Improving metagenomic binning results with overlapped bins using assembly graphs.

IF 1.5 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Vijini G Mallawaarachchi, Anuradha S Wickramarachchi, Yu Lin
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引用次数: 9

Abstract

Background: 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).

Results: 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.

Conclusion: 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 .

Abstract Image

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使用装配图改进重叠箱的宏基因组分类结果。
背景:宏基因组测序使我们能够在不需要获得纯培养物的情况下研究微生物群落的结构、多样性和生态。在许多宏基因组学研究中,从宏基因组学测序中获得的reads首先被组装成更长的contigs,然后这些contigs被分成contigs簇,其中一个簇中的contigs预计来自同一物种。由于不同的物种在其基因组中可能具有相同的序列,因此一个组装的contig可能属于多个物种。然而,现有的分组工具只支持非重叠分组,即每个分组最多分配给一个分组(物种)。结果:在本文中,我们引入了GraphBin2,它改进了从现有工具获得的分箱结果,更重要的是,它能够将contigs分配给多个bin。GraphBin2使用来自组装图的连通性和覆盖率信息来调整现有的组合结果,并推断出多个物种共享的组合。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箱结果,而且支持将contigs分配到多个bin。结论:GraphBin2将覆盖信息整合到装配图中,以完善现有分箱工具获得的分箱结果。GraphBin2还可以检测可能属于多个物种的contigs。我们证明GraphBin2在模拟和真实数据集上都优于其前身GraphBin。GraphBin2可在https://github.com/Vini2/GraphBin2免费获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Algorithms for Molecular Biology
Algorithms for Molecular Biology 生物-生化研究方法
CiteScore
2.40
自引率
10.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: Algorithms for Molecular Biology publishes articles on novel algorithms for biological sequence and structure analysis, phylogeny reconstruction, and combinatorial algorithms and machine learning. Areas of interest include but are not limited to: algorithms for RNA and protein structure analysis, gene prediction and genome analysis, comparative sequence analysis and alignment, phylogeny, gene expression, machine learning, and combinatorial algorithms. Where appropriate, manuscripts should describe applications to real-world data. However, pure algorithm papers are also welcome if future applications to biological data are to be expected, or if they address complexity or approximation issues of novel computational problems in molecular biology. Articles about novel software tools will be considered for publication if they contain some algorithmically interesting aspects.
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