name: 'cellfree-rna-agent'
description: 'AI-powered cell-free RNA analysis from liquid biopsy for cancer detection, tissue-of-origin identification, and non-invasive transcriptomic profiling.'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
Cell-Free RNA Analysis Agent
The Cell-Free RNA Analysis Agent provides comprehensive analysis of circulating cell-free RNA (cfRNA) from plasma and other biofluids for cancer detection, tissue-of-origin identification, and non-invasive transcriptomic profiling.
When to Use This Skill
- When analyzing plasma cfRNA for cancer detection and monitoring.
- To identify tissue-of-origin from circulating transcripts.
- For non-invasive transcriptomic profiling of tumors.
- When integrating cfRNA with cfDNA for comprehensive liquid biopsy.
- To discover RNA-based biomarkers from accessible biofluids.
Core Capabilities
cfRNA Profiling: Quantify mRNA, lncRNA, and small RNA from plasma.
Tissue Deconvolution: Identify tissue sources contributing to cfRNA pool.
Cancer Detection: ML models detecting cancer from cfRNA profiles.
Tumor Transcriptomics: Infer tumor gene expression non-invasively.
Integration with cfDNA: Combine RNA and DNA liquid biopsy analytes.
Biomarker Discovery: Identify diagnostic and prognostic RNA markers.
cfRNA Biology
Sources:
- Cell death (apoptosis, necrosis)
- Active secretion (EVs, RNA-binding proteins)
- Cell surface-associated RNA
Protection Mechanisms:
- Extracellular vesicles
- Protein complexes (AGO2, NPM1)
- Lipoproteins
Half-life: Minutes to hours (shorter than cfDNA)
Workflow
Input: Plasma cfRNA sequencing data (total RNA, small RNA, or targeted).
Quality Control: Assess library complexity, mapping rates, contamination.
Quantification: Normalize and quantify transcripts.
Deconvolution: Estimate tissue contributions.
Classification: Apply cancer detection models.
Integration: Combine with cfDNA if available.
Output: Tissue composition, cancer score, biomarker profiles.
Example Usage
User: "Analyze plasma cfRNA to detect cancer and identify tissue of origin."
Agent Action:
python3 Skills/Genomics/CellFree_RNA_Agent/cfrna_analyzer.py \
--input plasma_cfrna.fastq.gz \
--protocol total_rna \
--reference gencode_v44 \
--deconvolution true \
--cancer_detection true \
--output cfrna_results/
Tissue Deconvolution
Reference Transcriptomes:
- GTEx tissue expression atlas
- Single-cell reference atlases
- Tissue-specific marker genes
Methods:
- Non-negative least squares
- Support vector regression
- Deep learning deconvolution
Clinical Applications:
- Organ injury detection (liver, heart, brain)
- Tumor burden estimation
- Post-transplant monitoring
Cancer Detection Applications
| Cancer Type |
Key Markers |
Performance |
| Lung |
XIST, MALAT1, specific mRNAs |
AUC 0.80-0.90 |
| Breast |
HER2, ER/PR transcripts |
Monitoring |
| Colorectal |
KRAS, panel genes |
Early detection |
| Prostate |
PCA3, TMPRSS2-ERG |
Established |
| Liver |
AFP, specific ncRNAs |
HCC surveillance |
Technical Considerations
Pre-analytical Factors:
- Sample collection (EDTA, cell stabilization)
- Processing time (<4 hours recommended)
- Storage temperature (-80°C)
- Hemolysis avoidance (critical)
Library Preparation:
- Total RNA (captures mRNA, lncRNA)
- Small RNA (miRNA, piRNA)
- Targeted panels (specific genes)
- UMI-based for quantification
AI/ML Components
Cancer Classifier:
- Gradient boosting on gene panels
- Neural networks for full transcriptome
- Multi-cancer detection models
Tissue Predictor:
- Reference-based deconvolution
- Supervised tissue classifiers
- Anomaly detection for novel sources
Integration with Other Analytes
| Analyte |
Strength |
Combination Benefit |
| cfDNA |
Mutations, methylation |
Genomic + transcriptomic |
| CTCs |
Single-cell analysis |
Cellular confirmation |
| Exosomes |
Protected RNA |
Source identification |
| Proteins |
Functional markers |
Multi-modal biomarkers |
Prerequisites
- Python 3.10+
- STAR/Salmon for alignment
- DESeq2/edgeR for quantification
- Tissue deconvolution tools
Related Skills
- Liquid_Biopsy_Analytics_Agent - For comprehensive liquid biopsy
- Exosome_EV_Analysis_Agent - For EV-derived RNA
- ctDNA_Analysis - For DNA-based markers
Emerging Technologies
- Targeted cfRNA: Gene panels for specific cancers
- Single-molecule: Direct RNA sequencing
- Spatial deconvolution: Mapping cfRNA to tissue regions
- Longitudinal monitoring: Treatment response tracking
Author
AI Group - Biomedical AI Platform
1---2name: cellfree-rna-agent3description: <!--4---5<!--6# COPYRIGHT NOTICE7# This file is part of the "Universal Biomedical Skills" project.8# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>9# All Rights Reserved.10#11# This code is proprietary and confidential.12# Unauthorized copying of this file, via any medium is strictly prohibited.13#14# Provenance: Authenticated by MD BABU MIA1516-->1718---19name: 'cellfree-rna-agent'20description: 'AI-powered cell-free RNA analysis from liquid biopsy for cancer detection, tissue-of-origin identification, and non-invasive transcriptomic profiling.'21measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.22allowed-tools:23 - read_file24 - run_shell_command25---262728# Cell-Free RNA Analysis Agent2930The **Cell-Free RNA Analysis Agent** provides comprehensive analysis of circulating cell-free RNA (cfRNA) from plasma and other biofluids for cancer detection, tissue-of-origin identification, and non-invasive transcriptomic profiling.3132## When to Use This Skill3334* When analyzing plasma cfRNA for cancer detection and monitoring.35* To identify tissue-of-origin from circulating transcripts.36* For non-invasive transcriptomic profiling of tumors.37* When integrating cfRNA with cfDNA for comprehensive liquid biopsy.38* To discover RNA-based biomarkers from accessible biofluids.3940## Core Capabilities41421. **cfRNA Profiling**: Quantify mRNA, lncRNA, and small RNA from plasma.43442. **Tissue Deconvolution**: Identify tissue sources contributing to cfRNA pool.45463. **Cancer Detection**: ML models detecting cancer from cfRNA profiles.47484. **Tumor Transcriptomics**: Infer tumor gene expression non-invasively.49505. **Integration with cfDNA**: Combine RNA and DNA liquid biopsy analytes.51526. **Biomarker Discovery**: Identify diagnostic and prognostic RNA markers.5354## cfRNA Biology5556**Sources**:57- Cell death (apoptosis, necrosis)58- Active secretion (EVs, RNA-binding proteins)59- Cell surface-associated RNA6061**Protection Mechanisms**:62- Extracellular vesicles63- Protein complexes (AGO2, NPM1)64- Lipoproteins6566**Half-life**: Minutes to hours (shorter than cfDNA)6768## Workflow69701. **Input**: Plasma cfRNA sequencing data (total RNA, small RNA, or targeted).71722. **Quality Control**: Assess library complexity, mapping rates, contamination.73743. **Quantification**: Normalize and quantify transcripts.75764. **Deconvolution**: Estimate tissue contributions.77785. **Classification**: Apply cancer detection models.79806. **Integration**: Combine with cfDNA if available.81827. **Output**: Tissue composition, cancer score, biomarker profiles.8384## Example Usage8586**User**: "Analyze plasma cfRNA to detect cancer and identify tissue of origin."8788**Agent Action**:89```bash90python3 Skills/Genomics/CellFree_RNA_Agent/cfrna_analyzer.py \91 --input plasma_cfrna.fastq.gz \92 --protocol total_rna \93 --reference gencode_v44 \94 --deconvolution true \95 --cancer_detection true \96 --output cfrna_results/97```9899## Tissue Deconvolution100101**Reference Transcriptomes**:102- GTEx tissue expression atlas103- Single-cell reference atlases104- Tissue-specific marker genes105106**Methods**:107- Non-negative least squares108- Support vector regression109- Deep learning deconvolution110111**Clinical Applications**:112- Organ injury detection (liver, heart, brain)113- Tumor burden estimation114- Post-transplant monitoring115116## Cancer Detection Applications117118| Cancer Type | Key Markers | Performance |119|-------------|-------------|-------------|120| Lung | XIST, MALAT1, specific mRNAs | AUC 0.80-0.90 |121| Breast | HER2, ER/PR transcripts | Monitoring |122| Colorectal | KRAS, panel genes | Early detection |123| Prostate | PCA3, TMPRSS2-ERG | Established |124| Liver | AFP, specific ncRNAs | HCC surveillance |125126## Technical Considerations127128**Pre-analytical Factors**:129- Sample collection (EDTA, cell stabilization)130- Processing time (<4 hours recommended)131- Storage temperature (-80°C)132- Hemolysis avoidance (critical)133134**Library Preparation**:135- Total RNA (captures mRNA, lncRNA)136- Small RNA (miRNA, piRNA)137- Targeted panels (specific genes)138- UMI-based for quantification139140## AI/ML Components141142**Cancer Classifier**:143- Gradient boosting on gene panels144- Neural networks for full transcriptome145- Multi-cancer detection models146147**Tissue Predictor**:148- Reference-based deconvolution149- Supervised tissue classifiers150- Anomaly detection for novel sources151152## Integration with Other Analytes153154| Analyte | Strength | Combination Benefit |155|---------|----------|---------------------|156| cfDNA | Mutations, methylation | Genomic + transcriptomic |157| CTCs | Single-cell analysis | Cellular confirmation |158| Exosomes | Protected RNA | Source identification |159| Proteins | Functional markers | Multi-modal biomarkers |160161## Prerequisites162163* Python 3.10+164* STAR/Salmon for alignment165* DESeq2/edgeR for quantification166* Tissue deconvolution tools167168## Related Skills169170* Liquid_Biopsy_Analytics_Agent - For comprehensive liquid biopsy171* Exosome_EV_Analysis_Agent - For EV-derived RNA172* ctDNA_Analysis - For DNA-based markers173174## Emerging Technologies1751761. **Targeted cfRNA**: Gene panels for specific cancers1772. **Single-molecule**: Direct RNA sequencing1783. **Spatial deconvolution**: Mapping cfRNA to tissue regions1794. **Longitudinal monitoring**: Treatment response tracking180181## Author182183AI Group - Biomedical AI Platform184185186<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->