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终结者网络:在细菌中全面识别内在转录终结器

¹Department of Computer Science, Wellesley College, Wellesley, MA 02481, United States.

Bioinformatics (Oxford, England)

|March 11, 2026

概括

终结者网使用神经网络准确识别细菌转录终结器. 该系统显著改进了现有方法,并提供了跨多种 prokaryotes 的终结器的全面数据库.

科学领域:

微生物学微生物学
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

内在终结器对于细菌转录终止至关重要,影响转录稳定性和基因调节.
目前用于识别终端器的计算方法受到小数据集和狭窄的分类学范围的限制.
需要在各种细菌物种中对转录终结器进行强有力的全基因组识别.

研究的目的:

开发和介绍TerminatorNet,一种用于识别细菌内在转录终端器的新系统.
在实验验证的终结器的大型,多样化的数据集上训练机器学习模型.
创建一个全面的转录终止器库,跨越广泛的细菌基因组.

主要方法:

利用神经网络模型来识别内在转录终端器.
将模型训练在来自各种细菌基因组的大量实验性特征终结器数据集上.
将TerminatorNet应用于成千上万个 prokaryotic 基因组进行大规模识别.

主要成果:

终结者网络在终结者识别中达到98%的准确性,错误阳性率为3%,超过现有方法.
识别了数以千万计的终结者,遍及 prokaryotic 分类学谱.
在细菌群体 (例如,Bacillota与archaea) 中观察到内在终结的差异性使用,并确定了特定细菌的终结器内的DNA吸收信号序列.

结论:

终结者网为细菌转录终结器的全基因组识别提供了一个高度准确和可扩展的解决方案.
生成的存储库提供了有价值的见解进化使用和终结者跨 prokaryotes 的功能角色.
这些发现突出了在已识别的终结者中发现功能相关的DNA序列的潜力,例如DNA吸收信号序列.

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