Claude Skills by mdbabumiamssm

End-to-end Ribo-seq analysis from FASTQ to translation efficiency and ORF detection. Use when analyzing ribosome profiling data to study translation.

End-to-end single-cell RNA-seq workflow from 10X Genomics data to annotated cell types. Covers QC, normalization, clustering, marker detection, and cell type annotation. Use when analyzing single-cell RNA-seq data.

End-to-end small RNA-seq analysis from FASTQ to differential miRNA expression. Use when analyzing miRNA, piRNA, or other small RNA sequencing data.

End-to-end somatic variant calling from tumor-normal paired samples using Mutect2 or Strelka2. Covers preprocessing, variant calling, filtering, and annotation for cancer genomics. Use when calling somatic mutations from tumor-normal pairs.

End-to-end alternative splicing analysis from FASTQ to differential splicing results. Aligns with STAR 2-pass mode, performs junction QC, runs rMATS-turbo for differential analysis, and generates sashimi visualizations. Use when performing comprehensive splicing analysis from raw RNA-seq data.

End-to-end TCR/BCR repertoire analysis from FASTQ to clonotype diversity metrics. Use when analyzing immune repertoire sequencing data from bulk or single-cell experiments.

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.

Full-stack computational software development for biomedical and life science applications. Use when building AI-powered research platforms, clinical decision support systems, multi-LLM ensemble architectures, RAG pipelines, single-cell analysis tools, or biomedical web applications. Includes production-ready patterns for PyTorch deep learning, Flask web apps, citation verification systems, and scientific software distribution.

Publication-quality visualizations for biomedical and genomics data. Use when creating volcano plots, heatmaps, UMAP plots, dot plots, survival curves, forest plots, or multi-panel figures. Includes scanpy, matplotlib, seaborn, plotly workflows with journal-ready aesthetics and proper statistical annotations.

Myeloproliferative neoplasm (MPN) research expertise including JAK2/CALR/MPL mutations, myelofibrosis, polycythemia vera, essential thrombocythemia. Use for MPN literature search, driver mutation analysis, PPM1D pathway analysis, fibrosis markers, megakaryocyte biology, clinical trial data interpretation, and translational research.

Build and publish professional Python packages to PyPI. Use when creating pip-installable packages, converting scripts to packages, setting up pyproject.toml/setup.py, adding CLI interfaces, writing tests, or preparing for PyPI upload. Covers bioinformatics tool packaging.

High-impact scientific manuscript preparation for journals like Nature, Blood, Cell. Use when writing abstracts, introductions, methods, results, discussions, or figure legends. Includes citation management, statistical reporting standards, ICMJE guidelines, and journal-specific formatting for hematology/oncology publications.