Claude Skills by HolobiomicsLab

Use when when you have a binned Hi-C cooler file, an associated eigenvector track (from prior eigs_cis calculation), and need to measure how strongly the genome is partitioned into active (A) and inactive (B) compartments.

Use when you have a raw or preprocessed single-cell count matrix (from BAM-to-fragment or FASTQ-to-matrix pipelines) and need to apply matrix-free algorithms like tl.spectral, tl.multi_spectral, or other scalable dimension reduction methods that require dense or sparse matrix input.

Use when when an algorithm claims linear or sublinear time/space complexity (e.g., matrix-free spectral embedding) and you need to verify that claim holds for datasets at the scale intended (10 million+ cells).

Use when you need to deploy a complex multi-language pipeline (e.g., HiC-Pro) that requires Python >3.7 libraries (pysam, bx-python, numpy, scipy), R packages (ggplot2, RColorBrewer), and compiled tool binaries (bowtie2, samtools >=1.

Use when when deploying a complex bioinformatics pipeline (like HiC-Pro) across heterogeneous computing environments where required tools (bowtie2, samtools, R, Python) may be installed in non-standard locations, differ in version, or require scheduler-specific configuration (TORQUE, SGE, SLURM.

Use when you have a precomputed expected contact frequency table (TSV format with columns: dist_bp, contact_frequency, n_valid) derived from cooler files and need to compress distance-dependent contact probabilities into log-spaced bins.

Use when you have a cooler Hi-C contact matrix, a set of genomic features (e.g., CTCF peaks, enhancers, or TAD boundaries defined in BED format), and want to quantify average contact patterns around those features to detect local organization principles.

Use when you have a precomputed expected contact frequency table (TSV with columns: dist_bp, contact_frequency, n_valid) derived from cooler Hi-C matrices and need to generate a smoothed, log-binned P(s) curve for downstream analysis such as TAD detection, contact probability visualization, or.

Use when you have a Hi-C dataset stored in cooler format (e.

Use when your Hi-C data is stored in cooler format (a binary HDF5-based sparse matrix with associated genomic bins and genomic tracks); you need to programmatically access the contact matrix, bin coordinates, or track data (e.g., eigenvectors, GC content) for further analysis;

Use when you are performing dimensionality reduction on a sparse single-cell count matrix (in CSR format) and need to compute pairwise cell similarities before spectral decomposition.

Use when after calculateDiffMeth() has been run on a methylBase object and you have a methylDiff object with q-values and methylation difference estimates.

Use when you have a methylDiff object containing differentially methylated bases or regions from bisulfite sequencing, gene annotation in BED or similar format (RefSeq, Ensembl), and CpG island coordinate files, and need to understand what fraction of your differential methylation signal falls.

Use when you have paired scATAC-seq peak matrices and scRNA-seq gene expression matrices from the same cells (multiome data) and need to perform joint clustering, visualization, or correlation analysis across both chromatin accessibility and gene expression in a single coordinate system.

Use when when setting up HiC-Pro or similar multi-tool pipelines where tool availability and version constraints are prerequisites for downstream analysis.

Use when before running HiC-Pro or similar multi-stage pipelines on a new system or environment, especially when dependency installation is not automated (e.g., not in a conda environment or container).

Use when you have filtered ATAC-seq peak counts, matched motifs to those peaks, and want to measure which transcription factor motifs show elevated or reduced accessibility relative to GC-content and accessibility-matched background expectations—particularly when annotating TF motif usage across.

Use when after identifying differentially accessible peaks (via tl.

Use when you have pre-processed chromatin accessibility data (ATAC-seq or DNAse-seq) with chromVAR deviations already computed for individual cells or bulk samples across multiple cell types or conditions, and you need to identify which transcription factor motifs exhibit significant differential.

Use when you have aligned ATAC-seq BAM files and peak annotations from two or more experimental conditions (e.

Use when you have preprocessed, normalized beta-value matrices from EPIC or 450k methylation arrays with at least two sample groups (case/control, treatment/untreated, or similar contrasts) and seek to identify regions of coordinated differential methylation rather than individual CpG sites.

Use when you have raw .idat files or beta-valued matrices from Illumina HumanMethylation450 (450K) or EPIC array experiments and need to import them into R for quality control and downstream analysis.

Use when you have loaded normalized methylation data from EPIC or 450k arrays and need to identify differentially methylated blocks rather than individual CpG sites or DMRs.

Use when you have merged methylation call data across multiple biological replicates (samples per group ≥2) with base-pair-level coverage information, and you need to identify loci where methylation levels differ significantly between treatment groups.

Use when you have CpG methylation call files (from Bismark or MethylDackel) and need to load them into R for differential methylation analysis, but anticipate memory constraints or want to avoid loading the entire dataset into memory.

Use when immediately after loading raw .idat files or beta-value matrices from HumanMethylation450 or EPIC arrays when you need to exclude probes that fail quality control.

Use when you have raw ChIP-Seq and control BED files with potential PCR duplicates or unequal sequencing depths. Duplicate filtering is mandatory before estimating fragment length (predictd) or generating coverage pileups.

Use when when you are developing or contributing to a Python package (like cooltools) and need to test changes to utility functions, library integrations, or API implementations without reinstalling the package after each modification. Apply this when you must verify that a new function (e.

Use when you have computed eigenvector values from a prior eigs_cis calculation on a cooler Hi-C matrix and need to classify genomic regions into discrete A/B compartment categories before performing saddle analysis or computing compartment-level contact asymmetry metrics.

Use when you have raw Hi-C FASTQ files from a public repository (NCBI SRA, GEO, or ENCODE-deposited accession) and need to reproduce or validate Hi-C map generation following the ENCODE uniform processing standard, or you need to verify that your pipeline output conforms to reference format and.

Use when after cloning or installing a peak-calling or genomic analysis tool from a repository, before using it on production data.

Use when when developing or validating a DNA methylation array analysis pipeline using ChAMP, you need an independent ground-truth dataset to confirm that DMR detection is working correctly.

Use when after merging methylation call files into a unified methylBase object (covering all samples at common base positions), apply this skill to assess whether biological replicates cluster together, whether case/control or treatment groups separate as expected, and to identify potential sample.

Use when you have a backed AnnData object containing processed fragment data (stored in .obsm['fragment_paired'] or .

Use when after invoking pp.make_fragment_file to convert a coordinate-sorted BAM file (e.g., from 10X ATAC or standard alignment) into a compressed fragment file.

Use when when beginning ChIP-Seq analysis with single-end BED/SAM input and no prior knowledge of the library's fragment length.

Use when you have paired-end ChIP-Seq data (BEDPE format) and need to determine the empirical fragment length (insertion length) before peak calling. This is mandatory for paired-end ChIP-Seq workflows to ensure MACS3 correctly extends coverage tracks and calculates local bias;

Use when after filtering duplicate reads from ChIP-Seq data but before generating pileup coverage tracks.

Use when you have a SummarizedExperiment object containing peak counts from single-cell or bulk ATAC-seq/DNAse-seq data and need to prepare it for unbiased motif deviation analysis.

Use when you have raw Hi-C FASTQ files from a kilobase-resolution Hi-C experiment and need to produce a processed Hi-C contact map (.hic file) for visualization, loop calling, or chromatin structure analysis. This is the entry point for any Hi-C dataset that has not yet been aligned and normalized.

Use when you have filtered peak or chromatin accessibility counts and need to annotate each peak with the presence or absence of specific DNA sequence patterns—either predefined motifs (e.

Use when you need to map computed per-bin metrics (insulation scores, boundary calls, contact frequencies) back to genomic coordinates for export to BED/GFF format, cross-reference with external annotations, or validate that computed features fall within expected genomic ranges.

Use when after you have identified a set of differentially methylated bases or regions (e.

Use when you have a pre-generated .hic contact map file and need to identify and annotate chromatin loops or topologically associating domains (TADs) at high resolution.

Use when after bias-correcting ATAC-seq cutsite signal (via ATACorrect) when you have a bias-corrected bigWig file and need to compute per-position footprint scores within defined accessible regions (peaks, called footprints, or regulatory regions) to detect and quantify transcription factor.

Use when you have a cooler file (.cool or .mcool) from a Hi-C experiment and need to generate a genome-wide track of per-bin sequencing depth to assess coverage uniformity, identify poorly sequenced regions, or normalize downstream analyses by local sequencing intensity.

Use when you have a cooler-format Hi-C contact matrix and need to establish a genome-wide baseline contact frequency by genomic distance.

Use when you have raw Hi-C FASTQ files from a Hi-C wet-lab protocol and need to convert them into processed Hi-C contact maps (.hic files) for loop detection, TAD identification, or 3D structure inference. Use when starting from deposited public Hi-C datasets (e.

Use when you have raw Hi-C FASTQ files from a sequencing experiment and need to generate kilobase-resolution Hi-C contact maps conforming to ENCODE reference standards.

Use when you have a cooler-format Hi-C contact matrix and need to identify TAD boundaries and insulation strength along the genome. Use this skill when your research question requires quantifying local chromatin compartmentalization or annotating structural domain edges for downstream analysis (e.