VirusCircBase: a database of virus circular RNAs

2020 ◽  
Author(s):  
Zena Cai ◽  
Yunshi Fan ◽  
Zheng Zhang ◽  
Congyu Lu ◽  
Zhaozhong Zhu ◽  
...  
Keyword(s):  
Influenza A ◽  
Enrichment Analysis ◽  
Computational Prediction ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Circular Rnas ◽  
Specific Cell ◽  
Sequencing Data ◽  
Kegg Pathways ◽  
Dsdna Viruses

Abstract Circular RNAs (circRNAs) are covalently closed long noncoding RNAs critical in diverse cellular activities and multiple human diseases. Several cancer-related viral circRNAs have been identified in double-stranded DNA viruses (dsDNA), yet no systematic study about the viral circRNAs has been reported. Herein, we have performed a systematic survey of 11 924 circRNAs from 23 viral species by computational prediction of viral circRNAs from viral-infection-related RNA sequencing data. Besides the dsDNA viruses, our study has also revealed lots of circRNAs in single-stranded RNA viruses and retro-transcribing viruses, such as the Zika virus, the Influenza A virus, the Zaire ebolavirus, and the Human immunodeficiency virus 1. Most viral circRNAs had reverse complementary sequences or repeated sequences at the flanking sequences of the back-splice sites. Most viral circRNAs only expressed in a specific cell line or tissue in a specific species. Functional enrichment analysis indicated that the viral circRNAs from dsDNA viruses were involved in KEGG pathways associated with cancer. All viral circRNAs presented in the current study were stored and organized in VirusCircBase, which is freely available at http://www.computationalbiology.cn/ViruscircBase/home.html and is the first virus circRNA database. VirusCircBase forms the fundamental atlas for the further exploration and investigation of viral circRNAs in the context of public health.

scholarly journals Immunological pathways of macrophage response to Brucella ovis infection

2020 ◽  
Vol 26 (7) ◽  
pp. 635-648
Author(s):  
Zhixiong Zhou ◽  
Guojing Gu ◽  
Yichen Luo ◽  
Wenjie Li ◽  
Bowen Li ◽  
...  
Keyword(s):  
Immune Response ◽  
Molecular Mechanisms ◽  
Group Versus ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Sequencing Data ◽  
Brucella Ovis ◽  
Qrt Pcr ◽  
Raw264.7 Macrophage

As the molecular mechanisms of Brucella ovis pathogenicity are not completely clear, we have applied a transcriptome approach to identify the differentially expressed genes (DEGs) in RAW264.7 macrophage infected with B. ovis. The DEGs related to immune pathway were identified by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) functional enrichment analysis. Quantitative real-time PCR (qRT-PCR) was performed to validate the transcriptome sequencing data. In total, we identified 337 up-regulated and 264 down-regulated DEGs in B. ovis-infected group versus mock group. Top 20 pathways were enriched by KEGG analysis and 20 GO by functional enrichment analysis in DEGs involved in the molecular function, cellular component, and biological process and so on, which revealed multiple immunological pathways in RAW264.7 macrophage cells in response to B. ovis infection, including inflammatory response, immune system process, immune response, cytokine activity, chemotaxis, chemokine-mediated signaling pathway, chemokine activity, and CCR chemokine receptor binding. qRT-PCR results showed Ccl2 (ENSMUST00000000193), Ccl2 (ENSMUST00000124479), Ccl3 (ENSMUST00000001008), Hmox1 (ENSMUST00000005548), Hmox1 (ENSMUST00000159631), Cxcl2 (ENSMUST00000075433), Cxcl2 (ENSMUST00000200681), Cxcl2 (ENSMUST00000200919), and Cxcl2 (ENSMUST00000202317). Our findings firstly elucidate the pathways involved in B. ovis-induced host immune response, which may lay the foundation for revealing the bacteria–host interaction and demonstrating the pathogenic mechanism of B. ovis.

KEGG-PATH: Kyoto encyclopedia of genes and genomes-based pathway analysis using a path analysis model

2014 ◽  
Vol 10 (9) ◽  
pp. 2441-2447 ◽  
Author(s):  
Junli Du ◽  
Zhifa Yuan ◽  
Ziwei Ma ◽  
Jiuzhou Song ◽  
Xiaoli Xie ◽  
...  
Keyword(s):  
Time Course ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Analysis Model ◽  
Kegg Pathways ◽  
Regulation Mechanisms ◽  
Complex Regulation ◽  
Multiple Treatments ◽  
Path Analysis Model

The KEGG-PATH approach, a kind of data mining through functional enrichment analysis of time-course experiments or those involving multiple treatments, can uncover the complex regulation mechanisms of KEGG pathways through the subdivision of total effect.

Novel insights toward human stroke-related epigenetics: circular RNA and its impact in poststroke processes

Epigenomics ◽  
2020 ◽  
Vol 12 (22) ◽  
pp. 1957-1968
Author(s):  
Pablo W Silva ◽  
Samara M M Shimon ◽  
Leonardo M de Brito ◽  
Laís Reis-das-Mercês ◽  
Leandro Magalhães ◽  
...  
Keyword(s):  
Rna Binding ◽  
Rna Binding Proteins ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Circular Rna ◽  
Functional Enrichment ◽  
Circular Rnas ◽  
Global Expression Profile ◽  
Human Stroke ◽  
Target Rna

Aim: Circular RNAs (circRNAs) are dysregulated in complex diseases, so we investigated their global expression profile in stroke. Material & methods: Public RNA-Seq data of human ischemic stroke lesion tissues and controls were used to perform the global expression analysis. Target RNA binding proteins and microRNAs were predicted in silico. Functional enrichment analysis was performed to infer the circRNAs’ potential roles. Results: We found that circRNAs are potentially involved in synaptic components and transmission, inflammation and ataxia. An integrative analysis revealed that hsa_circ_0078299 and FXN may be major players in the molecular stroke-context. Conclusion: Our results suggest a broad involvement of circRNAs in some stroke-related processes, indicating their potential as therapeutic targets to allow neuroprotection and brain recovery.

78 Effects of invitro production on the epigenome and transcriptome of bovine embryos determined through a multi-omics data integration approach

2021 ◽  
Vol 33 (2) ◽  
pp. 147
Author(s):  
M. Rabaglino ◽  
J. B.-M. Secher ◽  
P. Hyttel ◽  
H. Kadarmideen
Keyword(s):  
Survival Rates ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
P Value ◽  
Kegg Pathways ◽  
The Past ◽  
Before And After

In cattle, ovarian superovulation followed by invivo embryo collection and transfer (MOET), and the invitro production (IVP) of embryos are used all over the world to improve animal genetics. Application of MOET has resulted in the production of billions of healthy animals during the past 40 years, and IVP has evolved and given rise to significant numbers of calves during the past 10 years. Nevertheless, the use of MOET and IVP can affect the embryo epigenome, and therefore its transcriptome, before and after elongation, as shown by different studies. The integration of publicly available epigenome-transcriptome datasets generated by these studies could lead to a robust characterisation of the impacts of the application of MOET and IVP. The goal of this study was to integrate all publicly available data about MOET and IVP embryos to determine temporally differentially methylated regions (DMRs) and differentially expressed genes (DEGs) from blastocyst to elongation between IVP and MOET embryos. Datasets were downloaded from the Gene Expression Omnibus (GEO) database. Accession numbers were (1) for epigenomics: GSE69173, GSE97517, and GSE101895, plus one provided dataset from O’Doherty et al. (2018 BMC Genomics, 19, 438; https://doi.org/10.1186/s12864-018-4818-3), all hybridized to the EDMA platform GPL18384; (2) for transcriptomics: GSE12327, GSE21030, GSE24596, GSE24936, GSE27817, and GSE40101, all hybridized to the Affymetrix platform GPL2112. Both types of data were analysed with the limma package for R software, and functional enrichment analysis was done with the DAVID database. For DMRs, comparisons between IVP and MOET were made from spherical blastocysts (n=16 per group) on Day 7, to embryos on Day 15, specifically in the trophectoderm (TE) or embryonic disc (ED) regions (n=4 per region and per group). For DEGs, comparisons between IVP and MOET were made from spherical blastocysts (n=9 per group) to elongated blastocysts on Day 13 and embryos undergoing gastrulation on Day 16 (n=6 per group). Considering a P-value <0.05 and fold-change >2, there were 16 672 (TE) and 26 264 (ED) DMRs and 2236 DEGs that temporally differed between IVP and MOET. Most of the identified DMRs were found in intronic regions (around 36%) rather than exonic regions (8%). However, DMRs that were more methylated at IVP compared with MOET contained exons encoding for genes that enriched the Wnt signalling Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway in the ED, and focal adhesion and ECM-receptor interaction KEGG pathways (P<0.05) in the TE. Accordingly, DEGs with lower expression in elongated embryos (Day 13 and Day 16) at IVP as opposed to MOET were mainly associated with these three pathways. In conclusion, this multi-omics analysis demonstrates that even when embryos are produced under different conditions and experiments, the main changes imposed by IVP affected genes involved in embryonic development and adhesion to the endometrium, which could explain the lower survival rates at IVP compared with MOET.

scholarly journals Comprehensive Bioinformatics Identifies Key microRNA Players in ATG7-Deficient Lung Fibroblasts

2020 ◽  
Vol 21 (11) ◽  
pp. 4126 ◽  
Author(s):  
Stevan D. Stojanović ◽  
Maximilian Fuchs ◽  
Jan Fiedler ◽  
Ke Xiao ◽  
Anna Meinecke ◽  
...  
Keyword(s):  
Large Scale ◽  
Expression Profiles ◽  
Quantitative Polymerase Chain Reaction ◽  
Human Fibroblasts ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Lung Fibroblasts ◽  
Specific Cell ◽  
Proteome Profiling

Background: Deficient autophagy has been recently implicated as a driver of pulmonary fibrosis, yet bioinformatics approaches to study this cellular process are lacking. Autophagy-related 5 and 7 (ATG5/ATG7) are critical elements of macro-autophagy. However, an alternative ATG5/ATG7-independent macro-autophagy pathway was recently discovered, its regulation being unknown. Using a bioinformatics proteome profiling analysis of ATG7-deficient human fibroblasts, we aimed to identify key microRNA (miR) regulators in autophagy. Method: We have generated ATG7-knockout MRC-5 fibroblasts and performed mass spectrometry to generate a large-scale proteomics dataset. We further quantified the interactions between various proteins combining bioinformatics molecular network reconstruction and functional enrichment analysis. The predicted key regulatory miRs were validated via quantitative polymerase chain reaction. Results: The functional enrichment analysis of the 26 deregulated proteins showed decreased cellular trafficking, increased mitophagy and senescence as the major overarching processes in ATG7-deficient lung fibroblasts. The 26 proteins reconstitute a protein interactome of 46 nodes and miR-regulated interactome of 834 nodes. The miR network shows three functional cluster modules around miR-16-5p, miR-17-5p and let-7a-5p related to multiple deregulated proteins. Confirming these results in a biological setting, serially passaged wild-type and autophagy-deficient fibroblasts displayed senescence-dependent expression profiles of miR-16-5p and miR-17-5p. Conclusions: We have developed a bioinformatics proteome profiling approach that successfully identifies biologically relevant miR regulators from a proteomics dataset of the ATG-7-deficient milieu in lung fibroblasts, and thus may be used to elucidate key molecular players in complex fibrotic pathological processes. The approach is not limited to a specific cell-type and disease, thus highlighting its high relevance in proteome and non-coding RNA research.

scholarly journals Comparative Transcriptomic Analysis to Identify the Important Coding and Non-coding RNAs Involved in the Pathogenesis of Pterygium

2021 ◽  
Vol 12 ◽  
Author(s):  
Xin Liu ◽  
Jing Zhang ◽  
Danyao Nie ◽  
Kun Zeng ◽  
Huiling Hu ◽  
...  
Keyword(s):  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Scientific Basis ◽  
Functional Enrichment ◽  
Circular Rnas ◽  
Messenger Rnas ◽  
Protein Protein Interaction ◽  
Matrix Organization ◽  
Non Coding Rnas

Pterygium is a common ocular surface disease characterized by abnormal fibrovascular proliferation and invasion, similar to tumorigenesis. The formation of tumors is related to a change in the expression of various RNAs; however, whether they are involved in the formation and development of pterygium remains unclear. In this study, transcriptome analysis of messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) of paired pterygium and normal conjunctiva was performed to explore key genes regulating the development of pterygium. In total, 579 mRNAs, 275 lncRNAs, and 21 circRNAs were differentially expressed (DE) in pterygium compared with paired conjunctival tissues. Functional enrichment analysis indicated that DE RNAs were associated with extracellular matrix organization, blood vessel morphogenesis, and focal adhesion. Furthermore, through protein-protein interaction network and mRNA-lncRNA co-expression network analysis, key mRNAs including FN1, VCAM1, and MMP2, and key lncRNAs including MIR4435-2HG and LINC00968 were screened and might be involved in the pathogenesis of pterygium. In addition, several circRNAs including hsa_circ_0007482 and hsa_circ_001730 were considered to be involved in the pterygium development. This study provides a scientific basis for elucidating the pathogenesis of pterygium and will be beneficial for the development of preventive and therapeutic strategies.

scholarly journals PEA: an integrated R toolkit for plant epitranscriptome analysis

2017 ◽  
Author(s):  
Jingjing Zhai ◽  
Jie Song ◽  
Qian Cheng ◽  
Yunjia Tang ◽  
Chuang Ma
Keyword(s):  
High Throughput Sequencing ◽  
Learning Algorithm ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Classification Problem ◽  
Learning Technologies ◽  
Functional Enrichment ◽  
Sequencing Data ◽  
Systematic Analysis ◽  
Comprehensive Collection

AbstractMotivationThe epitranscriptome, also known as chemical modifications of RNA (CMRs), is a newly discovered layer of gene regulation, the biological importance of which emerged through analysis of only a small fraction of CMRs detected by high-throughput sequencing technologies. Understanding of the epitranscriptome is hampered by the absence of computational tools for the systematic analysis of epitranscriptome sequencing data. In addition, no tools have yet been designed for accurate prediction of CMRs in plants, or to extend epitranscriptome analysis from a fraction of the transcriptome to its entirety.ResultsHere, we introduce PEA, an integrated R toolkit to facilitate the analysis of plant epitranscriptome data. The PEA toolkit contains a comprehensive collection of functions required for read mapping, CMR calling, motif scanning and discovery, and gene functional enrichment analysis. PEA also takes advantage of machine learning technologies for transcriptome-scale CMR prediction, with high prediction accuracy, using the Positive Samples Only Learning algorithm, which addresses the two-class classification problem by using only positive samples (CMRs), in the absence of negative samples (non-CMRs). Hence PEA is a versatile epitranscriptome analysis pipeline covering CMR calling, prediction, and annotation, and we describe its application to predict N6-methyladenosine (m6A) modifications in Arabidopsis thaliana. Experimental results demonstrate that the toolkit achieved 71.6% sensitivity and 73.7% specificity, which is superior to existing m6A predictors. PEA is potentially broadly applicable to the in-depth study of epitranscriptomics.AvailabilityPEA is implemented using R and available at https://github.com/cma2015/PEA.

scholarly journals Prognostic Value of a Novel Signature With Nine Hepatitis C Virus-Induced Genes in Hepatic Cancer by Mining GEO and TCGA Databases

2021 ◽  
Vol 9 ◽  
Author(s):  
Jianming Wei ◽  
Bo Wang ◽  
Xibo Gao ◽  
Daqing Sun
Keyword(s):  
Hepatitis C ◽  
High Risk ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hepatic Cancer ◽  
Prognostic Signature ◽  
Kegg Pathways ◽  
Induced Genes

BackgroundHepatitis C virus-induced genes (HCVIGs) play a critical role in regulating tumor development in hepatic cancer. The role of HCVIGs in hepatic cancer remains unknown. This study aimed to construct a prognostic signature and assess the value of the risk model for predicting the prognosis of hepatic cancer.MethodsDifferentially expressed HCVIGs were identified in hepatic cancer data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases using the library (“limma”) package of R software. The protein–protein interaction (PPI) network was constructed using the Cytoscape software. Functional enrichment analysis was performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Univariate and multivariate Cox proportional hazard regression analyses were applied to screen for prognostic HCVIGs. The signature of HCVIGs was constructed. Gene Set Enrichment Analysis (GSEA) compared the low-risk and high-risk groups. Finally, the International Cancer Genome Consortium (ICGC) database was used to validate this prognostic signature. Polymerase chain reaction (PCR) was performed to validate the expression of nine HCVIGs in the hepatic cancer cell lines.ResultsA total of 143 differentially expressed HCVIGs were identified in TCGA hepatic cancer dataset. Functional enrichment analysis showed that DNA replication was associated with the development of hepatic cancer. The risk score signature was constructed based on the expression of ZIC2, SLC7A11, PSRC1, TMEM106C, TRAIP, DTYMK, FAM72D, TRIP13, and CENPM. In this study, the risk score was an independent prognostic factor in the multivariate Cox regression analysis [hazard ratio (HR) = 1.433, 95% CI = 1.280–1.605, P < 0.001]. The overall survival curve revealed that the high-risk group had a poor prognosis. The Kaplan–Meier Plotter online database showed that the survival time of hepatic cancer patients with overexpression of HCVIGs in this signature was significantly shorter. The prognostic signature-associated GO and KEGG pathways were significantly enriched in the risk group. This prognostic signature was validated using external data from the ICGC databases. The expression of nine prognostic genes was validated in HepG2 and LO-2.ConclusionThis study evaluates a potential prognostic signature and provides a way to explore the mechanism of HCVIGs in hepatic cancer.

scholarly journals Regulatory role of non-coding RNA in ginseng rusty root symptom tissue

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xingbo Bian ◽  
Pengcheng Yu ◽  
Ling Dong ◽  
Yan Zhao ◽  
He Yang ◽  
...  
Keyword(s):  
Fatty Acid ◽  
Regulatory Networks ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Circular Rnas ◽  
Primary Metabolism ◽  
Non Coding Rna

AbstractGinseng rusty root symptom (GRS) is one of the primary diseases of ginseng. It leads to a severe decline in the quality of ginseng and significantly affects the ginseng industry. The regulatory mechanism of non-coding RNA (ncRNA) remains unclear in the course of disease. This study explored the long ncRNAs (lncRNAs), circular RNAs (circRNAs), and microRNAs (miRNAs) in GRS tissues and healthy ginseng (HG) tissues and performed functional enrichment analysis of the screened differentially expressed ncRNAs. Considering the predictive and regulatory effects of ncRNAs on mRNAs, we integrated ncRNA and mRNA data to analyze and construct relevant regulatory networks. A total of 17,645 lncRNAs, 245 circRNAs, and 299 miRNAs were obtained from HG and GRS samples, and the obtained ncRNAs were characterized, including the classification of lncRNAs, length and distribution of circRNA, and the length and family affiliations of miRNAs. In the analysis of differentially expressed ncRNA target genes, we found that lncRNAs may be involved in the homeostatic process of ginseng tissues and that lncRNAs, circRNAs, and miRNAs are involved in fatty acid-related regulation, suggesting that alterations in fatty acid-related pathways may play a key role in GRS. Besides, differentially expressed ncRNAs play an essential role in regulating transcriptional translation processes, primary metabolism such as starch and sucrose, and secondary metabolism such as alkaloids in ginseng tissues. Finally, we integrated the correlations between ncRNAs and mRNAs, constructed corresponding interaction networks, and identified ncRNAs that may play critical roles in GRS. These results provide a basis for revealing GRS's molecular mechanism and enrich our understanding of ncRNAs in ginseng.

scholarly journals CRAFT: a bioinformatics software for custom prediction of circular RNA functions

2021 ◽  
Author(s):  
Anna Dal Molin ◽  
Enrico Gaffo ◽  
Valeria Difilippo ◽  
Alessia Buratin ◽  
Caterina Tretti Parenzan ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Circular Rna ◽  
Functional Enrichment ◽  
Circular Rnas ◽  
Cellular Processes ◽  
Functional Investigation

Circular RNAs (circRNAs), transcripts generated by backsplicing, are particularly stable and pleiotropic molecules, whose dysregulation drives human diseases and cancer by modulating gene expression and signaling pathways. CircRNAs can regulate cellular processes by different mechanisms, including interaction with microRNAs (miRNAs) and RNA-binding proteins (RBP), and encoding specific peptides. The prediction of circRNA functions is instrumental to interpret their impact in diseases, and to prioritize circRNAs for functional investigation. Currently, circRNA functional predictions are provided by web databases that do not allow custom analyses, while self-standing circRNA prediction tools are mostly limited to predict only one type of function, mainly focusing on the miRNA sponge activity of circRNAs. To solve these issues, we developed CRAFT (CircRNA Function prediction Tool), a freely available computational pipeline that predicts circRNA sequence and molecular interactions with miRNAs and RBP, along with their coding potential. Analysis of a set of circRNAs with known functions has been used to appraise CRAFT predictions and to optimize its setting. CRAFT provides a comprehensive graphical visualization of the results, links to several knowledge databases, and extensive functional enrichment analysis. Moreover, it originally combines the predictions for different circRNAs. CRAFT is a useful tool to help the user explore the potential regulatory networks involving the circRNAs of interest and generate hypotheses about the cooperation of circRNAs into the modulation of biological processes.

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