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This skill performs A/B compartment shift analysis between two Hi-C samples.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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 | hic-compartment-shift |
| description | This skill performs A/B compartment shift analysis between two Hi-C samples. |
This skill performs A/B compartment shift analysis using PC1 eigenvector values extracted from Hi-C data, following the HOMER framework. It supports two conditions, each with two or more replicates, and uses the PC1 values (E1 column) from user-provided TSV files.
Major steps include:
Use this skill when you want to:
Example input set:
CT1_rep1.tsvCT1_rep2.tsvCT2_rep1.tsvCT2_rep2.tsvAdditional requirements:
compartments_shift_analysis/
shift_regions/
diff_PC1_CT2_vs_CT1.txt
regions.*.txt # other region files output by the tools used.
temp/
bins_PC1.txt
PC1_all_samples.txt
*.bedGraph # other bedGraph file
awk 'BEGIN{OFS=" "} NR>1 && NF==5 {print $1, $2, $3, $5}' CT1_rep1.tsv > CT1_rep1.PC1.bedGraph
Use any one TSV as the template:
awk 'BEGIN{OFS=" "} NR>1 && NF==5 {print $1, $2, $3}' CT1_rep1.tsv > bins_PC1.txt
The resulting bins_PC1.txt defines genomic intervals for PC1 extraction.
Call:
mcp_homer-tools__homer_differential_PC1with:
bins_pc1_path: Path to the bins_PC1.txt file generated earlier,genome: HOMER genome identifier, provided by user.bedgraph_paths: List of PC1 bedGraph files in the exact replicate order (e.g., CT1_rep1, CT1_rep2, CT2_rep1, CT2_rep2).experiment_labels: List of experiment group labels matching bedGraph order (e.g. ['CT1','CT1','CT2','CT2']).merged_output_path: Output path for merged PC1 table. Empty → '<bins_pc1_path>.merged_PC1.txt'.diff_output_path: Output path for differential PC1 table. Empty → 'diff_PC1.txt'.