Gene Expression & RNA-Seq Analysis

You send raw reads or a count matrix. We return the differentially expressed genes, the pathways they sit in, and figures ready for your manuscript.

Bulk RNA sequencing is the most common data type we handle. Most projects arrive in one of two states: raw FASTQ files straight from a sequencing provider, or a count matrix produced by someone else that you are not sure you trust.

We handle both. If you send raw reads we run the full pipeline and show you the quality control. If you send a count matrix we check it before we analyse it — library size distribution, batch structure, outliers — and tell you if something looks wrong.

Data We Accept

Raw FASTQ files (Illumina, BGI, Element, any provider)
Aligned BAM files with or without an index
Count matrices from featureCounts, HTSeq, Salmon, kallisto or RSEM
Microarray raw data (Affymetrix CEL, Agilent, Illumina BeadChip)
A sample metadata sheet describing groups, batches and covariates

Questions We Answer

Which genes respond to my treatment, and by how much?
Which biological pathways are affected?
Is there a batch effect in my data, and can it be corrected?
Do my samples cluster the way the experimental design predicts?
Does my published signature of interest separate my groups?

What We Do

Each project uses the subset of these that your research question requires.

Quality Control and Alignment

Adapter and quality trimming, rRNA and contamination screening, alignment to a reference genome or transcriptome, and per-sample QC reporting. We tell you if a sample should be dropped and why.

Differential Expression

Negative binomial modelling with DESeq2 or edgeR, or limma-voom where the design calls for it. Complex designs are handled properly: paired samples, time courses, multiple factors, batch correction and covariate adjustment.

Functional Enrichment

Over-representation and gene set enrichment analysis against GO, KEGG, Reactome, MSigDB and custom gene sets. Results are filtered and summarised so you are not handed 400 redundant terms.

Co-expression and Clustering

WGCNA module detection, hierarchical clustering and sample correlation analysis to find gene groups that move together across conditions.

Deconvolution and Signatures

Estimating cell type proportions from bulk data using CIBERSORTx or xCell, and scoring published signatures across your samples.

What You Receive

Analysis report with plain-language summary and manuscript-ready methods
Volcano, MA, heatmap, PCA and sample correlation figures in vector PDF and PNG
Full differential expression table with fold change, standard error and adjusted p-values
Enrichment tables for every gene set database tested
Normalised count matrix and the QC report for every sample
Complete analysis code with pinned software versions

Tools We Use

fastp, FastQC, MultiQC
STAR, HISAT2, Salmon, kallisto
featureCounts, HTSeq
DESeq2, edgeR, limma-voom
clusterProfiler, fgsea, GSEA
WGCNA, ComplexHeatmap

Typical turnaround: 5–10 working days from a count matrix; 2–3 weeks from raw reads for up to 24 samples

Indicative price: From $499 for a two-group comparison from an existing count matrix

Reduced rates are available for students and researchers at African public institutions. Every project is quoted in writing before work begins.

Gene Expression & RNA-Seq Analysis

Data We Accept

Questions We Answer

What We Do

Quality Control and Alignment

Differential Expression

Functional Enrichment

Co-expression and Clustering

Deconvolution and Signatures

What You Receive

Tools We Use

Tell us about your data and we will scope it — free, within one working day.

+233 558 017 827

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