How It Works
Three streamlined steps from raw data to actionable biological insights.
Upload & Configure
Bring your raw FASTQs, pre-aligned reads, or count matrices. Our high-speed chunked uploader securely transfers massive datasets directly to our computational fleet. Select a reference genome and map your metadata.
Automated Processing
Our Master Router dynamically executes the correct biological track (e.g., standard RNA-seq, small RNA, or epigenomics). The pipeline handles alignment, batch correction (ComBat-seq), and statistical normalization (DESeq2) automatically.
The CoreHub
Enter your isolated, secure workspace. Instantly explore interactive PCA and Volcano plots, download standardized matrices, and seamlessly trigger our 12 advanced analytical modules or single-cell deconvolutions right from the dashboard.
Platform Capabilities
From raw reads to spatial maps, every analysis module at your fingertips.
Alignment & QC
FastQC, Trimmomatic, HISAT2 alignment, featureCounts quantification, and compressed CRAM output.
DEG & Statistics
Batch correction (ComBat-seq), DESeq2 differential expression, PCA, UMAP, and Volcano plots.
Cell Deconvolution
DestVI / BayesPrism unmixes bulk RNA into pseudo-single-cell profiles for advanced downstream analysis.
Spatial Mapping
Tangram deep learning projects imputed cells onto H&E tissue slides with Moran’s I autocorrelation.
12 Standard Analytical Modules
Unlocked instantly after the core pipeline finishes.
Alternative Splicing
IsoformSwitchAnalyzeR detects transcript structure changes and lost protein domains.
RNA Editing & SNPs
REDItools2 scans for A-to-I editing events and high-confidence mutations.
WGCNA
Co-expression network analysis correlating gene modules to clinical traits.
Pathway Enrichment
GSEA/ORA with PathBank, KEGG, and Reactome pathway diagrams.
Causal Networks
GRNBoost2 regulatory inference and STRING protein-protein interaction maps.
Literature Mining
INDRA NLP reconstructs gene regulatory pathways from PubMed abstracts.
Survival Prediction
Kaplan-Meier curves and Log-Rank tests on gene expression cohorts.
TCGA Integration
Compare your data against massive public cancer cohorts for subtype classification.
Biomarker Discovery
Cross-reference DEGs against FDA-approved diagnostic and predictive biomarkers.
MOFA
Multi-omics factor analysis integrating RNA-seq with secondary omics data.
DIABLO
Supervised multi-omics integration for predictive modeling with AUROC curves.
Trajectory Inference
PAGA / Pseudotime mapping of pseudo-cell developmental lineages.