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Claude Skills by mdbabumiamssm
github.com/mdbabumiamssm1,577 skills5 installs3,735 views
- Gtf Gff HandlingParse, query, and convert GTF and GFF3 annotation files. Extract gene, transcript, and exon coordinates using gffread, gtfparse, and gffutils. Use when extracting specific features from gene annotations or converting between annotation formats.Votes: 0GitHub stars: 32
- Interval ArithmeticCore interval arithmetic operations including intersect, subtract, merge, complement, map, and groupby using bedtools and pybedtools. Use when finding overlapping regions, removing overlaps, combining adjacent intervals, or transferring annotations between interval files.Votes: 0GitHub stars: 32
- Proximity OperationsFind nearest features, search within windows, and extend intervals using closest, window, flank, and slop operations. Use when performing TSS proximity analysis, assigning enhancers to genes, defining promoter regions, or finding nearby genomic features.Votes: 0GitHub stars: 32
- Isoseq AnalysisAnalyze PacBio Iso-Seq data for full-length isoform discovery and quantification. Use when characterizing transcript diversity or identifying novel splice variants.Votes: 0GitHub stars: 32
- Long Read AlignmentAlign long reads using minimap2 for Oxford Nanopore and PacBio data. Supports various presets for different read types and applications. Use when aligning ONT or PacBio reads to a reference genome for variant calling, SV detection, or coverage analysis.Votes: 0GitHub stars: 32
- Long Read QcQuality control for long-read sequencing data using NanoPlot, NanoStat, and chopper. Generate QC reports, filter reads by length and quality, and visualize read characteristics. Use when assessing ONT or PacBio run quality or filtering reads before assembly or alignment.Votes: 0GitHub stars: 32
- Medaka PolishingPolish assemblies and call variants from Oxford Nanopore data using medaka. Uses neural networks trained on specific basecaller versions. Use when improving ONT-only assemblies or calling variants from Nanopore data without short-read polishing.Votes: 0GitHub stars: 32
- Structural VariantsDetect structural variants from long-read alignments using Sniffles, cuteSV, and SVIM. Use when detecting deletions, insertions, inversions, translocations, or complex rearrangements from ONT or PacBio data, especially those missed by short-read methods.Votes: 0GitHub stars: 32
- Genotype ImputationImpute missing genotypes using reference panels with Beagle or Minimac4. Use when increasing variant density for GWAS, harmonizing data across genotyping platforms, or inferring variants not directly typed in array data.Votes: 0GitHub stars: 32
- Imputation QcQuality control of phasing and imputation results. Filter by INFO scores, assess accuracy, and prepare imputed data for downstream analysis. Use when filtering low-quality imputed variants or validating imputation accuracy before GWAS.Votes: 0GitHub stars: 32
- Reference PanelsDownload, prepare, and manage reference panels for phasing and imputation. Covers 1000 Genomes, HRC, and TOPMed panels. Use when setting up imputation infrastructure or selecting appropriate reference panels for target populations.Votes: 0GitHub stars: 32
- ScFoundation Model AgentUnified agent for leveraging single-cell foundation models (scGPT, scBERT, Geneformer, scFoundation) for cross-species annotation, perturbation prediction, and gene network inference.Votes: 0GitHub stars: 32
- Bone Marrow AI AgentAI-powered bone marrow morphology analysis, cell classification, and hematologic disorder diagnosis using deep learning on aspirate and biopsy images.Votes: 0GitHub stars: 32
- CHIC ML Framework AgentMachine learning framework for inferring high-risk clonal hematopoiesis from complete blood count data without sequencing, reducing the number needed to sequence for CHIP screening.Votes: 0GitHub stars: 32
- CHIP Clonal Hematopoiesis AgentAI-powered clonal hematopoiesis of indeterminate potential (CHIP) detection, risk stratification, and cardiovascular/malignancy risk prediction using genomic and clinical data.Votes: 0GitHub stars: 32
- Coagulation Thrombosis AgentAI-powered analysis of coagulation disorders, thrombosis risk prediction, anticoagulation management, and platelet function assessment using machine learning.Votes: 0GitHub stars: 32
- Bead NormalizationBead-based normalization for CyTOF and high-parameter flow cytometry. Covers EQ bead normalization, signal drift correction, and batch normalization. Use when correcting instrument drift in CyTOF or harmonizing data across batches.Votes: 0GitHub stars: 32
- Clustering PhenotypingUnsupervised clustering and cell type identification for flow/mass cytometry. Covers FlowSOM, Phenograph, and CATALYST workflows. Use when discovering cell populations in high-dimensional cytometry data without predefined gates.Votes: 0GitHub stars: 32
- Compensation TransformationSpillover compensation and data transformation for flow cytometry. Covers compensation matrix calculation, application, and biexponential/arcsinh transforms. Use when correcting spectral overlap between fluorophores or transforming data for analysis.Votes: 0GitHub stars: 32
- Fcs HandlingRead and manipulate Flow Cytometry Standard (FCS) files. Covers loading data, accessing parameters, and basic data exploration. Use when loading and inspecting flow or mass cytometry data before preprocessing.Votes: 0GitHub stars: 32
- Hemoglobinopathy Analysis AgentAI-powered analysis of hemoglobin disorders including sickle cell disease, thalassemias, and variant hemoglobins using HPLC, electrophoresis, and molecular data.Votes: 0GitHub stars: 32
- MPN Progression Monitor AgentAI-powered myeloproliferative neoplasm monitoring for disease progression prediction, treatment response tracking, and transformation risk assessment in PV, ET, and myelofibrosis.Votes: 0GitHub stars: 32
- Myeloma MRD AgentAI-powered minimal residual disease (MRD) analysis for multiple myeloma using next-generation flow cytometry, NGS, and mass spectrometry approaches.Votes: 0GitHub stars: 32
- Quality MetricsQuality metrics for IMC data including signal-to-noise, channel correlation, tissue integrity, and acquisition QC. Use when assessing data quality before analysis or troubleshooting problematic acquisitions.Votes: 0GitHub stars: 32
- Spatial AnalysisSpatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing. Use when analyzing spatial relationships between cell types, testing for neighborhood enrichment, or identifying cell-cell interaction patterns in imaging mass cytometry data.Votes: 0GitHub stars: 32
- Armored CART Design AgentAI-powered design of armored CAR-T cells with cytokine/chemokine expression for enhanced solid tumor efficacy, including IL-12, IL-15, IL-18, and IL-7 armoring strategies.Votes: 0GitHub stars: 32
- CART Design Optimizer AgentAI-guided CAR-T cell design for solid tumors using antigen prioritization, safety-by-design architectures, and exhaustion-resistant engineering.Votes: 0GitHub stars: 32
- Cytokine Storm Analysis AgentAI-powered cytokine release syndrome (CRS) and cytokine storm analysis for prediction, monitoring, and management in immunotherapy and infectious disease.Votes: 0GitHub stars: 32
- Immune Checkpoint Combination AgentAI-powered analysis for predicting optimal immune checkpoint inhibitor combinations based on tumor microenvironment, biomarkers, and molecular profiling.Votes: 0GitHub stars: 32
- NK Cell Therapy AgentAI-powered NK cell therapy design for cancer immunotherapy including CAR-NK engineering, memory-like NK generation, and KIR/HLA matching optimization.Votes: 0GitHub stars: 32
- TCR Repertoire Analysis AgentAI-powered T-cell receptor repertoire analysis for cancer diagnosis, immunotherapy response prediction, and therapeutic TCR selection using deep learning and multi-layer ML approaches.Votes: 0GitHub stars: 32
- TCR PMHC Prediction AgentAI-powered TCR-peptide-MHC interaction prediction using AlphaFold3 and deep learning for therapeutic TCR discovery, neoantigen validation, and T cell immunogenicity assessment.Votes: 0GitHub stars: 32
- TCell Exhaustion Analysis AgentAI-powered analysis of T-cell exhaustion states, epigenetic scarring, stem-like T-cell populations, and checkpoint blockade response prediction in cancer immunotherapy.Votes: 0GitHub stars: 32
- TME Immune Profiling AgentComprehensive AI-powered tumor microenvironment immune profiling integrating bulk deconvolution, single-cell analysis, and spatial transcriptomics for immunotherapy biomarker discovery.Votes: 0GitHub stars: 32
- Epitope PredictionPredict B-cell and T-cell epitopes using BepiPred, IEDB tools, and structure-based methods for vaccine and antibody design. Identify immunogenic regions in antigens. Use when designing vaccines, mapping antibody binding sites, or predicting immunogenic peptides.Votes: 0GitHub stars: 32
- Immcantation AnalysisAnalyze BCR repertoires for somatic hypermutation, clonal lineages, and B cell phylogenetics using the Immcantation framework. Use when studying B cell affinity maturation, germinal center dynamics, or antibody evolution.Votes: 0GitHub stars: 32
- Immunogenicity ScoringScore and prioritize neoantigens and epitopes for immunogenicity using multi-factor models combining MHC binding, processing, expression, and sequence features. Rank candidates for vaccine design. Use when prioritizing epitopes for vaccine development or identifying the most immunogenic neoantigens.Votes: 0GitHub stars: 32
- Mhc Binding PredictionPredict peptide-MHC class I and II binding affinity using MHCflurry and NetMHCpan neural network models. Identify potential T-cell epitopes from protein sequences. Use when predicting MHC binding for vaccine design or neoantigen identification.Votes: 0GitHub stars: 32
- Mixcr AnalysisPerform V(D)J alignment and clonotype assembly from TCR-seq or BCR-seq data using MiXCR. Use when processing raw immune repertoire sequencing data to identify clonotypes and their frequencies.Votes: 0GitHub stars: 32
- Repertoire VisualizationCreate publication-quality visualizations of immune repertoire data including circos plots, clone tracking, diversity plots, and network graphs. Use when generating figures for repertoire comparisons, clonal dynamics, or V(D)J gene usage.Votes: 0GitHub stars: 32
- Tcr Epitope BindingPredict TCR-epitope specificity using ERGO-II and deep learning models for T-cell receptor antigen recognition. Match TCRs to their cognate epitopes or predict TCR targets. Use when analyzing TCR repertoire specificity or identifying antigen-reactive T-cells.Votes: 0GitHub stars: 32
- Vdjtools AnalysisCalculate immune repertoire diversity metrics, compare samples, and track clonal dynamics using VDJtools. Use when analyzing repertoire diversity, finding shared clonotypes, or comparing immune profiles between conditions.Votes: 0GitHub stars: 32
- Opentrons AgentGenerates executable Python protocols for Opentrons OT-2 and Flex robots from natural language descriptions.Votes: 0GitHub stars: 32
- Cellular Senescence AgentAI-powered analysis of cellular senescence for aging research, cancer therapy response, and senolytic drug development.Votes: 0GitHub stars: 32
- BioMCPDeploy and operate the BioMCP server so MCP-compatible clients (Claude Desktop, LobeChat, etc.) can query biomedical databases via a single standardized interface.Votes: 0GitHub stars: 32
- Atlas MappingMaps query single-cell data to reference atlases using scArches transfer learning with scVI and scANVI models. Transfers cell type labels without retraining on combined data. Use when annotating new single-cell datasets using pre-trained reference models.Votes: 0GitHub stars: 32
- Biomarker DiscoverySelects informative features for biomarker discovery using Boruta all-relevant selection, mRMR minimum redundancy, and LASSO regularization. Use when identifying biomarkers from high-dimensional omics data.Votes: 0GitHub stars: 32
- Model ValidationImplements nested cross-validation and stratified splits for unbiased model evaluation on biomedical datasets. Prevents data leakage and overfitting in biomarker discovery. Use when validating classifiers or optimizing hyperparameters on omics data.Votes: 0GitHub stars: 32
- Survival AnalysisAnalyzes time-to-event data using Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression with lifelines. Builds survival models from clinical and omics features. Use when predicting patient survival or modeling time-to-event outcomes.Votes: 0GitHub stars: 32
- Linear AlgebraTensor OperationsVotes: 0GitHub stars: 32