Differential expression analysis of urinary exosomal circular RNAs in patients with IgA nephropathy

Nephrology ◽  
2021 ◽  
Author(s):  
Rumei Luan ◽  
Geng Tian ◽  
Xin Ci ◽  
Qian Zheng ◽  
Linlin Wu ◽  
...  
Keyword(s):  
Iga Nephropathy ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Circular Rnas

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.

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 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 Best practices on the differential expression analysis of multi-species RNA-seq

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Matthew Chung ◽  
Vincent M. Bruno ◽  
David A. Rasko ◽  
Christina A. Cuomo ◽  
José F. Muñoz ◽  
...  
Keyword(s):  
Best Practices ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Single Species ◽  
Rna Seq ◽  
Species Analysis ◽  
Differential Gene ◽  
Multiple Species ◽  
Downstream Analysis

AbstractAdvances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

scholarly journals The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Xueyi Dong ◽  
Luyi Tian ◽  
Quentin Gouil ◽  
Hasaru Kariyawasam ◽  
Shian Su ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Transcriptomic Analysis ◽  
Statistical Testing ◽  
Rna Seq ◽  
Sequencing Data ◽  
Short Read ◽  
Sequencing Platform ◽  
Long Read

Abstract Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.

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