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This skill annotates chromatin loops, including enhancer/promoter assignments, CTCF-peak overlap. It automatically constructs enhancer and promoter sets when missing and outputs standardized loop categories.
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Basierend auf der SOC-Berufsklassifikation
| name | loop-annotation |
| description | This skill annotates chromatin loops, including enhancer/promoter assignments, CTCF-peak overlap. It automatically constructs enhancer and promoter sets when missing and outputs standardized loop categories. |
This skill performs loop annotation for Hi-C/HiChIP/ChIA-PET interaction data. It identifies regulatory and structural loop types (E–E, E–P, P–P, CTCF-CTCF).
Main steps include:
.bedpe file as the input of annotateInteractions.plannotateInteractions.pl with feature sets.Required:
Optional:
loop_annotation/
logs/
annotateInteractions.log
annotations/
interactionAnnotation.txt
lengthDist.txt
featureEnrichment.txt
pairwiseFeatureEnrichment.txt # assign feature pairs to 0x0 0x1 and so on, represent the feature pairs like CTCF-CTCF, E-P
... # other outputs by annotateInteractions.pl
features/
enhancers.bed
promoters.bed
plots:
loop_type.pdf
lengthDist.pdf
If enhancers.bed or promoters.bed missing, go on to Step 2, otherwise go on to step 4 directly.
bedtools intersect -a ATAC_peaks.bed -b H3K27ac_peaks.bed > enhancers_raw.bed
sort -k1,1 -k2,2n enhancers_raw.bed | bedtools merge -i - > enhancers.bed
Call:
with:
genome: HOMER genome identifier, provided by useroutput_promoters_bed: Output path for promoters.bedwith:
input_bedpe: Input loops.bedpe fileindex_count_column: Column index for interaction count, provided by useroutput_bedpe: Output standardized loops fileCall:
with:
standardized_loops: Path to standardized loops file for annotateInteractions.plgenome: HOMER genome identifier, provided by userfeature_beds: List of feature BED files for -p (CTCF, enhancers, promoters, etc.)annotations_dir: Base output directory for loop annotationlogs_dir: Output directory for logsCall:
with:
annotations_dir: Directory containing HOMER interactionAnnotation.txt and lengthDist.txtplots_dir: Output directory for plotsfeature_map: Mapping of feature index to feature name, you can infer the dict from pairwiseFeatureEnrichment.txt file.
(e.g. {'0': 'CTCF','1': 'E','2': 'P'})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.