Scholarlink

<p>Single-cell allele-specific expression (ASE) provides valuable insights into gene regulatory mechanisms. However, its utility is limited by the lack of dedicated computational tools. We present DAESC + , a dual-module end-to-end software package for the processing and analysis of single-cell ASE. The preprocessing module, DAESC-P, is a user-friendly bioinformatics pipeline to obtain ASE counts from multiplexed scRNA-seq data. The analysis module, DAESC-GPU, is a scalable tool for differential ASE analysis powered by graphics processing units (GPUs). We demonstrated that DAESC-P is more accurate than the existing SALSA pipeline. DAESC-GPU is dozens of times faster than our previous method (DAESC) and scalable to over a million cells. Applying DAESC + to a subset of the OneK1K cohort, we identified 15 genes exhibiting differential regulatory patterns between naïve and central memory CD4 + T cells, and 2 genes between naïve and memory B cells.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

DAESC + : high-performance, integrated software for single-cell allele-specific expression data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月18日

查看全文
  • Tengfei Cui,
  • Guanghao Qi

摘要

Single-cell allele-specific expression (ASE) provides valuable insights into gene regulatory mechanisms. However, its utility is limited by the lack of dedicated computational tools. We present DAESC + , a dual-module end-to-end software package for the processing and analysis of single-cell ASE. The preprocessing module, DAESC-P, is a user-friendly bioinformatics pipeline to obtain ASE counts from multiplexed scRNA-seq data. The analysis module, DAESC-GPU, is a scalable tool for differential ASE analysis powered by graphics processing units (GPUs). We demonstrated that DAESC-P is more accurate than the existing SALSA pipeline. DAESC-GPU is dozens of times faster than our previous method (DAESC) and scalable to over a million cells. Applying DAESC + to a subset of the OneK1K cohort, we identified 15 genes exhibiting differential regulatory patterns between naïve and central memory CD4 + T cells, and 2 genes between naïve and memory B cells.