RNA Sequencing Data Analysis: A 2025 Review and Updated Snakemake Pipeline

• Introduction
• Objectives
• Workflow
  • Preprocessing
  • Indexing
  • Alignment
• Conclusion
• References

---

Introduction

This project aims to provide an easy-to-use Snakemake pipeline, ensuring that it is ready to run "out-of-the-box" in 2025. The pipeline was developed by replicating the RNA-seq data analysis steps outlined in the original work by Clarissa M. Koch and colleagues ¹, whose well-structured article inspired this effort. I aim to assess whether the methods, tools, and approaches described in their paper are still relevant and effective in 2025, while making the process more accessible to users.

Objectives

• Replicate the original analysis: Recreate the RNA-seq data analysis pipeline outlined by Koch et al. and evaluate whether the tools and methods used in 2018 are still valid in 2025.
• Compare with other publications: In addition to replicating the original work, compare their methods and tools with those used in other recent publications.
• Provide an updated Snakemake pipeline: Develop a fully automated and easy-to-use Snakemake workflow to run the entire analysis from raw data to final results with minimal setup.
• Ensure modern compatibility: Ensure that the pipeline uses up-to-date tools and methods that are efficient and well-supported in 2025, replacing deprecated tools where necessary.

---

Workflow

Preprocessing

• PreAlignment-QC:

  • FastQC ²

  Highlights any areas where reads look unusual and requires a closer look.

  - The program is not tied to any specific type of sequencing technique and
  can be used to look at libraries coming from a large number of different
  experiment types (Genomic Sequencing, ChIP-Seq, RNA-Seq, BS-Seq etc etc).
  

  • MultiQC ³

  MultiQC is the standard in bioinformatics reporting

  - A tool to create a single report with interactive plots for
  multiple bioinformatics analyses across many samples.
  Reports are generated by scanning given directories for recognised files.
  

• Trimming - Filtering:

  • Trimmomatic
  • Fastp

• Indexing

  • Bowtie2:

• Alignment

  • TopHat2 ⁴

  TopHat2 produces number of files in its tophat_out/ output directory. Some of the generated files are:

  accepted_hits.bam: list of read alignments in BAM format.
  
  unmapped.bam: list of unmapped reads in BAM format.
  
  junctions.bed: BED track of reported junctions.
  
  insertions.bed: BED track of insertions reported by TopHat.
  
  deletions.bed: BED track of deletions reported by TopHat.
  
  prep_reads.info:
  - statistics about the input sequencing data (min/max read length, number of reads).
  
  align_summary.txt:
  - summary of the alignment counts (number of mapped reads, overall read mapping rate).
  

References

Footnotes

1. Koch CM, Chiu SF, Akbarpour M, Bharat A, Ridge KM, Bartom ET, Winter DR. A Beginner's Guide to Analysis of RNA Sequencing Data. Am J Respir Cell Mol Biol. 2018 Aug;59(2):145-157. doi: 10.1165/rcmb.2017-0430TR. PMID: 29624415; PMCID: PMC6096346. ↩

2. Andrews, Simon. Year. FastQC: A Quality Control Tool for High Throughput Sequence Data. Version [0.12.1]. GitHub. https://github.com/s-andrews/FastQC. ↩

3. Ewels, Philip, Måns Magnusson, Sverker Lundin, and Max Käller. 2016. "MultiQC: Summarize Analysis Results for Multiple Tools and Samples in a Single Report." Bioinformatics. https://doi.org/10.1093/bioinformatics/btw354. ↩

4. Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013). TopHat2: Accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology, 14(4), R36. https://doi.org/10.1186/gb-2013-14-4-r36 ↩