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Claude Skills by swaruplab
github.com/swaruplab586 skills2 installs975 views
- Immunoinformatics 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: 97
- Immunoinformatics 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: 97
- Immunoinformatics 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: 97
- Immunoinformatics Neoantigen PredictionIdentify tumor neoantigens from somatic mutations using pVACtools for personalized cancer immunotherapy. Predict mutant peptides that bind patient HLA and may elicit T-cell responses. Use when identifying vaccine targets or checkpoint inhibitor response biomarkers from tumor sequencing data.Votes: 0GitHub stars: 97
- Immunoinformatics 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: 97
- Kallisto BustoolsscRNA-seq quantification with kallisto + bustools via kb-python. Pseudoalignment-based — orders of magnitude faster than full alignment (STAR / cellranger) while producing comparable count matrices. Covers index generation (kb ref), per-sample quantification (kb count), supported chemistries (10X v1/v2/v3, Drop-seq, CEL-Seq, inDrops, SMART-Seq), the nac workflow for nascent/mature RNA (snRNA-seq + RNA velocity), and the handoff to scanpy / Seurat / SCE for downstream analysis.Votes: 0GitHub stars: 97
- Liquid Biopsy Cfdna PreprocessingPreprocesses cell-free DNA sequencing data including adapter trimming, alignment optimized for short fragments, and UMI-aware duplicate removal using fgbio. Applies cfDNA-specific quality thresholds and fragment length filtering. Use when processing plasma cfDNA sequencing data before downstream analysis.Votes: 0GitHub stars: 97
- Liquid Biopsy Ctdna Mutation DetectionDetects somatic mutations in circulating tumor DNA using variant callers optimized for low allele fractions with UMI-based error suppression. Reliably detects mutations at VAF above 0.5 percent using consensus-based approaches. Use when identifying tumor mutations from plasma DNA or tracking specific variants.Votes: 0GitHub stars: 97
- Liquid Biopsy Fragment AnalysisAnalyzes cfDNA fragment size distributions and fragmentomics features using FinaleToolkit or Griffin. Extracts nucleosome positioning patterns, fragment ratios, and DELFI-style fragmentation profiles for cancer detection. Use when leveraging fragment patterns for tumor detection or tissue-of-origin analysis.Votes: 0GitHub stars: 97
- Liquid Biopsy Longitudinal MonitoringTracks ctDNA dynamics over time for treatment response monitoring using serial liquid biopsy samples. Analyzes tumor fraction trends, mutation clearance kinetics, and defines molecular response criteria. Use when monitoring patients during therapy or detecting molecular relapse before clinical progression.Votes: 0GitHub stars: 97
- Liquid Biopsy Methylation Based DetectionAnalyzes cfDNA methylation patterns for cancer detection using cfMeDIP-seq or bisulfite sequencing with MethylDackel. Identifies cancer-specific methylation signatures and performs tissue-of-origin deconvolution. Use when using methylation biomarkers for early cancer detection or minimal residual disease.Votes: 0GitHub stars: 97
- Liquid Biopsy Tumor Fraction EstimationEstimates circulating tumor DNA fraction from shallow whole-genome sequencing using ichorCNA. Detects copy number alterations via HMM segmentation and calculates ctDNA percentage. Requires 0.1-1x sWGS coverage. Use when quantifying tumor burden from liquid biopsy or monitoring treatment response.Votes: 0GitHub stars: 97
- Long Read Sequencing AgentAI-powered analysis of long-read sequencing data (PacBio, ONT) for structural variant detection, isoform discovery, epigenetic modifications, and de novo assembly.Votes: 0GitHub stars: 97
- Long Read Sequencing BasecallingConvert raw Nanopore signal data (FAST5/POD5) to nucleotide sequences using Dorado basecaller. Covers model selection, GPU acceleration, modified base detection, and quality filtering. Use when processing raw Nanopore data before alignment. Note: Guppy is deprecated; use Dorado for all new analyses.Votes: 0GitHub stars: 97
- Long Read Sequencing Clair3 VariantsDeep learning-based variant calling from long reads using Clair3 for SNPs and small indels. Use when calling germline variants from ONT or PacBio alignments, particularly when high accuracy is needed for clinical or research applications.Votes: 0GitHub stars: 97
- Long Read Sequencing 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: 97
- Long Read Sequencing 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: 97
- Long Read Sequencing 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: 97
- Long Read Sequencing 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: 97
- Long Read Sequencing Nanopore MethylationCalls DNA methylation from Oxford Nanopore sequencing data using signal-level analysis. Use when detecting 5mC or 6mA modifications directly from nanopore reads without bisulfite conversion.Votes: 0GitHub stars: 97
- Long Read Sequencing 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: 97
- Machine Learning 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: 97
- Machine Learning 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: 97
- Machine Learning 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: 97
- Machine Learning Omics ClassifiersBuilds classification models for omics data using RandomForest, XGBoost, and logistic regression with sklearn-compatible APIs. Includes proper preprocessing and evaluation metrics for biomarker classifiers. Use when building diagnostic or prognostic classifiers from expression or variant data.Votes: 0GitHub stars: 97
- Machine Learning Prediction ExplanationExplains machine learning predictions on omics data using SHAP values and LIME for feature attribution. Identifies which genes or features drive classifier decisions. Use when interpreting biomarker classifiers or understanding model predictions.Votes: 0GitHub stars: 97
- Machine Learning 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: 97
- Metabolomics LipidomicsSpecialized lipidomics analysis for lipid identification, quantification, and pathway interpretation. Covers LC-MS lipidomics with LipidSearch, MS-DIAL, and LipidMaps annotation. Use when analyzing lipid classes, chain composition, or lipid-specific pathways.Votes: 0GitHub stars: 97
- Metabolomics Metabolite AnnotationMetabolite identification from m/z and retention time. Covers database matching, MS/MS spectral matching, and confidence level assignment. Use when assigning compound identities to detected features in untargeted metabolomics.Votes: 0GitHub stars: 97
- Metabolomics Msdial PreprocessingMS-DIAL-based metabolomics preprocessing as alternative to XCMS. Covers peak detection, alignment, annotation, and export for downstream analysis. Use when processing MS-DIAL output files for R/Python analysis or when preferring GUI-based preprocessing.Votes: 0GitHub stars: 97
- Metabolomics Normalization QcQuality control and normalization for metabolomics data. Covers QC-based correction, batch effect removal, and data transformation methods. Use when correcting technical variation in metabolomics data before statistical analysis.Votes: 0GitHub stars: 97
- Metabolomics Pathway MappingMap metabolites to biological pathways using KEGG, Reactome, and MetaboAnalyst. Perform pathway enrichment and topology analysis. Use when interpreting metabolomics results in the context of biochemical pathways.Votes: 0GitHub stars: 97
- Metabolomics Statistical AnalysisStatistical analysis for metabolomics data. Covers preprocessing (log2 transformation, normalization), limma moderated testing with empirical Bayes, Welch's t-tests with BH correction, fold change estimation, and multivariate methods (PCA, PLS-DA, OPLS-DA). Use when identifying differentially abundant metabolites or building classification models.Votes: 0GitHub stars: 97
- Metabolomics Targeted AnalysisTargeted metabolomics analysis using MRM/SRM with standard curves. Covers absolute quantification, method validation, and quality assessment. Use when quantifying specific metabolites using calibration curves and internal standards.Votes: 0GitHub stars: 97
- Metabolomics Xcms PreprocessingXCMS3 workflow for LC-MS/MS metabolomics preprocessing. Covers peak detection, retention time alignment, correspondence (grouping), and gap filling. Use when processing raw LC-MS data into a feature table for untargeted metabolomics.Votes: 0GitHub stars: 97
- Metagenomics Abundance EstimationSpecies abundance estimation using Bracken with Kraken2 output. Redistributes reads from higher taxonomic levels to species for more accurate estimates. Use when accurate species-level abundances are needed from Kraken2 classification output.Votes: 0GitHub stars: 97
- Metagenomics Amr DetectionDetect antimicrobial resistance genes using AMRFinderPlus, ResFinder, and CARD. Screen isolates and metagenomes for resistance determinants. Use when characterizing resistance profiles in clinical isolates, surveillance samples, or metagenomic data.Votes: 0GitHub stars: 97
- Metagenomics Functional ProfilingProfile functional potential of metagenomes using HUMAnN3 and similar tools. Use when obtaining pathway abundances, gene family counts, or functional annotations from metagenomic data.Votes: 0GitHub stars: 97
- Metagenomics Kraken ClassificationTaxonomic classification of metagenomic reads using Kraken2. Fast k-mer based classification against RefSeq database. Use when performing initial taxonomic classification of shotgun metagenomic reads before abundance estimation with Bracken.Votes: 0GitHub stars: 97
- Metagenomics Metagenome VisualizationVisualize metagenomic profiles using R (phyloseq, microbiome) and Python (matplotlib, seaborn). Create stacked bar plots, heatmaps, PCA plots, and diversity analyses. Use when creating publication-quality figures from MetaPhlAn, Bracken, or other taxonomic profiling output.Votes: 0GitHub stars: 97
- Metagenomics Metaphlan ProfilingMarker gene-based taxonomic profiling using MetaPhlAn 4. Provides accurate species-level relative abundances using clade-specific markers. Use when accurate taxonomic profiling is needed and computational resources are limited, or for comparison with HMP/other MetaPhlAn studies.Votes: 0GitHub stars: 97
- Metagenomics Strain TrackingTrack bacterial strains using MASH, sourmash, fastANI, and inStrain. Compare genomes, detect contamination, and monitor strain-level variation. Use when needing sub-species resolution for outbreak tracking, transmission analysis, or within-host strain dynamics.Votes: 0GitHub stars: 97
- Methylation Analysis Bismark AlignmentBisulfite sequencing read alignment using Bismark with bowtie2/hisat2. Handles genome preparation and produces BAM files with methylation information. Use when aligning WGBS, RRBS, or other bisulfite-converted sequencing reads to a reference genome.Votes: 0GitHub stars: 97
- Methylation Analysis Differential Cpg TestingPer-CpG differential methylation testing from bisulfite sequencing count data or beta-value matrices. Covers beta and M-value computation, coverage filtering, statistical tests (Welch t-test, Mann-Whitney, limma, DSS beta-binomial), multiple testing correction, and effect size calculation. Use when comparing methylation at individual CpG sites between experimental groups from WGBS, RRBS, or targeted bisulfite sequencing.Votes: 0GitHub stars: 97
- Methylation Analysis Dmr DetectionDifferentially methylated region (DMR) detection using methylKit tiles, bsseq BSmooth, and DMRcate. Use when identifying contiguous genomic regions with methylation differences between experimental conditions or cell types.Votes: 0GitHub stars: 97
- Methylation Analysis Methylation CallingExtract methylation calls from Bismark BAM files using bismark_methylation_extractor. Generates per-cytosine reports for CpG, CHG, and CHH contexts. Use when extracting methylation levels from aligned bisulfite sequencing data for downstream analysis.Votes: 0GitHub stars: 97
- Methylation Analysis Methylkit AnalysisDNA methylation analysis with methylKit in R. Import Bismark coverage files, filter by coverage, normalize samples, and perform statistical comparisons. Use when analyzing single-base methylation patterns, comparing samples, or preparing data for DMR detection.Votes: 0GitHub stars: 97
- Microbiome Amplicon ProcessingAmplicon sequence variant (ASV) inference from 16S rRNA or ITS amplicon sequencing using DADA2. Covers quality filtering, error learning, denoising, and chimera removal. Use when processing demultiplexed amplicon FASTQ files to generate an ASV table for downstream analysis.Votes: 0GitHub stars: 97
- Microbiome Cancer AgentAI-powered analysis of microbiome-cancer interactions including tumor microbiome profiling, immunotherapy response prediction, and microbiome-targeted therapeutic opportunities.Votes: 0GitHub stars: 97
- Microbiome Differential AbundanceDifferential abundance testing for microbiome data using compositionally-aware methods like ALDEx2, ANCOM-BC2, and MaAsLin2. Use when identifying taxa that differ between experimental groups while accounting for the compositional nature of microbiome data.Votes: 0GitHub stars: 97