Bioinformatics - RNA-seq

RNA sequencing (RNA-seq) provides a comprehensive and detailed view of gene expression by sequencing RNA molecules in a sample. This technique is essential for understanding gene regulation, identifying new transcripts, and studying differential gene expression.

Pipeline Overview

1. Demultiplexing: Sorting sequencing reads based on sample-specific barcodes.
2. Quality Control (QC): Assessing the raw data for quality issues such as adapter contamination and base quality.
3. Preprocessing: Trimming low-quality sequences and removing adapter sequences.
4. Mapping: Aligning the reads to a reference genome or transcriptome.
5. Quantification: Counting reads mapped to each gene or transcript.
6. Differential Expression: Identifying genes with significant expression differences across experimental conditions using DESeq2.
7. Post-processing: Filtering out lowly expressed genes, normalizing counts, and conducting pathway analysis.
8. Visualization: Generating plots such as heatmaps, PCA, and volcano plots to visualize expression patterns and differential expression results.

Expected Result Output

1. Main Results File: A .csv file with gene expression data, including counts, FoldChange and p.Adjusted value.
2. Additional Results:
   • Volcano Plot: For visualizing differential expression.
   • Heatmap: For visualizing gene expression across samples.
   • PCA Plot: To assess sample similarity and detect potential outliers.
   • Coverage Plot: To check read coverage across genes or the entire transcriptome using genomic browser such as IGV.