What is bulk RNA-seq data?

Bulk RNA-seq provides a snapshot of the average gene expression levels at a specific time point across a population of cells, rather than individual cells.

Key characteristics:

• High sensitivity: It’s good at detecting even low-abundance transcripts because of deeper sequencing per sample.

• Less expensive and simpler than single-cell RNA-seq.

• No cell-level resolution: You can’t distinguish what genes individual cells were expressing — only the average. (Cell types can be resolved with deconvolution tools)

What does bulk RNA-seq data look like?

fastq format after sample demultiplexing

SE data

PE data

Data analysis steps:

1. Read quality control
2. Applications of RNA-seq data
3. Read-mapping and quantification of features
4. Differential gene expression
5. Process / pathway enrichment

1. Read quality control

   FastQC

1. Applications of RNA-seq data

Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology Amarinder Singh Thind , Isha Monga , Prasoon Kumar Thakur , Pallawi Kumari , Kiran Dindhoria , Monika Krzak , Marie Ranson , Bruce Ashford Briefings in Bioinformatics, Volume 22, Issue 6, November 2021, bbab259, https://doi.org/10.1093/bib/bbab259

1. Read-mapping and quantification of features

   STAR Check output files for percentage of uniquely mapped reads, mapped read length, etc. - should align with experiment design and sequencing method

1. Sample quality and differential gene expression

   Tutorial for RNAseqQC R package: https://cran.r-project.org/web/packages/RNAseqQC/vignettes/introduction.html DESeq2 / EdgeR

1. Process / pathway enrichment

   ClusterProfiler