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Claude Skills by GPTomics
github.com/GPTomics537 skills2 installs584 views
- Polygenic RiskCalculate polygenic risk scores using PRSice-2, LDpred2, or PRS-CS from GWAS summary statistics. Use when predicting disease risk from genome-wide genetic variants.Votes: 0GitHub stars: 1,198
- Somatic SignaturesExtract and analyze mutational signatures from somatic variants using SigProfiler or MutationalPatterns to characterize mutagenic processes. Use when identifying DNA damage mechanisms or etiology in cancer genomes.Votes: 0GitHub stars: 1,198
- Tumor Mutational BurdenCalculate tumor mutational burden from panel or WES data with proper normalization and clinical thresholds. Use when assessing immunotherapy eligibility or characterizing tumor immunogenicity.Votes: 0GitHub stars: 1,198
- Variant PrioritizationFilter and prioritize variants by pathogenicity, population frequency, and clinical evidence for rare disease analysis. Use when identifying candidate disease-causing variants from exome or genome sequencing.Votes: 0GitHub stars: 1,198
- Binding Site AnnotationAnnotate CLIP-seq binding sites to genomic features including 3'UTR, 5'UTR, CDS, introns, and ncRNAs. Use when characterizing where an RBP binds in transcripts.Votes: 0GitHub stars: 1,198
- Clip AlignmentAlign CLIP-seq reads to the genome with crosslink site awareness. Use when mapping preprocessed CLIP reads for peak calling.Votes: 0GitHub stars: 1,198
- Clip Motif AnalysisIdentify enriched sequence motifs at CLIP-seq binding sites for RBP binding specificity. Use when characterizing the sequence preferences of an RNA-binding protein.Votes: 0GitHub stars: 1,198
- Clip Peak CallingCall protein-RNA binding site peaks from CLIP-seq data using CLIPper, PureCLIP, or Piranha. Use when identifying RBP binding sites from aligned CLIP reads.Votes: 0GitHub stars: 1,198
- Clip PreprocessingPreprocess CLIP-seq data including adapter trimming, UMI extraction, and PCR duplicate removal. Use when preparing raw CLIP, iCLIP, or eCLIP reads for peak calling.Votes: 0GitHub stars: 1,198
- Ancestral ReconstructionReconstruct ancestral sequences at phylogenetic nodes using PAML and IQ-TREE marginal likelihood methods. Infer ancient protein sequences and trace evolutionary trajectories through sequence history. Use when inferring ancestral states for protein resurrection or tracing evolutionary history.Votes: 0GitHub stars: 1,198
- Hgt DetectionDetect horizontal gene transfer events using HGTector, compositional analysis, and phylogenetic incongruence methods. Identify foreign genes in bacterial and archaeal genomes from anomalous composition or unexpected phylogenetic placement. Use when searching for horizontally transferred genes or analyzing genome evolution in prokaryotes.Votes: 0GitHub stars: 1,198
- Ortholog InferenceInfer orthologous gene groups across species using OrthoFinder and ProteinOrtho. Identify orthologs, paralogs, and co-orthologs for comparative genomics and functional annotation transfer. Use when identifying gene orthologs across species or building orthogroups for evolutionary analysis.Votes: 0GitHub stars: 1,198
- Positive SelectionDetect positive selection using dN/dS (omega) tests with PAML codeml and HyPhy. Identify sites and branches under adaptive evolution through codon models and branch-site tests. Use when testing for adaptive evolution in gene families or identifying positively selected sites.Votes: 0GitHub stars: 1,198
- Synteny AnalysisAnalyze genome collinearity and syntenic blocks using MCScanX, SyRI, and JCVI for comparative genomics. Detect conserved gene order, chromosomal rearrangements, and whole-genome duplications. Use when comparing genome structure between species or identifying conserved genomic regions.Votes: 0GitHub stars: 1,198
- Cnv AnnotationAnnotate CNVs with genes, pathways, and clinical significance. Use when interpreting CNV calls or identifying affected genes from copy number analysis.Votes: 0GitHub stars: 1,198
- Cnv VisualizationVisualize copy number profiles, segments, and compare across samples. Create publication-quality plots of CNV data from CNVkit, GATK, or other callers. Use when creating genome-wide CNV plots, sample heatmaps, or chromosome-level visualizations.Votes: 0GitHub stars: 1,198
- Cnvkit AnalysisDetect copy number variants from targeted/exome sequencing using CNVkit. Supports tumor-normal pairs, tumor-only, and germline CNV calling. Use when detecting CNVs from WES or targeted panel sequencing data.Votes: 0GitHub stars: 1,198
- Gatk CnvCall copy number variants using GATK best practices workflow. Supports both somatic (tumor-normal) and germline CNV detection from WGS or WES data. Use when following GATK best practices or integrating CNV calling with other GATK variant pipelines.Votes: 0GitHub stars: 1,198
- Base Editing AnalysisAnalyzes base editing and prime editing outcomes including editing efficiency, bystander edits, and indel frequencies. Use when quantifying CRISPR base editor results, comparing ABE vs CBE efficiency, or assessing prime editing fidelity.Votes: 0GitHub stars: 1,198
- Batch CorrectionBatch effect correction for CRISPR screens. Covers normalization across batches, technical replicate handling, and batch-aware analysis. Use when combining screens from multiple batches or correcting systematic technical variation.Votes: 0GitHub stars: 1,198
- Crispresso EditingCRISPResso2 for analyzing CRISPR gene editing outcomes. Quantifies indels, HDR efficiency, and generates comprehensive editing reports. Use when analyzing amplicon sequencing data from CRISPR editing experiments to assess editing efficiency.Votes: 0GitHub stars: 1,198
- Hit CallingStatistical methods for calling hits in CRISPR screens. Covers MAGeCK, BAGEL2, drugZ, and custom approaches for identifying essential and resistance genes. Use when identifying significant genes from screen count data after QC passes.Votes: 0GitHub stars: 1,198
- Jacks AnalysisJACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens) for modeling sgRNA efficacy and gene essentiality. Use when analyzing multiple CRISPR screens simultaneously or when accounting for variable sgRNA efficiency across experiments.Votes: 0GitHub stars: 1,198
- Library DesignCRISPR library design for genetic screens. Covers sgRNA selection, library composition, control design, and oligo ordering. Use when designing custom sgRNA libraries for knockout, activation, or interference screens.Votes: 0GitHub stars: 1,198
- Mageck AnalysisMAGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout) for pooled CRISPR screen analysis. Covers count normalization, gene ranking, and pathway analysis. Use when identifying essential genes, drug targets, or resistance mechanisms from dropout or enrichment screens.Votes: 0GitHub stars: 1,198
- Screen QcQuality control for pooled CRISPR screens. Covers library representation, read distribution, replicate correlation, and essential gene recovery. Use when assessing screen quality before hit calling or diagnosing poor screen performance.Votes: 0GitHub stars: 1,198
- Circos PlotsCreate circular genome visualizations with Circos and pyCircos. Display multi-track data including ideograms, genes, variants, CNVs, and interaction arcs. Use when creating circular genome visualizations.Votes: 0GitHub stars: 1,198
- Color PalettesSelect and apply colorblind-friendly palettes for scientific figures using viridis, RColorBrewer, and custom color schemes. Use when selecting colorblind-friendly palettes for figures.Votes: 0GitHub stars: 1,198
- Genome TracksCreate genome browser-style visualizations showing multiple data tracks (coverage, peaks, genes) using pyGenomeTracks, Gviz, and IGV. Use when visualizing genomic data at specific loci with multiple aligned tracks.Votes: 0GitHub stars: 1,198
- Ggplot2 FundamentalsCreate publication-quality scientific figures with ggplot2 including scatter plots, boxplots, heatmaps, and multi-panel layouts. Use when creating static figures for papers, presentations, or reports in R.Votes: 0GitHub stars: 1,198
- Heatmaps ClusteringCreate clustered heatmaps with row/column annotations using ComplexHeatmap, pheatmap, and seaborn for gene expression and omics data visualization. Use when visualizing expression patterns across samples or identifying co-expressed gene clusters.Votes: 0GitHub stars: 1,198
- Interactive VisualizationCreate interactive HTML plots with plotly and bokeh for exploratory data analysis and web-based sharing of omics visualizations. Use when building zoomable, hoverable plots for data exploration or web dashboards.Votes: 0GitHub stars: 1,198
- Multipanel FiguresCombine multiple plots into publication-ready multi-panel figures using patchwork, cowplot, or matplotlib GridSpec with shared legends and panel labels. Use when combining multiple plots into publication figures.Votes: 0GitHub stars: 1,198
- Network VisualizationVisualize biological networks including gene regulatory networks, protein interaction networks, and co-expression modules using NetworkX, PyVis, and Cytoscape automation. Produces interactive and publication-quality network figures. Use when creating network diagrams from interaction data, GRN results, or co-expression modules.Votes: 0GitHub stars: 1,198
- Upset PlotsCreate UpSet plots to visualize set intersections as an alternative to Venn diagrams using UpSetR or upsetplot. Use when comparing overlapping gene sets, peak sets, or sample groups with more than 3 sets.Votes: 0GitHub stars: 1,198
- Batch DownloadsDownload large datasets from NCBI efficiently using history server, batching, and rate limiting. Use when performing bulk sequence downloads, handling large query results, or production-scale data retrieval.Votes: 0GitHub stars: 1,198
- Blast SearchesRun remote BLAST searches against NCBI databases using Biopython Bio.Blast. Use when identifying unknown sequences, finding homologs, or searching for sequence similarity against NCBI's nr/nt databases.Votes: 0GitHub stars: 1,198
- Entrez FetchRetrieve records from NCBI databases using Biopython Bio.Entrez. Use when downloading sequences, fetching GenBank records, getting document summaries, or parsing NCBI data into Biopython objects.Votes: 0GitHub stars: 1,198
- Entrez LinkFind cross-references between NCBI databases using Biopython Bio.Entrez. Use when navigating from genes to proteins, sequences to publications, finding related records, or discovering database relationships.Votes: 0GitHub stars: 1,198
- Entrez SearchSearch NCBI databases using Biopython Bio.Entrez. Use when finding records by keyword, building complex search queries, discovering database structure, or getting global query counts across databases.Votes: 0GitHub stars: 1,198
- Geo DataQuery NCBI Gene Expression Omnibus (GEO) for expression datasets using Biopython Bio.Entrez. Use when finding microarray/RNA-seq datasets, downloading expression data, or linking GEO series to SRA runs.Votes: 0GitHub stars: 1,198
- Interaction DatabasesQuery protein-protein and gene interaction databases including STRING, BioGRID, and IntAct via their REST APIs and Python clients. Retrieve interaction networks, confidence scores, and functional enrichment. Use when building protein interaction networks, contextualizing gene lists with known interactions, or retrieving pathway-level interaction data.Votes: 0GitHub stars: 1,198
- Local BlastRun local BLAST searches using BLAST+ command-line tools. Use when running fast unlimited searches, building custom databases, performing large-scale analysis, or when NCBI servers are slow or unavailable.Votes: 0GitHub stars: 1,198
- Sequence SimilarityFind homologous sequences using iterative BLAST (PSI-BLAST), profile HMMs (HMMER), and reciprocal best hit analysis. Use when identifying orthologs, distant homologs, or protein family members where standard BLAST is not sensitive enough.Votes: 0GitHub stars: 1,198
- Sra DataDownload sequencing data from NCBI SRA using the SRA toolkit. Use when downloading FASTQ files from SRA accessions, prefetching large datasets, or validating SRA downloads.Votes: 0GitHub stars: 1,198
- Uniprot AccessAccess UniProt protein database for sequences, annotations, and functional information. Use when retrieving protein data, GO terms, domain annotations, or protein-protein interactions.Votes: 0GitHub stars: 1,198
- De ResultsExtract, filter, annotate, and export differential expression results from DESeq2 or edgeR. Use for identifying significant genes, applying multiple testing corrections, adding gene annotations, and preparing results for downstream analysis. Use when filtering and exporting DE analysis results.Votes: 0GitHub stars: 1,198
- De VisualizationVisualize differential expression results using DESeq2/edgeR built-in functions. Covers plotMA, plotDispEsts, plotCounts, plotBCV, sample distance heatmaps, and p-value histograms. Use when visualizing differential expression results.Votes: 0GitHub stars: 1,198
- Deseq2 BasicsPerform differential expression analysis using DESeq2 in R/Bioconductor. Use for analyzing RNA-seq count data, creating DESeqDataSet objects, running the DESeq workflow, and extracting results with log fold change shrinkage. Use when performing DE analysis with DESeq2.Votes: 0GitHub stars: 1,198
- Edger BasicsPerform differential expression analysis using edgeR in R/Bioconductor. Use for analyzing RNA-seq count data with the quasi-likelihood F-test framework, creating DGEList objects, normalization, dispersion estimation, and statistical testing. Use when performing DE analysis with edgeR.Votes: 0GitHub stars: 1,198