This project demonstrates a complete RNA-seq preprocessing workflow using publicly available sequencing data. The pipeline starts from raw paired-end FASTQ files and performs quality control, read trimming, genome alignment, BAM processing, and gene-level quantification.
The workflow was implemented on Ubuntu (WSL2) using widely adopted bioinformatics tools and follows best practices for RNA-seq preprocessing.
Raw FASTQ Files
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Quality Control (FastQC)
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Read Trimming (fastp)
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Genome Alignment (HISAT2)
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SAM to BAM Conversion (SAMtools)
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Sorting & Indexing BAM (SAMtools)
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Gene Quantification (featureCounts)
| Tool | Purpose |
|---|---|
| Linux (Ubuntu WSL2) | Bioinformatics environment |
| SRA Toolkit | Download sequencing data |
| FastQC | Raw read quality assessment |
| MultiQC | Aggregate quality reports |
| fastp | Adapter removal and quality trimming |
| HISAT2 | RNA-seq read alignment |
| SAMtools | BAM file processing |
| featureCounts | Gene-level read counting |
- Ubuntu 24.04 (WSL2)
- HISAT2
- SAMtools
- FastQC
- MultiQC
- fastp
- featureCounts (Subread)
RNAseq-Cancer-Pipeline/
│
├── alignment/
├── bam/
├── counts/
├── figures/
├── qc/
├── raw_data/
├── reference/
├── results/
├── scripts/
├── trimmed_reads/
├── README.md
└── .gitignore
- Public paired-end RNA-seq FASTQ files
- Human reference genome (GRCh38)
- Gene annotation (GTF)
Sequencing reads were downloaded using the SRA Toolkit.
Raw sequencing quality was evaluated using FastQC.
Outputs:
- HTML reports
- ZIP reports
Low-quality bases and adapter sequences were removed using fastp.
Outputs:
- Trimmed FASTQ files
- HTML report
- JSON report
Trimmed reads were aligned against the Human GRCh38 reference genome using HISAT2.
Output:
- SAM alignment file
SAMtools was used to:
- Convert SAM → BAM
- Sort BAM
- Index BAM
Outputs:
- Sorted BAM
- BAM Index (.bai)
featureCounts assigned aligned reads to annotated genes.
Outputs:
- gene_counts.txt
- gene_counts.txt.summary
| Metric | Result |
|---|---|
| Total Reads | 16,249,153 |
| Overall Mapping Rate | 91.95% |
| Primary Mapping Rate | 90.63% |
| Properly Paired Reads | 83.95% |
The high mapping rate indicates successful alignment against the human reference genome.
- Sorted BAM file
- BAM index
- Gene count matrix
- FastQC reports
- fastp reports
- Alignment statistics
- Linux command line
- Bash scripting
- RNA-seq preprocessing
- FASTQ quality assessment
- Adapter trimming
- Genome alignment
- BAM file manipulation
- Gene quantification
- Bioinformatics workflow management
This preprocessing workflow provides the required inputs for downstream analyses such as:
- Differential Gene Expression (DESeq2)
- edgeR
- limma-voom
- Functional Enrichment Analysis
- GO Analysis
- KEGG Pathway Analysis
Future versions of this project will include:
- Multiple biological replicates
- Differential expression analysis using DESeq2
- Volcano plots
- PCA visualization
- Heatmaps
- Gene Ontology enrichment
- KEGG pathway analysis
- Workflow automation using Bash
Kim D, Langmead B, Salzberg SL.
HISAT2: graph-based alignment of next-generation sequencing reads to a population of genomes.
Nature Biotechnology.
Liao Y, Smyth GK, Shi W.
featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features.
Bioinformatics.
Andrews S.
FastQC: A Quality Control Tool for High Throughput Sequence Data.
Chen S, Zhou Y, Chen Y, Gu J.
fastp: an ultra-fast all-in-one FASTQ preprocessor.
Bioinformatics.
Mehwish
Bioinformatics | RNA-seq Analysis | Linux | R | Python | NGS
GitHub: https://github.com/Mehwish55