Circular RNA expression profiling in dorsal root ganglion of rats with peripheral nerve injury-induced neuropathic pain

2020 ◽  
Author(s):  
Wanxia Xiong ◽  
Fan Liu ◽  
jie wang ◽  
zhiyao wang
Keyword(s):  
Neuropathic Pain ◽  
Differential Expression ◽  
Expression Analysis ◽  
Functional Annotation ◽  
Molecular Mechanisms ◽  
High Throughput Sequencing ◽  
Differential Expression Analysis ◽  
Mapk Signaling ◽  
Circular Rnas ◽  
Next Generation Sequencing Technology

Abstract Background : Circular RNAs (circRNAs) comprise a class of endogenous species of RNA consisting of a covalently closed loop structure that is crucial for genetic and epigenetic regulation. The significance of circRNA in neuropathic pain remains to be investigated. Methods : The sciatic nerve chronic constriction injury (CCI) model was established to induce neuropathic pain. We performed genome-wide circRNA analysis of 4 paired DRG sample from CCI and NC rats via next generation sequencing technology. The differentially expressed circRNAs (DEcircRNAs) were identified by differential expression analysis and the expression profile of circRNAs was validated by quantitative real-time PCR (qPCR). Functional annotation analysis was performed to predict the function of DEcircRNAs. Results : A total of 374 DEcirRNAs were identified between CCI and NC rats using circRNA High-throughput sequencing (HTS). Expression levels of 9 DEcircRNAs were validated by qPCR. Functional annotation analysis showed that DEcircRNAs were mainly enriched in pathways and functions such as ‘dopaminergic synapse’, ‘renin secretion’, ‘MAPK signaling pathway’ and ‘neurogenesis’. Competing endogenous RNAs analysis showed that top 50 circRNAs exhibited interactions with four pain related miRNAs. Circ:chr2:33950934-33955969 is the largest node in the circRNA-miRNA interaction network. Conclusion : DEcircRNAs may advance our understanding of the molecular mechanisms underlying neuropathic pain. Key words : neuropathic pain, circRNA, CCI, differential expression analysis

scholarly journals A Comparison of Methods: Normalizing High-Throughput RNA Sequencing Data

2015 ◽  
Author(s):  
Rahul Reddy
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Differential Expression Analysis ◽  
Simulated Data ◽  
Sequencing Data ◽  
Technical Variability ◽  
Expression Studies ◽  
Normalization Methods

As RNA-Seq and other high-throughput sequencing grow in use and remain critical for gene expression studies, technical variability in counts data impedes studies of differential expression studies, data across samples and experiments, or reproducing results. Studies like Dillies et al. (2013) compare several between-lane normalization methods involving scaling factors, while Hansen et al. (2012) and Risso et al. (2014) propose methods that correct for sample-specific bias or use sets of control genes to isolate and remove technical variability. This paper evaluates four normalization methods in terms of reducing intra-group, technical variability and facilitating differential expression analysis or other research where the biological, inter-group variability is of interest. To this end, the four methods were evaluated in differential expression analysis between data from Pickrell et al. (2010) and Montgomery et al. (2010) and between simulated data modeled on these two datasets. Though the between-lane scaling factor methods perform worse on real data sets, they are much stronger for simulated data. We cannot reject the recommendation of Dillies et al. to use TMM and DESeq normalization, but further study of power to detect effects of different size under each normalization method is merited.

scholarly journals Accurate quantification of circular RNAs identifies extensive circular isoform switching events

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jinyang Zhang ◽  
Shuai Chen ◽  
Jingwen Yang ◽  
Fangqing Zhao
Keyword(s):  
False Discovery Rate ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Circular Rnas ◽  
Rna Seq ◽  
Rnase R ◽  
False Discovery ◽  
Rrna Depletion ◽  
Accurate Expression

AbstractDetection and quantification of circular RNAs (circRNAs) face several significant challenges, including high false discovery rate, uneven rRNA depletion and RNase R treatment efficiency, and underestimation of back-spliced junction reads. Here, we propose a novel algorithm, CIRIquant, for accurate circRNA quantification and differential expression analysis. By constructing pseudo-circular reference for re-alignment of RNA-seq reads and employing sophisticated statistical models to correct RNase R treatment biases, CIRIquant can provide more accurate expression values for circRNAs with significantly reduced false discovery rate. We further develop a one-stop differential expression analysis pipeline implementing two independent measures, which helps unveil the regulation of competitive splicing between circRNAs and their linear counterparts. We apply CIRIquant to RNA-seq datasets of hepatocellular carcinoma, and characterize two important groups of linear-circular switching and circular transcript usage switching events, which demonstrate the promising ability to explore extensive transcriptomic changes in liver tumorigenesis.

Identification of Critical Functional Modules and Signaling Pathways in Osteoporosis

2020 ◽  
Vol 15 ◽  
Author(s):  
Xiaowei Jiang ◽  
Pu Ying ◽  
Yingchao Shen ◽  
Yiming Miu ◽  
Wenbin Kong ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Differential Expression ◽  
Expression Analysis ◽  
Network Topology ◽  
Molecular Mechanisms ◽  
Differential Expression Analysis ◽  
Osteoclast Differentiation ◽  
Enrichment Analysis ◽  
Osteoclast Formation ◽  
Functional Modules

Background: Osteoporosis is the most common bone metabolic disease. Abnormal osteoclast formation and resorption play a fundamental role in osteoporosis pathogenesis. Recent researches have greatly broadened our understanding of molecular mechanisms of osteoporosis. However, the molecular mechanisms leading to osteoporosis are still not entirely clear. Objective: The purpose of this work is to study the critical regulatory genes, functional modules, and signaling pathways. Methods: Differential expression analysis, network topology-based analysis, and overrepresentation enrichment analysis (ORA) were used to identify differentially expressed genes (DEGs), gene subnetworks, and signaling pathways related to osteoporosis, respectively. Results: Differential expression analysis identified DEGs, such as POGLUT1, DAPK3 and NFKBIA, associated with osteoclastogenesis, which highlighted Notch, apoptosis and NF-kB signaling pathways. Network topology-based analysis identified the upregulated subnetwork characterized by EXOSC8 and DIS3L from the RNA exosome complex, and the downregulated subnetwork composed of histone deacetylases and the cofactors, MORF4L1 and JDP2. Furthermore, the overrepresentation enrichment analysis highlighted that corticotrophin-releasing hormone signaling pathway may affect osteoclastogenesis through its component NR4A1, and suppressing osteoclast differentiation and osteoclast bone resorption with urocortin (UCN). Conclusion: Our systematic analysis not only discovered novel molecular mechanisms, but also proposed potential drug targets for osteoporosis.

scholarly journals A comparative study of techniques for differential expression analysis on RNA-Seq data

2014 ◽  
Author(s):  
Zong Hong Zhang ◽  
Dhanisha J. Jhaveri ◽  
Vikki M. Marshall ◽  
Denis C. Bauer ◽  
Janette Edson ◽  
...  
Keyword(s):  
Comparative Study ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
False Positives ◽  
Sequencing Depth ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Next Generation Sequencing Technology

Recent advances in next-generation sequencing technology allow high-throughput cDNA sequencing (RNA-Seq) to be widely applied in transcriptomic studies, in particular for detecting differentially expressed genes between groups. Many software packages have been developed for the identification of differentially expressed genes (DEGs) between treatment groups based on RNA-Seq data. However, there is a lack of consensus on how to approach an optimal study design and choice of suitable software for the analysis. In this comparative study we evaluate the performance of three of the most frequently used software tools: Cufflinks-Cuffdiff2, DESeq and edgeR. A number of important parameters of RNA-Seq technology were taken into consideration, including the number of replicates, sequencing depth, and balanced vs. unbalanced sequencing depth within and between groups. We benchmarked results relative to sets of DEGs identified through either quantitative RT-PCR or microarray. We observed that edgeR performs slightly better than DESeq and Cuffdiff2 in terms of the ability to uncover true positives. Overall, DESeq or taking the intersection of DEGs from two or more tools is recommended if the number of false positives is a major concern in the study. In other circumstances, edgeR is slightly preferable for differential expression analysis at the expense of potentially introducing more false positives.

scholarly journals Profiling of plasma extracellular vesicle transcriptome reveals that circRNAs are prevalent and differ between multiple sclerosis patients and healthy controls

2021 ◽  
Author(s):  
Leire Iparraguirre ◽  
Ainhoa Alberro ◽  
Thomas Birkballe Hansen ◽  
Tamara Castillo-Triviño ◽  
Maider Muñoz-Culla ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Differential Expression ◽  
Extracellular Vesicles ◽  
Expression Analysis ◽  
Secondary Structure Prediction ◽  
Differential Expression Analysis ◽  
Circular Rnas ◽  
Healthy Controls ◽  
Type Distribution ◽  
Multiple Sclerosis Patients

Abstract Background Extracellular vesicles (EVs) are released by almost all cell types and are implicated in a number of biological and pathological processes including autoimmune diseases such as multiple sclerosis (MS). Differences in the number and cargo of plasma derived EVs have been described in MS. In this work, we attempt to characterise the EV RNA cargo of MS patients with particular attention to a recently discovered non coding RNA type, circular RNAs (circRNAs), which have been shown to play important roles in physiology and disease and hold a great biomarker potential. Methods Plasma was collected from 20 MS patients and 8 healthy controls (HC) and total RNA was isolated from plasma-derived extracellular vesicles isolated by differential centrifugation. Samples were pooled in disease status, sex and age paired groups and RNA-Sequenced with Illumina HiSeq X Ten after rRNA depletion. CircRNAs were detected by both find_circ and CIRI2 and their quantification was based on BSJ-spanning reads. Linear transcripts were quantified by HTSEq. Differential expression analysis was performed using DESeq2. RNA type distribution was analyzed based on biomart classification. MiRNA binding site number and density for circRNAs was calculated based on the TargetScan prediction performed by Circinteractome. CircRNA secondary structure prediction was calculated by their length normalized Gibbs free energy. All the statistical analysis were performed in Rstudio. Results The EV linear and circular transcriptome of MS patients and controls is characterized and compared to the transcriptome previously described in leucocytes. Results reveal differences in the RNA type distribution, showing that circRNAs are enriched in EVs compared to leucocytes. Nevertheless, highly structured circRNAs are preferentially retained in leukocytes. Additionally, differential expression analysis reports significant differences in circRNA and linear RNA expression between MS patients and controls as well as between different MS types. Conclusions The plasma derived EV RNA cargo is not a representation of leukocytes’ cytoplasm but a message that must be studied. Moreover, our results reveal the interest of circRNAs as part of this message highlighting the importance to further understand the RNA regulation in MS.

scholarly journals Construction of a ceRNA network in glioma and analysis of its clinical significance

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Guangdong Liu ◽  
Haihong Li ◽  
Wenyang Ji ◽  
Haidong Gong ◽  
Yan Jiang ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Gene Set Enrichment Analysis ◽  
Normal Brain ◽  
Central Nervous System Tumor ◽  
Cox Regression Analysis ◽  
Cerna Network

Abstract Background Glioma is the most common central nervous system tumor with a poor survival rate and prognosis. Previous studies have found that long non-coding RNA (lncRNA) and competitive endogenous RNA (ceRNA) play important roles in regulating various tumor mechanisms. We obtained RNA-Seq data of glioma and normal brain tissue samples from TCGA and GTEx databases and extracted the lncRNA and mRNA expression data. Further, we analyzed these data using weighted gene co-expression network analysis and differential expression analysis, respectively. Differential expression analysis was also carried out on the mRNA data from the GEO database. Further, we predicted the interactions between lncRNA, miRNA, and targeted mRNA. Using the CGGA data to perform univariate and multivariate Cox regression analysis on mRNA. Results We constructed a Cox proportional hazard regression model containing four mRNAs and performed immune infiltration analysis. Moreover, we also constructed a ceRNA network including 21 lncRNAs, two miRNAs, and four mRNAs, and identified seven lncRNAs related to survival that have not been previously studied in gliomas. Through the gene set enrichment analysis, we found four lncRNAs that may have a significant role in tumors and should be explored further in the context of gliomas. Conclusions In short, we identified four lncRNAs with research value for gliomas, constructed a ceRNA network in gliomas, and developed a prognostic prediction model. Our research enhances our understanding of the molecular mechanisms underlying gliomas, providing new insights for developing targeted therapies and efficiently evaluating the prognosis of gliomas.

scholarly journals Negative Binomial Additive Model for RNA-Seq Data Analysis

2019 ◽  
Author(s):  
Xu Ren ◽  
Pei Fen Kuan
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
High Throughput Sequencing ◽  
Negative Binomial ◽  
Differential Expression Analysis ◽  
Additive Model ◽  
Rna Seq ◽  
Smooth Functions ◽  
Linear Effect ◽  
Bayesian Shrinkage

SUMMARYHigh-throughput sequencing experiments followed by differential expression analysis is a widely used approach for detecting genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and co-variates of interest. Existing models assume linear effect of covariates, which is restrictive and may not be sufficient for some phenotypes. In this paper, we introduce NBAMSeq, a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. Specifically, we model the logarithm of mean gene counts as sums of smooth functions with the smoothing parameters and coefficients estimated simultaneously within a nested iterative method. The variance is estimated by the Bayesian shrinkage approach to fully exploit the information across all genes. Based on extensive simulation and case studies of RNA-Seq data, we show that NBAMSeq offers improved performance in detecting nonlinear effect and maintains equivalent performance in detecting linear effect compared to existing methods. Our proposed NBAMSeq is available for download athttps://github.com/reese3928/NBAMSeqand in submission to Bioconductor repository.

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