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Claude Skills by swaruplab
github.com/swaruplab586 skills2 installs975 views
- Small Rna Seq Mirge3 AnalysisFast miRNA quantification with isomiR detection and A-to-I editing analysis using miRge3. Use when quantifying known miRNAs quickly or analyzing isomiR variants and RNA editing.Votes: 0GitHub stars: 97
- Small Rna Seq Smrna PreprocessingPreprocess small RNA sequencing data with adapter trimming and size selection optimized for miRNA, piRNA, and other small RNAs. Use when preparing small RNA-seq reads for downstream quantification or discovery analysis.Votes: 0GitHub stars: 97
- Small Rna Seq Target PredictionPredict miRNA target genes using sequence-based algorithms and database lookups. Use when identifying potential mRNA targets of differentially expressed or functionally important miRNAs.Votes: 0GitHub stars: 97
- Snapatac2Single-cell ATAC-seq analysis with SnapATAC2 (scverse). Covers the full pipeline — fragment import, TSS enrichment QC, tile-matrix construction, doublet filtering, spectral embedding, UMAP/leiden, MACS3 peak calling, gene activity matrices, differentially accessible regions, multi-sample integration via Harmony / MNN-correct, and cell-type annotation via scRNA-seq reference (SCANVI label transfer) or marker-based gene activity. Built on AnnData; interoperates with scanpy.Votes: 0GitHub stars: 97
- SoloteLocus-specific transposable element quantification from single-cell RNA-seq BAMs, producing a gene+TE 10x-style count matrix.Votes: 0GitHub stars: 97
- Spatial AgentAn agent that interprets spatial transcriptomics data to propose mechanistic hypotheses and analyze tissue organization.Votes: 0GitHub stars: 97
- Spatial Epigenomics AgentAI-powered spatial epigenomics analysis combining chromatin accessibility, histone modifications, and DNA methylation with spatial coordinates for tissue architecture mapping.Votes: 0GitHub stars: 97
- Spatial Transcriptomics AgentSpatial analystVotes: 0GitHub stars: 97
- Spatial Transcriptomics AnalysisAutomated analysis pipeline for Spatial Transcriptomics (Visium, Xenium) integrating histology and gene expression.Votes: 0GitHub stars: 97
- STAgentSpatial analystVotes: 0GitHub stars: 97
- Image AnalysisProcess and analyze tissue images from spatial transcriptomics data using Squidpy. Extract image features, segment cells/nuclei, and compute morphological features from H&E or IF images. Use when processing tissue images for spatial transcriptomics.Votes: 0GitHub stars: 97
- Spatial CommunicationAnalyze cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy. Infer intercellular signaling, identify communication pathways, and visualize interaction networks. Use when analyzing cell-cell communication in spatial context.Votes: 0GitHub stars: 97
- Spatial Data IoLoad spatial transcriptomics data from Visium, Xenium, MERFISH, Slide-seq, and other platforms using Squidpy and SpatialData. Read Space Ranger outputs, convert formats, and access spatial coordinates. Use when loading Visium, Xenium, MERFISH, or other spatial data.Votes: 0GitHub stars: 97
- Spatial DeconvolutionEstimate cell type composition in spatial transcriptomics spots using reference-based deconvolution. Use cell2location, RCTD, SPOTlight, or Tangram to infer cell type proportions from scRNA-seq references. Use when estimating cell type composition in spatial spots.Votes: 0GitHub stars: 97
- Spatial DomainsIdentify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions. Use when identifying tissue domains or spatial regions.Votes: 0GitHub stars: 97
- Spatial MultiomicsAnalyze high-resolution spatial platforms like Slide-seq, Stereo-seq, and Visium HD. Use when working with subcellular resolution or high-density spatial data.Votes: 0GitHub stars: 97
- Spatial NeighborsBuild spatial neighbor graphs for spatial transcriptomics data using Squidpy. Compute k-nearest neighbors, Delaunay triangulation, and radius-based connectivity for downstream spatial analyses. Use when building spatial neighborhood graphs.Votes: 0GitHub stars: 97
- Spatial PreprocessingQuality control, filtering, normalization, and feature selection for spatial transcriptomics data. Calculate QC metrics, filter spots/cells, normalize counts, and identify highly variable genes. Use when filtering and normalizing spatial transcriptomics data.Votes: 0GitHub stars: 97
- Spatial ProteomicsAnalyzes spatial proteomics data from CODEX, IMC, and MIBI platforms including cell segmentation and protein colocalization. Use when working with multiplexed imaging data, analyzing protein spatial patterns, or integrating spatial proteomics with transcriptomics.Votes: 0GitHub stars: 97
- Spatial StatisticsCompute spatial statistics for spatial transcriptomics data using Squidpy. Calculate Moran's I, Geary's C, spatial autocorrelation, co-occurrence analysis, and neighborhood enrichment. Use when computing spatial autocorrelation or co-occurrence statistics.Votes: 0GitHub stars: 97
- Spatial VisualizationVisualize spatial transcriptomics data using Squidpy and Scanpy. Create tissue plots with gene expression, clusters, and annotations overlaid on histology images. Use when visualizing spatial expression patterns.Votes: 0GitHub stars: 97
- Spatial TranscriptomicsSpatial transcriptomics analysis pipeline using squidpy + scanpy. Handles Visium, Xenium, CosMx, MERFISH, Slide-seq, and GeoMx data. Covers loading, quantile-based QC, normalization, clustering, spatial neighborhood analysis (enrichment, co-occurrence, spatially variable genes), and visualization. Includes minimal sections on cell-cell communication, niche detection, and Visium deconvolution.Votes: 0GitHub stars: 97
- Statistical Data AnalysisOmics data forgeVotes: 0GitHub stars: 97
- Stellar AtlasBuild deployable interactive web atlases from single-cell RNA-seq data using STELLAR. Turns a .h5ad (or Seurat .rds) into a UMAP + gene-expression + DE + hdWGCNA + CellChat + Milo + enrichment + AI-chat browser SPA. Covers the four-step CLI (init → ingest → build-frontend → serve/deploy), all six built-in modules, stellar.yaml configuration, and the parquet schemas each module ingests.Votes: 0GitHub stars: 97
- Structural Biology Alphafold PredictionsAccess and analyze AlphaFold protein structure predictions. Use when predicted structures are needed for proteins without experimental structures, or for confidence scores (pLDDT).Votes: 0GitHub stars: 97
- Structural Biology Geometric AnalysisPerform geometric calculations on protein structures using Biopython Bio.PDB. Use when measuring distances, angles, and dihedrals, superimposing structures, calculating RMSD, or computing solvent accessible surface area (SASA).Votes: 0GitHub stars: 97
- Structural Biology Modern Structure PredictionPredict protein structures using modern ML models including AlphaFold3, ESMFold, Chai-1, and Boltz-1. Use when predicting structures for novel proteins, protein complexes, or when comparing predictions across multiple methods.Votes: 0GitHub stars: 97
- Structural Biology Structure IoParse and write protein structure files using Biopython Bio.PDB. Use when reading PDB, mmCIF, and MMTF files, downloading structures from RCSB PDB, or writing structures to various formats.Votes: 0GitHub stars: 97
- Structural Biology Structure ModificationModify protein structures using Biopython Bio.PDB. Use when transforming coordinates, removing atoms or residues, adding new entities, modifying B-factors and occupancies, or building structures programmatically.Votes: 0GitHub stars: 97
- Structural Biology Structure NavigationNavigate protein structure hierarchy using Biopython Bio.PDB SMCRA model. Use when accessing models, chains, residues, and atoms, iterating over structure levels, or extracting sequences from PDB files.Votes: 0GitHub stars: 97
- Systems Biology Context Specific ModelsBuild tissue and condition-specific metabolic models using GIMME, iMAT, and INIT algorithms with expression data constraints. Create models that reflect cell-type specific metabolism. Use when building tissue-specific metabolic models or integrating transcriptomics with FBA.Votes: 0GitHub stars: 97
- Systems Biology Flux Balance AnalysisPerform flux balance analysis (FBA) and flux variability analysis (FVA) on genome-scale metabolic models using COBRApy. Predict growth rates, metabolic fluxes, and optimal resource utilization. Use when predicting metabolic phenotypes or optimizing flux distributions.Votes: 0GitHub stars: 97
- Systems Biology Gene EssentialityPerform in silico gene knockout analysis and synthetic lethality screens using COBRApy single and double deletions. Predict essential genes and identify synthetic lethal pairs for drug target discovery. Use when identifying essential genes or finding synthetic lethal drug targets.Votes: 0GitHub stars: 97
- Systems Biology Metabolic ReconstructionBuild genome-scale metabolic models from genome sequences using CarveMe and gapseq for automated reconstruction. Generate draft models ready for curation and analysis. Use when creating metabolic models for organisms without existing models.Votes: 0GitHub stars: 97
- Systems Biology Model CurationValidate, gap-fill, and curate genome-scale metabolic models using memote for quality scores and COBRApy for manual curation. Ensure models meet SBML standards and produce biologically meaningful predictions. Use when improving draft models or preparing models for publication.Votes: 0GitHub stars: 97
- 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: 97
- Tcr Bcr Analysis 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: 97
- Tcr Bcr Analysis 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: 97
- Tcr Bcr Analysis 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: 97
- Tcr Bcr Analysis Scirpy AnalysisAnalyze single-cell TCR and BCR data integrated with gene expression using scirpy. Use when working with 10x Genomics VDJ data alongside scRNA-seq or when integrating immune receptor information with cell state analysis.Votes: 0GitHub stars: 97
- Tcr Bcr Analysis 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: 97
- 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: 97
- 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: 97
- Temporal Genomics Circadian RhythmsDetects circadian and ultradian rhythms in time-series omics data using CosinorPy cosinor models, MetaCycle (JTK_CYCLE, ARSER), and RAIN non-parametric tests. Fits cosine models to estimate phase and amplitude, tests rhythmicity significance at pre-specified periods. Use when testing for 24-hour or other known-period oscillations in circadian, feeding-fasting, or light-dark cycle experiments. Not for unknown-period discovery (see temporal-genomics/periodicity-detection).Votes: 0GitHub stars: 97
- Temporal Genomics Periodicity DetectionDiscovers periodic signals of unknown period in time-series omics data using Lomb-Scargle periodograms (scipy), autocorrelation, and wavelet time-frequency decomposition (pywt). Identifies dominant frequencies, handles irregularly sampled data, and detects transient periodicity. Use when searching for periodic patterns of unknown period length, analyzing cell cycle oscillations, or processing unevenly spaced time-series. Not for testing known 24-hour rhythms (see temporal-genomics/circadian-r...Votes: 0GitHub stars: 97
- Temporal Genomics Temporal ClusteringClusters genes by temporal expression profile shape using Mfuzz soft clustering, TCseq, and DEGreport degPatterns. Groups co-regulated genes into shared trajectory patterns via fuzzy c-means or hierarchical approaches. Use when categorizing temporally dynamic genes into response groups or identifying co-expression modules across time points. Requires temporally variable genes identified first (see differential-expression/timeseries-de).Votes: 0GitHub stars: 97
- Temporal Genomics Temporal GrnInfers dynamic gene regulatory networks from bulk time-series expression data using Granger causality (statsmodels), dynGENIE3 (Extra-Trees on ODE-derived expression derivatives), and dynamic Bayesian networks (bnlearn). Identifies time-delayed regulatory relationships and tracks network rewiring across conditions. Use when inferring causal regulatory relationships from bulk temporal expression data or detecting TF influence propagation over time. Not for static co-expression networks (see ge...Votes: 0GitHub stars: 97
- Temporal Genomics Trajectory ModelingModels continuous temporal trajectories from bulk or time-resolved omics data using generalized additive models (mgcv), spline regression, and changepoint detection (segmented, ruptures). Fits smooth gene expression curves and tests trajectory differences between conditions. Use when fitting non-linear temporal models to bulk time-series data or comparing developmental trajectories across conditions. Not for single-cell pseudotime (see single-cell/trajectory-inference).Votes: 0GitHub stars: 97
- 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: 97
- Tooluniverse Gwas FinemappingIdentify and prioritize causal variants at GWAS loci using statistical fine-mapping and locus-to-gene predictions. Computes posterior probabilities for causal variants, links variants to genes via L2G predictions, annotates functional consequences, and suggests validation strategies. Use when asked to fine-map GWAS loci, prioritize causal variants, identify credible sets, or link GWAS signals to causal genes.Votes: 0GitHub stars: 97