Characterizing polymorphic inversions in human genomes by single-cell sequencing.
Characterizing polymorphic inversions in human genomes by single-cell sequencing.
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DOI：
10.1101/gr.201160.115
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发表时间：
2016-11
期刊：
影响因子：
7
通讯作者：
Lansdorp PM
中科院分区：
文献类型：
--
作者：
Sanders AD;Hills M;Porubský D;Guryev V;Falconer E;Lansdorp PM
识别个体和细胞之间不同的基因组特征可以帮助揭示驱动表型和疾病易感性的功能变异。为此，单细胞研究至关重要，因为越来越清楚的是，稀有但功能性的细胞亚群的贡献对于疾病的预后、管理和进展非常重要。到目前为止，研究这些关联一直面临着我们无法准确、全面地绘制结构重排的挑战。为了克服这个问题，我们将 DNA 模板链的单细胞测序 (Strand-seq) 与定制分析软件结合起来，以高分辨率快速发现、绘制基因组重排并进行基因分型。这使我们能够探索异质细胞群中倒位的分布和频率，识别基因组复杂区域中的多个多态性结构域，并在参考组装中定位罕见等位基因。然后，我们绘制了两个不相关个体内倒位的整个基因组互补图，以表征他们独特的倒位概况，并建立了人类基因组结构重排的非冗余全局参考。这里描述的工作提供了一个强大的新框架来研究单细胞样本中的结构变异和基因组异质性，无论是来自用于群体研究的个体还是用于发现生物标志物的组织类型。
Identifying genomic features that differ between individuals and cells can help uncover the functional variants that drive phenotypes and disease susceptibilities. For this, single-cell studies are paramount, as it becomes increasingly clear that the contribution of rare but functional cellular subpopulations is important for disease prognosis, management, and progression. Until now, studying these associations has been challenged by our inability to map structural rearrangements accurately and comprehensively. To overcome this, we coupled single-cell sequencing of DNA template strands (Strand-seq) with custom analysis software to rapidly discover, map, and genotype genomic rearrangements at high resolution. This allowed us to explore the distribution and frequency of inversions in a heterogeneous cell population, identify several polymorphic domains in complex regions of the genome, and locate rare alleles in the reference assembly. We then mapped the entire genomic complement of inversions within two unrelated individuals to characterize their distinct inversion profiles and built a nonredundant global reference of structural rearrangements in the human genome. The work described here provides a powerful new framework to study structural variation and genomic heterogeneity in single-cell samples, whether from individuals for population studies or tissue types for biomarker discovery.
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影响因子：
12.3
作者：
Bailey JA;Baertsch R;Kent WJ;Haussler D;Eichler EE
通讯作者：
Eichler EE
影响因子：
4.5
作者：
Feuk L;MacDonald JR;Tang T;Carson AR;Li M;Rao G;Khaja R;Scherer SW
通讯作者：
Scherer SW
影响因子：
12.3
作者：
Feuk L
通讯作者：
Feuk L
影响因子：
3.5
作者：
Antonacci F;Kidd JM;Marques-Bonet T;Ventura M;Siswara P;Jiang Z;Eichler EE
通讯作者：
Eichler EE
DOI：
10.1002/ajmg.c.30258
发表时间：
2010-05-15
影响因子：
3.1
作者：
Hobart, Holly H.;Morris, Colleen A.;Bray-Ward, Patricia
通讯作者：
Bray-Ward, Patricia