scholarly journals Quantitative RNA-seq meta analysis of alternative exon usage in C. elegans

2017 ◽  
Author(s):  
Nicolas Tourasse ◽  
Jonathan R. M. Millet ◽  
Denis Dupuy
Keyword(s):  
Dynamic Range ◽  
Meta Analysis ◽  
Large Fraction ◽  
Single Gene ◽  
Rna Seq ◽  
Splice Sites ◽  
C Elegans ◽  
Trans Splicing ◽  
Highly Expressed Genes ◽  
Exon Usage

ABSTRACTAlmost twenty years after the completion of the C. elegans genome sequence, gene structure annotation is still an ongoing process with new evidence for gene variants still being regularly uncovered by additional in-depth transcriptome studies. While alternative splice forms can allow a single gene to encode several functional isoforms the question of how much spurious splicing is tolerated is still heavily debated.Here we gathered a compendium of 1,682 publicly available C. elegans RNA-seq datasets to increase the dynamic range of detection of RNA isoforms and obtained robust measurements of the relative abundance of each splicing event. While most of the splicing reads come from reproducibly detected splicing events, a large fraction of purported junctions are only supported by a very low number of reads. We devised an automated curation method that takes into account the expression level of each gene to discriminate robust splicing events from potential biological noise. We found that rarely used splice sites disproportionately come from highly expressed genes and are significantly less conserved in other nematode genomes than splice sites with a higher usage frequency.Our increased detection power confirmed trans-splicing for at least 84% of C. elegans protein coding genes. The genes for which trans-splicing was not observed are overwhelmingly low expression genes, suggesting that the mechanism is pervasive but not full captured by organism-wide RNA-Seq.We generated annotated gene models including quantitative exon usage information for the entire C. elegans genome. This allows users to visualize at a glance the relative expression of each isoform for their gene of interest.

scholarly journals Quantitative RNA-seq meta-analysis of alternative exon usage in C. elegans

2017 ◽  
Vol 27 (12) ◽  
pp. 2120-2128 ◽  
Author(s):  
Nicolas J. Tourasse ◽  
Jonathan R.M. Millet ◽  
Denis Dupuy
Keyword(s):  
Meta Analysis ◽  
Alternative Exon ◽  
Rna Seq ◽  
C Elegans ◽  
Exon Usage

scholarly journals Meta-Analysis of Oxidative Transcriptomes in Insects

Antioxidants ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 345
Author(s):  
Hidemasa Bono
Keyword(s):  
Oxidative Stress ◽  
Stress Response ◽  
Meta Analysis ◽  
Adherens Junction ◽  
Enrichment Analysis ◽  
Oxidative Stress Response ◽  
Insect Species ◽  
Rna Seq ◽  
Manual Curation ◽  
Analysis Workflow

Data accumulation in public databases has resulted in extensive use of meta-analysis, a statistical analysis that combines the results of multiple studies. Oxidative stress occurs when there is an imbalance between free radical activity and antioxidant activity, which can be studied in insects by transcriptome analysis. This study aimed to apply a meta-analysis approach to evaluate insect oxidative transcriptomes using publicly available data. We collected oxidative stress response-related RNA sequencing (RNA-seq) data for a wide variety of insect species, mainly from public gene expression databases, by manual curation. Only RNA-seq data of Drosophila melanogaster were found and were systematically analyzed using a newly developed RNA-seq analysis workflow for species without a reference genome sequence. The results were evaluated by two metric methods to construct a reference dataset for oxidative stress response studies. Many genes were found to be downregulated under oxidative stress and related to organ system process (GO:0003008) and adherens junction organization (GO:0034332) by gene enrichment analysis. A cross-species analysis was also performed. RNA-seq data of Caenorhabditis elegans were curated, since no RNA-seq data of insect species are currently available in public databases. This method, including the workflow developed, represents a powerful tool for deciphering conserved networks in oxidative stress response.

scholarly journals CCL20/TNF/VEGFA Cytokine Secretory Phenotype of Tumor-Associated Macrophages Is a Negative Prognostic Factor in Cutaneous Melanoma

Cancers ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 3943
Author(s):  
Alba Gutiérrez-Seijo ◽  
Elena García-Martínez ◽  
Celia Barrio-Alonso ◽  
Miriam Pareja-Malagón ◽  
Alejandra Acosta-Ocampo ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Large Fraction ◽  
Prognostic Significance ◽  
Disease Free Survival ◽  
Rna Seq ◽  
Primary Cutaneous Melanoma ◽  
Kaplan Meier ◽  
Intracellular Cytokines ◽  
Negative Prognostic Factor

TAMs constitute a large fraction of infiltrating immune cells in melanoma tissues, but their significance for clinical outcomes remains unclear. We explored diverse TAM parameters in clinically relevant primary cutaneous melanoma samples, including density, location, size, and polarization marker expression; in addition, because cytokine production is a hallmark of macrophages function, we measured CCL20, TNF, and VEGFA intracellular cytokines by single-cell multiparametric confocal microscopy. The Kaplan–Meier method was used to analyze correlation with melanoma-specific disease-free survival and overall survival. No significant correlations with clinical parameters were observed for TAM density, morphology, or location. Significantly, higher contents of the intracellular cytokines CCL20, TNF, and VEGFA were quantified in TAMs infiltrating metastasizing compared to non-metastasizing skin primary melanomas (p < 0.001). To mechanistically explore cytokine up-regulation, we performed in vitro studies with melanoma-conditioned macrophages, using RNA-seq to explore involved pathways and specific inhibitors. We show that p53 and NF-κB coregulate CCL20, TNF, and VEGFA in melanoma-conditioned macrophages. These results delineate a clinically relevant pro-oncogenic cytokine profile of TAMs with prognostic significance in primary melanomas and point to the combined therapeutic targeting of NF-kB/p53 pathways to control the deviation of TAMs in melanoma.

scholarly journals Meta-analysis of RNA-seq expression data across species, tissues and studies

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Peter H. Sudmant ◽  
Maria S. Alexis ◽  
Christopher B. Burge
Keyword(s):  
Meta Analysis ◽  
Expression Data ◽  
Rna Seq

scholarly journals HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 244 ◽  
Author(s):  
Antonio Victor Campos Coelho ◽  
Rossella Gratton ◽  
João Paulo Britto de Melo ◽  
José Leandro Andrade-Santos ◽  
Rafael Lima Guimarães ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Differentially Expressed Genes ◽  
Cd4 T Cells ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Infected Cells ◽  
Hiv 1

HIV-1 infection elicits a complex dynamic of the expression various host genes. High throughput sequencing added an expressive amount of information regarding HIV-1 infections and pathogenesis. RNA sequencing (RNA-Seq) is currently the tool of choice to investigate gene expression in a several range of experimental setting. This study aims at performing a meta-analysis of RNA-Seq expression profiles in samples of HIV-1 infected CD4+ T cells compared to uninfected cells to assess consistently differentially expressed genes in the context of HIV-1 infection. We selected two studies (22 samples: 15 experimentally infected and 7 mock-infected). We found 208 differentially expressed genes in infected cells when compared to uninfected/mock-infected cells. This result had moderate overlap when compared to previous studies of HIV-1 infection transcriptomics, but we identified 64 genes already known to interact with HIV-1 according to the HIV-1 Human Interaction Database. A gene ontology (GO) analysis revealed enrichment of several pathways involved in immune response, cell adhesion, cell migration, inflammation, apoptosis, Wnt, Notch and ERK/MAPK signaling.

scholarly journals Multi-species transcriptome meta-analysis of the response to retinoic acid in vertebrates and comparative analysis of the effects of retinol and retinoic acid on gene expression in LMH cells

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Clemens Falker-Gieske ◽  
Andrea Mott ◽  
Sören Franzenburg ◽  
Jens Tetens
Keyword(s):  
Gene Expression ◽  
Retinoic Acid ◽  
Cellular Response ◽  
Homo Sapiens ◽  
Meta Analysis ◽  
Early Response ◽  
Rna Seq ◽  
Transcriptomic Response ◽  
Time Points ◽  
Lmh Cells

Abstract Background Retinol (RO) and its active metabolite retinoic acid (RA) are major regulators of gene expression in vertebrates and influence various processes like organ development, cell differentiation, and immune response. To characterize a general transcriptomic response to RA-exposure in vertebrates, independent of species- and tissue-specific effects, four publicly available RNA-Seq datasets from Homo sapiens, Mus musculus, and Xenopus laevis were analyzed. To increase species and cell-type diversity we generated RNA-seq data with chicken hepatocellular carcinoma (LMH) cells. Additionally, we compared the response of LMH cells to RA and RO at different time points. Results By conducting a transcriptome meta-analysis, we identified three retinoic acid response core clusters (RARCCs) consisting of 27 interacting proteins, seven of which have not been associated with retinoids yet. Comparison of the transcriptional response of LMH cells to RO and RA exposure at different time points led to the identification of non-coding RNAs (ncRNAs) that are only differentially expressed (DE) during the early response. Conclusions We propose that these RARCCs stand on top of a common regulatory RA hierarchy among vertebrates. Based on the protein sets included in these clusters we were able to identify an RA-response cluster, a control center type cluster, and a cluster that directs cell proliferation. Concerning the comparison of the cellular response to RA and RO we conclude that ncRNAs play an underestimated role in retinoid-mediated gene regulation.

scholarly journals Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods

2021 ◽  
Vol 15 ◽  
Author(s):  
Shu Zhao ◽  
Zhiwei Bao ◽  
Xinyi Zhao ◽  
Mengxiang Xu ◽  
Ming D. Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Meta Analysis ◽  
Single Gene ◽  
Support Vector ◽  
Svm Classifier ◽  
Diagnostic Model ◽  
Roc Curve Analysis ◽  
Major Depressive

BackgroundMajor depressive disorder (MDD) is a global health challenge that impacts the quality of patients’ lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes.Materials and MethodsWe collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package “MetaOmics” was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection.ResultsOur analysis revealed six differentially expressed genes (AKR1C3, ARG1, KLRB1, MAFG, TPST1, and WWC3) with a false discovery rate (FDR) &lt; 0.05 between MDD patients and control subjects. We then evaluated the diagnostic ability of these genes individually. With single gene prediction, we achieved a corresponding area under the curve (AUC) value of 0.63 ± 0.04, 0.67 ± 0.07, 0.70 ± 0.11, 0.64 ± 0.08, 0.68 ± 0.07, and 0.62 ± 0.09, respectively, for these genes. Next, we constructed the classifiers of SVM, RF, kNN, and NB with an AUC of 0.84 ± 0.09, 0.81 ± 0.10, 0.73 ± 0.11, and 0.83 ± 0.09, respectively, in validation datasets, suggesting that the SVM classifier might be superior for constructing an MDD diagnostic model. The final SVM classifier including 70 feature genes was capable of distinguishing MDD samples from healthy controls and yielded an AUC of 0.78 in an independent dataset.ConclusionThis study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.

scholarly journals RgCop-A regularized copula based method for gene selection in single cell rna-seq data

2021 ◽  
Vol 17 (10) ◽  
pp. e1009464
Author(s):  
Snehalika Lall ◽  
Sumanta Ray ◽  
Sanghamitra Bandyopadhyay
Keyword(s):  
Single Cell ◽  
Gene Selection ◽  
Real Life ◽  
Classification Performance ◽  
Rna Seq ◽  
Scale Invariant ◽  
Dependence Measure ◽  
Highly Expressed Genes ◽  
The Stability ◽  
Downstream Analysis

Gene selection in unannotated large single cell RNA sequencing (scRNA-seq) data is important and crucial step in the preliminary step of downstream analysis. The existing approaches are primarily based on high variation (highly variable genes) or significant high expression (highly expressed genes) failed to provide stable and predictive feature set due to technical noise present in the data. Here, we propose RgCop, a novel regularized copula based method for gene selection from large single cell RNA-seq data. RgCop utilizes copula correlation (Ccor), a robust equitable dependence measure that captures multivariate dependency among a set of genes in single cell expression data. We raise an objective function by adding a l1 regularization term with Ccor to penalizes the redundant co-efficient of features/genes, resulting non-redundant effective features/genes set. Results show a significant improvement in the clustering/classification performance of real life scRNA-seq data over the other state-of-the-art. RgCop performs extremely well in capturing dependence among the features of noisy data due to the scale invariant property of copula, thereby improving the stability of the method. Moreover, the differentially expressed (DE) genes identified from the clusters of scRNA-seq data are found to provide an accurate annotation of cells. Finally, the features/genes obtained from RgCop can able to annotate the unknown cells with high accuracy.

scholarly journals LeafCutter: annotation-free quantification of RNA splicing

2016 ◽  
Author(s):  
Yang I Li ◽  
David A Knowles ◽  
Jack Humphrey ◽  
Alvaro N. Barbeira ◽  
Scott P. Dickinson ◽  
...  
Keyword(s):  
Complex Traits ◽  
Rna Splicing ◽  
Population Variation ◽  
Disease Genes ◽  
Intron Splicing ◽  
Rna Seq ◽  
Exon Usage ◽  
Molecular Effects ◽  
Free Quantification ◽  
Gene Expression Levels

AbstractThe excision of introns from pre-mRNA is an essential step in mRNA processing. We developed LeafCutter to study sample and population variation in intron splicing. LeafCutter identifies variable intron splicing events from short-read RNA-seq data and finds alternative splicing events of high complexity. Our approach obviates the need for transcript annotations and circumvents the challenges in estimating relative isoform or exon usage in complex splicing events. LeafCutter can be used both for detecting differential splicing between sample groups, and for mapping splicing quantitative trait loci (sQTLs). Compared to contemporary methods, we find 1.4–2.1 times more sQTLs, many of which help us ascribe molecular effects to disease-associated variants. Strikingly, transcriptome-wide associations between LeafCutter intron quantifications and 40 complex traits increased the number of associated disease genes at 5% FDR by an average of 2.1-fold as compared to using gene expression levels alone. LeafCutter is fast, scalable, easy to use, and available at https://github.com/davidaknowles/leafcutter.

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