Use when your ChIP-Seq input is paired-end sequencing data stored in BEDPE format (e.g., CTCFPEChIPchr2250k.bedpe.gz), and you need to estimate fragment length and call peaks while respecting the paired nature of the reads rather than treating them as independent single-end alignments.
Use when after computeDeviations has generated a SummarizedExperiment object with z-score assays reflecting bias-corrected deviations of observed vs. expected accessibility at motif or kmer sites.
Use when after bias correction of ATAC-seq reads (via ATACorrect) when you have a bias-corrected bigWig file and need to measure transcription factor footprint strength within defined accessible regions (peaks, motif sites, or called footprint boundaries).
Use when you have a cooler file (.cool or .mcool) from a Hi-C or micro-C experiment and need to quantify the total number of sequencing reads assigned to each genomic bin to assess coverage uniformity, identify poorly-sequenced regions, or prepare bin-level weights for downstream normalization.
Use when when setting up a bioinformatics pipeline (particularly Hi-C data processing) that depends on multiple external binaries with version constraints, and you need to configure the environment in a way that is both portable across systems and reproducible across runs.
Use when when setting up a bioinformatics pipeline (such as HiC-Pro) that depends on multiple compiled or independently distributed binaries and you need to confirm that all required tools are installed, executable, meet version requirements (e.g., samtools ≥1.
Use when before executing a complex bioinformatics pipeline (such as Hi-C data processing) that depends on multiple third-party tools with explicit version constraints.
Use when you have raw methylation call files from Bismark, MethylDackel, or similar bisulfite alignment tools (bedGraph, cytosine report, or tabix-indexed formats) and need to import them into R as methylRaw or methylRawListDB objects for downstream differential methylation analysis, quality.
Use when when you have computed z-score deviations for genomic annotations (e.g., motifs) across multiple cells or samples and need to quantify uncertainty in their variability rankings before performing differential or comparative analyses.
Use when when you have computed per-bin insulation scores from a Hi-C cooler file using cooltools.insulation and need to identify discrete genomic boundaries that separate topological domains.
Use when you have executed a complex multi-step processing pipeline (e.g., ENCODE Hi-C uniform processing pipeline) and need to confirm that the generated output files match a known reference baseline.
Use when when you have aligned ChIP-Seq reads (single-end BED or paired-end BEDPE format) and need to identify enriched genomic regions by comparing ChIP signal against control background, with the ability to customize fragment length estimation, local bias calculation, and peak score thresholds.
Use when when beginning peak calling on ChIP-Seq data: you have raw single-end or paired-end BED/BEDPE alignment files for both ChIP and control samples and need to remove duplicate reads before predicting fragment length and building local bias models.
Use when after duplicate filtering and fragment length prediction (d) in ChIP-Seq analysis, when you need to convert discrete read alignments into continuous coverage signal for comparison against control background.
Use when you have ATAC-seq BAM files aligned to a reference genome, a set of transcription factor motif locations (BED format), and you need to determine which motifs are actually occupied by proteins in your cell type or condition.
Use when when you have filtered ATAC-seq or DNAse-seq peak counts (after GC bias correction, sample filtering, and peak filtering) and wish to measure how strongly each annotation (motif or kmer) influences chromatin accessibility variability in each sample relative to a background expectation.
Use when use this skill after performing Tn5 bias correction and footprint scoring on ATAC-seq BAM files when you need to inspect the spatial distribution of Tn5 insertions around transcription factor binding sites, validate footprinting quality, or communicate differential TF occupancy patterns.
Use when after you have (1) corrected ATAC-seq BAM files for Tn5 insertion bias using ATACorrect, (2) computed per-base footprint scores using ScoreBigwig, (3) obtained a motif database (e.
Use when you have sparse, single-cell or bulk ATAC/DNAse-seq data from multiple cell types or conditions (e.g., GM vs H1 cell lines), pre-filtered and GC-bias-corrected, with motif-to-peak matches already computed.
Use when you have generated a .hic contact map from Hi-C raw sequencing data and need to identify topologically associating domains (TADs) or other chromatin structural boundaries.
Use when when you have loaded a cooler file containing Hi-C contact matrices and need to quantify how contact probability decays with genomic distance within a single chromosome.