From bulk tissue to single-cell resolution — rigorous, publication-ready transcriptomic workflows enhanced by deep learning and tailored to your exact research question.
Transcriptomics powers discovery across the full spectrum of life science research.
Uncover gene expression dysregulation in cancer, neurodegeneration, autoimmune and rare diseases.
Identify novel therapeutic targets and characterize drug mechanisms of action at transcriptomic level.
Discover and validate expression-based biomarkers for diagnosis, prognosis or treatment stratification.
Define and annotate cell populations with single-cell resolution in complex tissues and organoids.
Map regulatory networks and signaling cascades altered under your experimental condition.
Publication-ready figures, methods sections and supplementary data for high-impact journals.
Whole-transcriptome profiling from bulk tissue or cell populations. We go beyond standard pipelines with custom statistical modeling, ensuring your differential expression results are biologically meaningful, not pipeline artifacts.
Robust DEG identification with DESeq2, edgeR and custom models. We account for confounders, batch effects and complex experimental designs.
Functional annotation and pathway-level interpretation. Gene Set Enrichment Analysis with pre-ranked lists and custom gene sets.
WGCNA-based module detection to identify gene clusters with coordinated expression. Correlate modules with clinical or phenotypic traits.
Machine learning-assisted identification of expression signatures with discriminant power. Feature selection for diagnostic or prognostic panels.
Rigorous quality control with removal of low-quality libraries. Batch effect correction preserving true biological variance using ComBat and limma.
All results delivered as fully interactive HTML reports (volcano plots, heatmaps, PCA) that you can explore and share directly.
FastQC, trimming, alignment to reference genome
STAR / HISAT2 + featureCounts / Salmon
TMM, VST, custom model selection
DESeq2 / edgeR with multi-factor design
Pathway enrichment, network analysis, report
Reference parameters for standard Bulk RNA-Seq projects. Custom designs available on request.
| Parameter | Specification |
|---|---|
| Input material | Total RNA · Poly-A enrichment or rRNA depletion |
| Minimum RNA input | 500 ng (optimal) · 100 ng (low-input protocol available) |
| RNA integrity (RIN) | ≥ 7.0 recommended · FFPE-compatible protocol available |
| Sequencing depth | 20–50 M reads per sample (standard) · up to 100 M for low-expression targets |
| Read length | Paired-end 150 bp (PE150) |
| Accepted organisms | Human · Mouse · Rat · Any organism with reference genome |
| Minimum samples | 3 biological replicates per condition (recommended) |
| Data quality | Q30 ≥ 85% · Raw + processed data delivered |
| Deliverables | Interactive HTML report · Count matrices · DEG tables · Pathway results · Methods text |
Going beyond standard pipelines with advanced Machine Learning, resolving cellular heterogeneity at unprecedented resolution.
By leveraging this scientific Generative AI, we achieve:
Standard PCA-based integration fails to remove technical variation. Our scVI pipeline separates biological signal from batch noise.
Doublet removal, low-quality cell exclusion, ambient RNA correction
Scran pooling or scVI probabilistic normalization
Deep generative model learns latent biological representation
Leiden clustering + marker-based cell type annotation
Pseudo-bulk DE, trajectory inference, cell-cell communication
Most providers run your data through a generic automated pipeline. We do the opposite: every dataset is analyzed with a workflow built specifically for your experimental design, biology, and research question.