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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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使用深度学习和变压器层对酶编码基因的功能注释.

Gi Bae Kim1,2,3, Ji Yeon Kim1,2,3, Jong An Lee1,2,3

  • 1Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.

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深度学习模型DeepECtransformer预测了未注释的微生物基因的酶委员会 (EC) 数量. 这通过使用序列数据和图案来识别酶功能的功能性基因注释来推进功能性基因注释.

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科学领域:

  • 基因组学和生物信息学
  • 计算生物学 计算生物学
  • 酶学 是一种酶学.

背景情况:

  • 微生物基因组的功能注释是不完整的,特别是对酶编码基因.
  • 酶委员会 (EC) 编号对酶的催化功能进行了分类,这些功能对于理解微生物代谢至关重要.
  • 准确的EC数预测可以显著改善未表征基因的注释.

研究的目的:

  • 开发一个深度学习模型来预测酶委员会 (EC) 的数字.
  • 为了增强微生物基因组中未表征的开放阅读框架的功能注释.
  • 通过实验验证模型的预测.

主要方法:

  • 开发了DeepECtransformer,这是一种使用变压器层进行EC数预测的深度学习模型.
  • 将模型应用于大肠杆菌K-12MG1655基因组,以预测未注释基因的EC数.
  • 实验验证了对选定蛋白质 (YgfF,YciO,YjdM) 的预测酶活性.

主要成果:

  • 深EC转换器成功预测了大肠杆菌中464个未注释的基因的EC数.
  • 实验验证证证实了三种蛋白质预测的酶活性.
  • 分析显示,该模型利用酶功能动机进行准确的预测.

结论:

  • DeepECtransformer是一种有效的深度学习方法,用于预测酶功能 (EC 数).
  • 该模型促进了以前未经表征的微生物基因的功能注释.
  • 这种方法有助于理解微生物基因功能和代谢途径.