Mallawaarachchi Vijini G, Wickramarachchi Anuradha S, Lin Yu
School of Computing, College of Engineering and Computer Science, Australian National University, Canberra, Australia.
Algorithms Mol Biol. 2021 May 4;16(1):3. doi: 10.1186/s13015-021-00185-6.
Metagenomic sequencing allows us to study the structure, diversity and ecology in microbial communities without the necessity of obtaining pure cultures. In many metagenomics studies, the reads obtained from metagenomics sequencing are first assembled into longer contigs and these contigs are then binned into clusters of contigs where contigs in a cluster are expected to come from the same species. As different species may share common sequences in their genomes, one assembled contig may belong to multiple species. However, existing tools for binning contigs only support non-overlapped binning, i.e., each contig is assigned to at most one bin (species).
In this paper, we introduce GraphBin2 which refines the binning results obtained from existing tools and, more importantly, is able to assign contigs to multiple bins. GraphBin2 uses the connectivity and coverage information from assembly graphs to adjust existing binning results on contigs and to infer contigs shared by multiple species. Experimental results on both simulated and real datasets demonstrate that GraphBin2 not only improves binning results of existing tools but also supports to assign contigs to multiple bins.
GraphBin2 incorporates the coverage information into the assembly graph to refine the binning results obtained from existing binning tools. GraphBin2 also enables the detection of contigs that may belong to multiple species. We show that GraphBin2 outperforms its predecessor GraphBin on both simulated and real datasets. GraphBin2 is freely available at https://github.com/Vini2/GraphBin2 .
宏基因组测序使我们能够研究微生物群落的结构、多样性和生态，而无需获得纯培养物。在许多宏基因组学研究中，从宏基因组测序获得的读段首先被组装成更长的重叠群，然后这些重叠群被分类到重叠群簇中，其中一个簇中的重叠群预计来自同一物种。由于不同物种在其基因组中可能共享共同序列，一个组装的重叠群可能属于多个物种。然而，现有的重叠群分类工具仅支持非重叠分类，即每个重叠群最多被分配到一个分类（物种）。
在本文中，我们介绍了GraphBin2，它改进了从现有工具获得的分类结果，更重要的是，能够将重叠群分配到多个分类中。GraphBin2利用组装图中的连通性和覆盖信息来调整现有的重叠群分类结果，并推断多个物种共享的重叠群。在模拟和真实数据集上的实验结果表明，GraphBin2不仅改进了现有工具的分类结果，还支持将重叠群分配到多个分类中。
GraphBin2将覆盖信息纳入组装图，以改进从现有分类工具获得的分类结果。GraphBin2还能够检测可能属于多个物种的重叠群。我们表明，GraphBin2在模拟和真实数据集上均优于其前身GraphBin。GraphBin2可在https://github.com/Vini2/GraphBin2上免费获取。
