القائمة
AI-powered analysis of cellular senescence for aging research, cancer therapy response, and senolytic drug development.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/swaruplab/operon --skill cellular-senescence-agentيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Install and run the BD Rhapsody™ Sequence Analysis Pipeline (v3.0) on a shared cluster or remote Linux server with no root and no container runtime. Covers the self-contained install bundle, reference archives, FASTQ manifests, per-library YML generation, SLURM array execution, outputs, sample-tag demultiplexing, and the failure modes that cost hours — wrong Sample_Tags_Version on nuclei runs, uncapped Maximum_Threads, node-local scratch, and pinning a stale `latest` bundle.
Advanced single-cell multi-omics analysis including scRNA-seq, scCITE-seq, scATAC-seq, and TARGET-seq. Use when analyzing single-cell data, cell type identification, trajectory analysis, differential expression, UMAP/clustering, integrating protein and RNA modalities (TotalVI), or working with Scanpy, Seurat, scvi-tools. Includes workflows for MPN, hematologic malignancies, megakaryocyte biology.
Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
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عرض SKILL.md
| name | cellular-senescence-agent |
| description | AI-powered analysis of cellular senescence for aging research, cancer therapy response, and senolytic drug development. |
| measurable_outcome | Execute skill workflow successfully with valid output within 15 minutes. |
| allowed-tools | ["read_file","run_shell_command"] |
The Cellular Senescence Agent provides comprehensive AI-driven analysis of cellular senescence signatures for aging research, cancer biology, and senolytic therapeutic development.
Senescence Scoring: Calculate senescence signatures from transcriptomic data.
SASP Profiling: Characterize senescence-associated secretory phenotype composition.
Single-Cell Detection: Identify senescent cells in scRNA-seq data.
Senolytic Prediction: Predict sensitivity to senolytic drugs.
Tissue Aging: Assess senescence burden across tissues.
Cancer Senescence: Analyze therapy-induced senescence.
| Category | Markers | Detection |
|---|---|---|
| Cell cycle | p16INK4a, p21CIP1, p53 | Expression, IHC |
| SA-β-gal | GLB1 (lysosomal) | Activity assay |
| SASP | IL-6, IL-8, MMP3, PAI-1 | Expression, secretion |
| DNA damage | γH2AX, 53BP1 foci | Immunofluorescence |
| Morphology | Enlarged, flattened | Imaging |
| Epigenetic | SAHF, SAHMs | Chromatin marks |
Input: Bulk or single-cell RNA-seq, proteomics, imaging data.
Signature Scoring: Apply senescence gene signatures.
SASP Analysis: Profile secretory phenotype.
Cell Identification: Flag senescent cells (single-cell).
Senolytic Prediction: Match to drug sensitivity profiles.
Burden Estimation: Quantify senescence load.
Output: Senescence scores, SASP profile, drug recommendations.
User: "Analyze senescence signatures in this aging tissue dataset and identify senolytic candidates."
Agent Action:
python3 Skills/Longevity_Aging/Cellular_Senescence_Agent/senescence_analyzer.py \
--rnaseq tissue_expression.tsv \
--singlecell tissue_scrnaseq.h5ad \
--signatures fridman_sasp,reactome_senescence \
--senolytic_prediction true \
--tissue liver \
--output senescence_report/
| Signature | Genes | Application |
|---|---|---|
| Fridman (2017) | CDKN1A, CDKN2A, SERPINE1... | Pan-senescence |
| SenMayo | 125 genes | Tissue senescence |
| SASP Core | IL6, IL8, CXCL1, MMP1... | Secretory phenotype |
| p16/p21 pathway | CDKN2A, CDKN1A, MDM2... | Cell cycle arrest |
Pro-inflammatory:
Matrix Remodeling:
Effects on Microenvironment:
| Drug | Target | Clinical Status |
|---|---|---|
| Dasatinib | Src/tyrosine kinases | Trials (with Q) |
| Quercetin | PI3K, serpins | Trials (with D) |
| Navitoclax | BCL-2/BCL-xL | Trials |
| Fisetin | Multiple | Early trials |
| UBX1325 | BCL-xL | Phase 2 (macular) |
Senescence Classifier:
Drug Response:
Aging Clock Integration:
Therapy-Induced Senescence (TIS):
Senescence + Senolytics: