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Claude Skills by David-Li0406
github.com/David-Li04065,064 skills17 installs250 views
- Bio Clip Seq 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: 2
- Bio Clip Seq 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: 2
- Bio Clip Seq 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: 2
- Bio Codon UsageAnalyze codon usage, calculate CAI (Codon Adaptation Index), and examine synonymous codon bias using Biopython. Use when analyzing coding sequences for expression optimization or evolutionary analysis.Votes: 0GitHub stars: 2
- Bio Compressed FilesRead and write compressed sequence files (gzip, bzip2, BGZF) using Biopython. Use when working with .gz or .bz2 sequence files. Use BGZF for indexable compressed files.Votes: 0GitHub stars: 2
- Bio 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: 2
- Bio Copy Number 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: 2
- Bio Copy Number 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: 2
- Bio Crispr 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: 2
- Bio Crispr Screens 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: 2
- Bio Crispr Screens 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: 2
- Bio Crispr Screens 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: 2
- Bio Crispr Screens 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: 2
- Bio Crispr Screens 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: 2
- Bio Data Visualization 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: 2
- Bio Data Visualization 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: 2
- Bio Data Visualization Multipanel FiguresCombine multiple plots into publication-ready multi-panel figures using patchwork, cowplot, and gridExtra with shared legends and annotations. Use when combining multiple plots into publication figures.Votes: 0GitHub stars: 2
- Bio De 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: 2
- Bio De 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: 2
- Bio 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: 2
- Bio Differential Expression Batch CorrectionRemove batch effects from RNA-seq data using ComBat, ComBat-Seq, limma removeBatchEffect, and SVA for unknown batch variables. Use when correcting batch effects in expression data.Votes: 0GitHub stars: 2
- Bio Duplicate HandlingMark and remove PCR/optical duplicates using samtools fixmate and markdup. Use when preparing alignments for variant calling or when duplicate reads would bias analysis.Votes: 0GitHub stars: 2
- Bio Epitranscriptomics M6a Peak CallingCall m6A peaks from MeRIP-seq IP vs input comparisons. Use when identifying m6A modification sites from methylated RNA immunoprecipitation data.Votes: 0GitHub stars: 2
- Bio Epitranscriptomics M6anet AnalysisDetect m6A modifications from Oxford Nanopore direct RNA sequencing using m6Anet. Use when analyzing epitranscriptomic modifications from long-read RNA data without immunoprecipitation.Votes: 0GitHub stars: 2
- Bio Epitranscriptomics Merip PreprocessingAlign and QC MeRIP-seq IP and input samples for m6A analysis. Use when preparing MeRIP-seq data for peak calling or differential methylation analysis.Votes: 0GitHub stars: 2
- Bio Epitranscriptomics Modification VisualizationCreate metagene plots and browser tracks for RNA modification data. Use when visualizing m6A distribution patterns around genomic features like stop codons.Votes: 0GitHub stars: 2
- Bio Expression Matrix Gene Id MappingConvert between gene identifier systems including Ensembl, Entrez, HGNC symbols, and UniProt. Use when you need to map IDs for pathway analysis or to match different data sources.Votes: 0GitHub stars: 2
- Bio Filter SequencesFilter and select sequences by criteria (length, ID, GC content, patterns) using Biopython. Use when subsetting sequences, removing unwanted records, or selecting by specific criteria.Votes: 0GitHub stars: 2
- Bio Flow Cytometry Bead NormalizationBead-based normalization for CyTOF and high-parameter flow cytometry. Covers EQ bead normalization, signal drift correction, and batch normalization. Use when correcting instrument drift in CyTOF or harmonizing data across batches.Votes: 0GitHub stars: 2
- Bio Flow Cytometry Clustering PhenotypingUnsupervised clustering and cell type identification for flow/mass cytometry. Covers FlowSOM, Phenograph, and CATALYST workflows. Use when discovering cell populations in high-dimensional cytometry data without predefined gates.Votes: 0GitHub stars: 2
- Bio Flow Cytometry Compensation TransformationSpillover compensation and data transformation for flow cytometry. Covers compensation matrix calculation, application, and biexponential/arcsinh transforms. Use when correcting spectral overlap between fluorophores or transforming data for analysis.Votes: 0GitHub stars: 2
- Bio Flow Cytometry Cytometry QcComprehensive quality control for flow cytometry and CyTOF data. Covers flow rate stability, signal drift, margin events, dead cell exclusion, and batch QC. Use when assessing acquisition quality or identifying problematic samples before analysis.Votes: 0GitHub stars: 2
- Bio Flow Cytometry Differential AnalysisDifferential abundance and state analysis for cytometry data. Compare cell populations between conditions using statistical methods. Use when testing for significant changes in cell frequencies or marker expression between groups.Votes: 0GitHub stars: 2
- Bio Flow Cytometry Doublet DetectionDetect and remove doublets from flow and mass cytometry data. Covers FSC/SSC gating and computational doublet detection methods. Use when filtering out cell aggregates before clustering or quantitative analysis.Votes: 0GitHub stars: 2
- Bio Flow Cytometry Fcs HandlingRead and manipulate Flow Cytometry Standard (FCS) files. Covers loading data, accessing parameters, and basic data exploration. Use when loading and inspecting flow or mass cytometry data before preprocessing.Votes: 0GitHub stars: 2
- Bio Flow Cytometry Gating AnalysisManual and automated gating for defining cell populations in flow cytometry. Covers rectangular, polygon, and data-driven gates. Use when identifying cell populations through hierarchical gating strategies.Votes: 0GitHub stars: 2
- Bio Format ConversionConvert between sequence file formats (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO. Use when changing file formats or preparing data for different tools.Votes: 0GitHub stars: 2
- Bio 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: 2
- Bio Genome Assembly Hifi AssemblyHigh-quality genome assembly from PacBio HiFi reads using hifiasm with phasing support. Use when building reference-quality diploid assemblies from HiFi data, especially with trio or Hi-C phasing for fully resolved haplotypes.Votes: 0GitHub stars: 2
- Bio Genome Intervals Bed File BasicsBED file format fundamentals, creation, validation, and basic operations. Covers BED3 through BED12 formats, coordinate systems, sorting, and format conversion using bedtools and pybedtools. Use when working with genomic coordinates or preparing interval files for downstream tools.Votes: 0GitHub stars: 2
- Bio Genome Intervals Coverage AnalysisCalculate read depth and coverage across genomic intervals using bedtools genomecov and coverage. Generate bedGraph files, compute per-base depth, and summarize coverage statistics. Use when assessing sequencing depth, creating coverage tracks, or evaluating target capture efficiency.Votes: 0GitHub stars: 2
- Bio Genome Intervals Interval ArithmeticCore interval arithmetic operations including intersect, subtract, merge, complement, map, and groupby using bedtools and pybedtools. Use when finding overlapping regions, removing overlaps, combining adjacent intervals, or transferring annotations between interval files.Votes: 0GitHub stars: 2
- Bio Genome Intervals Proximity OperationsFind nearest features, search within windows, and extend intervals using closest, window, flank, and slop operations. Use when performing TSS proximity analysis, assigning enhancers to genes, defining promoter regions, or finding nearby genomic features.Votes: 0GitHub stars: 2
- Bio 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: 2
- Bio Hi C Analysis Compartment AnalysisDetect A/B compartments from Hi-C data using cooltools and eigenvector decomposition. Identify active (A) and inactive (B) chromatin compartments from contact matrices. Use when identifying A/B compartments from Hi-C data.Votes: 0GitHub stars: 2
- Bio Hi C Analysis Contact PairsProcess Hi-C read pairs using pairtools. Parse alignments, filter duplicates, classify pairs, and generate contact statistics from Hi-C sequencing data. Use when processing raw Hi-C read pairs.Votes: 0GitHub stars: 2
- Bio Hi C Analysis Hic Data IoLoad, convert, and manipulate Hi-C contact matrices using cooler format. Read .cool/.mcool files, convert from .hic format, access matrix data, and export to different formats. Use when loading or converting Hi-C contact matrices.Votes: 0GitHub stars: 2
- Bio Hi C Analysis Hic DifferentialCompare Hi-C contact matrices between conditions to identify differential chromatin interactions. Compute log2 fold changes, statistical significance, and visualize differential contact maps. Use when comparing Hi-C contacts between conditions.Votes: 0GitHub stars: 2
- Bio Hi C Analysis Hic VisualizationVisualize Hi-C contact matrices, TADs, loops, and genomic features using matplotlib, cooltools, and HiCExplorer. Create triangle plots, virtual 4C, and multi-track figures. Use when visualizing contact matrices or genomic features.Votes: 0GitHub stars: 2
- Bio Hi C Analysis Loop CallingDetect chromatin loops and point interactions from Hi-C data using cooltools, chromosight, and HiCCUPS-like methods. Identify CTCF-mediated loops and enhancer-promoter contacts. Use when detecting chromatin loops from Hi-C data.Votes: 0GitHub stars: 2