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Claude Skills by peacezha
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- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- 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: 3
- Clinical InterpretationClinical variant interpretation using ClinVar, ACMG guidelines, and pathogenicity predictors. Prioritize variants for diagnostic and research applications. Use when interpreting clinical significance of variants.Votes: 0GitHub stars: 3
- Consensus SequencesGenerate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.Votes: 0GitHub stars: 3
- DeepvariantDeep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep learning caller or when highest germline calling accuracy is required.Votes: 0GitHub stars: 3
- Filtering Best PracticesComprehensive variant filtering including GATK VQSR, hard filters, bcftools expressions, and quality metric interpretation for SNPs and indels. Use when filtering variants using GATK best practices.Votes: 0GitHub stars: 3
- Gatk Variant CallingVariant calling with GATK HaplotypeCaller following best practices. Covers germline SNP/indel calling, GVCF workflow for cohorts, joint genotyping, and variant quality score recalibration (VQSR). Use when calling variants with GATK HaplotypeCaller.Votes: 0GitHub stars: 3
- Joint CallingJoint genotype calling across multiple samples using GATK CombineGVCFs and GenotypeGVCFs. Essential for cohort studies, population genetics, and leveraging VQSR. Use when performing joint genotyping across multiple samples.Votes: 0GitHub stars: 3
- Variant AnnotationComprehensive variant annotation using bcftools annotate/csq, VEP, SnpEff, and ANNOVAR. Add database annotations, predict functional consequences, and assess clinical significance with MANE transcript selection and pathogenicity scoring. Use when annotating variants with functional and clinical information.Votes: 0GitHub stars: 3
- Variant CallingCall SNPs and indels from aligned reads using bcftools mpileup and call. Use when detecting variants from BAM files or generating VCF from alignments.Votes: 0GitHub stars: 3
- Vcf BasicsView, query, and understand VCF/BCF variant files using bcftools and cyvcf2. Use when inspecting variants, extracting specific fields, or understanding VCF format structure.Votes: 0GitHub stars: 3
- Vcf ManipulationMerge, concatenate, sort, intersect, and subset VCF files using bcftools. Use when combining variant files, comparing call sets, or restructuring VCF data.Votes: 0GitHub stars: 3
- Vcf StatisticsGenerate variant statistics, sample concordance, and quality metrics using bcftools stats and gtcheck. Use when evaluating variant quality, comparing samples, or summarizing VCF contents.Votes: 0GitHub stars: 3
- Cwl WorkflowsCreate portable, standards-based bioinformatics pipelines with Common Workflow Language (CWL). Use when building workflows that need maximum portability across execution platforms, sharing pipelines with collaborators using different systems, or contributing to community workflow registries.Votes: 0GitHub stars: 3
- Nextflow PipelinesCreate scalable, containerized bioinformatics pipelines with Nextflow DSL2 supporting Docker, Singularity, and cloud execution. Use when building portable pipelines with container support, running workflows on cloud platforms (AWS, Google Cloud), or leveraging nf-core community pipelines.Votes: 0GitHub stars: 3
- Snakemake WorkflowsBuild reproducible bioinformatics pipelines with Snakemake using rules, wildcards, and automatic dependency resolution. Use when creating Python-based workflows, automating multi-step analyses with make-like dependency tracking, or running pipelines on HPC clusters with SLURM.Votes: 0GitHub stars: 3
- Wdl WorkflowsCreate portable bioinformatics pipelines with Workflow Description Language (WDL) using Cromwell or miniwdl execution engines. Use when running GATK best practices pipelines, working with Terra/AnVIL platforms, or building workflows for cloud execution on Google Cloud or AWS.Votes: 0GitHub stars: 3
- Atacseq PipelineEnd-to-end ATAC-seq workflow from FASTQ files to differential accessibility and TF footprinting. Covers alignment, peak calling with MACS3, QC metrics, and optional TOBIAS footprinting. Use when running end-to-end ATAC-seq analysis from FASTQ to differential accessibility.Votes: 0GitHub stars: 3
- Chipseq PipelineEnd-to-end ChIP-seq workflow from FASTQ files to annotated peaks. Covers QC, alignment, peak calling with MACS3 (or HOMER), and peak annotation with ChIPseeker. Use when processing ChIP-seq data from alignment through peak annotation.Votes: 0GitHub stars: 3
- Cnv PipelineEnd-to-end copy number variant detection workflow from BAM files. Covers CNVkit analysis for exome/targeted sequencing with visualization and annotation. Use when detecting copy number alterations from sequencing data.Votes: 0GitHub stars: 3
- Crispr Editing PipelineOrchestrates an end-to-end CRISPR editing experiment design from target gene to delivery-ready, validatable constructs. Sequences guide design, off-target assessment, edit-modality selection (knockout, base editing, prime editing, HDR knock-in), and template/donor design, with a QC checkpoint at each handoff. Use when designing a complete CRISPR experiment for knockout, point correction, or tagging and the order of operations, the modality decision, and the cross-cutting traps are needed rath...Votes: 0GitHub stars: 3
- Edna PipelineEnd-to-end eDNA metabarcoding from raw amplicons to community ecology. Covers QC, primer removal (mandatory before DADA2 filterAndTrim), denoising with OBITools3 v3 (obi stats plural; DMS-based) or DADA2 ASVs (Callahan 2017), decontam combined method as screening-not-classifier (Davis 2018), tag-jumping with NovaSeq 10x MiSeq caveat (Schnell 2015), Hill-number effective species counts with coverage-based rarefaction (Jost 2006; Chao & Jost 2012; doubling rule), beta-diversity decomposition wi...Votes: 0GitHub stars: 3
- Expression To PathwaysWorkflow from differential expression results to functional enrichment analysis. Covers GO, KEGG, Reactome enrichment with clusterProfiler and visualization. Use when taking DE results to pathway enrichment.Votes: 0GitHub stars: 3
- Fastq To VariantsEnd-to-end DNA sequencing workflow from FASTQ files to variant calls. Covers QC, alignment with BWA, BAM processing, and variant calling with bcftools or GATK HaplotypeCaller. Use when calling variants from raw sequencing reads.Votes: 0GitHub stars: 3
- Genome Annotation PipelineEnd-to-end genome annotation pipeline from assembled contigs to functional annotation, covering repeat masking, gene prediction, and functional assignment for both prokaryotic and eukaryotic genomes. Use when annotating a newly assembled genome from scratch.Votes: 0GitHub stars: 3
- Gwas PipelineEnd-to-end GWAS workflow from VCF to association results. Covers PLINK QC, population structure correction, and association testing for case-control or quantitative traits. Use when running genome-wide association studies.Votes: 0GitHub stars: 3
- Imc PipelineEnd-to-end imaging mass cytometry workflow from raw acquisitions to spatial cell analysis. Orchestrates image preprocessing, segmentation, phenotyping, and spatial statistics. Use when analyzing imaging mass cytometry data end-to-end.Votes: 0GitHub stars: 3