Deep Learning to Identify Transcription Start Sites from CAGE Data
Deep Learning to Identify Transcription Start Sites from CAGE Data
复制标题
DOI：
10.1109/bibm49941.2020.9313267
复制
发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Hansi Zheng;X. Li;Haiyan Hu
中科院分区：
文献类型：
--
作者：
Hansi Zheng;X. Li;Haiyan Hu
基因转录起始位点（TSS）的识别对于理解转录基因调控非常重要。帽分析基因表达（CAGE）实验最近已成为直接测量TSS的常见实践。目前，公共数据库中的CAGE数据为研究各种细胞条件下的基因转录起始机制创造了前所未有的机会。然而，由于CAGE数据中固有的潜在转录噪声，需要计算机模拟方法来进一步从噪声中识别真正的TSS。在这里，我们提出了一种计算方法dlCAGE，这是一种端到端的深度神经网络，用于从CAGE数据中识别TSS。dlCAGE结合了DeepBind模型架构发现的从头DNA调控基序特征，以及现有的序列和结构特征。与目前最先进的方法相比，dlCAGE在几种细胞系中的测试结果显示了其在从CAGE实验鉴定TSS中的上级性能和前景。
Gene transcription start site (TSS) identification is important to understanding transcriptional gene regulation. Cap Analysis Gene Expression (CAGE) experiments have recently become common practice for direct measurement of TSSs. Currently, CAGE data available in public databases created unprecedented opportunities to study gene transcriptional initiation mechanisms under various cellular conditions. However, due to potential transcriptional noises inherent in CAGE data, in-silico methods are required to identify bonafide TSSs from noises further. Here we present a computational approach dlCAGE, an end-to-end deep neural network to identify TSSs from CAGE data. dlCAGE incorporate de-novo DNA regulatory motif features discovered by DeepBind model architecture, as well as existing sequence and structural features. Testing results of dlCAGE in several cell lines in comparison with current state-of-the-art approaches showed its superior performance and promise in TSS identification from CAGE experiments.