7.replicates-incorporation/SKILL.md
--- name: replicates-incorporation description: This skill manages experimental reproducibility, pooling, and consensus strategies. This skill operates in two distinct modes based on the input state. (1) Pre-Peak Calling (BAM Mode): It merges all BAMs, generate the merge BAM file to prepare for track generation and (if provided with >3 biological replicates) splits them into 2 balanced "pseudo-replicates" to prepare for peak calling. (2) Post-Peak Calling (Peak Mode): If provided with peak files
npx skillsauth add bisnake2001/chromskills 7.replicates-incorporationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill provides two modes for replicates incorporation:
${proj_dir} in Step 0.*.filtered.bam) if available.Call:
mcp__project-init-tools__project_initwith:
sample: alltask: rep_mergeThe tool will:
all_rep_merge directory.all_rep_merge directory, which will be used as ${proj_dir}Call:
mcp__bw_tools__pool_bams
with:bam_files: [${rep1_bam}, ${rep2_bam}, ${rep3_bam}] (Add as many as needed)output_bam: ${proj_dir}/temp/${sample}.pooled.bamCall: (call this only when more than two replicates are provided and user prompt for generating pseudo replicates)
mcp__bw_tools__split_pseudo_replicates
with:
bam_file: ${proj_dir}/temp/${sample}.pooled.bam
output_rep1: ${proj_dir}/temp/${sample}.pseudo1.bam
output_rep2: ${proj_dir}/temp/${sample}.pseudo2.bamA. Narrow Peaks / ATAC (IDR) Use this to combine reproducible peaks. You should ideally run IDR on:
Call:
mcp__bw_tools__filter_idr_peaks
with:peak_file_a: Path to Replicate 1 narrowPeak file.peak_file_b: Path to Replicate 2 narrowPeak file.output_optimal: ${proj_dir}/peaks/${sample}.idr.narrowPeaksoutput_raw_idr: ${proj_dir}/temp/${sample}_idr_results.tsvinput_file_type: narrowPeakrank_measure: q.valueB. Broad Peaks (Consensus) Call:
mcp__bw_tools__merge_consensus_peaks
with:
peak_file_a: Path to Replicate 1 broadPeak file.
peak_file_b: Path to Replicate 2 broadPeak file.
output_peak: ${proj_dir}/peaks/${sample}.consensus.broadPeaks
overlap_fraction: 0.5development
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.
development
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.
data-ai
Perform peak calling for ChIP-seq or ATAC-seq data using MACS3, with intelligent parameter detection from user feedback. Use it when you want to call peaks for ChIP-seq data or ATAC-seq data.
devops
The TF-differential-binding pipeline performs differential transcription factor (TF) binding analysis from ChIP-seq datasets (TF peaks) using the DiffBind package in R. It identifies genomic regions where TF binding intensity significantly differs between experimental conditions (e.g., treatment vs. control, mutant vs. wild-type). Use the TF-differential-binding pipeline when you need to analyze the different function of the same TF across two or more biological conditions, cell types, or treatments using ChIP-seq data or TF binding peaks. This pipeline is ideal for studying regulatory mechanisms that underlie transcriptional differences or epigenetic responses to perturbations.