Genomics & transcriptomics
RNA-seq Data Analysis
We analyse bulk RNA-seq experiments from raw reads or count matrices and deliver results you can interpret, defend and publish: quality control, differential expression with the correct design, enrichment and figures, with every step documented.
Who it is for
- Academic labs without in-house bioinformatics support
- Biotech teams that need a documented analysis on a deadline
- PhD students who need expert review of a thesis dataset
What you send us
- FASTQ (single- or paired-end)Any Illumina-style library; stranded or unstranded.
- Count matricesfeatureCounts, Salmon, RSEM or nf-core/rnaseq output.
- Sample sheetConditions, batches, pairing and any covariates.
What you receive
- HTML and PDF report: QC, results and interpretation
- Differential-expression tables for all genes and significant genes
- Publication-ready figures (PCA, volcano, MA, heatmaps, enrichment) in vector formats
- Methods section with software versions and parameters
- Reproducible code and environment files
Methodology
How the work runs
Each step is agreed with you and documented. Typical tools: FastQC · MultiQC · STAR · Salmon · DESeq2 · edgeR · clusterProfiler · fgsea · nf-core/rnaseq
- ScopingWe review the design, sample sheet and questions, and flag problems such as confounded batches or too few replicates before any analysis starts.
- Processing and QCRead QC and trimming, alignment (STAR) or pseudo-alignment (Salmon), quantification and a MultiQC report shared with you before the main analysis.
- Exploratory analysisNormalisation, PCA and sample-distance analysis to identify outliers, batch structure and sample swaps.
- Differential expressionDESeq2 or edgeR with a design formula that matches the experiment, explicit contrasts and shrunken fold changes.
- Interpretation and hand-overGO, Reactome and preranked GSEA enrichment, gene-level plots, a written summary and a results call.
Quality controls
Checks applied to every project
- Per-sample read quality, duplication, rRNA and mapping rates reported
- Library-size and strandedness checks against the stated protocol
- Sample-level PCA and correlation to detect outliers and swaps
- Batch and confounding assessed before any contrast is tested
Scope and limits
What the work can and cannot show
- A design with fewer than three biological replicates per group can be analysed, but power is low and the report will say so.
- Batch effects that are fully confounded with the condition cannot be corrected statistically.
- Research use only: results are not a clinical or diagnostic report.
Free quote
Start a RNA-seq project.
Describe your data and questions. We reply with questions or a written quote.
It depends on the data type, the number of samples and the depth of interpretation you need. Every project receives a written quote with scope, deliverables, price and delivery date after a short exchange about your data and questions. The quote is free and carries no obligation.
Yes. Many projects start from a count matrix produced by a sequencing provider or by nf-core/rnaseq. We check it for the same problems we would look for in raw data.
Data are used only for your project, transferred through a channel you approve and deleted at the end of the engagement on request. We will sign your institution’s confidentiality agreement before any data are shared.
Yes. You receive a methods section with software versions and parameters, and every figure can be regenerated from the delivered code. Authorship or acknowledgement follows your field’s norms and is agreed at the start.
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