TFBSFootprinter:一种用于预测脊椎动物物种转录因子结合部位的多组学工具
Harlan R Barker1,2,3, Seppo Parkkila1,2, MarttiE E Tolvanen4
1Tampere University Hospital and Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Transcription
|July 12, 2025
概括
TFBSFootprinter使用多原子数据准确地预测了317种脊椎动物的转录因子结合部位. 这种工具增强了对基因调节和疾病机制的理解,优于现有的方法.
科学领域:
- 基因组学和生物信息学
- 分子生物学分子生物学
- 计算生物学 计算生物学
背景情况:
- 转录因子 (TF) 蛋白通过结合特定的DNA序列 (转录因子结合位,TFBS) 来调节真核基因表达.
- 准确的TFBS预测对于了解基因调节,疾病机制和跨不同物种的药物发现至关重要.
- 在促进区域中用于TFBS分析的现有工具的跨物种可用性有限.
研究的目的:
- 开发一种新的计算工具,TFBSFootprinter,用于在广泛的脊椎动物物种中自动分析基因促进器区域的TFBS.
- 整合多原子数据以提高功能TFBS预测的准确性.
- 为跨物种TFBS分析提供一个用户友好的平台.
主要方法:
- TFBSFootprinter集成了多原子数据,包括序列保存 (GERP),接近转录起点 (FANTOM5),基因表达相关性 (FANTOM5),TF结合数据 (ChIP-Seq,GTRD),染色质可访问性 (ATAC-Seq,ENCODE),eQTLs (GTEx) 和CpG含量 (Ensembl).
- 分析基于Ensembl转录ID,以简化使用.
- 基准测试涉及将TFBSFootprinter的性能与DeepBind,DeepSEA,FIMO和传统的位置权重矩阵 (PWM) 等既有工具进行比较,使用精选的数据集.
主要成果:
- 使用所有多原子数据时,TFBSFootprinter实现了卓越的预测准确性,接收器运行特征曲线 (AUC) 下的平均面积为0.881,优于其他方法.
- 优化多原子数据组合进一步提高了性能,AUC为0.910.
- 该工具确定了最佳的数据组合,用于预测单个TF的约束性.
结论:
- TFBSFootprinter在转录因子结合部位的自动化跨物种分析方面取得了重大进展.
- 多原子数据的整合大大提高了TFBS预测的准确性.
- 该工具以Conda和Python包提供,促进在研究中更广泛地采用.
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