片段路径复形网络用于病毒基因组分类

中文摘要 AI 辅助

病毒家族的准确分类对于理解基因组多样性至关重要，但快速进化和异质的基因组结构使得可靠分配变得复杂。新发现的病毒，特别是来自代表性不足生态系统的病毒，扩展了病毒基因组的已知多样性。这种日益增长的多样性对依赖与现有参考序列相似性的分类方法构成了挑战。我们提出了片段路径复形网络（FPCN），该框架将病毒基因组表示为不同尺度下有序片段的序列。FPCN捕获每个片段内的上下文序列信息，并使用路径复形对相邻片段之间的复杂关系进行建模。我们在公认的NCBI基准上评估了FPCN，FPCN取得了比竞争方法更高的分类准确率。对新发布数据集的进一步评估揭示了FPCN对训练中未出现的基因组的泛化能力。总之，这些结果使FPCN成为在基准数据集和新发布的基因组中，对已知病毒家族内病毒基因组进行分类的有效工具。

英文摘要

Accurate classification of viral families is essential to understanding genomic diversity, but rapid evolution and heterogeneous genome structure complicate reliable assignment. Newly discovered viruses, particularly those from underrepresented ecosystems, expand the known diversity of viral genomes. This growing diversity poses challenges for classification methods that rely on similarity to existing references. We present Fragment Path Complex Networks (FPCN), a framework that represents viral genomes as sequences of ordered fragments at various scales. FPCN captures contextual sequence information within each fragment and uses path complexes to model complex relationships between neighboring fragments. We evaluated FPCN on well-established NCBI benchmarks, and FPCN achieved higher classification accuracy than competing methods. Further evaluations on newly released datasets revealed FPCN's ability to generalize to genomes that were not presented in the training. Together, these results position FPCN as an effective tool for classifying viral genomes within known families across benchmark datasets and newly released genomes.

发表机构

• University of Georgia（佐治亚大学）

机构由 AI 辅助整理，请以论文原文为准。