qDRIP: a method to quantitatively assess RNA-DNA hybrid formation genome-wide.
qDRIP: a method to quantitatively assess RNA-DNA hybrid formation genome-wide.
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DOI：
10.1093/nar/gkaa500
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发表时间：
2020-08-20
影响因子：
14.9
通讯作者：
Cimprich KA
中科院分区：
文献类型：
--
作者：
Crossley MP;Bocek MJ;Hamperl S;Swigut T;Cimprich KA
R环是动态的、共转录的核酸结构，其促进生理过程，但在某些情况下也可引起DNA损伤。预期转录或R环解析的扰动会改变它们的基因组分布。绘制RNA-DNA杂交体（R环的一个组成部分）的下一代测序方法迄今为止还不允许在这些条件之间进行定量比较。在这里，我们描述了定量差异DNA-RNA免疫沉淀（qDRIP），一种结合合成RNA-DNA杂交内标与高分辨率，链特异性测序的方法。我们表明，qDRIP通过准确分析人类细胞中不受转录抑制影响的区域中的信号，并促进条件之间准确的差异峰调用，避免了读数计数标准化固有的偏差。我们还使用这些定量比较对每个细胞的RNA-DNA杂交体的绝对计数及其全基因组半衰期进行了首次估计。最后，我们确定了一个子集的RNA-DNA杂交与高GC偏斜是部分耐RNase H。总的来说，qDRIP允许在R环受到干扰的条件下进行准确的归一化，并允许进行定量测量，从而提供以前无法实现的生物学见解。
R-loops are dynamic, co-transcriptional nucleic acid structures that facilitate physiological processes but can also cause DNA damage in certain contexts. Perturbations of transcription or R-loop resolution are expected to change their genomic distribution. Next-generation sequencing approaches to map RNA–DNA hybrids, a component of R-loops, have so far not allowed quantitative comparisons between such conditions. Here, we describe quantitative differential DNA–RNA immunoprecipitation (qDRIP), a method combining synthetic RNA–DNA-hybrid internal standards with high-resolution, strand-specific sequencing. We show that qDRIP avoids biases inherent to read-count normalization by accurately profiling signal in regions unaffected by transcription inhibition in human cells, and by facilitating accurate differential peak calling between conditions. We also use these quantitative comparisons to make the first estimates of the absolute count of RNA–DNA hybrids per cell and their half-lives genome-wide. Finally, we identify a subset of RNA–DNA hybrids with high GC skew which are partially resistant to RNase H. Overall, qDRIP allows for accurate normalization in conditions where R-loops are perturbed and for quantitative measurements that provide previously unattainable biological insights.
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影响因子：
3.5
作者：
Bhatia V;Herrera-Moyano E;Aguilera A;Gómez-González B
通讯作者：
Gómez-González B
DOI：
10.1016/j.tig.2016.10.002
发表时间：
2016-12
期刊：
Trends in genetics : TIG
影响因子：
--
作者：
Chédin F
通讯作者：
Chédin F
影响因子：
46.9
作者：
Chambers, Vicki S.;Marsico, Giovanni;Balasubramanian, Shankar
通讯作者：
Balasubramanian, Shankar
影响因子：
10.5
作者：
Hu Z;Chen K;Xia Z;Chavez M;Pal S;Seol JH;Chen CC;Li W;Tyler JK
通讯作者：
Tyler JK
影响因子：
64.5
作者：
Li, XL;Manley, JL
通讯作者：
Manley, JL