scholarly journals CircMiner: accurate and rapid detection of circular RNA through splice-aware pseudo-alignment scheme

2020 ◽  
Vol 36 (12) ◽  
pp. 3703-3711 ◽  
Author(s):  
Hossein Asghari ◽  
Yen-Yi Lin ◽  
Yang Xu ◽  
Ehsan Haghshenas ◽  
Colin C Collins ◽  
...  
Keyword(s):  
Cell Line ◽  
Rapid Detection ◽  
High Throughput Sequencing ◽  
Circular Rna ◽  
Supplementary Information ◽  
Circular Rnas ◽  
Alignment Technique ◽  
Nucleotide Resolution ◽  
High Degree ◽  
Splice Junctions

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.

scholarly journals circtools—a one-stop software solution for circular RNA research

2018 ◽  
Vol 35 (13) ◽  
pp. 2326-2328 ◽  
Author(s):  
Tobias Jakobi ◽  
Alexey Uvarovskii ◽  
Christoph Dieterich
Keyword(s):  
High Throughput Sequencing ◽  
Circular Rna ◽  
Statistical Testing ◽  
Supplementary Information ◽  
Circular Rnas ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Multi Stage ◽  
Sequence Reconstruction ◽  
One Stop

Abstract Motivation Circular RNAs (circRNAs) originate through back-splicing events from linear primary transcripts, are resistant to exonucleases, are not polyadenylated and have been shown to be highly specific for cell type and developmental stage. CircRNA detection starts from high-throughput sequencing data and is a multi-stage bioinformatics process yielding sets of potential circRNA candidates that require further analyses. While a number of tools for the prediction process already exist, publicly available analysis tools for further characterization are rare. Our work provides researchers with a harmonized workflow that covers different stages of in silico circRNA analyses, from prediction to first functional insights. Results Here, we present circtools, a modular, Python-based framework for computational circRNA analyses. The software includes modules for circRNA detection, internal sequence reconstruction, quality checking, statistical testing, screening for enrichment of RBP binding sites, differential exon RNase R resistance and circRNA-specific primer design. circtools supports researchers with visualization options and data export into commonly used formats. Availability and implementation circtools is available via https://github.com/dieterich-lab/circtools and http://circ.tools under GPLv3.0. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Biogenesis mechanisms of circular RNA can be categorized through feature extraction of a machine learning model

2019 ◽  
Vol 35 (23) ◽  
pp. 4867-4870
Author(s):  
Chengyu Liu ◽  
Yu-Chen Liu ◽  
Hsien-Da Huang ◽  
Wei Wang
Keyword(s):  
Feature Extraction ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Cpg Island ◽  
Cpg Islands ◽  
Circular Rna ◽  
Training Data ◽  
Supplementary Information ◽  
Circular Rnas ◽  
Genomic Locus

Abstract Motivation In recent years, multiple circular RNAs (circRNA) biogenesis mechanisms have been discovered. Although each reported mechanism has been experimentally verified in different circRNAs, no single biogenesis mechanism has been proposed that can universally explain the biogenesis of all tens of thousands of discovered circRNAs. Under the hypothesis that human circRNAs can be categorized according to different biogenesis mechanisms, we designed a contextual regression model trained to predict the formation of circular RNA from a random genomic locus on human genome, with potential biogenesis factors of circular RNA as the features of the training data. Results After achieving high prediction accuracy, we found through the feature extraction technique that the examined human circRNAs can be categorized into seven subgroups, according to the presence of the following sequence features: RNA editing sites, simple repeat sequences, self-chains, RNA binding protein binding sites and CpG islands within the flanking regions of the circular RNA back-spliced junction sites. These results support all of the previously reported biogenesis mechanisms of circRNA and solidify the idea that multiple biogenesis mechanisms co-exist for different subset of human circRNAs. Furthermore, we uncover a potential new links between circRNA biogenesis and flanking CpG island. We have also identified RNA binding proteins putatively correlated with circRNA biogenesis. Availability and implementation Scripts and tutorial are available at http://wanglab.ucsd.edu/star/circRNA. This program is under GNU General Public License v3.0. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Episo: quantitative estimation of RNA 5-methylcytosine at isoform level by high-throughput sequencing of RNA treated with bisulfite

2019 ◽  
Vol 36 (7) ◽  
pp. 2033-2039 ◽  
Author(s):  
Junfeng Liu ◽  
Ziyang An ◽  
Jianjun Luo ◽  
Jing Li ◽  
Feifei Li ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Quantitative Estimation ◽  
Supplementary Information ◽  
Biological Processes ◽  
Single Nucleotide ◽  
Rna Immunoprecipitation ◽  
Nucleotide Resolution ◽  
Human And Mouse ◽  
Single Nucleotide Resolution

Abstract Motivation RNA 5-methylcytosine (m5C) is a type of post-transcriptional modification that may be involved in numerous biological processes and tumorigenesis. RNA m5C can be profiled at single-nucleotide resolution by high-throughput sequencing of RNA treated with bisulfite (RNA-BisSeq). However, the exploration of transcriptome-wide profile and potential function of m5C in splicing remains to be elucidated due to lack of isoform level m5C quantification tool. Results We developed a computational package to quantify Epitranscriptomal RNA m5C at the transcript isoform level (named Episo). Episo consists of three tools: mapper, quant and Bisulfitefq, for mapping, quantifying and simulating RNA-BisSeq data, respectively. The high accuracy of Episo was validated using an improved m5C-specific methylated RNA immunoprecipitation (meRIP) protocol, as well as a set of in silico experiments. By applying Episo to public human and mouse RNA-BisSeq data, we found that the RNA m5C is not evenly distributed among the transcript isoforms, implying the m5C may subject to be regulated at isoform level. Availability and implementation Episo is released under the GNU GPLv3+ license. The resource code Episo is freely accessible from https://github.com/liujunfengtop/Episo (with Tophat/cufflink) and https://github.com/liujunfengtop/Episo/tree/master/Episo_Kallisto (with Kallisto). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals High-throughput sequencing identified circular RNA circUBE2K mediating RhoA associated bladder cancer phenotype via regulation of miR-516b-5p/ARHGAP5 axis

2021 ◽  
Vol 12 (8) ◽  
Author(s):  
Chen Yang ◽  
Zezhong Mou ◽  
Siqi Wu ◽  
Yuxi Ou ◽  
Zheyu Zhang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Circular Rna ◽  
Luciferase Reporter ◽  
Circular Rnas ◽  
Cell Phenotype ◽  
Invasion And Migration ◽  
Rhoa Activity

AbstractBladder cancer (BC) is known as a common and lethal urinary malignancy worldwide. Circular RNAs (circRNAs), an emerging non-coding RNA, participate in carcinogenesis process of several cancers including BC. In this study, high-throughput sequencing and RT-qPCR were applied to discover and validate abnormal high expression of circUBE2K in BC tissues. Fluorescence in situ hybridization (FISH) was used to detect hsa_circ_0009154 (circUBE2K) expression and subcellular localization in BC tissues. High circUBE2K predicted unfavorable prognoses in BCs, as well as correlated with clinical features. CCK8, transwell, EdU and wound healing assays demonstrated down-regulating circUBE2K decreased BC cell phenotype as proliferation, invasion, and migration, respectively. Further studies showed that circUBE2K promoted BC progression via sponging miR-516b-5p and enhancing ARHGAP5 expression through regulating RhoA activity. Dual-luciferase reporter, FISH and RNA pulldown assays were employed to verify the relationships among circUBE2K/miR-516b-5p/ARHGAP5/RhoA axis. Down-regulating miR-516b-5p or overexpressing ARHGAP5 restored RhoA activity mediated BC cell properties after silencing circUBE2K. Subcutaneous xenograft and metastasis model identified circUBE2K significantly increased BC cell metastasis and proliferation in-vivo. Taken together, we found that circUBE2K is a tumor-promoting circRNA in BC that functions as a ceRNA to regulate ARHGAP5 expression via sponging miR-516b-5p.

scholarly journals STAR Chimeric Post For Rapid Detection of Circular RNA and Fusion Transcripts

2017 ◽  
Author(s):  
Nicholas K. Akers ◽  
Eric E. Schadt ◽  
Bojan Losic
Keyword(s):  
False Positive ◽  
Rapid Detection ◽  
Software Package ◽  
Circular Rna ◽  
Large Datasets ◽  
Supplementary Information ◽  
High Dimensional ◽  
Rna Detection ◽  
Chimeric Rna ◽  
Detection And Quantification

AbstractMotivationThe biological relevance of chimeric RNA alignments is now well established. Chimera arising as chromosomal fusions are often drivers of cancer, and recently discovered circular RNA are only now being characterized. While software already exists for fusion discovery and quantitation, high false positive rates and high run-times hamper scalable fusion discovery on large datasets. Furthermore, very little software is available for circular RNA detection and quantification.ResultsHere we present STAR Chimeric Post (STARChip), a novel software package that processes chimeric alignments from the STAR aligner and produces annotated circular RNA and high precision fusions in a rapid, efficient, and scalable manner that is appropriate for high dimensional medical omics datasets.Availability and ImplementationSTARChip is available at https://github.com/LosicLab/[email protected] or [email protected] InformationSupplementary figures and tables are available online.

scholarly journals High-Throughput Data of Circular RNA Profiles in Human Temporal Cortex Tissue Reveals Novel Insights into Temporal Lobe Epilepsy

2018 ◽  
Vol 45 (2) ◽  
pp. 677-691 ◽  
Author(s):  
Jiaxin Li ◽  
Haijun Lin ◽  
Zhenrong Sun ◽  
Guanyi Kong ◽  
Xu Yan ◽  
...  
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Expression Profiles ◽  
Temporal Cortex ◽  
Bioinformatic Analysis ◽  
Circular Rna ◽  
Expression Level ◽  
Circular Rnas

Background/Aims: Circular RNAs (circRNAs) are a class of long noncoding RNAs with a closed loop structure that regulate gene expression as microRNA sponges. CircRNAs are more enriched in brain tissue, but knowledge of the role of circRNAs in temporal lobe epilepsy (TLE) has remained limited. This study is the first to identify the global expression profiles and characteristics of circRNAs in human temporal cortex tissue from TLE patients. Methods: Temporal cortices were collected from 17 TLE patients and 17 non-TLE patients. Total RNA was isolated, and high-throughput sequencing was used to profile the transcriptome of dysregulated circRNAs. Quantitative PCR was performed for the validation of changed circRNAs. Results: In total, 78983 circRNAs, including 15.29% known and 84.71% novel circRNAs, were detected in this study. Intriguingly, 442 circRNAs were differentially expressed between the TLE and non-TLE groups (fold change≥2.0 and FDR≤0.05). Of these circRNAs, 188 were up-regulated, and 254 were down-regulated in the TLE patient group. Eight circRNAs were validated by real-time PCR. Remarkably, circ-EFCAB2 was intensely up-regulated, while circ-DROSHA expression was significantly lower in the TLE group than in the non-TLE group (P<0.05). Bioinformatic analysis revealed that circ-EFCAB2 binds to miR-485-5p to increase the expression level of the ion channel CLCN6, while circ-DROSHA interacts with miR-1252-5p to decrease the expression level of ATP1A2. Conclusions: The dysregulations of circRNAs may reflect the pathogenesis of TLE and circ-EFCAB2 and circ-DROSHA might be potential therapeutic targets and biomarkers in TLE patients.

scholarly journals Analysis of Circular RNA Expression Profile in HEK 293T Cells Exposed to Ionizing Radiation

Dose-Response ◽  
2019 ◽  
Vol 17 (2) ◽  
pp. 155932581983779 ◽  
Author(s):  
Ningning He ◽  
Yuxiao Sun ◽  
Mengmeng Yang ◽  
Qianying Lu ◽  
Jinhan Wang ◽  
...  
Keyword(s):  
Ionizing Radiation ◽  
Messenger Rna ◽  
High Throughput Sequencing ◽  
Kinase Inhibitor ◽  
Mammalian Target Of Rapamycin ◽  
Growth Factor Receptor ◽  
Circular Rna ◽  
Circular Rnas ◽  
Signal Pathways ◽  
Network Analyses

Radiation therapy is one of the most common cancer treatments. It is important to understand how cells respond to ionizing radiation (IR) to improve therapeutic efficacy. Circular RNAs (circRNAs) recently have been found to regulate a variety of cellular processes. However, it is poorly defined that their expression pattern and their identity in cells following IR exposure. Here, we performed high-throughput sequencing and comprehensive analysis of circRNA expression in human embryonic kidney (HEK) 293T cells before and after irradiation. We identified totally 5592 circRNAs and discovered 1038 new circRNAs. We found 158 circRNAs with significantly differential expression after IR exposure. Among them, there were 61 upregulated and 97 downregulated circRNAs. Using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, and circRNA-microRNA-messenger RNA network analyses, we found the differentially expressed circRNAs might be involved in the signal pathways of oxidative phosphorylation, epithelial growth factor receptor (EGFR) tyrosine kinase inhibitor resistance, and mammalian target of rapamycin (mTOR) signaling.

scholarly journals circDeep: deep learning approach for circular RNA classification from other long non-coding RNA

2019 ◽  
Vol 36 (1) ◽  
pp. 73-80 ◽  
Author(s):  
Mohamed Chaabane ◽  
Robert M Williams ◽  
Austin T Stephens ◽  
Juw Won Park
Keyword(s):  
Deep Learning ◽  
Circular Rna ◽  
Supplementary Information ◽  
Circular Rnas ◽  
Protein Coding ◽  
Non Coding Rna ◽  
Gene Transcripts ◽  
Non Coding Rnas ◽  
Increase In Accuracy ◽  
High Level

Abstract Motivation Over the past two decades, a circular form of RNA (circular RNA), produced through alternative splicing, has become the focus of scientific studies due to its major role as a microRNA (miRNA) activity modulator and its association with various diseases including cancer. Therefore, the detection of circular RNAs is vital to understanding their biogenesis and purpose. Prediction of circular RNA can be achieved in three steps: distinguishing non-coding RNAs from protein coding gene transcripts, separating short and long non-coding RNAs and predicting circular RNAs from other long non-coding RNAs (lncRNAs). However, the available tools are less than 80 percent accurate for distinguishing circular RNAs from other lncRNAs due to difficulty of classification. Therefore, the availability of a more accurate and fast machine learning method for the identification of circular RNAs, which considers the specific features of circular RNA, is essential to the development of systematic annotation. Results Here we present an End-to-End deep learning framework, circDeep, to classify circular RNA from other lncRNA. circDeep fuses an RCM descriptor, ACNN-BLSTM sequence descriptor and a conservation descriptor into high level abstraction descriptors, where the shared representations across different modalities are integrated. The experiments show that circDeep is not only faster than existing tools but also performs at an unprecedented level of accuracy by achieving a 12 percent increase in accuracy over the other tools. Availability and implementation https://github.com/UofLBioinformatics/circDeep. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Circular RNA circYPEL2: A Novel Biomarker in Cervical Cancer

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 38
Author(s):  
Xinyang Zhang ◽  
Siqi Yang ◽  
Wenbo Chen ◽  
Xin Dong ◽  
Rongyu Zhang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cell Lines ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Strong Stability ◽  
Circular Rna ◽  
Migration And Invasion ◽  
Circular Rnas ◽  
Cancer Hallmarks ◽  
Potential Biomarker

Cervical cancer (CC) is one of the most threatening diseases in women. Circular RNAs (circRNAs) have been reported to be cancer hallmarks, but typical circRNAs in CC were rarely indicated. Through high-throughput sequencing in CC and normal cervix tissues, circYPEL2 (hsa_circ_0005600) was proposed as a candidate circRNA. CircYPEL2 exhibited significantly high expression in CC tissue and strong stability in CC cell lines. Furthermore, knockdown and overexpression of circYPEL2 indicated the potential involvement in CC proliferation, migration and invasion. Finally, the downstream regulatory genes of circYPEL2 were investigated by knockdown experiment in CC cell lines with high-throughput sequencing. In summary, our work identified circYPEL2 as a potential biomarker for clinical research of cervical cancer.

scholarly journals The role of circular RNA circ_0008285 in gestational diabetes mellitus by regulating the biological functions of trophoblasts

2021 ◽  
Vol 54 (1) ◽  
Author(s):  
Haitian Chen ◽  
Shaofeng Zhang ◽  
Yanxin Wu ◽  
Zhuyu Li ◽  
Dongyu Wang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
High Throughput Sequencing ◽  
Expression Profiles ◽  
Circular Rna ◽  
Circular Rnas ◽  
Invasion And Migration ◽  
And Migration

Abstract Background Circular RNAs (circRNAs) has emerged as vital regulator involved in various diseases. In this study, we identified and investigated the potential circRNAs involved in gestational diabetes mellitus (GDM). Methods High-throughput sequencing was used to collect the plasma circRNAs expression profiles of GDM patients. Quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) was used to measure the expressions of circ_0008285 and circ_0001173 in the plasma specimens. The Pearson’s correlation test was employed to assess the correlation between 2 circRNAs expression and the clinicopathologic data. Two circRNAs expression was verified in high glucose (HG)-induced HTR-8/SVneo cells. MTS, transwell assay was used to evaluate the effects of circ_0008285 expression on HG-induced HTR-8/SVneo cells. The network of circ_0008285 was constructed using cytocape. Results In GDM patients, the expression of circ_0008285 was significantly upregulated, while that of circ_0001173 was decreased. Circ_0008285 was significantly correlated with the total cholesterol and LDL-C levels. Circ_0001173 was significantly correlated with glycated hemoglobin. HG promoted the proliferation, invasion, and migration in HTR-8/SVneo cells, while the knockdown of circ_0008285 exerted reverse effects. In addition, network construction exhibited that circ_0008285 had 45 miRNA binding sites, which correlated with 444 mRNA. Conclusions circ_0008285 plays an important role and provides a clue for the usage of therapeutic targets in the development of GDM.

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