القائمة
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).
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Align bisulfite sequencing DNA methylation reads using Bismark only, with explicit validation of reference preparation, library layout detection, output organization, logging, and alignment QC. Use it for WGBS, RRBS, or other bisulfite-converted DNA methylation sequencing data when raw FASTQ files must be aligned before methylation extraction and downstream analysis.
Align ChIP-seq or ATAC-seq FASTQ files to a reference genome using Bowtie2, with strict input validation, library layout detection, output organization and logging. Use it when raw sequencing reads must be converted into sorted/indexed BAM files before downstream QC, peak calling, or footprinting.
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. ChIPseeker mode is also supported according to requirements.
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.
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.
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.
| name | local-methylation-profile |
| description | 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). |
methylation.bed
target_regions.bed
local_methyl_profile/
stats/
CpG_around_target.tsv
plots/
CpG_around_target.pdf
temp/
... # other temp file generated
awk -F'\t' 'BEGIN {OFS="\t"} {print $1, $2, $3, $<i_methylation>}, $<i_coverage>}' methylation.bed # n is provide by user, *100 if is fraction
awk -F'\t' 'BEGIN {OFS="\t"} {print $1, $2, $3}' target_regions.bed
Call:
mcp__methyl-tools__build_local_methylation_profilewith:
methyl_bed_path: 5-column BED-like file from preprocess_methylation.regions_bed_path: 3-column BED-like file from preprocess_regions.output_profile_tsv_path: path for aggregated profile table (TSV).flank_size: flank size in bp around region center (default 2000).bin_size: bin size in bp (default 50).min_coverage: minimum coverage threshold for CpGs (default 10).Call:
mcp__methyl-tools__plot_profilewith:
profile_tsv_path: TSV from build_methylation_profile.output_plot_path: output figure path (PNG/PDF; format inferred from extension).title: plot title (optional).| Context | Flank | Bin | Min cov |
|---|---|---|---|
| TF peaks | ±2 kb | 50bp | 10x |
| Promoters | ±1 kb | 50bp | 10x |
| Enhancers | ±5 kb | 100bp | 5x |
| Motifs | ±0.5kb | 10–20 | 10x |