alignment-level-QC
Calculates technical mapping statistics for any aligned BAM file (ChIP or ATAC). It assesses the performance of the aligner itself by generating metrics on read depth, mapping quality, error rates, and read group data using samtools and Picard.Use this skill to check "how well the reads mapped" or to validate BAM formatting/sorting before further processing. Do NOT use this skill for biological signal validation (like checking for peaks or open chromatin) or for filtering/removing reads.
genomic-feature-annotation
This skill is used to perform genomic feature annotation and visualization for any file containing genomic region information using Homer (Hypergeometric Optimization of Motif EnRichment). It annotates regions such as promoters, exons, introns, intergenic regions, and TSS proximity, and generates visual summaries of feature distributions.
functional-enrichment
Perform GO and KEGG functional enrichment using HOMER from genomic regions (BED/narrowPeak/broadPeak) or gene lists, and produce R-based barplot/dotplot visualizations. Use this skill when you want to perform GO and KEGG functional enrichment using HOMER from genomic regions or just want to link genomic region to genes.
De-novo-motif-discovery
This skill identifies novel transcription factor binding motifs in the promoter regions of genes, or directly from genomic regions of interest such as ChIP-seq peaks, ATAC-seq accessible sites, or differentially acessible regions. It employs HOMER (Hypergeometric Optimization of Motif Enrichment) to detect both known and previously uncharacterized sequence motifs enriched within the supplied genomic intervals. Use the skill when you need to uncover sequence motifs enriched or want to know which TFs might regulate the target regions.
known-motif-enrichment
This skill should be used when users need to perform known motif enrichment analysis on ChIP-seq, ATAC-seq, or other genomic peak files using HOMER (Hypergeometric Optimization of Motif EnRichment). It identifies enrichment of known transcription factor binding motifs from established databases in genomic regions.
motif-scanning
This skill identifies the locations of known transcription factor (TF) binding motifs within genomic regions such as ChIP-seq or ATAC-seq peaks. It utilizes HOMER to search for specific sequence motifs defined by position-specific scoring matrices (PSSMs) from known motif databases. Use this skill when you need to detect the presence and precise genomic coordinates of known TF binding motifs within experimentally defined regions such as ChIP-seq or ATAC-seq peaks.
chromatin-state-inference
This skill should be used when users need to infer chromatin states from histone modification ChIP-seq data using chromHMM. It provides workflows for chromatin state segmentation, model training, state annotation.
hic-normalization
Automatically detect and normalize Hi-C data. Only .cool or .mcool file is supported. All .mcool files are then checked for existing normalization (supports bins/weight only) and balanced if none of the normalizations exist.
hic-compartments-calling
This skill performs PCA-based A/B compartments calling on Hi-C .mcool datasets using pre-defined MCP tools from the cooler-tools, cooltools-tools, and plot-hic-tools servers.
hic-tad-calling
This skill should be used when users need to identify topologically associating domains (TADs) from Hi-C data in .mcools (or .cool) files or when users want to visualize the TAD in target genome loci. It provides workflows for TAD calling and visualization.
hic-loop-calling
This skill performs chromatin loop detection from Hi-C .mcool files using cooltools.
BAM-filtration
Performs data cleaning and removal operations. This skill takes a raw BAM and creates a new, "clean" BAM file by actively removing artifacts: mitochondrial reads, blacklisted regions, PCR duplicates, and unmapped reads. Use this skill to "clean," "filter," or "remove bad reads" from a dataset. This is a prerequisite step before peak calling. Do NOT use this skill if you only want to view statistics without modifying the file.
differential-tad-analysis
This skill performs differential topologically associating domain (TAD) analysis using HiCExplorer's hicDifferentialTAD tool. It compares Hi-C contact matrices between two conditions based on existing TAD definitions to identify significantly altered chromatin domains.
differential-methylation
This skill performs differential DNA methylation analysis (DMRs and DMCs) between experimental conditions using WGBS methylation tracks (BED/BedGraph). It standardizes input files into per-sample four-column Metilene tables, constructs a merged methylation matrix, runs Metilene for DMR detection, filters the results, and generates quick visualizations.
local-methylation-profile
This skill analyzes the local DNA methylation profiles around target genomic regions provide by user. Use this skill when you want to vasulize the average methylation profile around target regions (e.g. TSS, CTCF peak or other target regions).
integrative-DMR-DEG
This skill performs correlation analysis between differential methylation and differential gene expression, identifying genes with coordinated epigenetic regulation. It provides preprocessing and integration workflows, using promoter-level methylation–expression relationships.
global-methylation-profile
This skill performs genome-wide DNA methylation profiling. It supports single-sample and multi-sample workflows to compute methylation density distributions, genomic feature distribution of the methylation profile, and sample-level clustering/PCA. Use it when you want to systematically characterize global methylation patterns from WGBS or similar per-CpG methylation call files.
methylation-variability-analysis
This skill provides a complete and streamlined workflow for performing methylation variability and epigenetic heterogeneity analysis from whole-genome bisulfite sequencing (WGBS) data. It is designed for researchers who want to quantify CpG-level variability across biological samples or conditions, identify highly variable CpGs (HVCs), and explore epigenetic heterogeneity.
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