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 A Systematic Analysis of Candidate Genes Associated with Nicotine Addiction

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Meng Liu ◽  
Xia Li ◽  
Rui Fan ◽  
Xinhua Liu ◽  
Ju Wang
Keyword(s):  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Nicotine Addiction ◽  
Clustering Coefficient ◽  
Functional Enrichment ◽  
Moderate Degree ◽  
Cancer Genes ◽  
Systematic Analysis ◽  
The Central Nervous System

Nicotine, as the major psychoactive component of tobacco, has broad physiological effects within the central nervous system, but our understanding of the molecular mechanism underlying its neuronal effects remains incomplete. In this study, we performed a systematic analysis on a set of nicotine addiction-related genes to explore their characteristics at network levels. We found that NAGenes tended to have a more moderate degree and weaker clustering coefficient and to be less central in the network compared to alcohol addiction-related genes or cancer genes. Further, clustering of these genes resulted in six clusters with themes in synaptic transmission, signal transduction, metabolic process, and apoptosis, which provided an intuitional view on the major molecular functions of the genes. Moreover, functional enrichment analysis revealed that neurodevelopment, neurotransmission activity, and metabolism related biological processes were involved in nicotine addiction. In summary, by analyzing the overall characteristics of the nicotine addiction related genes, this study provided valuable information for understanding the molecular mechanisms underlying nicotine addiction.

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.

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 Systematic analysis of the expression and prognosis relevance of FBXO family reveals the significance of FBXO1 in human breast cancer

2020 ◽  
Author(s):  
Yaqian Liu ◽  
Bo Pan ◽  
Weikun Qu ◽  
Yilong Cao ◽  
Jun Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Systematic Analysis ◽  
Cellular Processes ◽  
Kaplan Meier ◽  
Disease Indicators ◽  
Kaplan Meier Plotter

Abstract Background: Breast cancer (BC) remains a prevalent and common form of cancer with high heterogeneity. Making efforts to explore novel molecular biomarkers and serve as potential disease indicators, which is essential to effectively enhance the prognosis and individualized treatment of BC. FBXO proteins act as the core component of E3 ubiquitin ligase, which play essential regulators roles in multiple cellular processes. Recently, research has indicated that FBXOs also play significant roles in cancer development. However, the molecular functions of these family members in BC have not been fully elucidated.Methods: In this research, we investigated the expression data, survival relevance and mutation situation of 10 FBXO members (FBXO1, 2, 5, 6, 16, 17, 22, 28, 31 and 45) in patients with BC from the Oncomine, GEPIA, HPA, Kaplan-Meier Plotter, UALCAN and cBioPortal databases. The high transcriptional levels of FBXO1 in different subtypes of BC were verified by immunohistochemical staining and the specific mutations of FBXO1 were obtained from COSMIC database. Top 10 genes with the highest correlation to FBXO1 were identified through cBioPortal and COXPRESdb tools. Additionally, functional enrichment analysis, PPI network and survival relevance of FBXO1 and co-expressed genes in BC were obtained from DAVID, STRING, UCSC Xena, GEPIA, bc-GenExMiner and Kaplan-Meier Plotter databases.Results: We found that FBXO2, FBXO6, FBXO16 and FBXO17 were potential favorable prognostic factors for BC. FBXO1, FBXO5, FBXO22, FBXO28, FBXO31 and FBXO45 may be the independent poor prognostic factors for BC. All of them were correlated to clinicopathological staging. Moreover, we identified that FBXO1 was an excellent molecular biomarker and therapeutic target for BC. Conclusion: This study implies that FBXO1, FBXO2, FBXO5, FBXO6, FBXO16, FBXO17, FBXO22, FBXO28, FBXO31 and FBXO45 genes are potential clinical targets and prognostic biomarkers for patients with BC.

scholarly journals Construction and Validation of Predictive Model to Identify Critical Genes Associated with Advanced Kidney Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Guangda Xin ◽  
Guangyu Zhou ◽  
Wenlong Zhang ◽  
Xiaofei Zhang
Keyword(s):  
Kidney Disease ◽  
Prediction Model ◽  
Predictive Model ◽  
Learning Algorithm ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Functional Feature ◽  
Stage Renal Disease ◽  
End Stage

Background. Chronic kidney disease (CKD) is characterized by progressive renal function loss, which may finally lead to end-stage renal disease (ESRD). The study is aimed at identifying crucial genes related to CKD progressive and constructing a disease prediction model to investigate risk factors. Methods. GSE97709 and GSE37171 datasets were downloaded from the GEO database including peripheral blood samples from subjects with CKD, ESRD, and healthy controls. Differential expressed genes (DEGs) were identified and functional enrichment analysis. Machine learning algorithm-based prediction model was constructed to identify crucial functional feature genes related to ESRD. Results. A total of 76 DEGs were screened from CDK vs. normal samples while 10,114 DEGs were identified from ESRD vs. CDK samples. For numerous genes related to ESRD, several GO biological terms and 141 signaling pathways were identified including markedly upregulated olfactory transduction and downregulated platelet activation pathway. The DEGs were clustering in three modules according to WGCNA access, namely, ME1, ME2, and ME3. By construction of the XGBoost model and dataset validation, we screened cohorts of genes associated with progressive CKD, such as FZD10, FOXD4, and FAM215A. FZD10 represented the highest score ( F score = 21) in predictive model. Conclusion. Our results demonstrated that FZD10, FOXD4, PPP3R1, and UCP2 might be critical genes in CKD progression.

scholarly journals High-throughput sequence analysis reveals variation in the relative abundance of components of the bacterial and fungal microbiota in the rhizosphere of Ginkgo biloba

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8051
Author(s):  
Rujue Ruan ◽  
Zhifang Jiang ◽  
Yuhuan Wu ◽  
Maojun Xu ◽  
Jun Ni
Keyword(s):  
High Throughput ◽  
Ginkgo Biloba ◽  
High Throughput Sequencing ◽  
Sampling Site ◽  
Enrichment Analysis ◽  
Normal Growth ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Bulk Soil ◽  
Rrna Gene

Background The narrow region of soil, in contact with and directly influenced by plant roots, is called the rhizosphere. Microbes living in the rhizosphere are considered to be important factors for the normal growth and development of plants. In this research, the structural and functional diversities of microbiota between the Ginkgo biloba root rhizosphere and the corresponding bulk soil were investigated. Methods Three independent replicate sites were selected, and triplicate soil samples were collected from the rhizosphere and the bulk soil at each sampling site. The communities of bacteria and fungi were investigated using high-throughput sequencing of the 16S rRNA gene and the internal transcribed spacer (ITS) of the rRNA gene, respectively. Results A number of bacterial genera showed significantly different abundance in the rhizosphere compared to the bulk soil, including Bradyrhizobium, Rhizobium, Sphingomonas, Streptomyces and Nitrospira. Functional enrichment analysis of bacterial microbiota revealed consistently increased abundance of ATP-binding cassette (ABC) transporters and decreased abundance of two-component systems in the rhizosphere community, compared to the bulk soil community. In contrast, the situation was more complex and inconsistent for fungi, indicating the independency of the rhizosphere fungal community on the local microenvironment.

scholarly journals Comprehensive profiling of aberrant prognostic alternative splicing signatures in clear cell renal cell carcinoma: a retrospective study based on large-scale sequencing data

2020 ◽  
Author(s):  
Dong Zhang ◽  
Zhao Zhang ◽  
Yi Duan ◽  
Guangxu Ji ◽  
Hongliang Wu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Alternative Splicing ◽  
Renal Cell ◽  
Large Scale ◽  
Clear Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Sequencing Data ◽  
Cell Renal Cell Carcinoma

Abstract Backgroud: Clear cell renal cell carcinoma(ccRCC) is the most common type with poor prognosis in kidney tumor. Growing evidence has indicated that aberrant alternative splicing (AS) events are efficacious signatures for tumor prognosis predicting and therapeutic targets. Systematic and comprehensive analysis of AS in ccRCC is in urgent need.Methods: Level 3 RNA-seq data were acquired from TCGA data portal and the AS profiles were performed with assistance of SpliceSeq software. Univariate cox regression analysis was applied for screening prognosis-related AS events. Gene functional enrichment analysis revealed the pathways enriched by prognosis-related AS. The final AS panel was developed by LASSO-penalized method for predicting prognosis and compared with traditional clinical factors. The potential regulatory network was analyzed via Spearman correlation between splicing factors (SFs).Results: A total of 2100 survival-associated AS events were filtered from 1666 parent genes. Gene functional enrichment analysis suggested that the regulation of autophagy could be a potential mechanism of splicing regulatory in ccRCC. 17 aberrant AS events formed the final AS panel which can estimate OS probability in ccRCC patients. The AUC values of ROC curves for the final AS panel can keep above 0.7 spanning 1 year to 5 years.Conclusion: We developed a robust and individualized predictive model based on large-scale sequencing data. The identified vital AS events and splicing networks may be valuable in deciphering the potential mechanisms of AS on tumorigenesis of ccRCC.

scholarly journals bcbioRNASeq: R package for bcbio RNA-seq analysis

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1976 ◽  
Author(s):  
Michael J. Steinbaugh ◽  
Lorena Pantano ◽  
Rory D. Kirchner ◽  
Victor Barrera ◽  
Brad A. Chapman ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
R Package ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Control Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Post Process

RNA-seq analysis involves multiple steps from processing raw sequencing data to identifying, organizing, annotating, and reporting differentially expressed genes. bcbio is an open source, community-maintained framework providing automated and scalable RNA-seq methods for identifying gene abundance counts. We have developed bcbioRNASeq, a Bioconductor package that provides ready-to-render templates and wrapper functions to post-process bcbio output data. bcbioRNASeq automates the generation of high-level RNA-seq reports, including identification of differentially expressed genes, functional enrichment analysis and quality control analysis.

Identification of lncRNA-associated ceRNA network in high-grade serous ovarian cancer metastasis

Epigenomics ◽  
2020 ◽  
Vol 12 (14) ◽  
pp. 1175-1191
Author(s):  
Xi Li ◽  
Sihui Yu ◽  
Rui Yang ◽  
Qi Wang ◽  
Xiangnan Liu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Metastasis ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Sequencing Data ◽  
Expression Levels

Aim: To uncover a novel lncRNA–miRNA–mRNA network associated with high-grade serous ovarian cancer metastasis. Material & methods: The candidate differentially expressed lncRNAs were obtained from RNA-sequencing data and determined by functional experiments. The downstream miRNAs and mRNAs were identified by bioinformatic prediction and subjected to functional enrichment analysis. Results: The expression levels of lncRNA ENTPD1-AS1/PRANCR/NR2F2-AS1 were reduced in omental metastatic tissues. Similar differential expression patterns of these lncRNAs were also found in lnCAR database and we verified their tumor suppressive roles by performing functional experiments. Furthermore, we predicted miRNAs and mRNAs via bioinformatic tools and validated their alteration in expression levels in presence of lncRNA interference. Conclusion: We proposed a potential ceRNA regulatory mechanism in high-grade serous ovarian cancer omental metastasis

scholarly journals Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients

2019 ◽  
Author(s):  
Akram Mohammed ◽  
Yan Cui ◽  
Valeria R. Mas ◽  
Rishikesan Kamaleswaran
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
High Throughput Sequencing ◽  
Pediatric Intensive Care ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Health Condition ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Shock Gene

AbstractSeptic shock is a severe health condition caused by uncontrolled sepsis. Advancements in the high-throughput sequencing techniques have risen the number of potential genetic biomarkers under review. Multiple genetic markers and functional pathways play a part in the development and progression of pediatric septic shock. Fifty-four differentially expressed pediatric septic shock gene biomarkers were identified using gene expression data from 181 pediatric intensive care unit (PICU) patients within the first 24 hours of admission. The gene expression signatures discovered showed discriminatory power between pediatric septic shock survivors and nonsurvivors types. Using functional enrichment analysis of differentially expressed genes (DEGs), the known genes and pathways in septic shock were validated, and unexplored septic shock-related genes and functional groups were identified. Septic shock survivors were distinguished from septic shock non-survivors by differential expression of genes involved in the immune response, chemokine-mediated signaling, neutrophil chemotaxis, and chemokine activity. The identification of the septic shock gene biomarkers may facilitate in septic shock diagnosis, treatment, and prognosis.

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