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 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 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 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 identifying differentially methylated bases (q-value 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.importfragments and before performing spectral embedding (tl.spectral) or other dimension reduction.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: