scholarly journals DEBKS: A Tool to Detect Differentially Expressed Circular RNA

2020 ◽  
Author(s):  
Zelin Liu ◽  
Huiru Ding ◽  
Jianqi She ◽  
Chunhua Chen ◽  
Weiguang Zhang ◽  
...  
Keyword(s):  
Open Source ◽  
Rna Sequencing ◽  
Open Source Software ◽  
Simulated Data ◽  
Circular Rna ◽  
Host Gene ◽  
Circular Rnas ◽  
Biological Processes ◽  
Rna Seq ◽  
Disease Pathogenesis

AbstractCircular RNAs (circRNAs) are involved in various biological processes and in disease pathogenesis. However, only a small number of functional circRNAs have been identified among hundreds of thousands of circRNA species, partly because most current methods are based on circular junction counts and overlook the fact that circRNA is formed from the host gene by back-splicing (BS). To distinguish between expression originating from BS and that from the host gene, we present DEBKS, a software program to streamline the discovery of differential BS between two rRNA-depleted RNA sequencing (RNA-seq) sample groups. By applying real and simulated data and employing RT-qPCR for validation, we demonstrate that DEBKS is efficient and accurate in detecting circRNAs with differential BS events between paired and unpaired sample groups. DEBKS is available at https://github.com/yangence/DEBKS as open-source software.

scholarly journals Circall: fast and accurate methodology for discovery of circular RNAs from paired-end RNA-sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dat Thanh Nguyen ◽  
Quang Thinh Trac ◽  
Thi-Hau Nguyen ◽  
Ha-Nam Nguyen ◽  
Nir Ohad ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Simulated Data ◽  
High Sensitivity ◽  
Circular Rna ◽  
Computational Time ◽  
Circular Rnas ◽  
Rna Seq ◽  
Sequencing Data ◽  
Mapping Algorithm ◽  
False Discovery Rate Method

Abstract Background Circular RNA (circRNA) is an emerging class of RNA molecules attracting researchers due to its potential for serving as markers for diagnosis, prognosis, or therapeutic targets of cancer, cardiovascular, and autoimmune diseases. Current methods for detection of circRNA from RNA sequencing (RNA-seq) focus mostly on improving mapping quality of reads supporting the back-splicing junction (BSJ) of a circRNA to eliminate false positives (FPs). We show that mapping information alone often cannot predict if a BSJ-supporting read is derived from a true circRNA or not, thus increasing the rate of FP circRNAs. Results We have developed Circall, a novel circRNA detection method from RNA-seq. Circall controls the FPs using a robust multidimensional local false discovery rate method based on the length and expression of circRNAs. It is computationally highly efficient by using a quasi-mapping algorithm for fast and accurate RNA read alignments. We applied Circall on two simulated datasets and three experimental datasets of human cell-lines. The results show that Circall achieves high sensitivity and precision in the simulated data. In the experimental datasets it performs well against current leading methods. Circall is also substantially faster than the other methods, particularly for large datasets. Conclusions With those better performances in the detection of circRNAs and in computational time, Circall facilitates the analyses of circRNAs in large numbers of samples. Circall is implemented in C++ and R, and available for use at https://www.meb.ki.se/sites/biostatwiki/circall and https://github.com/datngu/Circall.

scholarly journals Rail-RNA: Scalable analysis of RNA-seq splicing and coverage

2015 ◽  
Author(s):  
Abhinav Nellore ◽  
Leonardo Collado-Torres ◽  
Andrew E Jaffe ◽  
José Alquicira-Hernández ◽  
Jacob Pritt ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Web Services ◽  
Open Source ◽  
Rna Sequencing ◽  
Open Source Software ◽  
Rna Seq ◽  
Spliced Alignment ◽  
Amazon Web Services ◽  
Scalable Analysis ◽  
Multiple Samples

RNA sequencing (RNA-seq) experiments now span hundreds to thousands of samples. Current spliced alignment software is designed to analyze each sample separately. Consequently, no information is gained from analyzing multiple samples together, and it is difficult to reproduce the exact analysis without access to original computing resources. We describe Rail-RNA, a cloud-enabled spliced aligner that analyzes many samples at once. Rail-RNA eliminates redundant work across samples, making it more efficient as samples are added. For many samples, Rail-RNA is more accurate than annotation-assisted aligners. We use Rail-RNA to align 667 RNA-seq samples from the GEUVADIS project on Amazon Web Services in under 16 hours for US$0.91 per sample. Rail-RNA produces alignments and base-resolution bigWig coverage files, ready for use with downstream packages for reproducible statistical analysis. We identify expressed regions in the GEUVADIS samples and show that both annotated and unannotated (novel) expressed regions exhibit consistent patterns of variation across populations and with respect to known confounders. Rail-RNA is open-source software available at http://rail.bio.

P5398CircRNAs deregulation in heart failure patients

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S Greco ◽  
A Made' ◽  
M Longo ◽  
R Tikhomirov ◽  
S Castelvecchio ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Functional Characterization ◽  
Enrichment Analysis ◽  
Host Gene ◽  
Circular Rnas ◽  
Amplification Efficiency ◽  
Rna Seq ◽  
Bioinformatics Pipeline

Abstract Background Circular RNAs (circRNAs) are an emerging class of noncoding RNAs stemming from the splicing and circularization of pre-mRNAs exons. CircRNAs can regulate transcription and splicing, sequester microRNAs acting as “sponge” and inducing the respective targets, and bind to RNA binding proteins. Recently, they have been found deregulated in dilated cardiomyopathies (DCM), one of the cardiovascular diseases with the worst rate of morbidity and mortality, and whose molecular mechanisms are only partially known. Purpose Therein, we will evaluate in ischemic DCM patients the modulation of 17 circRNAs, 14 out of them obtained from literature data on DCM ischemic or not, while the other 3 were circRNAs not characterized in the heart previously. The study aims to identify circRNAs candidates for further functional characterization in DCM. In addition, as differential expression (DE) analysis is not easily performed for circRNAs in RNA-seq datasets, the validated circRNAs will be used to set up the most specific and sensitive bioinformatics pipeline for circRNA-DE analysis. Methods We designed divergent and convergent specific primers for 17 circRNAs and their host gene, respectively, and their amplification efficiency was measured by RT-qPCR. Transcripts expression was measured in left ventricle biopsies of 12 patients affected by non end-stage ischemic HF and of 12 matched controls. Results We identified cPVT1, cANKRD17, cBPTF as DE, and validated the modulation of 5 out of the 14 DCM-related circRNAs (cHIPK3, cALPK2, cPCMTD1, cNEBL, cSLC8A1), while cPDRM5, cTTN1 showed opposite modulation, which may be due to the specific disease condition. All of them were modulated differently from the respective host gene. CircRNA/miRNA interactions were predicted using Starbase 3.0. Next, mRNAs-targets of the identified miRNAs were predicted by mirDIP 4.1 and intersected with gene expression datasets of the same patients, previously obtained by microarray analysis. We found that cBPTF and cANKRD17 might sponge 12 and 2 miRNAs, respectively. Enrichment analysis of the relevant targets identified several important pathways implicated in DCM, such as MAPK, FoxO, EGFR, VEGF and Insulin/IGF pathways. In addition, deep RNA-Seq analysis that is currently ongoing and the validated circRNAs will be used to optimize the bioinformatics pipeline for circRNA DE analysis. Conclusions We identified a subset of circRNAs deregulated in ischemic HF potentially implicated in HF pathogenesis.

scholarly journals The Landscape of MicroRNA, Piwi-Interacting RNA, and Circular RNA in Human Saliva

2015 ◽  
Vol 61 (1) ◽  
pp. 221-230 ◽  
Author(s):  
Jae Hoon Bahn ◽  
Qing Zhang ◽  
Feng Li ◽  
Tak-Ming Chan ◽  
Xianzhi Lin ◽  
...  
Keyword(s):  
Body Fluid ◽  
Expression Profiles ◽  
Embryonic Stem ◽  
Bioinformatic Analysis ◽  
Circular Rna ◽  
Human Saliva ◽  
Body Fluids ◽  
Individual Variability ◽  
Circular Rnas ◽  
Rna Seq

Abstract BACKGROUND Extracellular RNAs (exRNAs) in human body fluids are emerging as effective biomarkers for detection of diseases. Saliva, as the most accessible and noninvasive body fluid, has been shown to harbor exRNA biomarkers for several human diseases. However, the entire spectrum of exRNA from saliva has not been fully characterized. METHODS Using high-throughput RNA sequencing (RNA-Seq), we conducted an in-depth bioinformatic analysis of noncoding RNAs (ncRNAs) in human cell-free saliva (CFS) from healthy individuals, with a focus on microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and circular RNAs (circRNAs). RESULTS Our data demonstrated robust reproducibility of miRNA and piRNA profiles across individuals. Furthermore, individual variability of these salivary RNA species was highly similar to those in other body fluids or cellular samples, despite the direct exposure of saliva to environmental impacts. By comparative analysis of >90 RNA-Seq data sets of different origins, we observed that piRNAs were surprisingly abundant in CFS compared with other body fluid or intracellular samples, with expression levels in CFS comparable to those found in embryonic stem cells and skin cells. Conversely, miRNA expression profiles in CFS were highly similar to those in serum and cerebrospinal fluid. Using a customized bioinformatics method, we identified >400 circRNAs in CFS. These data represent the first global characterization and experimental validation of circRNAs in any type of extracellular body fluid. CONCLUSIONS Our study provides a comprehensive landscape of ncRNA species in human saliva that will facilitate further biomarker discoveries and lay a foundation for future studies related to ncRNAs in human saliva.

scholarly journals SimFuse: A Novel Fusion Simulator for RNA Sequencing (RNA-Seq) Data

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Yuxiang Tan ◽  
Yann Tambouret ◽  
Stefano Monti
Keyword(s):  
Sample Size ◽  
Rna Sequencing ◽  
High Throughput Sequencing ◽  
Performance Metrics ◽  
Simulated Data ◽  
Real Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
Detection Algorithms ◽  
Fusion Detection

The performance evaluation of fusion detection algorithms from high-throughput sequencing data crucially relies on the availability of data with known positive and negative cases of gene rearrangements. The use of simulated data circumvents some shortcomings of real data by generation of an unlimited number of true and false positive events, and the consequent robust estimation of accuracy measures, such as precision and recall. Although a few simulated fusion datasets from RNA Sequencing (RNA-Seq) are available, they are of limited sample size. This makes it difficult to systematically evaluate the performance of RNA-Seq based fusion-detection algorithms. Here, we present SimFuse to address this problem. SimFuse utilizes real sequencing data as the fusions’ background to closely approximate the distribution of reads from a real sequencing library and uses a reference genome as the template from which to simulate fusions’ supporting reads. To assess the supporting read-specific performance, SimFuse generates multiple datasets with various numbers of fusion supporting reads. Compared to an extant simulated dataset, SimFuse gives users control over the supporting read features and the sample size of the simulated library, based on which the performance metrics needed for the validation and comparison of alternative fusion-detection algorithms can be rigorously estimated.

scholarly journals P218 Interrogation of transcriptome data in RA for identifying disease susceptibility loci and predictors of treatment response

Rheumatology ◽  
2020 ◽  
Vol 59 (Supplement_2) ◽  
Author(s):  
Rhodri Smith ◽  
Nicola Goodson ◽  
Robert J Moots ◽  
Helen L Wright
Keyword(s):  
Treatment Response ◽  
Rna Sequencing ◽  
Pathway Analysis ◽  
Direct Sequencing ◽  
Group Analysis ◽  
Human Neutrophils ◽  
Analysis Tool ◽  
Transcriptome Data ◽  
Rna Seq ◽  
Disease Pathogenesis

Abstract Background Rheumatoid arthritis (RA) is a chronic autoimmune disease affecting approximately 1% of the Caucasian population worldwide causing significant morbidity. The genetics of disease pathogenesis remains poorly understood despite recent advances in high throughput genotyping and sequencing. Biological agents (e.g. TNF inhibitors, TNFi) have significantly impacted on disease management, however 30-40% of RA patients do not respond to this therapy. The aim of this study was to use transcriptome sequencing (RNA sequencing) data from human neutrophils to identify variants in RA that may underpin disease pathogenesis and predict response to biologic therapy. Methods RNA sequencing (RNA-seq) data from peripheral blood neutrophils isolated pre-TNFi treatment was analysed from 27 RA patients and 6 healthy controls. 21 RA patients subsequently responded to TNFi therapy (change in DAS28 >1.2). RNA-seq reads were mapped to the human genome (hg19) using TopHat2 and annotated using Cufflinks. Data was combined, calibrated and filtered using the Genome Analysis Tool Kit (GATK) to create a file of identified variants. These variants were subsequently interrogated using the VCFtools program package. Quality control parameters were applied in accordance with guidance and available literature, excluding variants that were: PHRED < 30, Minimum read depth < 4 and a loci sequencing success rate < 80%, with SNP clusters and indels also removed. Tajima D was used as a statistic for identifying regions of interest within the RNA-seq data. Identified variants were annotated and interrogated using the UCSC bioinformatics platform and pathway analysis of identified genes predicted through Ingenuity Pathway Analysis (IPA). Results GATK analysis identified 536,668 variants, which were refined to 5230 variants following application of QC parameters as specified with over 99% of variants excluded. RA patients had a mean Tajima-D score of 0.51 vs -0.19 in the controls (p < 0.0001) and furthermore had significantly more regions of transcriptome with extreme positive Tajima-D values (p < 0.0001). Bioinformatics analysis identified the variants with high Tajima-D scores to be within a number of biologically relevant loci, including NCF1, which has been associated with autoimmune diseases including SLE and is predictor of RA severity in rat models. IPA revealed that a number of the highest scoring variants were within loci that were linked via a gene network regulated by activation of Fcgamma receptors (FCGR1A/B/C, FCGR2A/B, FCGR3B) and p38 MAPK. Conclusion This study suggests that interrogation of transcriptome data has a role in elucidating the components underpinning RA pathogenesis, identifying a number of interesting loci that may contribute towards its missing heritability. However, such preliminary data will require validation through direct sequencing of variants and investigation in independent data sets as well sub-group analysis of treatment response to biological therapy. Disclosures R. Smith None. N. Goodson None. R.J. Moots None. H.L. Wright None.

scholarly journals Identification and integrated analysis of neuropathic pain-related circular RNA signature

2020 ◽  
Author(s):  
Kun Wang ◽  
Zhimin Zhou ◽  
Junping Bao ◽  
Dong Liu ◽  
Yuanbin Hu ◽  
...  
Keyword(s):  
Neuropathic Pain ◽  
Molecular Mechanisms ◽  
Circular Rna ◽  
Gene Expression Omnibus ◽  
Circular Rnas ◽  
Integrated Analysis ◽  
Biological Processes ◽  
Non Coding Rnas ◽  
Microarray Datasets ◽  
Competitive Endogenous Rna

Abstract Background: More and more evidences show that non-coding RNAs are involved in neuropathic pain, however, there are few reports on the regulatory mechanism of competitive endogenous RNA (ceRNA) in neuropathic pain. The purpose of this study is to explore the possible molecular mechanisms of neuropathic pain. Methods: We collected neuropathic pain-related microarray datasets providing expression profile of circular RNAs (circRNAs) and mRNAs from the Gene Expression Omnibus (GEO) and then performed bioinformatics analysis on them. Results: The present study has identified that up-regulated circRNAs primarily regulate the activity of focal adhesion-associated biological processes and down-regulated primarily regulate the activity of metabolic-associated biological processes by means of ceRNAs. Conclusions: Our data suggest that circRNAs may be candidates for pathogenesis in neuropathic pain and may be considered as promising therapeutic targets in the future.

scholarly journals Recent Applications of RNA Sequencing in Food and Agriculture

2021 ◽  
Author(s):  
Venkateswara R. Sripathi ◽  
Varsha C. Anche ◽  
Zachary B. Gossett ◽  
Lloyd T. Walker
Keyword(s):  
Rna Sequencing ◽  
Alternative Polyadenylation ◽  
Cost Effective ◽  
Circular Rnas ◽  
Rna Seq ◽  
Complete Collection ◽  
Specific Expression ◽  
Food And Agriculture ◽  
Allele Specific ◽  
Next Generation Sequencing Ngs

RNA sequencing (RNA-Seq) is the leading, routine, high-throughput, and cost-effective next-generation sequencing (NGS) approach for mapping and quantifying transcriptomes, and determining the transcriptional structure. The transcriptome is a complete collection of transcripts found in a cell or tissue or organism at a given time point or specific developmental or environmental or physiological condition. The emergence and evolution of RNA-Seq chemistries have changed the landscape and the pace of transcriptome research in life sciences over a decade. This chapter introduces RNA-Seq and surveys its recent food and agriculture applications, ranging from differential gene expression, variants calling and detection, allele-specific expression, alternative splicing, alternative polyadenylation site usage, microRNA profiling, circular RNAs, single-cell RNA-Seq, metatranscriptomics, and systems biology. A few popular RNA-Seq databases and analysis tools are also presented for each application. We began to witness the broader impacts of RNA-Seq in addressing complex biological questions in food and agriculture.

scholarly journals Circular RNA Foxo3: A Promising Cancer-Associated Biomarker

2021 ◽  
Vol 12 ◽  
Author(s):  
Tianli Yang ◽  
Yang Li ◽  
Feng Zhao ◽  
Liuhua Zhou ◽  
Ruipeng Jia
Keyword(s):  
Cancer Diagnosis ◽  
Circular Rna ◽  
Clinical Applications ◽  
Lymphocytic Leukemia ◽  
Circular Rnas ◽  
Biological Processes ◽  
Diagnosis And Prognosis ◽  
Forkhead Box ◽  
Non Coding Rnas ◽  
Downstream Gene Expression

Circular RNAs (circRNAs) are a class of novel non-coding RNAs (ncRNAs). Emerging evidence demonstrates that circRNAs play crucial roles in many biological processes by regulating linear RNA transcription, downstream gene expression and protein or peptide translation. Meanwhile, recent studies have suggested that circRNAs have the potential to be oncogenic or anti-oncogenic and play vital regulatory roles in the initiation and progression of tumors. Circular RNA Forkhead box O3 (circ-Foxo3, hsa_circ_0006404) is encoded by the human FOXO3 gene and is one of the most studied circular RNAs acting as a sponge for potential microRNAs (miRNAs) (Du et al., 2016). Previous studies have reported that circ-Foxo3 is involved in the development and tumorigenesis of a variety of cancers (bladder, gastric, acute lymphocytic leukemia, glioma, etc.). In this review, we summarize the current studies concerning circ-Foxo3 deregulation and the correlative mechanism in various human cancers. We also point out the potential clinical applications of this circRNA as a biomarker for cancer diagnosis and prognosis.

scholarly journals RNA Sequencing Reveals Novel LncRNAs/mRNAs Network Associated with Puerarin-mediated Inhibition of Cardiac Hypertrophy in Mice

2021 ◽  
Author(s):  
Shan Ye ◽  
Wei-Yang Chen ◽  
Caiwen Ou ◽  
Min-Sheng Chen
Keyword(s):  
Mouse Model ◽  
Cardiac Hypertrophy ◽  
Protective Effect ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Translation Initiation Factor ◽  
Initiation Factor ◽  
Biological Processes ◽  
Rna Seq ◽  
Qrt Pcr

Abstract Background: Evidence has demonstrated that puerarin is a potential drug for the treatment of cardiac hypertrophy. However, the precise underlying molecular mechanisms of the protective effect of puerarin are still unclear. Here, we aimed to explore the regulatory mechanisms of lncRNAs/mRNAs in a cardiac hypertrophy mouse model after puerarin treatment.Methods: A mouse model of cardiac hypertrophy was established by transverse aortic constriction (TAC). The echocardiography, tissue staining and western blot were used to examine the protective effect of puerarin. Then RNA sequencing (RNA-seq) was carried out to systematically analyze global gene expression. The target lncRNAs were confirmed using qRT-PCR. Moreover, a coding/non-coding gene co-expression (CNC) network was established to find the interaction of lncRNAs and mRNAs. The molecular functions, biological processes, molecular components and pathways of different expression mRNAs targeted by lncRNA were explored using Gene Ontology (GO) analysis and Kyto Encyclopedia of Genes and Genomes (KEGG) pathways analysis.Results: Puerarin exhibited obvious inhibitory effect in cardiac hypertrophy in TAC model. RNA-seq analysis was performed to investigate the lncRNAs and mRNAs expression patterns of cardiomyocytes in sham and TAC groups treated with or without puerarin. RNA-seq identified that TAC upregulated 19 lncRNAs and downregulated 18 lncRNAs, which could be revised by puerarin treatment (Fold change ≥ 3 and P< 0.05). Expression alterations of selected lncRNAs ENSMUST00000125726, ENSMUST00000143044 and ENSMUST00000212795 were confirmed by qRT-PCR. Pearson’s correlation coefficients of co-expression levels suggested that there was interactive relationship between those 3 validated altered lncRNAs and 5,500 mRNAs (r > 0.95 or r < −0.95). Those co-expressed mRNAs were enriched in some important biological processes such as vesicle-mediated transport, sin 3 complex, and translation initiation factor activity. KEGG analyses suggested that those lncRNA-interacted mRNAs were enriched in RNA transport, ribosome biogenesis in eukaryotes and proteasome signaling pathway. Conclusion: Puerarin may exert beneficial effects on cardiac hypertrophy through regulating the ENSMUST00000125726 /ENSMUST00000143044 / ENSMUST00000212795 -mRNAs network.

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