HiCHap: a package to correct and analyze the diploid Hi-C data.
HiCHap: a package to correct and analyze the diploid Hi-C data.
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HICHAP：纠正和分析二倍体HI-C数据的软件包。
DOI：
10.1186/s12864-020-07165-x
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发表时间：
2020-10-27
期刊：
影响因子：
4.4
通讯作者：
Peng C
中科院分区：
文献类型：
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作者：
Luo H;Li X;Fu H;Peng C
在二倍体细胞中，重要的是分别构建母本和父本Hi-C接触图，因为两个同源染色体在染色质三维（3D）组织中可能不同。以往的软件虽然可以利用定相遗传变异构建二倍体（母本和父本）Hi-C接触图谱，但都忽略了基因组中变异密度不同所导致的二倍体Hi-C接触图谱的系统性偏差。此外，还没有软件对等位基因特异性的染色质三维结构（包括区室、拓扑结构域和染色质环）进行定量分析。本研究揭示了二倍体Hi-C接触图谱中由变异密度引起的等位基因分配偏差的特征，并提出了一种新的校正策略。基于偏倚校正，我们开发了一个集成工具，称为HiCHap，用于执行读段作图，接触图构建，全基因组鉴定隔室，拓扑结构域和染色质环，以及二倍体Hi-C数据的等位基因特异性测试。我们的研究结果表明，在HiCHap等位基因分配偏差的校正显着提高二倍体Hi-C接触图的质量，随后促进二倍体染色质3D组织的全基因组鉴定，包括区室，拓扑结构域和染色质环。最后，HiCHap还支持不区分两个同源染色体的单倍体Hi-C图谱的数据分析。我们提供了一个集成的软件包HiCHap，用于对二倍体Hi-C数据进行数据处理、偏倚校正和结构分析。HiCHap软件的源代码和教程可在https://pypi.org/project/HiCHap/免费获得。在线版本包含补充材料，可通过10.1186/s12864-020-07165-x获得。
In diploid cells, it is important to construct maternal and paternal Hi-C contact maps respectively since the two homologous chromosomes can differ in chromatin three-dimensional (3D) organization. Though previous softwares could construct diploid (maternal and paternal) Hi-C contact maps by using phased genetic variants, they all neglected the systematic biases in diploid Hi-C contact maps caused by variable genetic variant density in the genome. In addition, few of softwares provided quantitative analyses on allele-specific chromatin 3D organization, including compartment, topological domain and chromatin loop. In this work, we revealed the feature of allele-assignment bias caused by the variable genetic variant density, and then proposed a novel strategy to correct the systematic biases in diploid Hi-C contact maps. Based on the bias correction, we developed an integrated tool, called HiCHap, to perform read mapping, contact map construction, whole-genome identification of compartments, topological domains and chromatin loops, and allele-specific testing for diploid Hi-C data. Our results show that the correction on allele-assignment bias in HiCHap does significantly improve the quality of diploid Hi-C contact maps, which subsequently facilitates the whole-genome identification of diploid chromatin 3D organization, including compartments, topological domains and chromatin loops. Finally, HiCHap also supports the data analysis for haploid Hi-C maps without distinguishing two homologous chromosomes. We provided an integrated package HiCHap to perform the data processing, bias correction and structural analysis for diploid Hi-C data. The source code and tutorial of software HiCHap are freely available at https://pypi.org/project/HiCHap/. The online version contains supplementary material available at 10.1186/s12864-020-07165-x.
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影响因子：
64.5
作者：
Nora EP;Goloborodko A;Valton AL;Gibcus JH;Uebersohn A;Abdennur N;Dekker J;Mirny LA;Bruneau BG
通讯作者：
Bruneau BG
通过图结构熵解码具有超低分辨率 Hi-C 数据的拓扑关联域
影响因子：
16.6
作者：
Li A;Yin X;Xu B;Wang D;Han J;Wei Y;Deng Y;Xiong Y;Zhang Z
通讯作者：
Zhang Z
影响因子：
64.5
作者：
Phillips-Cremins JE;Sauria ME;Sanyal A;Gerasimova TI;Lajoie BR;Bell JS;Ong CT;Hookway TA;Guo C;Sun Y;Bland MJ;Wagstaff W;Dalton S;McDevitt TC;Sen R;Dekker J;Taylor J;Corces VG
通讯作者：
Corces VG
影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
来自1,092个人基因组的遗传变异的综合图。
影响因子：
64.8
作者：
通讯作者：
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