Data & Analytics

Load when running over-representation analysis (ORA) on a list of proteins via Fisher's exact test against a built-in 8-pathway DEMO dictionary, with BH-FDR correction. Skip when needing a real pathway database (this skill is demo-only — use `bulkrna-enrichment` for real KEGG / Reactome / MSigDB) or for rank-based GSEA.

Load when computing two-group differential protein abundance (group2 vs group1, log2FC + p-value + BH-adjusted FDR) via Welch t-test, equal-variance t-test, or Mann-Whitney on a wide protein × sample CSV. Skip when you need multi-condition DE (run pairwise contrasts manually) or label-based TMT linear-mixed models.

Load when ingesting a MaxQuant `proteinGroups.txt`, FragPipe `combined_protein.tsv`, DIA-NN report, or generic CSV / TSV protein-quantification table — normalises columns to a standard schema, emits `tables/proteins.csv`. Skip when raw spectra are the input (run the search engine first) or when the file is already OmicsClaw schema.

Load when scaffolding a NEW OmicsClaw skill from a natural-language request — generates the skill directory layout (SKILL.md, parameters.yaml, references/, tests/) under the chosen domain. Skip when modifying an existing skill (edit its files directly) or when only routing a query (use `orchestrator`).

Load when routing a natural-language omics query to the correct domain skill across spatial / singlecell / genomics / proteomics / metabolomics / bulkrna domains via keyword / LLM / hybrid matching. Skip when the target skill is already known — invoke that skill directly.

Load when running univariate two-group testing (t-test / Wilcoxon / ANOVA / Kruskal-Wallis) on a feature × sample metabolomics CSV with `--group1-prefix` / `--group2-prefix` column matching, BH-FDR adjusted. Skip when working with raw spectra (run `metabolomics-xcms-preprocessing`) or for two-group DE with default `ctrl` / `treat` prefixes (use `metabolomics-de`).

Load when imputing missing values (min / median / KNN) and normalising (TIC / median / log) a feature × sample metabolomics CSV. Skip when only normalisation is needed (use `metabolomics-normalization`) or when the input is raw spectra (run `metabolomics-xcms-preprocessing` first).

Load when running over-representation analysis (ORA) on a metabolite list via Fisher's exact test against a built-in 9-pathway DEMO dictionary, BH-FDR adjusted. Skip when needing real KEGG / Reactome (this skill is demo-only) or `mummichog` / `fella` topology methods (CLI accepts them but only ORA runs).

Load when normalising a feature × sample metabolomics CSV via median, quantile, total (sum), PQN (probabilistic quotient), or log methods — emits a normalised wide-form table. Skip when also imputing (use `metabolomics-quantification`) or for raw spectra (run `metabolomics-xcms-preprocessing` first).

Load when running two-group metabolomics DE (t-test + log2FC + BH-FDR + PCA) on a feature × sample CSV using `--group-a-prefix` / `--group-b-prefix` (default `ctrl` / `treat`). Skip when needing tunable test backends (use `metabolomics-statistics` for Wilcoxon / ANOVA / Kruskal) or for raw spectra.

Load when annotating LC-MS features against a built-in 15-metabolite HMDB demo dictionary by m/z within a `--ppm` tolerance — emits a per-feature annotation table. Skip when needing real HMDB / KEGG / LipidMaps / METLIN look-up (this skill is demo-only) or for raw spectra (use `metabolomics-xcms-preprocessing` first).

Load when summarising / filtering a VCF — variant classification (SNP / MNP / INS / DEL / COMPLEX), Ti/Tv ratio, QUAL / DP threshold filtering, INFO-field parsing. Skip when the input is a BAM (use `genomics-variant-calling` upstream first) or when adding functional annotations (use `genomics-variant-annotation`).

Load when summarising small variants (SNVs / indels) from a VCF or computing demo-pattern variant statistics (Ti/Tv ratio, per-chromosome distribution, SNP / indel split). Skip when filtering / merging VCFs (use `genomics-vcf-operations`), when calling structural variants (use `genomics-sv-detection`), or when adding functional annotations (use `genomics-variant-annotation`).

Load when summarising a peak file (BED / narrowPeak) from ATAC-seq / ChIP-seq / CUT&Tag — peak count, width distribution, per-chromosome counts, score statistics. Skip when calling peaks from BAM (run MACS / Genrich externally first) or when working with single-cell ATAC (use `scatac-preprocessing`).

Load when computing genome-assembly QC metrics — N50/N90, L50/L90, total length, contig count, GC content, longest-contig — from a FASTA produced by any assembler (SPAdes / Megahit / Flye / Canu). Skip when running the assembly itself or when assessing alignment quality (use `genomics-alignment`).

Load when placing bulk RNA-seq samples on a single-cell reference's pseudotime axis (NNLS deconvolution + nearest-neighbour mapping). Skip for plain cell-type proportions (use bulkrna-deconvolution alone) or for native single-cell trajectory inference (use sc-pseudotime).

Load when summarising STAR / HISAT2 / Salmon alignment-rate logs in bulk RNA-seq. Skip if data is raw FASTQ (use bulkrna-read-qc) or already counted (use bulkrna-qc), or for genome-DNA alignment (use genomics-alignment).

Load when comparing gene expression between two conditions in bulk RNA-seq count data. Skip when the data is single-cell (use sc-de) or spatial (use spatial-de), or when you need exon-level alternative splicing (use bulkrna-splicing).

--> --- name: bio-methylation-methylkit description: DNA 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. tool_type: r primary_tool: methylKit measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-chipseq-peak-annotation description: Annotate ChIP-seq peaks to genomic features and genes using ChIPseeker. Assign peaks to promoters, exons, introns, and intergenic regions. Find nearest genes and calculate distance to TSS. Generate annotation plots and statistics. Use when annotating ChIP-seq peaks to genomic features. tool_type: r primary_tool: ChIPseeker measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_fi...

--> --- name: bio-chipseq-motif-analysis description: De novo motif discovery and known motif enrichment analysis using HOMER and MEME-ChIP. Identify transcription factor binding motifs in ChIP-seq, ATAC-seq, or other genomic peak data. Use when finding enriched DNA motifs in peak sequences. tool_type: cli primary_tool: HOMER measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Identify DNA sequence mot...

--> --- name: bio-chipseq-differential-binding description: Differential binding analysis using DiffBind. Compare ChIP-seq peaks between conditions with statistical rigor. Requires replicate samples. Outputs differentially bound regions with fold changes and p-values. Use when comparing ChIP-seq binding between conditions. tool_type: r primary_tool: DiffBind measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_comm...

--> --- name: bio-atac-seq-differential-accessibility description: Find differentially accessible chromatin regions between conditions using DiffBind or DESeq2. Use when comparing chromatin accessibility between treatment groups, cell types, or developmental stages in ATAC-seq experiments. tool_type: r primary_tool: DiffBind measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-atac-seq-atac-qc description: Quality control metrics for ATAC-seq data including fragment size distribution, TSS enrichment, FRiP, and library complexity. Use when assessing ATAC-seq library quality before or after peak calling to identify problematic samples. tool_type: mixed primary_tool: deeptools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---