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Pipeline for RNA sequencing data analysis by combination of Nextflow and R
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With the development of high-throughput technologies, RNA sequencing (RNA-seq) has become a widely used technology in biological studies and thus a large number of RNA-seq data are emerging and remain to be analyzed. Although there are many different options for analysis methods and tools, a unified pipeline for RNA-seq data analysis is always necessary for a laboratory. Given the update of new methods and tools, I summarized my customized analysis codes to generate an updated pipeline for RNA-seq data analysis. During aging, gene mutations accumulate, and hormone regulation is disrupted, which may exacerbate age-related diseases. Therefore, we generated a dataset from mice with a gene mutation or not and under different hormone treatments to study the effects of two factors, i.e., hormone and gene mutation, on the transcriptome. Based on the Nextflow nf-core rnaseq pipeline, this project established this pipeline consisting of three stages: (1) upstream analysis containing quality control of fastq files before and after trimming, trimming, alignment, and quantification; (2) midstream analysis containing count normalization, differentially expressed genes analysis, and visualization via boxplot, PCA, t-SNE, sample distance heatmap, MA plot, volcano plot, and gene expression heatmap; and (3) downstream analysis containing functional enrichments of KEGG pathways and GO terms. Results showed distinct effects of the single factor as well as interactive effects of the two factors. Codes are also provided for readers who want to customize their analysis pipeline adapted from this pipeline easily.
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Pipeline for RNA sequencing data analysis by combination of Nextflow and R
)
Abstract
With the development of high-throughput technologies, RNA sequencing (RNA-seq) has become a widely used technology in biological studies and thus a large number of RNA-seq data are emerging and remain to be analyzed. Although there are many different options for analysis methods and tools, a unified pipeline for RNA-seq data analysis is always necessary for a laboratory. Given the update of new methods and tools, I summarized my customized analysis codes to generate an updated pipeline for RNA-seq data analysis. During aging, gene mutations accumulate, and hormone regulation is disrupted, which may exacerbate age-related diseases. Therefore, we generated a dataset from mice with a gene mutation or not and under different hormone treatments to study the effects of two factors, i.e., hormone and gene mutation, on the transcriptome. Based on the Nextflow nf-core rnaseq pipeline, this project established this pipeline consisting of three stages: (1) upstream analysis containing quality control of fastq files before and after trimming, trimming, alignment, and quantification; (2) midstream analysis containing count normalization, differentially expressed genes analysis, and visualization via boxplot, PCA, t-SNE, sample distance heatmap, MA plot, volcano plot, and gene expression heatmap; and (3) downstream analysis containing functional enrichments of KEGG pathways and GO terms. Results showed distinct effects of the single factor as well as interactive effects of the two factors. Codes are also provided for readers who want to customize their analysis pipeline adapted from this pipeline easily.
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