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Serpentine:一种用于差异 Hi-C 分析的灵活 2D 分箱方法。

Serpentine: a flexible 2D binning method for differential Hi-C analysis.

机构信息

Institut Pasteur, Unité Régulation Spatiale des Génomes, UMR3525 CNRS, Paris 75015, France.

Sorbonne Université, Collège Doctoral, Paris 75005, France.

出版信息

Bioinformatics. 2020 Jun 1;36(12):3645-3651. doi: 10.1093/bioinformatics/btaa249.

Abstract

MOTIVATION

Hi-C contact maps reflect the relative contact frequencies between pairs of genomic loci, quantified through deep sequencing. Differential analyses of these maps enable downstream biological interpretations. However, the multi-fractal nature of the chromatin polymer inside the cellular envelope results in contact frequency values spanning several orders of magnitude: contacts between loci pairs separated by large genomic distances are much sparser than closer pairs. The same is true for poorly covered regions, such as repeated sequences. Both distant and poorly covered regions translate into low signal-to-noise ratios. There is no clear consensus to address this limitation.

RESULTS

We present Serpentine, a fast, flexible procedure operating on raw data, which considers the contacts in each region of a contact map. Binning is performed only when necessary on noisy regions, preserving informative ones. This results in high-quality, low-noise contact maps that can be conveniently visualized for rigorous comparative analyses.

AVAILABILITY AND IMPLEMENTATION

Serpentine is available on the PyPI repository and https://github.com/koszullab/serpentine; documentation and tutorials are provided at https://serpentine.readthedocs.io/en/latest/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

Hi-C 接触图谱反映了基因组序列对之间的相对接触频率,通过深度测序进行量化。对这些图谱进行差异分析可以进行下游的生物学解释。然而,细胞内的染色质聚合物具有多重分形特性,导致接触频率值跨越几个数量级:相隔较大基因组距离的基因座对之间的接触要稀疏得多,而距离较近的基因座对则更为紧密。对于覆盖较差的区域(如重复序列)也是如此。无论是远距离还是覆盖较差的区域,都会导致低信噪比。目前还没有明确的共识来解决这一限制。

结果

我们提出了 Serpentine,这是一种快速、灵活的原始数据处理程序,它考虑了接触图谱中每个区域的接触。只有在噪声区域才需要进行分箱,而保留有信息的区域。这产生了高质量、低噪声的接触图谱,可以方便地进行严格的比较分析。

可用性和实现

Serpentine 可在 PyPI 存储库和 https://github.com/koszullab/serpentine 上获得;文档和教程可在 https://serpentine.readthedocs.io/zh_CN/latest/ 上获得。

补充信息

补充数据可在生物信息学在线获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a63f/7320618/6c2212cdc927/btaa249f1.jpg

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