bulk RNA-seq
Analysis of bulk gene expression data generated by RNA-seq
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8
Bulk RNA-seq is a powerful tool for quantifying gene expression, offering higher sensitivity and resolution compared to microarrays. It generates millions of short reads stored in FASTQ files, which require significant computational resources for their analysis. The primary analysis involves quality control, adapter trimming, alignment to a reference genome using tools like STAR, and quantification of gene expression using tools like Salmon. The nf-core/rnaseq pipeline automates these steps, ensuring reproducibility and portability through containerization. Once gene counts per sample matrix obtained, the statistical analysis is performed with theDESeq2 R package, that normalizes and models count data to identify differentially expressed genes while considering technical and biological variability.
Required assays
| assay | omic | resolution | link |
|---|---|---|---|
| Bulk gene expression | Transcriptomics | Bulk | https://pubmed.ncbi.nlm.nih.gov/19015660/ |
Modules list
| Module | Type | Implementation | Description |
|---|---|---|---|
| Bulk_RNAseq-nfcore_pipeline | Primary analysis | Bash | Primary analysis of RNA-seq data requires significant computing capacity due to the volume and complexity of the data. The process begins with pre-processing, where reads are quality-checked, trimmed to remove adapter sequences, and shortened at error-prone ends to facilitate alignment. Alignment involves mapping each read to the reference genome, which is computationally intensive due to the need to account for potential misreads, polymorphisms, and repeated regions. Different alignment algorithms, such as STAR for exact alignment and Kallisto or Salmon for faster but less precise alignment, have been developed to handle these challenges. After alignment, transcripts are quantified by counting reads overlapping exonic regions, with results presented as count matrices. The nf-core/rnaseq Nextflow pipeline integrates these tools into a reproducible workflow, enhancing standardization and version control. Nextflow facilitates portability through containerization systems like Docker and Singularity, ensuring that each tool runs in a specific version independently of the operating system. This pipeline is part of the nf-core project, which provides state-of-the-art, community-reviewed workflows that can be easily customized and run with a single command. |
| Bulk_RNAseq-Differential_analysis | Statistical analysis | R | Differential expression analysis involves measuring the difference in mean gene abundance between two groups and evaluating its statistical significance. In R, this is accomplished using established workflows like DESeq2 or edgeR, which model counts with a negative binomial distribution parameterized by normalization coefficients and corrected dispersion. These models account for experimental conditions and sample-specific covariates. Based on the modeled counts, a statistical test determines differential gene expression, with p-values adjusted for multiple comparisons and expression changes quantified by log2 fold change. |
Reference publications
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