Integrating chromatin conformation information in a self-supervised learning model improves metagenome binning.
Integrating chromatin conformation information in a self-supervised learning model improves metagenome binning.
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作者：
Ho H;Chovatia M;Egan R;He G;Yoshinaga Y;Liachko I;O'Malley R;Wang Z
宏基因组分仓是宏基因组组装下游的关键步骤，用于根据支架的起源基因组对支架进行分组。虽然已经实现了准确的分箱数据集包含多个样本来自同一社区，分箱的完整性往往是低的数据集与少量的样本，由于缺乏强大的物种共丰度信息。在这项研究中，我们利用从Hi-C测序获得的染色质构象信息，并开发了一种新的参考独立算法，宏基因组分箱与abband-Tetra-nucleotide frequencies-Long Range（MetaBAT-LR），以提高这些数据集的分箱完整性。这种自监督算法从一组高质量的基因组箱中构建模型，以预测可能来自相同基因组的支架对。然后，它将这些预测应用于合并不完整的基因组箱，以及招募未装箱的支架。我们验证了MetaBAT-LR在不同复杂度的合成和真实世界宏基因组数据集上合并和招募支架的能力。对类似软件工具的基准测试表明，MetaBAT-LR发现了所有其他方法遗漏的独特箱。MetaBAT-LR是开源的，可在。
Metagenome binning is a key step, downstream of metagenome assembly, to group scaffolds by their genome of origin. Although accurate binning has been achieved on datasets containing multiple samples from the same community, the completeness of binning is often low in datasets with a small number of samples due to a lack of robust species co-abundance information. In this study, we exploited the chromatin conformation information obtained from Hi-C sequencing and developed a new reference-independent algorithm, Metagenome Binning with Abundance and Tetra-nucleotide frequencies—Long Range (metaBAT-LR), to improve the binning completeness of these datasets. This self-supervised algorithm builds a model from a set of high-quality genome bins to predict scaffold pairs that are likely to be derived from the same genome. Then, it applies these predictions to merge incomplete genome bins, as well as recruit unbinned scaffolds. We validated metaBAT-LR’s ability to bin-merge and recruit scaffolds on both synthetic and real-world metagenome datasets of varying complexity. Benchmarking against similar software tools suggests that metaBAT-LR uncovers unique bins that were missed by all other methods. MetaBAT-LR is open-source and is available at .
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