CoCoNet: an efficient deep learning tool for viral metagenome binning
CoCoNet: an efficient deep learning tool for viral metagenome binning
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DOI：
10.1093/bioinformatics/btab213
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发表时间：
2021-04-05
期刊：
影响因子：
5.8
通讯作者：
Belcaid, Mahdi
中科院分区：
文献类型：
--
作者：
Arisdakessian, Cedric G.;Nigro, Olivia;Belcaid, Mahdi
动机：宏基因组学方法具有表征微生物群落和揭示微生物组与生物过程之间复杂联系的潜力。组装是宏基因组学实验中最关键的步骤之一。它包括将重叠的DNA测序读数转化为足够精确的社区基因组表示。这个过程在计算上是困难的，通常会导致基因组在许多组群中碎片化。计算分箱方法通过基于序列组成、丰度或染色体组织将组群划分到代表群落基因组的箱中来减轻碎片化。现有的分箱方法主要针对细菌基因组进行了调整，对病毒宏基因组的分箱效果不佳。结果：我们提出了组合和覆盖网络（CoCoNet），这是一种新的病毒宏基因组分簇方法，它利用深度学习的灵活性和有效性来模拟属于同一病毒基因组的组合，并为病毒组合分簇提供了一个严格的框架。我们的结果表明，CoCoNet在病毒数据集上的性能大大优于现有的分箱方法。
Motivation: Metagenomic approaches hold the potential to characterize microbial communities and unravel the intricate link between the microbiome and biological processes. Assembly is one of the most critical steps in metagenomics experiments. It consists of transforming overlapping DNA sequencing reads into sufficiently accurate representations of the community's genomes. This process is computationally difficult and commonly results in genomes fragmented across many contigs. Computational binning methods are used to mitigate fragmentation by partitioning contigs based on their sequence composition, abundance or chromosome organization into bins representing the community's genomes. Existing binning methods have been principally tuned for bacterial genomes and do not perform favorably on viral metagenomes.Results: We propose Composition and Coverage Network (CoCoNet), a new binning method for viral metagenomes that leverages the flexibility and the effectiveness of deep learning to model the co-occurrence of contigs belonging to the same viral genome and provide a rigorous framework for binning viral contigs. Our results show that CoCoNet substantially outperforms existing binning methods on viral datasets.