Haplotype and minimum-chimerism consensus determination using short sequence data.
Haplotype and minimum-chimerism consensus determination using short sequence data.
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DOI：
10.1186/1471-2164-13-s2-s4
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发表时间：
2012-04-12
期刊：
影响因子：
4.4
通讯作者：
Emrich SJ
中科院分区：
文献类型：
--
作者：
O'Neil ST;Emrich SJ
给定来自单个个体的序列数据，组装单倍型是一个经过充分研究的问题，但直到最近才考虑将单倍型组装用于群体采样数据。我们讨论了一个软件工具，称为哈普勒，这是专为低多样性，低覆盖率的数据，如生态样本来自自然种群。因为这样的数据可能包含错误以及模糊的单倍型信息，我们开发的方法，增加这些组件的信心。Hapler还重建了完整的共有序列，同时最小化和识别可能的嵌合点。模拟数据的实验表明，Hapler是有效的组装单倍型从基因大小的短读段的比对。此外，在我们的测试中，Hapler产生的共识序列比多数投票和病毒准种估计的替代共识方法嵌合性更低，无论错误率、读段长度或群体单倍型偏倚如何。遗传多样性序列数据的分析越来越普遍，特别是在生态信息学领域，其中自然群体的转录组测序是基因组测序的成本有效的替代方案。对于这样的研究，重要的是要考虑和确定单倍型多样性。Hapler提供了强大的单倍型信息，并确定了可能的定相错误的共识序列，提供有价值的信息，人口研究和下游使用所得的组件。
Assembling haplotypes given sequence data derived from a single individual is a well studied problem, but only recently has haplotype assembly been considered for population-sampled data. We discuss a software tool called Hapler, which is designed specifically for low-diversity, low-coverage data such as ecological samples derived from natural populations. Because such data may contain error as well as ambiguous haplotype information, we developed methods that increase confidence in these assemblies. Hapler also reconstructs full consensus sequences while minimizing and identifying possible chimeric points. Experiments on simulated data indicate that Hapler is effective at assembling haplotypes from gene-sized alignments of short reads. Further, in our tests Hapler-generated consensus sequences are less chimeric than the alternative consensus approaches of majority vote and viral quasispecies estimation regardless of error rate, read length, or population haplotype bias. The analysis of genetically diverse sequence data is increasingly common, particularly in the field of ecoinformatics where transcriptome sequencing of natural populations is a cost effective alternative to genome sequencing. For such studies, it is important to consider and identify haplotype diversity. Hapler provides robust haplotype information and identifies possible phasing errors in consensus sequences, providing valuable information for population studies and downstream usage of resulting assemblies.