DETIRE:一种混合深度学习模型,用于从元基因组中识别病毒序列
Yan Miao1, Jilong Bian1, Guanghui Dong1
1College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
Frontiers in microbiology
|July 3, 2023
概括
在元基因组中检测病毒DNA碎片是具有挑战性的,因为其短序列. 新的DETIRE模型使用深度学习来准确识别这些病毒序列,改善病原体检测.
科学领域:
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- 大基因组包含多样化的DNA,包括对了解疾病至关重要的病毒序列.
- 在元基因组数据中识别病毒片段对于临床诊断至关重要,但由于序列长度短,因此具有挑战性.
研究的目的:
- 开发一种准确的方法,从复杂的元基因组样本中识别病毒序列.
- 为了应对检测短病毒DNA片段的挑战.
主要方法:
- 开发了一种混合深度学习模型,DETIRE (Deep lEarning用于识别mEtagenomes中的病毒序列).
- 利用基于图的核酸序列嵌入和结合卷积神经网络 (CNN) 和双向长短期记忆 (BiLSTM) 网络.
- 来自空间和顺序分析的特征被加权并结合起来进行最终分类.
主要成果:
- 与DeepVirFinder,PPR-Meta和CHEER等现有方法相比,DETIRE在识别短病毒序列 (<1,000 bp) 中表现出卓越的性能.
- 该模型在220,000个病毒和宿主RefSeq基因组序列上进行了训练.
结论:
- 拟议的DETIRE模型为检测元基因组数据中的病毒序列提供了有效的解决方案.
- 这一进步有助于分析病毒成分,并通过改善短病毒片段的识别来支持临床诊断.
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