RNASeq workflow

A Nextflow pipeline for bioinformatic workflow for RNASeq analysis on human or mouse transcriptomic data. It performs quality control, ribodepletion, alignment on reference genome, and transcript-level quantification.

This repository includes a custom bioinformatic workflow for our lab (LPM IKEM, Prague, Czechia), all parameters are set to work properly on specific data from our lab. Before any usage, please check if it fits your data as well

Table of Contents

• 🧬 Overview
• 🚀 Quick Usage
• 📦 Requirements
• 🛠️ Installation
• 🔬 Pipeline Description
• 📥 Inputs
• 📤 Outputs
• 📚 References

---

Overview

The pipeline takes paired-end FASTQ files and a samplesheet as input and produces:

• Quality-controlled and trimmed reads
• Genome-coordinate alignments
• Transcriptome-coordinate alignments
• Transcript-level quantifications

---

Quick Usage

nextflow run main.nf \
  --input samplesheet.csv \
  --outdir results/ 

Requirements

User needs conda environment with Nextflow (>=23.04.0) & Singularity installed. Everything else needed for the pipeline execution can be prepared with the setup_pipeline.sh script.

---

Software

Tool	Version	Purpose
Nextflow	≥ 23.04	Workflow manager
FastQC	any	Read quality assessment
fastp	any	Read trimming and filtering
SORTMERNA	4.3.7	Ribodepletion
STAR	2.7.11b	Spliced Transcripts Alignment to a Reference
SAMTOOLS	1.23.1	Sorting & indexing BAM files
QUALIMAP	2.2.2	Alignment evaluation
SALMON	2.3.4	Transcript-level quantification
MultiQC	any	QC report aggregation

Reference genomes and databases

File	Used by
SortMeRNA db (smr_v4.3_default_db.fasta.gz)	SORTMERNA
Homo sapiens genome (GRCh38) (Homo_sapiens.GRCh38.dna_sm.primary_assembly.fa)	STAR
Homo sapiens transcripts (GRCh38) (Homo_sapiens.GRCh38.transcripts.fa)	SALMON
Homo sapiens annotation (GRCh38) (Homo_sapiens.GRCh38.116.gtf)	STAR, SALMON
Mus musculus genome (GRCm39) (Mus_musculus.GRCm39.dna_sm.primary_assembly.fa)	STAR
Mus musculus transcripts (GRCm39) (Mus_musculus.GRCm39.transcripts.fa)	SALMON
Mus musculus annotation (GRCm39) (Mus_musculus.GRCm39.116.gtf)	STAR, SALMON

Installation

1. Clone the repository

git clone https://github.com/xpolak37/RNAseq_workflow.git
cd RNAseq_workflow

2. Install Nextflow & Singularity

conda create --prefix </path/to/your/new/nf-env/> bioconda::nextflow
conda activate </path/to/your/new/nf-env/>
conda install conda-forge::singularity

3. Install pipeline dependencies

Via setup_pipeline.sh script:

bash setup_pipeline.sh

Pipeline Description

flowchart TB
    A["Input samplesheet"] --> B["FastQC on raw reads"]
    A --> C["Fastp quality trimming"] 
    C --> D["SortMeRNA"]
    D --> E["STAR alignment"]
    E --> F["Samtools sorting & indexing"]
    F --> G["Qualimap BAMQC & RNASEQ"]
    E --> H["Salmon quantification"]
    C --> Z["MultiQC"]
    B --> Z["MultiQC"]
    E --> Z["MultiQC"]
    G --> Z["MultiQC"]
    H --> Z["MultiQC"]

Loading

Steps in detail

Quality control (FastQC + fastp)
Raw reads are assessed with FastQC before and after trimming. fastp handles adapter removal, quality-based filtering, polyG/X trimming.

SortMeRNA
Filters out ribosomal RNA reads so quantification focuses on mRNA.

STAR alignment
Splice-aware mapping of reads to the genome; also emits a transcriptome BAM for Salmon.

Samtools sorting & indexing
Coordinate-sorts and indexes the genome BAM so downstream tools can read it efficiently.

Qualimap BAMQC & RNASEQ
Post-alignment QC: mapping quality, coverage, and gene-body / read-distribution metrics.

Salmon quantification
Estimates transcript-level abundances (quant.sf) from the transcriptome BAM, with GC/sequence-bias correction.

MultiQC
Aggregates all QC outputs (FastQC, Fastp, STAR, Qualimap, Salmon) into one interactive report.

---

Inputs

Samplesheet

A CSV file with three columns — no spaces, no extra headers:

sample,read1,read2
SampleID1,/data/SampleID1_R1.fastq.gz,/data/SampleID1_R2.fastq.gz
SampleID2,/data/SampleID2_R1.fastq.gz,/data/SampleID2_R2.fastq.gz

Column	Description
sample	Unique sample identifier
read1	Absolute path to forward reads (gzipped FASTQ)
read2	Absolute path to reverse reads (gzipped FASTQ)

Pipeline parameters

Parameter	Default	Description
--input	required	Path to samplesheet CSV (sample,fastq_1,fastq_2)
--outdir	${projectDir}/results	Output directory
--host	human	Reference genome name (human / mouse)
--star_index	./host_genomes/	Path to the pre-built STAR genome index directory (generated with setup_pipeline.sh)
--smr_refs	./smr_v4.3_default_db.fasta	SortMeRNA rRNA reference FASTA for ribodepletion (downloaded with setup_pipeline.sh)
--fastp_length_required	35	Minimum read length kept after Fastp trimming
--star_sjdb_overhang	150	STAR splice-junction overhang (read length − 1)
--singularity_cache_dir	./singularity_cache	Directory for cached Singularity container images
--multiqc_title	RNASEQ workflow	Title shown in the MultiQC report
--publish_dir_mode	copy	How results are published to outdir (copy / symlink / …)
--help	false	Print help message and exit

Outputs

results/
├── fastqc_raw/             # FastQC quality reports on raw reads
├── fastp/                  # Trimmed reads + fastp JSON/HTML reports
├── sortmerna/              # rRNA-depleted reads + SortMeRNA logs
├── star/                   # Genome & transcriptome BAMs + STAR alignment logs
├── salmon/                 # Transcript quantifications (quant.sf) per sample
└── multiqc/                # Aggregated MultiQC report

References

• FastQC: Andrews S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
• fastp: Chen S et al. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34(17):i884–i890. https://doi.org/10.1093/bioinformatics/bty560
• SortMeRNA: Kopylova E, Noé L, Touzet H. (2012). SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics, 28(24):3211–3217. https://doi.org/10.1093/bioinformatics/bts611
• SILVA: Quast C et al. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research, 41(D1):D590–D596. https://doi.org/10.1093/nar/gks1219
• STAR: Dobin A et al. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635
• Salmon: Patro R et al. (2017). Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods, 14:417–419. https://doi.org/10.1038/nmeth.4197
• SAMtools: Danecek P et al. (2021). Twelve years of SAMtools and BCFtools. GigaScience, 10(2):giab008. https://doi.org/10.1093/gigascience/giab008
• Qualimap 2: Okonechnikov K, Conesa A, García-Alcalde F. (2016). Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics, 32(2):292–294. https://doi.org/10.1093/bioinformatics/btv566
• Ensembl: Harrison PW et al. (2024). Ensembl 2024. Nucleic Acids Research, 52(D1):D891–D899. https://doi.org/10.1093/nar/gkad1049
• MultiQC: Ewels P et al. (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19):3047–3048. https://doi.org/10.1093/bioinformatics/btw354
• Nextflow: Di Tommaso P et al. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35:316–319. https://doi.org/10.1038/nbt.3820