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