Science & Research for Claude Code

ChIP-seq peak calling using MACS3 (or MACS2). Call narrow peaks for transcription factors or broad peaks for histone modifications. Supports input control, fragment size modeling, and various output formats including narrowPeak and broadPeak BED files. Use when calling peaks from ChIP-seq alignments.

ChIP-seq quality control metrics including FRiP (Fraction of Reads in Peaks), cross-correlation analysis (NSC/RSC), library complexity, and IDR (Irreproducibility Discovery Rate) for replicate concordance. Use to assess experiment quality before downstream analysis. Use when assessing ChIP-seq data quality metrics.

Identifies super-enhancers from H3K27ac ChIP-seq data using ROSE and related tools. Use when studying cell identity genes, cancer-associated regulatory elements, or master transcription factor binding regions that cluster into large enhancer domains.

Visualize ChIP-seq data using deepTools, Gviz, and ChIPseeker. Create heatmaps, profile plots, and genome browser tracks. Visualize signal around peaks, TSS, or custom regions. Use when visualizing ChIP-seq signal and peaks.

Query ClinVar for variant pathogenicity classifications, review status, and disease associations via REST API or local VCF. Use when determining clinical significance of variants for diagnostic or research purposes.

Query dbSNP for rsID lookups, variant annotations, and cross-references to other databases. Use when mapping between rsIDs and genomic coordinates or retrieving basic variant information.

Query gnomAD for population allele frequencies to assess variant rarity. Use when filtering variants by population frequency for rare disease analysis or determining if a variant is common in the general population.

Call HLA alleles from NGS data using OptiType, HLA-HD, or arcasHLA for immunogenomics applications. Use when determining HLA genotype for transplant matching, neoantigen prediction, or pharmacogenomic screening.

Query myvariant.info API for aggregated variant annotations from multiple databases (ClinVar, gnomAD, dbSNP, COSMIC, etc.) in a single request. Use when annotating variants with clinical and population data from multiple sources simultaneously.

Query PharmGKB and CPIC for drug-gene interactions, pharmacogenomic annotations, and dosing guidelines. Use when predicting drug response from genetic variants or implementing clinical pharmacogenomics.

Calculate polygenic risk scores using PRSice-2, LDpred2, or PRS-CS from GWAS summary statistics. Use when predicting disease risk from genome-wide genetic variants.

Extract 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.

Calculate tumor mutational burden from panel or WES data with proper normalization and clinical thresholds. Use when assessing immunotherapy eligibility or characterizing tumor immunogenicity.

Filter 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.

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Read 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.

Generate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.

Annotate CNVs with genes, pathways, and clinical significance. Use when interpreting CNV calls or identifying affected genes from copy number analysis.

Visualize 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.

Detect 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.

Call 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.

Analyzes 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.

Batch 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.

CRISPResso2 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.

Statistical 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.

JACKS (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.

CRISPR 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.

MAGeCK (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.

Quality 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.

Detects 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.