Claude Skills by HolobiomicsLab

Use when you have run a peak-calling algorithm on sparse CUT&RUN bedGraph data and received a BED-format output file;

Use when after clustering single-cell ATAC-seq data (e.g., via Leiden clustering on spectral embeddings), use this skill to identify peaks within each cluster. Triggering conditions: (1) you have sparse, per-cell insertion counts organized in a tile matrix;

Use when developing or reviewing Python code for a scientific package (e.g., cooltools) that targets collaborative development with multiple contributors.

Use when after extending ChIP sample reads to their predicted fragment length and constructing local lambda bias tracks (incorporating d-scaled, 1 kb, 10 kb, and genome-wide backgrounds).

Use when after generating ChIP pileup and local lambda (background) BEDGRAPH tracks with matched sequencing depth, use this skill to assign statistical significance scores to each genomic region.

Use when you have ATAC-seq BAM alignments with classified motif sites (bound vs. unbound based on chromatin accessibility or binding thresholds) and wish to detect and visualize the characteristic Tn5 insertion depletion signal (footprints) around transcription factor binding sites.

Use when after merging methylation call files from multiple samples using unite() to create a methylBase object, apply PCA when you need to visualize sample-level relationships based on overall methylation similarity across all covered bases, or when you want to determine which principal components.

Use when immediately after loading raw methylation array data (.idat files or beta-valued matrix) from HumanMethylation450 (450k) or EPIC arrays when conducting primary quality control.

Use when after implementing or modifying Python library functions (such as utility functions in cooltools.lib subpackages) to verify correctness and identify gaps in test coverage before merging changes or releasing code.

Use when when setting up a new conda environment for a Python-based bioinformatics pipeline and you need to confirm that all declared dependencies (e.g., pysam >=0.15.4, bx-python >=0.8.8, numpy >=1.18.1, scipy >=1.4.

Use when you are preparing to run Hi-C data normalization or read alignment filtering steps that depend on Python modules (iced, pysam, numpy, scipy) and you need to ensure consistent module versions across multiple runs or compute nodes.

Use when you are building or refactoring a scientific Python library and need to decide how to organize and expose utility functions (e.g., adaptive coarse-graining, filtering, analysis routines) so that end users can import and call them reliably.

Use when you have precomputed expected contact frequency tables (TSV format with columns like dist_bp, contact_frequency, n_valid) and need to apply log-binning and smoothing to group distance values into log-spaced bins, aggregate statistics within each bin, and export a cleaned, annotated output.

Use when when you have raw methylation array data (450K or EPIC format) in .idat files or as a beta-valued matrix and need to conduct a complete analysis pipeline including data import, quality filtering, normalization, batch effect correction, DMR detection, or gene set enrichment.

Use when after installing R or modifying an R environment via conda, package managers, or container images; before running any pipeline step that depends on R packages for statistical analysis, visualization, or data normalization.

Use when when analyzing normalized DNA methylation beta matrices (450K or EPIC arrays) and you need to identify the true number of latent batch or technical factors present in the data.

Use when you have identified a published computational tool (e.

Use when you have paired scATAC-seq and scRNA-seq data from the same cells (multiome experiment) and want to perform integrated analysis that leverages both chromatin accessibility and gene expression signals.

Use when you have a cooler Hi-C contact matrix file and an associated eigenvector track (from prior eigs_cis calculation or similar), and you need to quantify the preferential interaction patterns between A and B chromatin compartments.

Use when after merging methylation calls across all samples using unite() to create a methylBase object, apply this skill to characterize whether replicate samples cluster together and to visualize methylation-driven separation between biological groups (e.g., test vs. control).

Use when after completing peak calling and cell annotation in an ArchR project, when you intend to perform trajectory analysis using STREAM rather than ArchR's native monocle3 or Slingshot integrations, or when you need to share peak-by-cell matrices with collaborators using STREAM pipelines.

Use when use this skill during pipeline installation or initialization when you need to locate required external binaries (e.g., bowtie2, samtools, R, Python) but the user has not explicitly provided their installation paths in a configuration file.

Use when you have cloned a bioinformatics repository (e.g., FredHutch/SEACR) and need to confirm that its shell and R scripts are executable and will run successfully on your input data (e.g., CUT&RUN bedGraph files) before investing time in a full analysis.

Use when when you need to verify that a statistical correction (e.

Use when when you have installed or updated a DNA methylation analysis tool (e.g., ChAMP) and need to verify that it produces documented expected outputs on a reference simulation dataset before applying it to real experimental data.

Use when you have aligned single-cell ATAC-seq data as BAM files or fragment files (TSV format with genomic coordinates) and need to prepare it for spectral embedding, clustering, and peak calling. This is the entry point after alignment but before any dimension reduction or statistical analysis.

Use when after loading and preprocessing raw scATAC-seq data into an ArchR project object when you need to compute low-dimensional embeddings for clustering, UMAP/tSNE visualization, or integrated multi-omic analysis.

Use when after calling peaks and annotating cells in an ArchR project, when you need to perform trajectory analysis using STREAM or other external tools that require a peak-by-cell matrix in a specific tabular format (CSV or TSV) rather than native ArchR objects.

Use when after loading fragment counts into a SummarizedExperiment object (e.g., via getCounts) but before motif matching or deviation computation.

Use when you have preprocessed single-cell ATAC-seq fragment files or count matrices and need to identify open chromatin regions (peaks) to support downstream differential accessibility analysis, motif discovery, or regulatory network inference.

Use when after loading and normalizing a beta-valued methylation matrix (450K or EPIC array), apply SVD interpretation when you need to assess whether observed variation is driven by batch effects rather than biological signal, or when you want to determine the true dimensionality of latent.

Use when you have an ArchR project with clustered single-cell ATAC-seq cells and want to reconstruct developmental or cellular transition trajectories. Use this skill when your research question requires ordering cells along a developmental continuum (e.

Use when you have a bioinformatics pipeline (like HiC-Pro) with mixed Python, R, and compiled tool dependencies, and you need to ensure consistent reproducibility across machines and team members without manual per-tool installation. Use this when dependencies include version-pinned libraries (e.

Use when when you have a chromVARDeviations object with multiple annotation sets (e.

Use when after generating a count matrix from fragment data using pp.add_tile_matrix, pp.make_peak_matrix, or pp.make_gene_matrix in SnapATAC2.

Use when after generating a tile matrix or feature count matrix from single-cell ATAC-seq, RNA-seq, Hi-C, or methylation data, before clustering or UMAP visualization, when you need unsupervised dimension reduction that scales to millions of cells and is agnostic to the underlying data modality.

Use when you have a large single-cell count matrix (≥10 million cells) in CSR format and need to verify whether the matrix-free spectral embedding in SnapATAC2 achieves its documented linear scaling behavior on your hardware and dataset characteristics.

Use when after implementing or modifying utility functions in a library subpackage (e.g., cooltools.

Use when after identifying differentially methylated bases (q-value < 0.01, methylation difference > 25%) using calculateDiffMeth(), use this skill to determine what fraction of those bases overlap with specific gene features (promoters, exons, introns) and CpG contexts (islands vs. shores).

Use when when you have run differential methylation analysis in methylKit and need to validate whether overdispersion correction (overdispersion='MN') produces appropriately stringent statistical tests.

Use when you have completed peak calling and cell annotation in ArchR and want to perform trajectory inference or visualization in STREAM. Apply it specifically when your analysis goal requires STREAM's specialized trajectory reconstruction methods (e.

Use when you are preparing to run a complex multi-tool bioinformatics pipeline (such as HiC-Pro) on a new system or cluster, and need to confirm that all required binaries exist in the execution environment and meet minimum version thresholds (e.g., samtools >=1.9, Python >3.

Use when you are setting up HiC-Pro or a similar multi-tool pipeline for the first time, or you need to validate that all required dependencies are installed and discoverable.

Use when after creating a methylRawListDB object via methRead() with dbtype='tabix', or when loading pre-existing tabix-backed methylation files, to verify that bgzipped files are correctly indexed and contain required methylKit metadata headers (≥v1.13.

Use when you have aligned ATAC-seq BAM files, corrected Tn5 insertion bias and computed footprint scores (via TOBIAS ATACorrect and ScoreBigwig), a motif database in JASPAR or compatible format, and you need to assign occupancy states (bound/unbound) and confidence scores at specific TF binding.

Use when after importing fragment files into AnnData using pp.import_fragments and before performing spectral embedding (tl.spectral) or other dimension reduction.

Use when you have aligned ATAC-seq BAM files from Tn5-based chromatin accessibility assays and need to perform footprinting analysis.

Use when you have ATAC-seq BAM files and a set of genomic coordinates (e.g., transcription factor motif sites, peak regions) and need to quantify the spatial distribution of Tn5 cleavage events relative to those coordinates.

Use when when you have an ArchR project object with processed single-cell ATAC-seq data and want to infer developmental or cell-state trajectories.

Use when after bias-correcting ATAC-seq cutsite signal (using ATACorrect or equivalent) when you have a set of genomic regions of interest (e.