3′ mRNA-Seq (Digital RNA-Seq)

Purpose

A form of RNA-Seq that focuses reads on the 3′ ends of polyadenylated transcripts, generally around their 3′UTRs, to estimate gene expression. By sequencing a limited portion of each transcript, it can reduce sequencing requirements compared with full-length RNA-Seq when the goal is to compare gene expression. Actual costs and savings depend on the protocol and experimental design.

Selected Methodological References

• Sholder G. et al. (2020). 3′Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling. BMC Genomics, 21, 64. Describes a 3′-focused gene-expression profiling approach and evaluates its accuracy and scalability.
• Chen J. et al. (2021). MP3RNA-seq: Massively parallel 3′ end RNA sequencing for high-throughput gene expression profiling and genotyping. Journal of Integrative Plant Biology, 63(7), 1227–1239. Presents a 3′-end RNA-Seq method for gene-expression profiling across many samples.
• Dobin A. et al. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15–21. Describes the splice-aware aligner used to map reads to a reference.
• Anders S. et al. (2015). HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics, 31(2), 166–169. Describes HTSeq and its read-counting utility for RNA-Seq.
• Wood D. E. et al. (2019). Improved metagenomic analysis with Kraken 2. Genome Biology, 20, 257. Describes the taxonomic classification method used for screening sequence data.

From Samples to Results

Workflow & Configuration

1. Input: FASTQ reads generated with a 3′-end RNA-Seq library protocol, together with a compatible reference genome and gene annotation for the selected organism.
2. Quality control and preprocessing: FastQC assesses read quality; BBDuk is used for read filtering and cleanup.
3. Alignment and screening: STAR aligns reads to the reference, while Kraken2 supports taxonomic classification and contamination screening.
4. Gene-level quantification: HTSeq counts reads assigned to annotated genes; MultiQC aggregates available quality-control reports.

The workflow is managed with Nextflow DSL2 and is configured for containerized execution with Docker and Google Cloud Batch. Reference files, annotation, read preprocessing and library settings must be appropriate for the organism, sequencing protocol and experimental design.

The specific library preparation determines which 3′-end molecules and transcript regions are captured. Published cost or throughput results for a particular 3′-sequencing protocol should not be assumed to apply to every experiment.

Outputs & Scope

• Main output: raw gene-level counts for the samples, organised as a gene-by-sample count matrix for downstream expression analysis.
• Quality outputs: reports from the workflow steps and an aggregated MultiQC report.
• Downstream analyses: differential expression can identify differentially expressed genes (DEGs); pathway enrichment can then interpret those results. They require a suitable experimental design and annotations and are not direct outputs of this workflow.

Quantification is performed at gene level. The workflow does not reconstruct transcripts, quantify isoforms or analyse splicing. It is also not, by itself, an analysis of alternative polyadenylation site usage; that requires a dedicated objective and method.

Technologies

• Nextflow DSL2 (version 24.04.4 or later)
• Docker containerization and Google Cloud Batch execution
• FastQC and BBDuk (quality control and preprocessing)
• STAR and Kraken2 (alignment and taxonomic screening)
• HTSeq and MultiQC (gene-level counting and report aggregation)