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使用染色质聚合物模型比较 Hi-C、GAM 和 SPRITE 方法。

Comparison of the Hi-C, GAM and SPRITE methods using polymer models of chromatin.

机构信息

Dipartimento di Fisica, Università di Napoli Federico II and INFN Napoli, Complesso Universitario di Monte Sant'Angelo, Naples, Italy.

Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin, Germany.

出版信息

Nat Methods. 2021 May;18(5):482-490. doi: 10.1038/s41592-021-01135-1. Epub 2021 May 7.

Abstract

Hi-C, split-pool recognition of interactions by tag extension (SPRITE) and genome architecture mapping (GAM) are powerful technologies utilized to probe chromatin interactions genome wide, but how faithfully they capture three-dimensional (3D) contacts and how they perform relative to each other is unclear, as no benchmark exists. Here, we compare these methods in silico in a simplified, yet controlled, framework against known 3D structures of polymer models of murine and human loci, which can recapitulate Hi-C, GAM and SPRITE experiments and multiplexed fluorescence in situ hybridization (FISH) single-molecule conformations. We find that in silico Hi-C, GAM and SPRITE bulk data are faithful to the reference 3D structures whereas single-cell data reflect strong variability among single molecules. The minimal number of cells required in replicate experiments to return statistically similar contacts is different across the technologies, being lowest in SPRITE and highest in GAM under the same conditions. Noise-to-signal levels follow an inverse power law with detection efficiency and grow with genomic distance differently among the three methods, being lowest in GAM for genomic separations >1 Mb.

摘要

Hi-C、通过标签扩展(SPRITE)进行的分池相互作用识别和基因组结构作图(GAM)是用于探测全基因组染色质相互作用的强大技术,但它们在多大程度上真实地捕获三维(3D)接触以及彼此之间的表现如何尚不清楚,因为没有基准。在这里,我们在一个简化但受控制的框架中,在计算机上对这些方法进行了比较,以对抗已知的鼠类和人类基因座聚合物模型的 3D 结构,这些模型可以重现 Hi-C、GAM 和 SPRITE 实验以及多重荧光原位杂交(FISH)单分子构象。我们发现,计算机模拟的 Hi-C、GAM 和 SPRITE 整体数据与参考 3D 结构相符,而单细胞数据则反映了单个分子之间的强烈可变性。在相同条件下,不同技术中重复实验所需的最小细胞数量不同,在 SPRITE 中最低,在 GAM 中最高。噪声与信号比随检测效率呈逆幂律增长,三种方法之间的基因组距离增长不同,在基因组分离>1Mb 时,GAM 中最低。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ae2/8416658/b14b69c7edee/41592_2021_1135_Fig1_HTML.jpg

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