Bulk mRNA-seq Analysis
End-to-End Transcriptomic Profiling
We analyze bulk RNA-seq datasets to identify differentially expressed genes, altered pathways, and biologically meaningful transcriptional signatures across conditions, tissues, treatments, genotypes, and time points. Every step, from raw FASTQ processing to final figures, is documented in reproducible R/Python notebooks so your results are fully auditable and re-runnable.
Key Features
FASTQ Processing & Quantification
QC, adapter trimming, genome alignment, and count matrix generation form a robust, reproducible foundation for all downstream analyses.
Differential Expression Analysis
DESeq2, edgeR, and limma with careful experimental-design modelling to identify statistically robust changes across your contrasts of interest.
Visualisation & Sample QC
PCA, volcano plots, heatmaps, and clustering reveal structure in your data and flag outlier samples before they affect conclusions.
Pathway & Gene-Set Enrichment
GO, KEGG, Reactome, MSigDB, and GSEA analyses translate gene lists into biological mechanisms, with custom gene-set support.