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<p>N6-methyladenosine (m<sup>6</sup>A) modifications are prevalent across all mammals and represent the most abundant type of epigenetic modification. With advancements in research, new methods for single-cell m<sup>6</sup>A modification sequencing and prediction have continuously emerged. These methods provide researchers with powerful tools to explore the landscape of epigenetic modifications at the single-cell level. However, challenges such as operational complexity, limited sensitivity, resolution, and consistency across different techniques remain major obstacles for researchers in this field. In this study, we compared four representative single-cell m<sup>6</sup>A sequencing and prediction methods based on different principles. We also developed a single-cell m<sup>6</sup>A database, which is freely accessible online. The database allows users to search for the localization and modification levels of single-cell m<sup>6</sup>A modifications in human and mouse species on the basis of these methods. It also offers cell definitions, data visualization, and data download options. Additionally, we applied Scm<sup>6</sup>A to single-cell transcriptome data across cancers and spatial transcriptome data from UCEC to predict and visualize m<sup>6</sup>A modifications, demonstrating its unique superior performance.</p>

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Systematic evaluation of tools used for single-cell m6A identification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月23日

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  • Yueqi Li,
  • Xinyue Xu,
  • Mingcong Chen,
  • Jun Meng,
  • Dan Jiang,
  • Yi Bao,
  • Yelongzi Cao,
  • Yajing Chen,
  • Chun Hung Chang,
  • Shiou Yih Lee,
  • Yafei Chen,
  • Jia Lu,
  • Yang Chen,
  • Xiaoping Lv,
  • Hao Liang,
  • Kaikai Meng,
  • Sanqi An

摘要

N6-methyladenosine (m6A) modifications are prevalent across all mammals and represent the most abundant type of epigenetic modification. With advancements in research, new methods for single-cell m6A modification sequencing and prediction have continuously emerged. These methods provide researchers with powerful tools to explore the landscape of epigenetic modifications at the single-cell level. However, challenges such as operational complexity, limited sensitivity, resolution, and consistency across different techniques remain major obstacles for researchers in this field. In this study, we compared four representative single-cell m6A sequencing and prediction methods based on different principles. We also developed a single-cell m6A database, which is freely accessible online. The database allows users to search for the localization and modification levels of single-cell m6A modifications in human and mouse species on the basis of these methods. It also offers cell definitions, data visualization, and data download options. Additionally, we applied Scm6A to single-cell transcriptome data across cancers and spatial transcriptome data from UCEC to predict and visualize m6A modifications, demonstrating its unique superior performance.