular system. Noncoding RNA Res 2018; 3:75-98. [PMID: 30159442 PMCID: PMC6096412 DOI: 10.1016/j.ncrna.2018.05.002] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Revised: 05/11/2018] [Accepted: 05/11/2018] [Indexed: 12/25/2022] Open
Abstract
As part of the superfamily of long noncoding RNAs, circular RNAs (circRNAs) are emerging as a new type of regulatory molecules that partake in gene expression control. Here, we review the current knowledge about circRNAs in cardiovascular disease. CircRNAs are not only associated with different types of cardiovascular disease, but they have also been identified as intracellular effector molecules for pathophysiological changes in cardiovascular tissues, and as cardiovascular biomarkers. This evidence is put in the context of the current understanding of general circRNA biogenesis and of known interactions of circRNAs with DNA, RNA, and proteins.
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Affiliation(s)
- Lesca M. Holdt
- Institute of Laboratory Medicine, University Hospital, LMU Munich, Germany
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28
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Izuogu OG, Alhasan AA, Mellough C, Collin J, Gallon R, Hyslop J, Mastrorosa FK, Ehrmann I, Lako M, Elliott DJ, Santibanez-Koref M, Jackson MS. Analysis of human ES cell differentiation establishes that the dominant isoforms of the lncRNAs RMST and FIRRE are circular. BMC Genomics 2018; 19:276. [PMID: 29678151 PMCID: PMC5910558 DOI: 10.1186/s12864-018-4660-7] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 04/12/2018] [Indexed: 01/16/2023] Open
Abstract
Background Circular RNAs (circRNAs) are predominantly derived from protein coding genes, and some can act as microRNA sponges or transcriptional regulators. Changes in circRNA levels have been identified during human development which may be functionally important, but lineage-specific analyses are currently lacking. To address this, we performed RNAseq analysis of human embryonic stem (ES) cells differentiated for 90 days towards 3D laminated retina. Results A transcriptome-wide increase in circRNA expression, size, and exon count was observed, with circRNA levels reaching a plateau by day 45. Parallel statistical analyses, controlling for sample and locus specific effects, identified 239 circRNAs with expression changes distinct from the transcriptome-wide pattern, but these all also increased in abundance over time. Surprisingly, circRNAs derived from long non-coding RNAs (lncRNAs) were found to account for a significantly larger proportion of transcripts from their loci of origin than circRNAs from coding genes. The most abundant, circRMST:E12-E6, showed a > 100X increase during differentiation accompanied by an isoform switch, and accounts for > 99% of RMST transcripts in many adult tissues. The second most abundant, circFIRRE:E10-E5, accounts for > 98% of FIRRE transcripts in differentiating human ES cells, and is one of 39 FIRRE circRNAs, many of which include multiple unannotated exons. Conclusions Our results suggest that during human ES cell differentiation, changes in circRNA levels are primarily globally controlled. They also suggest that RMST and FIRRE, genes with established roles in neurogenesis and topological organisation of chromosomal domains respectively, are processed as circular lncRNAs with only minor linear species. Electronic supplementary material The online version of this article (10.1186/s12864-018-4660-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Osagie G Izuogu
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK.,Present Address: The European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK
| | - Abd A Alhasan
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK.,Present Address: Institute of Cellular Medicine, William Leech Building, Medical School, Newcastle University, Newcastle-upon-Tyne, NE2 4HH, UK
| | - Carla Mellough
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK.,Present Address: Lions Eye Institute, 2 Verdun Street, Nedlands, WA, 6009, Australia
| | - Joseph Collin
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK
| | - Richard Gallon
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK
| | - Jonathon Hyslop
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK
| | - Francesco K Mastrorosa
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK
| | - Ingrid Ehrmann
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK
| | - Majlinda Lako
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK
| | - David J Elliott
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK
| | - Mauro Santibanez-Koref
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK
| | - Michael S Jackson
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK.
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29
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Gomes CP, Salgado-Somoza A, Creemers EE, Dieterich C, Lustrek M, Devaux Y. Circular RNAs in the cardiovascular system. Noncoding RNA Res 2018; 3:1-11. [PMID: 30159434 PMCID: PMC6084836 DOI: 10.1016/j.ncrna.2018.02.002] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 01/16/2018] [Accepted: 02/22/2018] [Indexed: 02/06/2023] Open
Abstract
Until recently considered as rare, circular RNAs (circRNAs) are emerging as important regulators of gene expression. They are ubiquitously expressed and represent a novel branch of the family of non-coding RNAs. Recent investigations showed that circRNAs are regulated in the cardiovascular system and participate in its physiological and pathological development. In this review article, we will provide an overview of the role of circRNAs in cardiovascular health and disease. After a description of the biogenesis of circRNAs, we will summarize what is known of the expression, regulation and function of circRNAs in the cardiovascular system. We will then address some technical aspects of circRNAs research, discussing how artificial intelligence may aid in circRNAs research. Finally, the potential of circRNAs as biomarkers of cardiovascular disease will be addressed and directions for future research will be proposed.
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Key Words
- Artificial intelligence
- Biomarker
- CRISPR, clustered regularly interspaced short palindromic repeats
- CV, cardiovascular
- Cardiovascular disease
- Cardiovascular system
- Circular RNAs
- DCM, dilated cardiomyopathy
- EMT, epithelial-mesenchymal transition
- Non-coding RNAs
- RNA-seq, RNA sequencing
- RPAD, RNase R treatment followed by polyadenylation and poly(A)+ RNA depletion
- RT-qPCR, reverse transcription quantitative polymerase chain reaction
- circRNAs, circular RNAs
- lncRNAs, long non-coding RNAs
- miRNAs, microRNAs
- ncRNAs, non-coding RNAs
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Affiliation(s)
- Clarissa P.C. Gomes
- Cardiovascular Research Unit, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | | | - Esther E. Creemers
- Experimental Cardiology, Academic Medical Center, Amsterdam, The Netherlands
| | - Christoph Dieterich
- German Center for Cardiovascular Research, University Hospital Heidelberg, Heidelberg, Germany
| | - Mitja Lustrek
- Department of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Yvan Devaux
- Cardiovascular Research Unit, Luxembourg Institute of Health, Luxembourg, Luxembourg
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30
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Gao Y, Zhao F. Computational Strategies for Exploring Circular RNAs. Trends Genet 2018; 34:389-400. [PMID: 29338875 DOI: 10.1016/j.tig.2017.12.016] [Citation(s) in RCA: 103] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Revised: 12/12/2017] [Accepted: 12/20/2017] [Indexed: 12/22/2022]
Abstract
Recent studies have demonstrated that circular RNAs (circRNAs) are ubiquitous and have diverse functions and mechanisms of biogenesis. In these studies, computational profiling of circRNAs has been prevalently used as an indispensable method to provide high-throughput approaches to detect and analyze circRNAs. However, without an overall understanding of the underlying strategies, these computational methods may not be appropriately selected or used for a specific research purpose, and some misconceptions may result in biases in the analyses. In this review we attempt to illustrate the key steps and summarize tradeoff of different strategies, covering all popular algorithms for circRNA detection and various downstream analyses. We also clarify some common misconceptions and put emphasis on the fields of application for these computational methods.
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Affiliation(s)
- Yuan Gao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fangqing Zhao
- Laboratory of Computational genomics, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China.
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31
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RNA sequencing and Prediction Tools for Circular RNAs Analysis. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1087:17-33. [DOI: 10.1007/978-981-13-1426-1_2] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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32
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Sulaiman SA, Abdul Murad NA, Mohamad Hanif EA, Abu N, Jamal R. Prospective Advances in Circular RNA Investigation. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1087:357-370. [PMID: 30259380 DOI: 10.1007/978-981-13-1426-1_28] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
circRNAs have emerged as one of the key regulators in many cellular mechanisms and pathogenesis of diseases. However, with the limited knowledge and current technologies for circRNA investigations, there are several challenges that need to be addressed for. These include challenges in understanding the regulation of circRNA biogenesis, experimental designs, and sample preparations to characterize the circRNAs in diseases as well as the bioinformatics pipelines and algorithms. In this chapter, we discussed the above challenges and possible strategies to overcome those limitations. We also addressed the differences between the existing applications and technologies to study the circRNAs in diseases. By addressing these challenges, further understanding of circRNAs roles and regulations as well as the discovery of novel circRNAs could be achieved.
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Affiliation(s)
- Siti Aishah Sulaiman
- Universiti Kebangsaan Malaysia (UKM) Medical Molecular Biology Institute (UMBI), Kuala Lumpur, Malaysia
| | - Nor Azian Abdul Murad
- Universiti Kebangsaan Malaysia (UKM) Medical Molecular Biology Institute (UMBI), Kuala Lumpur, Malaysia.
| | | | - Nadiah Abu
- Universiti Kebangsaan Malaysia (UKM) Medical Molecular Biology Institute (UMBI), Kuala Lumpur, Malaysia.
| | - Rahman Jamal
- Universiti Kebangsaan Malaysia (UKM) Medical Molecular Biology Institute (UMBI), Kuala Lumpur, Malaysia
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33
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Circular RNAs: Biogenesis, Function, and a Role as Possible Cancer Biomarkers. Int J Genomics 2017; 2017:6218353. [PMID: 29349062 PMCID: PMC5733622 DOI: 10.1155/2017/6218353] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Revised: 08/29/2017] [Accepted: 09/28/2017] [Indexed: 01/17/2023] Open
Abstract
Circular RNAs (circRNAs) are a class of noncoding RNAs (ncRNAs) that form covalently closed continuous loop structures, lacking the terminal 5' and 3' ends. CircRNAs are generated in the process of back-splicing and can originate from different genomic regions. Their unique circular structure makes circRNAs more stable than linear RNAs. In addition, they also display insensitivity to ribonuclease activity. Generally, circRNAs function as microRNA (miRNA) sponges and have a regulatory role in transcription and translation. They may be also translated in a cap-independent manner in vivo, to generate specific proteins. In the last decade, next-generation sequencing techniques, especially RNA-seq, have revealed great abundance and also dysregulation of many circRNAs in various diseases, suggesting their involvement in disease development and progression. Regarding their high stability and relatively specific differential expression patterns in tissues and extracellular environment (e.g., body fluids), they are regarded as promising novel biomarkers in cancer. Therefore, we focus this review on describing circRNA biogenesis, function, and involvement in human cancer development and address the potential of circRNAs to be effectively used as novel cancer diagnostic and prognostic biomarkers.
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34
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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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Affiliation(s)
- Xiangxiang Zeng
- School of Information Science and Engineering, Xiamen University, Xiamen, China
| | - Wei Lin
- School of Information Science and Engineering, Xiamen University, Xiamen, China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
| | - Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin, China
- * E-mail:
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35
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CirComPara: A Multi-Method Comparative Bioinformatics Pipeline to Detect and Study circRNAs from RNA-seq Data. Noncoding RNA 2017; 3:ncrna3010008. [PMID: 29657280 PMCID: PMC5832002 DOI: 10.3390/ncrna3010008] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Accepted: 01/27/2017] [Indexed: 12/22/2022] Open
Abstract
Circular RNAs (circRNAs) are generated by back-splicing of immature RNA forming covalently closed loops of intron/exon RNA molecules. Pervasiveness, evolutionary conservation, massive and regulated expression, and post-transcriptional regulatory roles of circRNAs in eukaryotes have been appreciated and described only recently. Moreover, being easily detectable disease markers, circRNAs undoubtedly represent a molecular class with high bearing on molecular pathobiology. CircRNAs can be detected from RNA-seq data using appropriate computational methods to identify the sequence reads spanning back-splice junctions that do not co-linearly map to the reference genome. To this end, several programs were developed and critical assessment of various strategies and tools suggested the combination of at least two methods as good practice to guarantee robust circRNA detection. Here, we present CirComPara (http://github.com/egaffo/CirComPara), an automated bioinformatics pipeline, to detect, quantify and annotate circRNAs from RNA-seq data using in parallel four different methods for back-splice identification. CirComPara also provides quantification of linear RNAs and gene expression, ultimately comparing and correlating circRNA and gene/transcript expression levels. We applied our method to RNA-seq data of monocyte and macrophage samples in relation to haploinsufficiency of the RNA-binding splicing factor Quaking (QKI). The biological relevance of the results, in terms of number, types and variations of circRNAs expressed, illustrates CirComPara potential to enlarge the knowledge of the transcriptome, adding details on the circRNAome, and facilitating further computational and experimental studies.
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36
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Izuogu OG, Alhasan AA, Alafghani HM, Santibanez-Koref M, Elliott DJ, Jackson MS. Erratum to: PTESFinder: a computational method to identify post-transcriptional exon shuffling (PTES) events. BMC Bioinformatics 2016; 17:92. [PMID: 26892454 PMCID: PMC4759711 DOI: 10.1186/s12859-016-0949-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Osagie G Izuogu
- Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK.
| | - Abd A Alhasan
- Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK
| | - Hani M Alafghani
- Security Forces Hostpital, P. O. Box 2748-24268-8541, Makkah, Kingdom of Saudi Arabia
| | | | - David J Elliott
- Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK
| | - Michael S Jackson
- Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, UK
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