plugin/skills/pipeline-cutandrun

You can analyze CUT&RUN sequencing data by automating the workflow from raw FASTQ reads through alignment, spike-in normalization, and peak identification using Nextflow pipelines. This process generates high-confidence peak sets and quality control metrics.

Spike-in normalization for CUT&Tag data is a calibration technique used to accurately quantify DNA-protein interactions across samples. You need it when comparing epigenomic profiles between different experimental conditions to ensure precise mapping and reduce manual analysis time.

Yes, you can use SEACR and MACS2 together for CUT&RUN peak calling. The pipeline integrates multiple peak callers alongside alignment tools like bowtie2 and samtools to process sequencing data and identify precise protein-DNA interactions.

The best way to perform end-to-end epigenomics quality control for CUT&Tag is by running automated QC scripts that utilize fastqc and multiqc. This generates comprehensive QC metrics alongside peak calls and spike-in calibration results.

Yes, you need Nextflow to run this CUT&RUN analysis pipeline. The workflow is built on Nextflow to orchestrate the end-to-end process, coordinating dependencies like bowtie2, picard, and bedtools for robust epigenomic data processing.

CUT&RUN alignment may fail during sequencing data processing if library preparation parameters are incorrect or if references are mismatched. The pipeline recommends parameters based on target type and experimental setup to optimize alignment and prevent failures.