Bulk RNA-seq & DE
Gene- and transcript-level quantification with rigorous differential-expression testing across conditions, genotypes, or treatments.
Core Bioinformatics
Transcriptomics & Expression Analysis
From raw reads to a defensible expression story. We run bulk and single-cell RNA-seq pipelines—quantifying transcripts, calling differential expression, resolving cell types and splicing, and layering pathway enrichment—and hand back tables and figures documented for publication and review.
FASTQ · count matrix in
Counts · DE tables · figures out
Typical turnaround 3–7 days
Individual pricing from $149
Illustrative sample output
up-regulated
down-regulated
n.s.
Overview
Rigorous expression analysis, without the in-house pipeline overhead
Transcriptomics turns RNA sequencing reads into a quantified, interpretable picture of which genes and transcripts are active—and how that activity shifts between conditions, cell types, or time points. The hard part is rarely running a single tool; it is choosing quantification and statistical models suited to your design, controlling batch effects and false discovery, and documenting every decision so the result survives peer review.
We build and run that workflow for you. Whether you have a bulk RNA-seq comparison across a handful of groups or a single-cell atlas of a whole tissue, we take your data through QC, quantification, normalization, differential expression, and functional interpretation using established, peer-reviewed tools—never opaque in-house black boxes—and return results with the tool versions and parameters recorded for full reproducibility.
Capabilities
What we analyse
One service spanning the full range of RNA measurement—from bulk expression across conditions to single cells, splicing, and space.
Single-cell & single-nucleus
QC, doublet removal, integration, clustering, cell-type annotation, and marker discovery that resolve heterogeneity within a sample.
Alternative splicing
Differential exon usage, isoform switching, and splicing events quantified between conditions from short- or long-read data.
Gene-fusion detection
Chimeric transcripts and fusion events called and filtered from RNA-seq—valuable for oncology and disease-mechanism studies.
Enrichment & pathways
Over-representation and gene-set enrichment against curated ontologies turn a gene list into interpretable biology.
Spatial transcriptomics
Expression mapped back onto tissue architecture—spot and cell segmentation, clustering, and spatial domain analysis.
Long-read isoforms
Full-length transcript discovery, isoform quantification, and novel-isoform characterization from PacBio and Nanopore reads.
Small RNA & miRNA
miRNA identification and quantification, novel-miRNA prediction, and small-RNA differential expression with target context.
The Pipeline
How an expression analysis runs
A transparent, best-practice sequence—each step chosen for your data type and documented in the final report. Nothing is a black box.
Steps are adapted to your design: bulk vs. single-cell, model vs. non-model organism, two groups vs. a time course. We confirm the plan with you before any compute begins.
Quality control & trimming
Raw-read QC, adapter and quality trimming, and a per-sample quality summary before anything downstream.
Tools: FastQC · MultiQC · fastp · Trim Galore
Alignment & quantification
Spliced alignment or fast transcript-level pseudoalignment to your reference, producing a gene- or transcript-count matrix.
Tools: STAR · HISAT2 · Salmon · kallisto
Post-quantification QC
Library-complexity, gene-body coverage, and mapping-rate checks, plus PCA and clustering to catch batch effects and outliers early.
Tools: RSeQC · Qualimap · tximport · PCA
Normalization & modelling
Count normalization and an appropriate statistical model for your design—covariates and batch terms included where needed.
Tools: DESeq2 · edgeR · limma-voom
Differential expression & structure
FDR-controlled differential expression—plus clustering, cell-type annotation, or splicing analysis for single-cell and isoform designs.
Tools: DESeq2 · Seurat · Scanpy · rMATS
Functional enrichment
Over-representation and gene-set enrichment against curated ontologies to turn ranked gene lists into interpretable biology.
Tools: clusterProfiler · fgsea · GO · MSigDB
Reporting & delivery
Annotated result tables, publication-quality figures, a QC report, and reproducible methods with every tool version.
Tools: ggplot2 · MultiQC · versioned methods manifest
Tools & Technologies
Established, peer-reviewed tools—matched to your data
We select from the field's standard toolkit rather than forcing every dataset through one pipeline. A representative set of what we work with:
Read QC & preprocessing
FastQC
MultiQC
fastp
Trim Galore
Cutadapt
Alignment & quantification
STAR
HISAT2
Salmon
kallisto
RSEM
featureCounts
StringTie
Differential expression
DESeq2
edgeR
limma-voom
tximport
ComBat / sva
Single-cell & single-nucleus
Seurat
Scanpy
Cell Ranger
Harmony
scVI
Scrublet
SingleR
Splicing & fusion
rMATS
SUPPA2
LeafCutter
STAR-Fusion
Arriba
FusionCatcher
Enrichment & networks
clusterProfiler
fgsea
GSEA
GSVA
WGCNA
STRINGdb
Long-read & isoform
IsoSeq
FLAIR
SQANTI3
TALON
minimap2
QC, visualization & review
RSeQC
Qualimap
ggplot2
IGV
Reference Resources
Public knowledge behind every result
Expression numbers are only as useful as the annotation around them. We build on the community's authoritative, versioned resources.
- Ensembl / GENCODE
- Comprehensive gene models and transcript annotation underpinning quantification.
- RefSeq
- Curated reference transcripts and gene definitions for cross-referencing.
- Gene Ontology (GO)
- Structured vocabulary of biological process, function, and component for enrichment.
- KEGG & Reactome
- Curated pathway maps for interpreting coordinated expression changes.
- MSigDB
- Hallmark and curated gene-set collections powering GSEA.
- STRING
- Protein–protein interaction networks for connecting differentially expressed genes.
- CellMarker & PanglaoDB
- Marker-gene references supporting single-cell cell-type annotation.
- GEO / SRA / ArrayExpress
- Public expression archives for reprocessing, meta-analysis, and validation.
- miRBase
- Reference catalogue of published miRNA sequences and annotation.
Choosing a Design
Bulk vs. single-cell vs. spatial RNA-seq
There is no single best design—only the right one for your question and budget. A quick orientation; we will help you decide.
| Dimension | Bulk RNA-seq | Single-cell RNA-seq | Spatial transcriptomics |
|---|---|---|---|
| Resolution | Average expression across a whole sample | Per-cell profiles resolving distinct populations | Expression tied to tissue location |
| Best answers | How does expression differ between conditions? | Which cell types are present and how do they change? | Where in the tissue is expression happening? |
| Typical output | DE gene tables, volcano/heatmap, enrichment | Clusters, UMAP, cell-type maps, markers | Spatial domains, niches, co-localisation |
| Relative cost | Lowest per sample; most established | Higher per sample; richer resolution | Highest; specialised platforms |
| Best suited to | Condition comparisons, large cohorts, time courses | Heterogeneous tissue, rare populations, atlases | Architecture-dependent biology & microenvironment |
What You Receive
A complete, documented deliverable
Not just a count matrix dropped in a folder—every output you need to analyse, publish, and reproduce the work.
- Gene- and transcript-level count matrices (raw & normalized)
- Differential-expression tables with fold-changes & adjusted p-values (TSV / XLSX)
- Publication-quality figures (volcano, MA, heatmap, PCA / UMAP)
- Functional enrichment & pathway results
- Single-cell: annotated clusters, markers & cell-type maps
- QC report with per-sample mapping & quality metrics
- Publication-ready methods text with tool versions
Built for reproducibility, not just a result
Every pipeline follows documented best practices, with appropriate normalization, batch handling, and false-discovery control so significance is earned rather than assumed. Where it applies, we use spike-in controls and established benchmark datasets to check quantification behaves as expected. Each run records its tool versions, parameters, and reference builds.
The practical payoff: your methods section writes itself, a reviewer can re-run the analysis, and a result from today can be reproduced a year from now. We will also tell you honestly when a design or sample size won't support the conclusion you're after.
FAQ
Transcriptomics & expression questions
What researchers and project leads most often ask before starting an RNA-seq project.
Bulk RNA-seq measures average expression across a sample and is cost-effective for comparing conditions or genotypes. Single-cell resolves distinct cell types and states within a sample. We help you match the design to your biological question, budget, and the resolution you actually need.
Which genes and transcripts change significantly between your conditions, by how much, and with what statistical confidence — returned as ranked tables with fold-changes and adjusted p-values, plus volcano, MA, and heatmap figures ready for your manuscript.
Yes. We run QC and doublet removal, normalization, integration and batch correction, clustering, cell-type annotation, marker discovery, and trajectory or RNA-velocity analysis using Seurat and Scanpy-based workflows.
Yes. We can retrieve and reprocess published datasets from GEO, SRA, or ArrayExpress and integrate them with your own samples — useful for meta-analysis, validation, or building a reference to compare against.
We start from raw reads (FASTQ) or an existing count or expression matrix. If you only have sequencing files from your core facility and a reference or annotation, that is enough to begin. We return count matrices, normalized values, and annotated result tables.
Yes. Given a reference genome and annotation — or a de novo transcriptome assembly where none exists — we can build and run the pipeline for non-model plants, animals, microbes, and multi-species (dual RNA-seq) designs.
Related
Where expression work often leads next
Have sequencing data ready to analyse?
Tell us your organism, data type, and question—we'll scope it honestly, including if a different design would serve you better.