Methylation arrays, bisulfite sequencing, ChIP-Seq, ATAC-Seq and CUT&Tag — processed, tested and interpreted against the right background model.
Epigenomic data carries more technical structure than most data types: bisulfite conversion efficiency, probe cross-reactivity on arrays, fragment size distribution in ATAC-Seq, and background in ChIP all shape the result before any biology is measured.
We check each of those explicitly and report them, because a differentially methylated region that turns out to track a batch is worse than no result at all.
Data We Accept
- Illumina methylation array IDAT files (450K, EPIC, EPICv2)
- Whole genome or reduced representation bisulfite sequencing FASTQ
- ChIP-Seq, CUT&RUN or CUT&Tag FASTQ with matched input or IgG
- ATAC-Seq or scATAC-Seq FASTQ files
- Sample sheets with batch, chip position and cell composition information
Questions We Answer
- Which regions are differentially methylated between my groups?
- Where does my transcription factor bind, and does that change with treatment?
- Which regions become accessible during differentiation or disease?
- Which motifs are enriched in my peak set?
- Is my methylation signal confounded by cell composition?
What We Do
Each project uses the subset of these that your research question requires.
Methylation Array Analysis
Normalisation, probe filtering, cell type deconvolution, and differential methylation at probe and region level with minfi, limma and DMRcate.
Bisulfite Sequencing
Alignment with Bismark, conversion efficiency assessment, coverage filtering and differential methylation calling at CpG and region level.
ChIP-Seq, CUT&RUN and CUT&Tag
Alignment, quality metrics including FRiP and cross-correlation, peak calling against matched controls, and differential binding analysis across conditions.
ATAC-Seq
Fragment size assessment, mitochondrial read removal, peak calling, differential accessibility, and footprinting where coverage permits.
Annotation and Motif Analysis
Peak and region annotation to genomic features, motif enrichment with HOMER or MEME, and integration with expression data where you have it.
Epigenetic Clocks
Age acceleration estimates from methylation data using established clock models, with the caveats about non-European populations stated clearly.
What You Receive
- Differentially methylated positions and regions with annotation
- Peak sets in BED and narrowPeak format with QC metrics
- Differential binding or accessibility results
- Motif enrichment tables and genome browser tracks
- Cell composition estimates where relevant
- Full processing code and reference versions
Tools We Use
- minfi, limma, DMRcate, sesame
- Bismark, MethylDackel, methylKit
- Bowtie2, MACS3, SEACR
- DiffBind, csaw, DESeq2
- HOMER, MEME Suite, ChIPseeker
- deepTools, IGV tracks
Typical turnaround: 3–5 weeks depending on data type and sample number
Every project is scoped and quoted in writing before work begins. Reduced rates are available for students and researchers at African public institutions.
Services are provided for research purposes only. They are not intended for clinical diagnosis, treatment decisions or individual health assessment. See how it works, data submission guidelines and what you receive.
