> For the complete documentation index, see [llms.txt](https://ycl6.gitbook.io/guide-to-rna-seq-analysis/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ycl6.gitbook.io/guide-to-rna-seq-analysis/master.md).
# Introduction
## Guide to RNA-seq Data Analysis
**I-Hsuan Lin**
The University of Manchester
RNA-seq utilizes the sequencing technology to assay the presence and quantity of RNA molecules in the given sample. RNA-seq offers many advantages and supersede the microarray technology that was introduced in 2000s. This includes detects known and novel transcripts, increased specificity and sensitivity, and identification of low-abundance transcripts and isoforms with sufficient sequencing depth. RNA-seq has also been used to discover alternative splicing variants, chimeric RNAs result from fusion genes and RNA editing sites. We can compare RNA-seq data between conditions to detect differences across groups of samples in terms of (1) gene-level expression, (2) transcript/isoform-level expression, and (3) transcript/isoform usage within a gene.
I will use a real-world illumina paired-end RNA sequencing dataset to demonstrate a step-by-step guide where readers can reproduced the analysis. The complete workflow includes:
* Performing QC and trimming to pre-process RNA-seq raw data
* Mapping of trimmed reads using either alignment-free (Salmon) and alignment-based (STAR) methods
* Quantifing gene and transcript expression
* Performing statistical testing to identified differentially expressed genes, transcripts and also detected expression switching between transcripts
## License
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