会议:从单细胞多组数据的转录因子结合预测和监管关联推断
Yang Yang1,2, Dana Pe'er1,2
1Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States.
Bioinformatics (Oxford, England)
|June 28, 2024
概括
聚会预测转录因子 (TF) 结合和基因调节使用单细胞多基因组数据. 该框架识别了超越传统动机的新型TF绑定站点,改善了监管关联预测.
科学领域:
- 基因组学和转录基因组学
- 计算生物学 计算生物学
- 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.
背景情况:
- 单细胞多组学使得基因表达和染色质可访问性的分析成为可能.
- 了解转录因子 (TF) 通过cis区域调节基因表达是至关重要的.
- 整合用于监管协会发现的多模式数据是具有挑战性的,通常受到模式扫描的限制.
研究的目的:
- 开发REUNION,用于预测全基因组TF结合和cis区域-TF基因调控协会的框架.
- 克服图案扫描的局限性,通过在缺乏正规图案的地区识别TF绑定站点来克服.
- 通过全面的监管三重体识别,提高目标基因表达的预测.
主要方法:
- 汇聚集成TF表达,染色体可访问性,并使用信息理论目标基因表达.
- 该框架采用伪半监督学习来预测可访问的基因组区域中的TF结合.
- REUNION利用隐性染色体可访问性和序列特征,不需要用于训练的ChIP-seq数据.
主要成果:
- 与对外围血液单核细胞数据的替代方法相比,REUNION在TF结合预测方面表现优越.
- 该框架成功地识别了缺乏被检测的动机的地区的TF区域协会,从而绕过了对动机扫描的依赖.
- 新发现的区域-TF关联,即使没有动机,也提高了调节性三胞胎中目标基因表达的预测准确性.
结论:
- 汇聚提供了一个强大的框架,用于剖析TF监管网络使用单细胞多omics数据.
- 该方法有助于发现新的调节机制和潜在的共同结合的转录调节器.
- REUNION能够识别独立于动机的TF结合位点,这有助于我们更好地理解基因调节.
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