基于图卷积网络的噬菌体重叠群分类
Bacteriophage classification for assembled contigs using Graph Convolutional Network
摘要：
动机：噬菌体（又名噬菌体），主要感染细菌，在微生物生物学中起着关键作用。作为地球上最丰富的生物实体，已发现的恐龙数量只是冰山一角。最近，使用高通量测序，特别是宏基因组测序，已经揭示了许多新的突变。与噬菌体样序列的快速积累相比，真菌的分类学研究严重滞后。高度的多样性、丰富性和有限的已知物种给分类学分析带来了巨大的挑战。特别地，基于重叠群的工具难以对从宏基因组数据组装的快速积累的重叠群进行分类。结果：在这项工作中，我们提出了一种新的半监督学习模型，命名为PhaGCN，进行分类的噬菌体重叠群。在该学习模型中，我们将卷积神经网络（CNN）学习到的DNA序列特征与基因共享网络获得的蛋白质序列相似度相结合，构建知识图。然后，我们应用图卷积网络（GCN）在训练中同时利用标记和未标记的样本来增强学习能力。我们在模拟和真实测序数据上测试了PhaGCN。结果清楚地表明，我们的方法与可用的噬菌体分类工具竞争有利。
英文摘要：
Motivation: Bacteriophages (aka phages), which mainly infect bacteria, play key roles in the biology of microbes. As the most abundant biological entities on the planet, the number of discovered phages is only the tip of the iceberg. Recently, many new phages have been revealed using high throughput sequencing, particularly metagenomic sequencing. Compared to the fast accumulation of phage-like sequences, there is a serious lag in taxonomic classification of phages. High diversity, abundance, and limited known phages pose great challenges for taxonomic analysis. In particular, alignment-based tools have difficulty in classifying fast accumulating contigs assembled from metagenomic data. Results: In this work, we present a novel semi-supervised learning model, named PhaGCN, to conduct taxonomic classification for phage contigs. In this learning model, we construct a knowledge graph by combining the DNA sequence features learned by convolutional neural network (CNN) and protein sequence similarity gained from gene-sharing network. Then we apply graph convolutional network (GCN) to utilize both the labeled and unlabeled samples in training to enhance the learning ability. We tested PhaGCN on both simulated and real sequencing data. The results clearly show that our method competes favorably against available phage classification tools.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
