Pipeline Overview
This pipeline integrates:
- RNA-seq for transcript quantification and differential expression
- Proteomics data for protein abundance and interaction mapping
- WES data for somatic/germline variant calling and annotation
All outputs are combined into a unified multi-omics interpretation layer powered by machine learning and AI-based prioritization.
Step-by-Step Workflow
1. Data Input & Preprocessing
- Accepts FASTQ files (RNA-seq/WES), or raw proteomics output (e.g., MaxQuant)
- Optional: Combined human/mouse read separation for xenograft data
- Quality control via FastQC, MultiQC, Picard, and proteomics QC scripts
AI Advantage: Our AI modules learn from prior datasets to flag samples with unusual QC signatures early in the pipeline, helping identify outliers and technical artifacts in real time.
2. RNA-seq Analysis
- Alignment via STAR or Salmon
- Transcript quantification via RSEM
- Differential expression with DESeq2 or edgeR
- Functional enrichment: GO, KEGG, Reactome
AI Advantage:
- Gene prioritization based on known disease associations, pathway centrality, and AI-trained models using literature co-occurrence and omics integration
- Optional anomaly detection on expression profiles using unsupervised learning (e.g., UMAP/t-SNE + clustering + outlier scoring)
3. Proteomics Analysis
- Peptide quantification normalization
- Protein abundance and differential analysis
- Protein-protein interaction inference (STRING, BioGRID overlay)
AI Advantage:
- AI models infer protein modules with strongest cross-correlation to transcriptomic dysregulation
- Suggests candidate protein targets for therapeutic or diagnostic research
4. Whole-Exome Sequencing (WES)
- Alignment (BWA-MEM2) and preprocessing (GATK)
- Variant calling using GATK, Strelka, and FreeBayes
- CNV detection via CNVkit with support for tumor-only and matched analysis
- Variant annotation: SnpEff, VEP, ClinVar, COSMIC
AI Advantage:
- Variant prioritization using multi-feature ML scoring (frequency, pathogenicity, context-aware impact)
- AI-based classification: Likely driver vs. passenger mutation, and inferred gene-drug interactions
- Predictive classification of CNVs (oncogenic potential, recurrence across datasets)
5. Multi-Omics Integration
- Integrates RNA, protein, and variant data into a unified AI-driven model
- Identifies concordant dysregulation (e.g., mutation -> expression shift -> protein effect)
- Highlights key dysregulated axes and offers ranked hypotheses for therapeutic targets or biomarkers
AI Advantage:
- Multi-layer neural network trained on known clinical datasets and drug screens
- Suggests clinically actionable insights that may be missed by traditional siloed analyses
6. Reporting and Dashboard Delivery
- Output:
- Interactive HTML reports
- CSV/TSV summary tables
- Publication-ready figures (volcano plots, CNV heatmaps, expression heatmaps)
- All reports published to client-facing dashboard
- Includes AI-generated insights section: explains rationale behind top-ranked genes, variants, and pathway disruptions