Enhance README with detailed examples for complex scientific workflows across drug discovery, bioinformatics, and clinical genomics, showcasing multi-step processes and integration of various tools.

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Timothy Kassis
2025-10-22 08:24:40 -07:00
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@@ -106,36 +106,70 @@ Use our newly released MCP server that allows you to use any Claude Skill in any
## 💡 Quick Examples
Once you've installed the skills, you can ask Claude:
Once you've installed the skills, you can ask Claude to execute complex multi-step scientific workflows:
### Cheminformatics
### End-to-End Drug Discovery Pipeline
```
"Use PubChem to find information about aspirin and calculate its molecular properties"
"I need to find novel EGFR inhibitors for lung cancer treatment. Query ChEMBL for existing
EGFR inhibitors with IC50 < 50nM, analyze their structure-activity relationships using RDKit,
generate similar molecules with improved properties using datamol, perform virtual screening
with DiffDock against the AlphaFold-predicted EGFR structure, and search PubMed for recent
papers on resistance mechanisms to prioritize scaffolds. Finally, check COSMIC for common
EGFR mutations and assess how our candidates might interact with mutant forms."
```
### Bioinformatics
### Comprehensive Single-Cell Analysis Workflow
```
"Analyze this protein sequence using BioPython and predict its secondary structure"
"Load this 10X Genomics dataset using Scanpy, perform quality control and doublet removal,
integrate with public data from Cellxgene Census for the same tissue type, identify cell
populations using known markers from NCBI Gene, perform differential expression analysis
with PyDESeq2, run gene regulatory network inference with Arboreto, query Reactome and
KEGG for pathway enrichment, and create publication-quality visualizations with matplotlib.
Then cross-reference top dysregulated genes with Open Targets to identify potential
therapeutic targets."
```
### Data Analysis
### Multi-Omics Integration for Biomarker Discovery
```
"Perform exploratory data analysis on this RNA-seq dataset and create publication-quality plots"
"I have RNA-seq, proteomics, and metabolomics data from cancer patients. Use PyDESeq2 for
differential expression, pyOpenMS to analyze mass spec data, and integrate metabolite
information from HMDB and Metabolomics Workbench. Map proteins to pathways using UniProt
and KEGG, identify protein-protein interactions via STRING, correlate multi-omics layers
using statsmodels, and build a machine learning model with scikit-learn to predict patient
outcomes. Search ClinicalTrials.gov for ongoing trials targeting the top candidates."
```
### Drug Discovery
### Structure-Based Virtual Screening Campaign
```
"Search ChEMBL for kinase inhibitors with IC50 < 100nM and visualize their structures"
"I want to discover allosteric modulators for a protein-protein interaction. Retrieve the
AlphaFold structure for both proteins, identify the interaction interface using BioPython,
search ZINC15 for molecules with suitable properties for allosteric binding (MW 300-500,
logP 2-4), filter for drug-likeness using RDKit, perform molecular docking with DiffDock
to identify potential allosteric sites, rank candidates using DeepChem's property prediction
models, check PubChem for suppliers, and search USPTO patents to assess freedom to operate.
Finally, generate analogs with MedChem and molfeat for lead optimization."
```
### Literature Review
### Clinical Genomics Variant Interpretation Pipeline
```
"Search PubMed for recent papers on CRISPR-Cas9 applications in cancer therapy"
"Analyze this VCF file from a patient with suspected hereditary cancer. Use pysam to parse
variants, annotate with Ensembl for functional consequences, query ClinVar for known
pathogenic variants, check COSMIC for somatic mutations in cancer, retrieve gene information
from NCBI Gene, analyze protein impact using UniProt, search PubMed for case reports of
similar variants, query ClinPGx for pharmacogenomic implications, and generate a clinical
report with ReportLab. Then search ClinicalTrials.gov for precision medicine trials matching
the patient's profile."
```
### Protein Structure
### Systems Biology Network Analysis
```
"Retrieve the AlphaFold structure prediction for human p53 and analyze confidence scores"
"Starting with a list of differentially expressed genes from my RNA-seq experiment, query
NCBI Gene for detailed annotations, retrieve protein sequences from UniProt, identify
protein-protein interactions using STRING, map to biological pathways in Reactome and KEGG,
analyze network topology with Torch Geometric, identify hub genes and bottleneck proteins,
perform gene regulatory network reconstruction with Arboreto, integrate with Open Targets
for druggability assessment, use PyMC for Bayesian network modeling, and create interactive
network visualizations. Finally, search GEO for similar expression patterns across diseases."
```
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