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Related Concept Videos

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Related Experiment Video

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Generation of Native Chromatin Immunoprecipitation Sequencing Libraries for Nucleosome Density Analysis
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scNucMap: mapping the nucleosome landscapes at single-cell resolution.

Qianming Xiang1,2, Binbin Lai1,2,3,4

  • 1Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China.

Bioinformatics (Oxford, England)
|May 27, 2025
PubMed
Summary

A new tool, scNucMap, decodes single-cell micrococcal nuclease sequencing (scMNase-seq) data to map nucleosome-free regions. It accurately clusters cells and identifies transcription factors, advancing our understanding of gene regulation at single-cell resolution.

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Area of Science:

  • Epigenetics
  • Genomics
  • Computational Biology

Background:

  • Nucleosome depletion near cis-regulatory elements (CREs) correlates with gene regulatory activity.
  • Single-cell micrococcal nuclease sequencing (scMNase-seq) captures chromatin accessibility and nucleosome positioning at single-cell resolution.
  • A lack of specialized computational tools hinders the analysis of scMNase-seq data for precise chromatin dynamics and gene regulation insights.

Purpose of the Study:

  • To introduce scNucMap, a computational tool designed for analyzing scMNase-seq data.
  • To enable the mapping of nucleosome-free regions (NFRs) and facilitate gene regulatory network inference.

Main Methods:

  • Development of scNucMap, a novel computational tool tailored for scMNase-seq data.
  • Benchmarking scNucMap against existing tools (Signac, chromVAR) for cell clustering accuracy.
  • Application of scNucMap to identify transcription factors (TFs) associated with CREs and to analyze scATAC-seq data.

Main Results:

  • scNucMap demonstrated superior performance in cell clustering compared to Signac and chromVAR, showing higher accuracy and Kappa coefficients.
  • The tool successfully identified significant TFs linked to nucleosome depletion at CREs at both single-cell and cluster levels.
  • scNucMap provided complementary insights when applied to scATAC-seq data, highlighting its cross-modality utility.

Conclusions:

  • scNucMap is a reliable and adaptable tool for analyzing scMNase-seq data, improving cell clustering and regulatory network inference.
  • The tool enhances multimodal studies by integrating nucleosome positioning insights with other epigenetic data.
  • scNucMap facilitates a deeper understanding of the relationship between gene regulatory networks and nucleosome positioning at single-cell resolution.