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Claude Skills by swaruplab

github.com/swaruplab

586 skillsA× 578B× 4C× 1D× 32 installs975 views

Tooluniverse Gwas Snp InterpretationAInterpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple databases (GWAS Catalog, Open Targets Genetics, ClinVar). Retrieves variant annotations, GWAS trait associations, fine-mapping evidence, locus-to-gene predictions, and clinical significance. Use when asked to interpret a SNP by rsID, find disease associations for a variant, assess clinical significance, or answer questions like "What diseases is rs429358 associated with?" or "Interpret rs7903146".datapythonapiVotes: 0GitHub stars: 97
Tooluniverse Metabolomics AnalysisAAnalyze metabolomics data including metabolite identification, quantification, pathway analysis, and metabolic flux. Processes LC-MS, GC-MS, NMR data from targeted and untargeted experiments. Performs normalization, statistical analysis, pathway enrichment, metabolite-enzyme integration, and biomarker discovery. Use when analyzing metabolomics datasets, identifying differential metabolites, studying metabolic pathways, integrating with transcriptomics/proteomics, discovering metabolic biomark...datapythonreactVotes: 0GitHub stars: 97
Tooluniverse MetabolomicsAComprehensive metabolomics research skill for identifying metabolites, analyzing studies, and searching metabolomics databases. Integrates HMDB (220k+ metabolites), MetaboLights, Metabolomics Workbench, and PubChem. Use when asked to identify or annotate metabolites (HMDB IDs, chemical properties, pathways), retrieve metabolomics study information from MetaboLights (MTBLS*) or Metabolomics Workbench (ST*), search for studies by keywords or disease, or generate comprehensive metabolomics resea...researchpythonapiVotes: 0GitHub stars: 97
Tooluniverse Spatial Omics AnalysisAComputational analysis framework for spatial multi-omics data integration. Given spatially variable genes (SVGs), spatial domain annotations, tissue type, and disease context from spatial transcriptomics/proteomics experiments (10x Visium, MERFISH, DBiTplus, SLIDE-seq, etc.), performs comprehensive biological interpretation including pathway enrichment, cell-cell interaction inference, druggable target identification, immune microenvironment characterization, and multi-modal integration. Prod...researchgoreactVotes: 0GitHub stars: 97
Tooluniverse Spatial TranscriptomicsAAnalyze spatial transcriptomics data to map gene expression in tissue architecture. Supports 10x Visium, MERFISH, seqFISH, Slide-seq, and imaging-based platforms. Performs spatial clustering, domain identification, cell-cell proximity analysis, spatial gene expression patterns, tissue architecture mapping, and integration with single-cell data. Use when analyzing spatial transcriptomics datasets, studying tissue organization, identifying spatial expression patterns, mapping cell-cell interact...devopspythonexpressVotes: 0GitHub stars: 97
Tpd Ternary Complex AgentAAI-powered ternary complex prediction for targeted protein degradation, modeling POI-degrader-E3 ligase assemblies to optimize PROTAC and molecular glue efficacy.ai-agentspythongoVotes: 0GitHub stars: 97
Trial Eligibility AgentAParse trial protocols and patient data to produce criterion-level MET/NOT/UNKNOWN determinations with evidence and gaps for clinical trial screening tasks.ai-agentsgoshellVotes: 0GitHub stars: 97
Trialgpt MatchingATrial shortlistai-agentspythongoVotes: 0GitHub stars: 97
Tumor Clonal Evolution AgentAAI-powered analysis of tumor clonal architecture, subclonal dynamics, and evolutionary trajectories from multi-region sequencing and longitudinal liquid biopsy data.devopspythongoVotes: 0GitHub stars: 97
Tumor Heterogeneity AgentAAI-powered intratumor heterogeneity analysis for clonal architecture reconstruction, subclonal evolution tracking, and therapy resistance prediction using multi-region and longitudinal sequencing.devopspythongoVotes: 0GitHub stars: 97
Tumor Mutational Burden AgentACalculates and harmonizes Tumor Mutational Burden (TMB) across platforms to predict immunotherapy response.devopspythongoVotes: 0GitHub stars: 97
Universal Single Cell AnnotatorAAnnotate scRNA-seqai-agentspythonshellVotes: 0GitHub stars: 97
Variant Calling Clinical InterpretationAClinical variant interpretation using ClinVar, ACMG guidelines, and pathogenicity predictors. Prioritize variants for diagnostic and research applications. Use when interpreting clinical significance of variants.devopspythongoVotes: 0GitHub stars: 97
Variant Calling Consensus SequencesAGenerate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.businesspythongoVotes: 0GitHub stars: 97
Variant Calling DeepvariantADeep 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.devopsgobashVotes: 0GitHub stars: 97
Variant Calling Filtering Best PracticesAComprehensive 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.devopspythongoVotes: 0GitHub stars: 97
Variant Calling Gatk Variant CallingAVariant 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.datagobashVotes: 0GitHub stars: 97
Variant Calling Joint CallingAJoint 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.devopsgojavaVotes: 0GitHub stars: 97
Variant Calling Structural Variant CallingACall structural variants (SVs) from sequencing data using Manta, Delly, GRIDSS, and LUMPY. Detects deletions, insertions, inversions, duplications, and translocations too large for standard SNV callers. Use when detecting structural variants from short-read or long-read data and building consensus callsets.devopsgobashVotes: 0GitHub stars: 97
Variant Calling Variant AnnotationAComprehensive 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.devopspythongoVotes: 0GitHub stars: 97
Variant Calling Variant CallingACall SNPs and indels from aligned reads using bcftools mpileup and call. Use when detecting variants from BAM files or generating VCF from alignments.ai-agentsgobashVotes: 0GitHub stars: 97
Variant Calling Variant NormalizationANormalize indel representation, decompose MNPs, and split multiallelic variants using bcftools norm. Use when comparing variants from different callers, preparing VCF for database annotation, or merging VCFs from multiple sources.businesspythongoVotes: 0GitHub stars: 97
Variant Calling Vcf BasicsAView, query, and understand VCF/BCF variant files using bcftools and cyvcf2. Use when inspecting variants, extracting specific fields, or understanding VCF format structure.ai-agentspythongoVotes: 0GitHub stars: 97
Variant Calling Vcf ManipulationAMerge, concatenate, sort, intersect, and subset VCF files using bcftools. Use when combining variant files, comparing call sets, or restructuring VCF data.businesspythongoVotes: 0GitHub stars: 97
Variant Calling Vcf StatisticsAGenerate variant statistics, sample concordance, and quality metrics using bcftools stats and gtcheck. Use when evaluating variant quality, comparing samples, or summarizing VCF contents.devopspythongoVotes: 0GitHub stars: 97
Alphafold DatabaseAAccess AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.datapythongoVotes: 0GitHub stars: 97
AnndataAData structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.datapythongoVotes: 0GitHub stars: 97
Gene DatabaseAQuery NCBI Gene via E-utilities/Datasets API. Search by symbol/ID, retrieve gene info (RefSeqs, GO, locations, phenotypes), batch lookups, for gene annotation and functional analysis.ai-agentspythongoVotes: 0GitHub stars: 97
Geo DatabaseDAccess NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.datapythongoVotes: 0GitHub stars: 97
Kegg DatabaseADirect REST API access to KEGG (academic use only). Pathway analysis, gene-pathway mapping, metabolic pathways, drug interactions, ID conversion. For Python workflows with multiple databases, prefer bioservices. Use this for direct HTTP/REST work or KEGG-specific control.datapythongoVotes: 0GitHub stars: 97
Latchbio IntegrationALatch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.devopspythongoVotes: 0GitHub stars: 97
Pubmed DatabaseADirect REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython (Bio.Entrez). Use this for direct HTTP/REST work or custom API implementations.researchpythongoVotes: 0GitHub stars: 97
Pydeseq2ADifferential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.datapythongoVotes: 0GitHub stars: 97
ScanpyAStandard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.devopspythonbashVotes: 0GitHub stars: 97
Umap LearnAUMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.datapythongoVotes: 0GitHub stars: 97
ForgeDInstall, validate, configure and run FORGE, the Swarup Lab Nextflow pipeline for end-to-end single-cell and single-nucleus multiome (RNA + ATAC) analysis, on a SLURM cluster with no root — covers the pinned Nextflow window and the NXF_VER trap, the 15-second pre-flight preview, the manifest CSV and dataset config (including the GTF params that must be set explicitly), the five Singularity containers and their bind mounts, minimal versus full (~600 GB) references, adapting slurm_* parameters a...devopspythonrustVotes: 0GitHub stars: 97