Computer Science and Engineering, Michigan State University, East Lansing, 48824, USA.
Electrical Engineering, City University of Hong Kong, Hong Kong, China.
BMC Bioinformatics. 2019 Nov 4;20(1):544. doi: 10.1186/s12859-019-3138-1.
Infections by RNA viruses such as Influenza, HIV still pose a serious threat to human health despite extensive research on viral diseases. One challenge for producing effective prevention and treatment strategies is high intra-species genetic diversity. As different strains may have different biological properties, characterizing the genetic diversity is thus important to vaccine and drug design. Next-generation sequencing technology enables comprehensive characterization of both known and novel strains and has been widely adopted for sequencing viral populations. However, genome-scale reconstruction of haplotypes is still a challenging problem. In particular, haplotype assembly programs often produce contigs rather than full genomes. As a mutation in one gene can mask the phenotypic effects of a mutation at another locus, clustering these contigs into genome-scale haplotypes is still needed.
We developed a contig binning tool, VirBin, which clusters contigs into different groups so that each group represents a haplotype. Commonly used features based on sequence composition and contig coverage cannot effectively distinguish viral haplotypes because of their high sequence similarity and heterogeneous sequencing coverage for RNA viruses. VirBin applied prototype-based clustering to cluster regions that are more likely to contain mutations specific to a haplotype. The tool was tested on multiple simulated sequencing data with different haplotype abundance distributions and contig sizes, and also on mock quasispecies sequencing data. The benchmark results with other contig binning tools demonstrated the superior sensitivity and precision of VirBin in contig binning for viral haplotype reconstruction.
In this work, we presented VirBin, a new contig binning tool for distinguishing contigs from different viral haplotypes with high sequence similarity. It competes favorably with other tools on viral contig binning. The source codes are available at: https://github.com/chjiao/VirBin .
尽管对病毒疾病进行了广泛的研究，但 RNA 病毒（如流感、HIV）的感染仍然对人类健康构成严重威胁。产生有效预防和治疗策略的一个挑战是种内遗传多样性高。由于不同的毒株可能具有不同的生物学特性，因此对遗传多样性进行特征描述对于疫苗和药物设计很重要。下一代测序技术能够全面描述已知和新型毒株，并已广泛应用于病毒群体测序。然而，单倍型的基因组规模重建仍然是一个具有挑战性的问题。特别是，单倍型组装程序通常产生重叠群而不是完整的基因组。由于一个基因的突变可以掩盖另一位点突变的表型效应，因此仍然需要将这些重叠群聚类为基因组规模的单倍型。
我们开发了一种重叠群 binning 工具 VirBin，它将重叠群聚类到不同的组中，使每个组代表一个单倍型。由于 RNA 病毒的序列相似性高且测序覆盖度不均匀，基于序列组成和重叠群覆盖度的常用特征无法有效地区分病毒单倍型。VirBin 应用基于原型的聚类方法来聚类更有可能包含特定于单倍型的突变的区域。该工具在具有不同单倍型丰度分布和重叠群大小的多种模拟测序数据以及模拟准种测序数据上进行了测试。与其他重叠群 binning 工具的基准测试结果表明，VirBin 在病毒单倍型重建的重叠群 binning 方面具有更高的灵敏度和精度。
在这项工作中，我们提出了 VirBin，这是一种用于区分具有高序列相似性的不同病毒单倍型的重叠群 binning 新工具。它在病毒重叠群 binning 方面与其他工具竞争激烈。源代码可在 https://github.com/chjiao/VirBin 上获得。
