scholarly journals Identification, annotation and visualisation of extreme changes in splicing from RNA-seq experiments with SwitchSeq

2014 ◽  
Author(s):  
Mar Gonzàlez-Porta ◽  
Alvis Brazma
Keyword(s):  
Enrichment Analysis ◽  
R Package ◽  
Third Party ◽  
Pathway Enrichment Analysis ◽  
Rna Seq ◽  
Differential Splicing ◽  
Protein Coding ◽  
Downstream Analysis ◽  
Intuitive Manner ◽  
Abundant Transcript

In the past years, RNA sequencing has become the method of choice for the study of transcriptome composition. When working with this type of data, several tools exist to quantify differences in splicing across conditions and to address the significance of those changes. However, the number of genes predicted to undergo differential splicing is often high, and further interpretation of the results becomes a challenging task. Here we present SwitchSeq, a novel set of tools designed to help the users in the interpretation of differential splicing events that affect protein coding genes. More specifically, we provide a framework to identify switch events, i.e., cases where, for a given gene, the identity of the most abundant transcript changes across conditions. The identified events are then annotated by incorporating information from several public databases and third-party tools, and are further visualised in an intuitive manner with the independent R package tviz. All the results are displayed in a self-contained HTML document, and are also stored in txt and json format to facilitate the integration with any further downstream analysis tools. Such analysis approach can be used complementarily to Gene Ontology and pathway enrichment analysis, and can also serve as an aid in the validation of predicted changes in mRNA and protein abundance. The latest version of SwitchSeq, including installation instructions and use cases, can be found at https://github.com/mgonzalezporta/SwitchSeq. Additionally, the plot capabilities are provided as an independent R package at https://github.com/mgonzalezporta/tviz.

scholarly journals Bioinformatics-based Screening of Key Genes for Saponin Metabolism in Quinoa

2021 ◽  
Author(s):  
Chengang Guo ◽  
Zhimin wei ◽  
Wei Lyu ◽  
Yanlou Geng
Keyword(s):  
Differentially Expressed Genes ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Hierarchical Cluster ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Differential Analysis ◽  
Rna Seq ◽  
Protein Coding ◽  
Key Genes

Abstract Quinoa saponins have complex, diverse and evident physiologic activities. However, the key regulatory genes for quinoa saponin metabolism are not yet well studied. The purpose of this study was to explore genes closely related to quinoa saponin metabolism. In this study, the significantly differentially expressed genes in yellow quinoa were firstly screened based on RNA-seq technology. Then, the key genes for saponin metabolism were selected by gene set enrichment analysis (GSEA) and principal component analysis (PCA) statistical methods. Finally, the specificity of the key genes was verified by hierarchical clustering. The results of differential analysis showed that 1654 differentially expressed genes were achieved after pseudogenes deletion. Therein, there were 142 long non-coding genes and 1512 protein-coding genes. Based on GSEA analysis, 116 key candidate genes were found to be significantly correlated with quinoa saponin metabolism. Through PCA dimension reduction analysis, 57 key genes were finally obtained. Hierarchical cluster analysis further demonstrated that these key genes can clearly separate the four groups of samples. The present results could provide references for the breeding of sweet quinoa and would be helpful for the rational utilization of quinoa saponins.

scholarly journals TrancriptomeReconstructoR, A Data-Driven Annotation of Complex Transcriptomes

2020 ◽  
Author(s):  
Maxim Ivanov ◽  
Albin Sandelin ◽  
Sebastian Marquardt
Keyword(s):  
De Novo ◽  
Gene Annotation ◽  
R Package ◽  
Sequence Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Model ◽  
Preparation Methods ◽  
Downstream Analysis

Abstract Background: The quality of gene annotation determines the interpretation of results obtained in transcriptomic studies. The growing number of genome sequence information calls for experimental and computational pipelines for de novo transcriptome annotation. Ideally, gene and transcript models should be called from a limited set of key experimental data. Results: We developed TranscriptomeReconstructoR, an R package which implements a pipeline for automated transcriptome annotation. It relies on integrating features from independent and complementary datasets: i) full-length RNA-seq for detection of splicing patterns and ii) high-throughput 5' and 3' tag sequencing data for accurate definition of gene borders. The pipeline can also take a nascent RNA-seq dataset to supplement the called gene model with transient transcripts.We reconstructed de novo the transcriptional landscape of wild type Arabidopsis thaliana seedlings as a proof-of-principle. A comparison to the existing transcriptome annotations revealed that our gene model is more accurate and comprehensive than the two most commonly used community gene models, TAIR10 and Araport11. In particular, we identify thousands of transient transcripts missing from the existing annotations. Our new annotation promises to improve the quality of A.thaliana genome research.Conclusions: Our proof-of-concept data suggest a cost-efficient strategy for rapid and accurate annotation of complex eukaryotic transcriptomes. We combine the choice of library preparation methods and sequencing platforms with the dedicated computational pipeline implemented in the TranscriptomeReconstructoR package. The pipeline only requires prior knowledge on the reference genomic DNA sequence, but not the transcriptome. The package seamlessly integrates with Bioconductor packages for downstream analysis.

scholarly journals The G Protein-Coupled Estrogen Receptor (GPER) Expression Correlates with Pro-Metastatic Pathways in ER-Negative Breast Cancer: A Bioinformatics Analysis

Cells ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 622 ◽  
Author(s):  
Marianna Talia ◽  
Ernestina De Francesco ◽  
Damiano Rigiracciolo ◽  
Maria Muoio ◽  
Lucia Muglia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Signaling Pathways ◽  
G Protein ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
G Protein Coupled

The G protein-coupled estrogen receptor (GPER, formerly known as GPR30) is a seven-transmembrane receptor that mediates estrogen signals in both normal and malignant cells. In particular, GPER has been involved in the activation of diverse signaling pathways toward transcriptional and biological responses that characterize the progression of breast cancer (BC). In this context, a correlation between GPER expression and worse clinical-pathological features of BC has been suggested, although controversial data have also been reported. In order to better assess the biological significance of GPER in the aggressive estrogen receptor (ER)-negative BC, we performed a bioinformatics analysis using the information provided by The Invasive Breast Cancer Cohort of The Cancer Genome Atlas (TCGA) project and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets. Gene expression correlation and the statistical analysis were carried out with R studio base functions and the tidyverse package. Pathway enrichment analysis was evaluated with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway on the Database for Annotation, Visualization and Integrated Discovery (DAVID) website, whereas gene set enrichment analysis (GSEA) was performed with the R package phenoTest. The survival analysis was determined with the R package survivALL. Analyzing the expression data of more than 2500 primary BC, we ascertained that GPER levels are associated with pro-migratory and metastatic genes belonging to cell adhesion molecules (CAMs), extracellular matrix (ECM)-receptor interaction, and focal adhesion (FA) signaling pathways. Thereafter, evaluating the disease-free interval (DFI) in ER-negative BC patients, we found that the subjects expressing high GPER levels exhibited a shorter DFI in respect to those exhibiting low GPER levels. Overall, our results may pave the way to further dissect the network triggered by GPER in the breast malignancies lacking ER toward a better assessment of its prognostic significance and the action elicited in mediating the aggressive features of the aforementioned BC subtype.

MetENP/MetENPWeb: An R package and web application for metabolomics enrichment and pathway analysis in Metabolomics Workbench

2020 ◽  
Author(s):  
Kumari Sonal Choudhary ◽  
Eoin Fahy ◽  
Kevin Coakley ◽  
Manish Sud ◽  
Mano R Maurya ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Web Application ◽  
Enrichment Analysis ◽  
R Package ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment ◽  
Kegg Pathways ◽  
Link Type ◽  
Species Specific ◽  
User Friendly

ABSTRACTWith the advent of high throughput mass spectrometric methods, metabolomics has emerged as an essential area of research in biomedicine with the potential to provide deep biological insights into normal and diseased functions in physiology. However, to achieve the potential offered by metabolomics measures, there is a need for biologist-friendly integrative analysis tools that can transform data into mechanisms that relate to phenotypes. Here, we describe MetENP, an R package, and a user-friendly web application deployed at the Metabolomics Workbench site extending the metabolomics enrichment analysis to include species-specific pathway analysis, pathway enrichment scores, gene-enzyme information, and enzymatic activities of the significantly altered metabolites. MetENP provides a highly customizable workflow through various user-specified options and includes support for all metabolite species with available KEGG pathways. MetENPweb is a web application for calculating metabolite and pathway enrichment analysis.Availability and ImplementationThe MetENP package is freely available from Metabolomics Workbench GitHub: (https://github.com/metabolomicsworkbench/MetENP), the web application, is freely available at (https://www.metabolomicsworkbench.org/data/analyze.php)

scholarly journals Differential Expression Profiling of Long Noncoding RNA and mRNA during Osteoblast Differentiation in Mouse

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Minjung Kim ◽  
Youngseok Yu ◽  
Ji-Hoi Moon ◽  
InSong Koh ◽  
Jae-Hyung Lee
Keyword(s):  
Noncoding Rna ◽  
Osteoblast Differentiation ◽  
Enrichment Analysis ◽  
Osteoblastic Differentiation ◽  
Pathway Enrichment Analysis ◽  
Rna Seq ◽  
Fuzzy C Means Clustering ◽  
Differentiation Stage ◽  
And Function ◽  
Differential Expression Profiling

Long noncoding RNAs (lncRNAs) are emerging as an important controller affecting metabolic tissue development, signaling, and function. However, little is known about the function and profile of lncRNAs in osteoblastic differentiation in mice. Here, we analyzed the RNA-sequencing (RNA-Seq) datasets obtained for 18 days in two-day intervals from neonatal mouse calvarial pre-osteoblast-like cells. Over the course of osteoblast differentiation, 4058 mRNAs and 3948 lncRNAs were differentially expressed, and they were grouped into 12 clusters according to the expression pattern by fuzzy c-means clustering. Using weighted gene coexpression network analysis, we identified 9 modules related to the early differentiation stage (days 2–8) and 7 modules related to the late differentiation stage (days 10–18). Gene ontology and KEGG pathway enrichment analysis revealed that the mRNA and lncRNA upregulated in the late differentiation stage are highly associated with osteogenesis. We also identified 72 mRNA and 89 lncRNAs as potential markers including several novel markers for osteoblast differentiation and activation. Our findings provide a valuable resource for mouse lncRNA study and improves our understanding of the biology of osteoblastic differentiation in mice.

scholarly journals Transcriptome Analyses Revealed Adaptive-Responses in Livers of Mice Treated With Hua-Feng-Dan and Its “Guide Drug” Yaomu

2021 ◽  
Author(s):  
Jia-Jia Liu ◽  
Ya Zhang ◽  
Shang-Fu Xu ◽  
Feng Zhang ◽  
Jing-Shan Shi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Toxicity ◽  
Cholesterol Metabolism ◽  
Enrichment Analysis ◽  
Stroke Recovery ◽  
Pathway Enrichment Analysis ◽  
High Dose ◽  
Hepatic Gene Expression ◽  
Rna Seq ◽  
Adaptive Responses

Abstract BackgroundHua-Feng-Dan is a patent Chinese medicine for stroke recovery and is effective against Parkinson’s disease models with modulatory effects on gut microbiota, but its effects on hepatic gene expression are unknown. This study used RNA-Seq to profile hepatic gene expression by Hua-Feng-Dan and its “Guide Drug” Yaomu.MethodsMice received orally Hua-Feng-Dan 1.2 g/kg, Yaomu 0.1-0.3 g/kg, or vehicle for 7 days. Liver pathology was examined, and total RNA was isolated for RNA-Seq. The bioinformatics, including GO and KEGG pathway enrichment analysis, two-dimensional clustering, Ingenuity Pathways Analysis (IPA), and Illumina BaseSpace Correlation Engine were used to analyze differentially expressed genes (DEGs). qPCR was performed to verify selected genes.ResultsHua-Feng-Dan and Yaomu did not produce liver toxicity as evidenced by histopathology and serum ALT and AST. GO Enrichment revealed Hua-Feng-Dan affected lipid homeostasis, protein folding and cell adhesion. KEGG showed activated cholesterol metabolism, bile secretion and PPAR signaling pathways. DEGs were identified by DESeq2 with p < 0.05 compared to controls. Hua-Feng-Dan produced 806 DEGs, Yaomu-0.1 had 235, and Yaomu-0.3 had 92 DEGs. qPCR on selected genes largely verified RNA-Seq results. IPA upstream regulator analysis revealed activation of MAPK and adaptive responses. Yaomu-0.1 had similar effects, but Yaomu-0.3 had little effects. Hua-Feng-Dan-induced DEGs were highly correlated with the GEO database of chemical-induced adaptive transcriptome changes in the liver. ConclusionHua-Feng-Dan at clinical dose did not produce liver pathological changes but induced metabolic and signaling pathway activations. Low dose of its Guide Drug Yaomu produced similar changes to a lesser extent, but high dose of Yaomu had little effects. The effects of Hua-Feng-Dan on liver transcriptome changes may produce adaptive responses to program the liver to produce beneficial or detrimental (over-dosed) pharmacological effects.

scholarly journals ­Transcriptome profiling of non-climacteric ‘Yellow’ melon during ripening: insights on sugar metabolism

2020 ◽  
Author(s):  
Michelle Orane Schemberger ◽  
Marília Aparecida Stroka ◽  
Letícia Reis ◽  
Kamila Karoline de Souza Los ◽  
Gillize Aparecida Telles de Araujo ◽  
...  
Keyword(s):  
Cucumis Melo ◽  
Sugar Content ◽  
Sugar Metabolism ◽  
Enrichment Analysis ◽  
Transcriptome Profiling ◽  
Sucrose Metabolism ◽  
Pathway Enrichment Analysis ◽  
Rna Seq ◽  
Pathway Enrichment ◽  
Maturation Stages

Abstract Background: The non-climacteric ‘Yellow’ melon ( Cucumis melo , inodorus group) is an economically important crop and its quality is mainly determined by the sugar content. Thus, knowledge of sugar metabolism and its related pathways can contribute to the development of new field management and post-harvest practices, making it possible to deliver better quality fruits to consumers. Results: The RNA-seq associated with RT-qPCR analyses of four maturation stages were performed to identify important enzymes and pathways that are involved in the ripening profile of non-climacteric ‘Yellow’ melon fruit focusing on sugar metabolism. We identified 895 genes 10 days after pollination (DAP)-biased and 909 genes 40 DAP-biased. The KEGG pathway enrichment analysis of these differentially expressed (DE) genes revealed that ‘hormone signal transduction’, ‘carbon metabolism’, ‘sucrose metabolism’, ‘protein processing in endoplasmic reticulum’ and ‘spliceosome’ were the most differentially regulated processes occurring during melon development. In the sucrose metabolism, five DE genes are up-regulated and twelve are down-regulated during fruit ripening. Conclusions: The results demonstrated important enzymes in the sugar pathway that are responsible for the sucrose content and maturation profile in non-climacteric ‘Yellow’ melon. New DE genes were first detected for melon in this study such as invertase inhibitor LIKE 3 ( CmINH3 ), trehalose phosphate phosphatase ( CmTPP1 ) and trehalose phosphate synthases ( CmTPS5 , CmTPS7 , CmTPS9 ). Furthermore, the results of the protein-protein network interaction demonstrated general characteristics of the transcriptome of young and full-ripe melon and provide new perspectives for the understanding of ripening.

scholarly journals ­Transcriptome profiling of non-climacteric ‘Yellow’ melon during ripening: insights on sugar metabolism

2020 ◽  
Author(s):  
Michelle Orane Schemberger ◽  
Marília Aparecida Stroka ◽  
Letícia Reis ◽  
Kamila Karoline de Souza Los ◽  
Gillize Aparecida Telles de Araujo ◽  
...  
Keyword(s):  
Cucumis Melo ◽  
Sugar Content ◽  
Sugar Metabolism ◽  
Enrichment Analysis ◽  
Transcriptome Profiling ◽  
Sucrose Metabolism ◽  
Pathway Enrichment Analysis ◽  
Rna Seq ◽  
Pathway Enrichment ◽  
Maturation Stages

Abstract Background: The non-climacteric ‘Yellow’ melon ( Cucumis melo , inodorus group) is an economically important crop and its quality is mainly determined by the sugar content. Thus, knowledge of sugar metabolism and its related pathways can contribute to the development of new field management and post-harvest practices, making it possible to deliver better quality fruits to consumers. Results: The RNA-seq associated with RT-qPCR analyses of four maturation stages were performed to identify important enzymes and pathways that are involved in the ripening profile of non-climacteric ‘Yellow’ melon fruit focusing on sugar metabolism. We identified 895 genes 10 days after pollination (DAP)-biased and 909 genes 40 DAP-biased. The KEGG pathway enrichment analysis of these differentially expressed (DE) genes revealed that ‘hormone signal transduction’, ‘carbon metabolism’, ‘sucrose metabolism’, ‘protein processing in endoplasmic reticulum’ and ‘spliceosome’ were the most differentially regulated processes occurring during melon development. In the sucrose metabolism, five DE genes are up-regulated and twelve are down-regulated during fruit ripening. Conclusions: The results demonstrated important enzymes in the sugar pathway that are responsible for the sucrose content and maturation profile in non-climacteric ‘Yellow’ melon. New DE genes were first detected for melon in this study such as invertase inhibitor LIKE 3 ( CmINH3 ), trehalose phosphate phosphatase ( CmTPP1 ) and trehalose phosphate synthases ( CmTPS5 , CmTPS7 , CmTPS9 ). Furthermore, the results of the protein-protein network interaction demonstrated general characteristics of the transcriptome of young and full-ripe melon and provide new perspectives for the understanding of ripening.

scholarly journals TrancriptomeReconstructoR: data-driven annotation of complex transcriptomes

2020 ◽  
Author(s):  
Maxim Ivanov ◽  
Albin Sandelin ◽  
Sebastian Marquardt
Keyword(s):  
De Novo ◽  
Gene Annotation ◽  
R Package ◽  
Sequence Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Model ◽  
Preparation Methods ◽  
Downstream Analysis

AbstractBackgroundThe quality of gene annotation determines the interpretation of results obtained in transcriptomic studies. The growing number of genome sequence information calls for experimental and computational pipelines for de novo transcriptome annotation. Ideally, gene and transcript models should be called from a limited set of key experimental data.ResultsWe developed TranscriptomeReconstructoR, an R package which implements a pipeline for automated transcriptome annotation. It relies on integrating features from independent and complementary datasets: i) full-length RNA-seq for detection of splicing patterns and ii) high-throughput 5’ and 3’ tag sequencing data for accurate definition of gene borders. The pipeline can also take a nascent RNA-seq dataset to supplement the called gene model with transient transcripts.We reconstructed de novo the transcriptional landscape of wild type Arabidopsis thaliana seedlings as a proof-of-principle. A comparison to the existing transcriptome annotations revealed that our gene model is more accurate and comprehensive than the two most commonly used community gene models, TAIR10 and Araport11. In particular, we identify thousands of transient transcripts missing from the existing annotations. Our new annotation promises to improve the quality of A.thaliana genome research.ConclusionsOur proof-of-concept data suggest a cost-efficient strategy for rapid and accurate annotation of complex eukaryotic transcriptomes. We combine the choice of library preparation methods and sequencing platforms with the dedicated computational pipeline implemented in the TranscriptomeReconstructoR package. The pipeline only requires prior knowledge on the reference genomic DNA sequence, but not the transcriptome. The package seamlessly integrates with Bioconductor packages for downstream analysis.

scholarly journals Network Pharmacology-Based Study on the Mechanism of Gegen Qinlian Decoction against Colorectal Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Qiaowei Fan ◽  
Lin Guo ◽  
Jingming Guan ◽  
Jing Chen ◽  
Yujing Fan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Target Genes ◽  
Enrichment Analysis ◽  
R Package ◽  
Gastrointestinal Diseases ◽  
Gene Expression Omnibus ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Ppi Network

Purpose. Gegen Qinlian decoction (GQD) has been used to treat gastrointestinal diseases, such as diarrhea and ulcerative colitis (UC). A recent study demonstrated that GQD enhanced the effect of PD-1 blockade in colorectal cancer (CRC). This study used network pharmacology analysis to investigate the mechanisms of GQD as a potential therapeutic approach against CRC. Materials and Methods. Bioactive chemical ingredients (BCIs) of GQD were collected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. CRC-specific genes were obtained using the gene expression profile GSE110224 from the Gene Expression Omnibus (GEO) database. Target genes related to BCIs of GQD were then screened out. The GQD-CRC ingredient-target pharmacology network was constructed and visualized using Cytoscape software. A protein-protein interaction (PPI) network was subsequently constructed and analyzed with BisoGenet and CytoNCA plug-in in Cytoscape. Gene Ontology (GO) functional and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis for target genes were then performed using the R package of clusterProfiler. Results. One hundred and eighteen BCIs were determined to be effective on CRC, including quercetin, wogonin, and baicalein. Twenty corresponding target genes were screened out including PTGS2, CCNB1, and SPP1. Among these genes, CCNB1 and SPP1 were identified as crucial to the PPI network. A total of 212 GO terms and 6 KEGG pathways were enriched for target genes. Functional analysis indicated that these targets were closely related to pathophysiological processes and pathways such as biosynthetic and metabolic processes of prostaglandins and prostanoids, cytokine and chemokine activities, and the IL-17, TNF, Toll-like receptor, and nuclear factor-kappa B (NF-κB) signaling pathways. Conclusion. The study elucidated the “multiingredient, multitarget, and multipathway” mechanisms of GQD against CRC from a systemic perspective, indicating GQD to be a candidate therapy for CRC treatment.

Sign in / Sign up

Export Citation Format

Share Document