1. Smart Sample QC
- AI classifiers detect low-quality or outlier samples before downstream analysis begins
- Automated flags for adapter contamination, low complexity, and sequencing issues
2. Adaptive Trimming & Alignment
- AI models guide optimal trimming strategies based on read patterns
- Dynamically choose the best alignment strategy (e.g., STAR, BWA) per sample type
3. Species Separation
- Deep learning classifier splits mixed-species reads with high precision
- Automatically routes human reads forward, stores other species reads separately
4. Expression & Variant Prioritization
- AI highlights high-impact genes or variants based on biological relevance
- Factors include expression patterns, mutation deleteriousness, conservation, and databases like ClinVar & CIViC
5. AI-Powered Differential Expression & CNV Detection
- Enhanced filtering of DEGs and CNVs using biological context and machine learning
- Reduced false positives and more biologically meaningful results
6. Clustering & Outlier Detection
- Unsupervised learning (t-SNE, UMAP, HDBSCAN) reveals hidden patterns and sample subgroups
- Identifies outlier samples and subtypes automatically
7. AI-Driven Interpretation
- Natural language summaries of key findings
- Explains gene functions, potential therapeutic targets, and biological implications
- Optional prompts tailored to oncology, neuro, metabolic disease, and more
8. Interactive AI Reports
- Branded HTML and PDF reports can include:
- Charts
- DE summaries
- AI-generated clinical insights
- Exportable gene/variant tables
9. Web Dashboard + Notifications
- Auto-upload to web dashboard (optional)
- Email notification sent upon completion