scholarly journals A step-by-step guide to analyzing CAGE data using R/Bioconductor

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 886 ◽  
Author(s):  
Malte Thodberg ◽  
Albin Sandelin
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Differential Expression Analysis ◽  
Transcription Start ◽  
Single Experiment ◽  
Transcription Start Sites ◽  
Bioconductor Project ◽  
Advanced Analysis ◽  
Cap Analysis

Cap Analysis of Gene Expression (CAGE) is one of the most popular 5'-end sequencing methods. In a single experiment, CAGE can be used to locate and quantify the expression of both Transcription Start Sites (TSSs) and enhancers. This is workflow is a case study on how to use the CAGEfightR package to orchestrate analysis of CAGE data within the Bioconductor project. This workflow starts from BigWig-files and covers both basic CAGE analyses such as identifying, quantifying and annotating TSSs and enhancers, advanced analysis such as finding interacting TSS-enhancer pairs and enhancer clusters, to differential expression analysis and alternative TSS usage. R-code, discussion and references are intertwined to help provide guidelines for future CAGE studies of the same kind.

scholarly journals MAPCap: A fast and quantitative transcription start site profiling protocol

2019 ◽  
Author(s):  
Vivek Bhardwaj ◽  
Giuseppe Semplicio ◽  
Niyazi Umut Erdogdu ◽  
Asifa Akhtar
Keyword(s):  
High Resolution ◽  
Differential Expression ◽  
Transcription Start Site ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Start Site ◽  
Transcription Start ◽  
Transcription Start Sites

Abstract Below we present a simple and quick TSS quantification protocol, MAPCap (Multiplexed Affinity Purification of Capped RNA) that enables users to combine high-resolution detection of transcription start-sites and differential expression analysis. MAPCap can be used to profile TSS from dozens of samples in a multiplexed way, in 16-18 hours. MAPCap data can be analyzed using our easy-to-use software icetea (https://bioconductor.org/packages/icetea), which allows users to detect robust TSS using replicates, and perform differential TSS analysis.

Cap Analysis of Gene Expression (CAGE): A Quantitative and Genome-Wide Assay of Transcription Start Sites

2020 ◽  
pp. 277-301 ◽  
Author(s):  
Masaki Suimye Morioka ◽  
Hideya Kawaji ◽  
Hiromi Nishiyori-Sueki ◽  
Mitsuyoshi Murata ◽  
Miki Kojima-Ishiyama ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Start ◽  
Transcription Start Sites ◽  
Genome Wide ◽  
Cap Analysis

scholarly journals Cap analysis of gene expression reveals alternative promoter usage in a rat model of hypertension

2022 ◽  
Vol 5 (4) ◽  
pp. e202101234
Author(s):  
Sonal Dahale ◽  
Jorge Ruiz-Orera ◽  
Jan Silhavy ◽  
Norbert Hübner ◽  
Sebastiaan van Heesch ◽  
...  
Keyword(s):  
Gene Expression ◽  
Left Ventricle ◽  
Rat Model ◽  
Alternative Promoter ◽  
Brown Norway ◽  
Transcription Start ◽  
Transcription Start Sites ◽  
Promoter Usage ◽  
Cap Analysis

The role of alternative promoter usage in tissue-specific gene expression has been well established; however, its role in complex diseases is poorly understood. We performed cap analysis of gene expression (CAGE) sequencing from the left ventricle of a rat model of hypertension, the spontaneously hypertensive rat (SHR), and a normotensive strain, Brown Norway to understand the role of alternative promoter usage in complex disease. We identified 26,560 CAGE-defined transcription start sites in the rat left ventricle, including 1,970 novel cardiac transcription start sites. We identified 28 genes with alternative promoter usage between SHR and Brown Norway, which could lead to protein isoforms differing at the amino terminus between two strains and 475 promoter switching events altering the length of the 5′ UTR. We found that the shift in Insr promoter usage was significantly associated with insulin levels and blood pressure within a panel of HXB/BXH recombinant inbred rat strains, suggesting that hyperinsulinemia due to insulin resistance might lead to hypertension in SHR. Our study provides a preliminary evidence of alternative promoter usage in complex diseases.

scholarly journals CAGEfightR: Cap Analysis of Gene Expression (CAGE) in R/Bioconductor

2018 ◽  
Author(s):  
Malte Thodberg ◽  
Axel Thieffry ◽  
Kristoffer Vitting-Seerup ◽  
Robin Andersson ◽  
Albin Sandelin
Keyword(s):  
Gene Expression ◽  
Transcription Start ◽  
Transcription Start Sites ◽  
Link Type ◽  
Shape Statistics ◽  
Downstream Analysis ◽  
Cap Analysis ◽  
Memory Efficient

AbstractWe developed the CAGEfightR R/Biconductor-package for analyzing CAGE data. CAGEfightR allows for fast and memory efficient identification of transcription start sites (TSSs) and predicted enhancers. Downstream analysis, including annotation, quantification, visualization and TSS shape statistics are implemented in easy-to-use functions. The package is freely available at https://bioconductor.org/packages/CAGEfightR

scholarly journals Retinal transcriptome profiling at transcription start sites: a cap analysis of gene expression early after axonal injury

BMC Genomics ◽  
2014 ◽  
Vol 15 (1) ◽  
pp. 982 ◽  
Author(s):  
Masayuki Yasuda ◽  
Yuji Tanaka ◽  
Koji M Nishiguchi ◽  
Morin Ryu ◽  
Satoru Tsuda ◽  
...  
Keyword(s):  
Gene Expression ◽  
Axonal Injury ◽  
Transcriptome Profiling ◽  
Transcription Start ◽  
Transcription Start Sites ◽  
Cap Analysis

Abstract 545: Differential mRNA Expression is Influenced by Apolipoprotein A-I in Order to Promote Foam Cell Regression

2016 ◽  
Vol 36 (suppl_1) ◽  
Author(s):  
Elisa C Maruko ◽  
Hao Xu ◽  
Sushma Kaul ◽  
Brian J Capaldo ◽  
Nathalie Pamir ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Expression Analysis ◽  
Immune Cell ◽  
Foam Cell ◽  
Differential Expression Analysis ◽  
Ldl Receptor ◽  
Gene Set Enrichment Analysis ◽  
Control Group ◽  
Apolipoprotein A

Atherosclerosis is a disease of both lipids and inflammatory immune cells. More specifically, elevated plasma levels of low-density lipoproteins (LDL) leads to migration of circulating monocytes into the artery wall. Lipid loaded monocyte cells subsequently proliferate in the arterial walls becoming macrophage foam cells; a hallmark of atherosclerotic lesions. A proposed mechanism of the protective effects of high-density lipoprotein (HDL) is apolipoprotein A-I (apo A-I) acting as a mediator of cholesterol efflux and subsequent foam cell regression. To better understand the biological changes stimulated by apo A-I treatment, differential expression analysis of microarray data was performed on spleen cells from apo A-I treated mice. LDL receptor null (LDLr -/- ) and LDL receptor and apo A-I null (LDLr -/- , apoA-I -/- ) mice were fed a western diet consisting of 0.2% cholesterol and 42% of calories as fat for 12 weeks. After 6 weeks of diet, a subset of mice for each genotype was subcutaneously injected with 200 micrograms of apo A-I 3 times a week for the remaining 6 weeks. The control group mice were subcutaneously injected with 200 micrograms of saline or BSA. Spleen cell RNA was isolated, purified, and analyzed for differential expression analysis using Illumina BeadArray Microarray Technology Analysis. Individual gene expression analysis for LDLr -/- , apoA-I -/- apo A-I treated mice showed 281 significantly differentially expressed genes compared to BSA treated mice. LDLr -/- A-I treated mice had 1502. Of the significant genes, 189 intersected across both genotypes. LDLr -/- , apoA-I -/- A-I mice showed 73 up-regulated and 116 down-regulated genes. Similarly, LDLr -/- A-I mice had 71 up-regulated and 118 down-regulated. One-directional Gene Set Enrichment Analysis (GSEA) of LDLr -/- , apoA-I -/- A-I mice revealed 49 significant pathways while a total of 63 were found for LDLr -/- . Of these pathways, 21 were up-regulated and 13 were down-regulated in both genotypes. Eight of the top 10 most significant up-regulated pathways in both genotypes were immune cell related. Their functions involve receptor, adhesion, and chemokine signaling. Overall, preliminary analysis suggests A-I treatment induces similar gene expression changes across different genotypes.

scholarly journals Distinct molecular etiologies of male and female hepatocellular carcinoma

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Heini M. Natri ◽  
Melissa A. Wilson ◽  
Kenneth H. Buetow
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Sex Differences ◽  
Differential Expression ◽  
Differential Expression Analysis ◽  
Regulation Of Transcription ◽  
Male And Female ◽  
Differential Effects ◽  
Pathway Enrichment

Abstract Background Sex-differences in cancer occurrence and mortality are evident across tumor types; men exhibit higher rates of incidence and often poorer responses to treatment. Targeted approaches to the treatment of tumors that account for these sex-differences require the characterization and understanding of the fundamental biological mechanisms that differentiate them. Hepatocellular Carcinoma (HCC) is the second leading cause of cancer death worldwide, with the incidence rapidly rising. HCC exhibits a male-bias in occurrence and mortality, but previous studies have failed to explore the sex-specific dysregulation of gene expression in HCC. Methods Here, we characterize the sex-shared and sex-specific regulatory changes in HCC tumors in the TCGA LIHC cohort using combined and sex-stratified differential expression and eQTL analyses. Results By using a sex-specific differential expression analysis of tumor and tumor-adjacent samples, we uncovered etiologically relevant genes and pathways differentiating male and female HCC. While both sexes exhibited activation of pathways related to apoptosis and cell cycle, males and females differed in the activation of several signaling pathways, with females showing PPAR pathway enrichment while males showed PI3K, PI3K/AKT, FGFR, EGFR, NGF, GF1R, Rap1, DAP12, and IL-2 signaling pathway enrichment. Using eQTL analyses, we discovered germline variants with differential effects on tumor gene expression between the sexes. 24.3% of the discovered eQTLs exhibit differential effects between the sexes, illustrating the substantial role of sex in modifying the effects of eQTLs in HCC. The genes that showed sex-specific dysregulation in tumors and those that harbored a sex-specific eQTL converge in clinically relevant pathways, suggesting that the molecular etiologies of male and female HCC are partially driven by differential genetic effects on gene expression. Conclusions Sex-stratified analyses detect sex-specific molecular etiologies of HCC. Overall, our results provide new insight into the role of inherited genetic regulation of transcription in modulating sex-differences in HCC etiology and provide a framework for future studies on sex-biased cancers.

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