DeepMAsED: evaluating the quality of metagenomic assemblies
DeepMAsED: evaluating the quality of metagenomic assemblies
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DOI：
10.1093/bioinformatics/btaa124
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发表时间：
2020-05-15
期刊：
影响因子：
5.8
通讯作者：
Youngblut, Nicholas D.
中科院分区：
文献类型：
--
作者：
Mineeva, Olga;Rojas-Carulla, Mateo;Youngblut, Nicholas D.
动机：宏基因组组装方法学的进步使已发表的宏基因组组装数量迅速增加。然而，由于缺乏可以作为伪基础事实的密切相关的参考基因组，识别错误组装是具有挑战性的。现有的无参考方法不再被维护，可以做出强有力的假设，可能不适用于各种研究项目，并且尚未在大规模宏基因组组装中得到验证。结果：我们提出了DeepMAsED，这是一种无需参考基因组就能识别错误组装的深度学习方法。此外，我们还提供了一个用于生成大规模、逼真的宏基因组组装的硅管道，用于全面的模型训练和测试。当应用于大型和复杂的宏基因组组装时，DeepMAsED的精度大大超过了最先进的技术。我们的模型估计，在最近的两个大规模宏基因组组装出版物中，连续错装配率为1%。结论：DeepMAsED在不需要参考基因组或强大的建模假设的情况下，准确地识别了来自广泛多样性的细菌和古细菌的宏基因组组装组群中的错误组装。运行DeepMAsED很简单，并且使用我们的数据集生成管道对模型进行重新训练。因此，DeepMAsED是一个灵活的错误组装分类器，可以应用于广泛的宏基因组组装项目。
Motivation: Methodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large-scale metagenome assemblies.Results: We present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates a 1% contig misassembly rate in two recent large-scale metagenome assembly publications.Conclusions: DeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modeling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects.