Reese M G
Berkeley Drosophila Genome Project, Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720-3200, USA.
Comput Chem. 2001 Dec;26(1):51-6. doi: 10.1016/s0097-8485(01)00099-7.
Computational methods for automated genome annotation are critical to understanding and interpreting the bewildering mass of genomic sequence data presently being generated and released. A neural network model of the structural and compositional properties of a eukaryotic core promoter region has been developed and its application for analysis of the Drosophila melanogaster genome is presented. The model uses a time-delay architecture, a special case of a feed-forward neural network. The structure of this model allows for variable spacing between functional binding sites, which is known to play a key role in the transcription initiation process. Application of this model to a test set of core promoters not only gave better discrimination of potential promoter sites than previous statistical or neural network models, but also revealed indirectly subtle properties of the transcription initiation signal. When tested in the Adh region of 2.9 Mbases of the Drosophila genome, the neural network for promoter prediction (NNPP) program that incorporates the time-delay neural network model gives a recognition rate of 75% (69/92) with a false positive rate of 1/547 bases. The present work can be regarded as one of the first intensive studies that applies novel gene regulation technologies to the identification of the complex gene regulation sites in the genome of Drosophila melanogaster.
自动基因组注释的计算方法对于理解和解释目前正在生成和发布的大量令人困惑的基因组序列数据至关重要。已经开发了一种真核生物核心启动子区域结构和组成特性的神经网络模型，并展示了其在果蝇基因组分析中的应用。该模型使用时间延迟架构，这是前馈神经网络的一种特殊情况。该模型的结构允许功能结合位点之间存在可变间距，已知这在转录起始过程中起关键作用。将该模型应用于一组核心启动子测试集，不仅比以前的统计或神经网络模型能更好地区分潜在的启动子位点，还间接揭示了转录起始信号的微妙特性。在果蝇基因组2.9兆碱基的Adh区域进行测试时，纳入时间延迟神经网络模型的启动子预测神经网络程序（NNPP）识别率为75%（69/92），误报率为1/547碱基。目前的工作可被视为最早将新型基因调控技术应用于果蝇基因组中复杂基因调控位点识别的深入研究之一。
