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

ABSTRACT: 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.

SUBMITTER: Thodberg M

PROVIDER: S-EPMC6613478 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Thodberg Malte M Sandelin Albin A

F1000Research 20190618

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 ...[more]

PMID: 31327999

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Project description:BackgroundThis paper describes the results of a Gene Ontology (GO) term enrichment analysis of chicken microarray data using the Bioconductor packages. By checking the enriched GO terms in three contrasts, MM8-PM8, MM8-MA8, and MM8-MM24, of the provided microarray data during this workshop, this analysis aimed to investigate the host reactions in chickens occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. The results of GO enrichment analysis using GO terms annotated to chicken genes and GO terms annotated to chicken-human orthologous genes were also compared. Furthermore, a locally adaptive statistical procedure (LAP) was performed to test differentially expressed chromosomal regions, rather than individual genes, in the chicken genome after Eimeria challenge.ResultsGO enrichment analysis identified significant (raw p-value < 0.05) GO terms for all three contrasts included in the analysis. Some of the GO terms linked to, generally, primary immune responses or secondary immune responses indicating the GO enrichment analysis is a useful approach to analyze microarray data. The comparisons of GO enrichment results using chicken gene information and chicken-human orthologous gene information showed more refined GO terms related to immune responses when using chicken-human orthologous gene information, this suggests that using chicken-human orthologous gene information has higher power to detect significant GO terms with more refined functionality. Furthermore, three chromosome regions were identified to be significantly up-regulated in contrast MM8-PM8 (q-value < 0.01).ConclusionOverall, this paper describes a practical approach to analyze microarray data in farm animals where the genome information is still incomplete. For farm animals, such as chicken, with currently limited gene annotation, borrowing gene annotation information from orthologous genes in well-annotated species, such as human, will help improve the pathway analysis results substantially. Furthermore, LAP analysis approach is a relatively new and very useful way to be applied in microarray analysis.

Dataset Information

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

Publications

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

Similar Datasets

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