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Decoding Transcriptome

Discovering biomarkers and therapeutic targets with real-world data. A hands-on internship in transcriptomics analysis using industry-standard bioinformatics tools.

8 WeeksWeekends Only2.5 hrs / SessionOnline & GlobalCertificate + Thesis
Program Overview

The Decoding Transcriptome internship teaches participants how to extract meaningful biological insights from RNA-Seq data using state-of-the-art bioinformatics methods. Working with real-world transcriptomics datasets, participants will identify differentially expressed genes, build co-expression networks, and validate biomarkers and drug targets using major public databases.

The program culminates in an individual thesis assignment on a real-world dataset — a significant scientific project for any academic or professional portfolio.

Program Modules
01
Introduction to Transcriptomics and RNA-Seq Data
Fundamentals, experimental design, data formats, QC and preprocessing pipelines.
02
Differential Expression Analysis — DESeq2 / EdgeR
Statistical frameworks for identifying DEGs. Hands-on R analysis with DESeq2 and EdgeR.
03
Weighted Co-Expressed Gene Analysis — WGCNA
Building co-expression networks and identifying gene modules correlated with biological traits.
04
Network Analysis — Igraph in R and Cytoscape
Graph theory for biological networks. Visualisation using Igraph in R and Cytoscape.
05
Functional Enrichment and Annotation
ClusterProfiler, KEGG, Metascape, Enrichr and Gene Ontology for pathway interpretation.
06
Target Identification and Network Analysis
Hub gene identification, PPI networks, and prioritising therapeutic targets.
07
Biomarker and Drug Target Validation
Validation using GEO, TCGA, GTEx, STRING and DGIdb.
08
Thesis on Assignment
Independent analysis of a real-world transcriptomics dataset with written thesis.
What You Will Learn
Process and analyse RNA-Seq datasets from raw reads to biological insights
Identify differentially expressed genes using DESeq2 and EdgeR
Build weighted co-expression gene networks and interpret biological modules
Perform functional enrichment analysis to understand pathway-level biology
Identify hub genes and prioritise biomarkers and drug targets
Validate findings using GEO, TCGA, GTEx and other public databases
Complete an independent thesis-quality analysis on a real transcriptomics dataset
Software and Tools
R / RStudioDESeq2EdgeRWGCNAIgraphCytoscapeClusterProfilerKEGGMetascapeEnrichrGene OntologyGEOTCGAGTExSTRINGDGIdb