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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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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 | integrative-DMR-DEG |
| description | 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. |
This skill integrates differential methylation and differential expression datasets to reveal coordinated epigenetic regulation patterns.
dmr_results.txt # DMR results output by the metilene
dge_result.csv # DEG results output by DESeq2
corr_DMR_DEG/
stats/
integrated_results.tsv
pattern_counts.tsv
summary_stats.tsv
correlation_plot.pdf
temp/
homer_dmr.bed
... # Other temp files
awk -F'\t' 'BEGIN {OFS="\t"} {print $1, $2, $3, "peak_"NR, "*", "+"}' dmr_results.txt > homer_dmr.bed
Call:
with:
peaks_path: 6-column standard format BED file from Step 1.genome: Provide by user.output_path: Output path of the annotated fileCall:
(1) with:
input_path: dmr_results.txtoutput_pathdata_type: methylsource: metilene(2) with:
input_path: dge_result.csvoutput_pathdata_type: exprsource: deseq2Call:
with:
methyl_path: Path to standardized methylation TSV with columns: chr,start,end,pvalue,meth_diff (from Step 3)
methyl_annot_path: Path to methylation annotation TSV from HOMER (from Step 2).
expr_path: Path to standardized expression TSV with columns: gene,pvalue,log2FoldChange (from Step 3).
output_prefix: Prefix for all output files (e.g. 'corr_DMR_DEG/stats/integrative').
methyl_diff: Absolute methylation difference threshold (fraction points).
expr_fc: Fold-change threshold for expression (absolute, e.g. 1.5 for 1.5x).