A Heuristic Strategy for Multi-Mapping Reads to Enhance Hi-C Data
A Heuristic Strategy for Multi-Mapping Reads to Enhance Hi-C Data
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DOI：
10.1109/bibe52308.2021.9635215
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Chanaka Bulathsinghalage;Lu Liu
中科院分区：
文献类型：
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作者：
Chanaka Bulathsinghalage;Lu Liu
目前的Hi-C分析方法集中于独特映射的读段，并且很少进行包括多映射读段的研究，这导致缺乏来自DNA重复区域的生物信号。我们提出了一种启发式的策略，分配多映射读取的基因座，根据其最近的限制性内切酶切割位点的距离。我们证明了启发式策略可以拯救多映射读段，从而提高Hi-C数据的质量。与mHi-C相比，它不仅提高了同一细胞类型中的重复重现性，而且保持了不同细胞类型重复之间的差异。此外，该策略识别了不同限制性内切酶的Hi-C实验之间更常见的统计学显著的染色质相互作用，并且在计算资源上具有巨大的优势。因此，可以使用启发式策略通过利用多映射读取来增强Hi-C数据。
Current Hi-C analysis approaches focus on uniquely mapped reads and little research has been carried out to include multi-mapping reads, which leads to a lack of biological signals from DNA repetitive regions. We propose a heuristic strategy to assign multi-mapping reads to loci according to the distance to their closest restriction enzyme cutting sites. We demonstrate that the heuristic strategy can rescue multi-mapping reads thus enhance the quality of Hi-C data. Compared with mHi-C, it not only improves replicate reproducibility in the same cell type, but also maintains the difference between replicates of different cell types. Moreover, the strategy identifies much more common statistically significant chromatin interactions between Hi-C experiments of different restriction enzymes and has a huge advantage on computing resources. Therefore, the heuristic strategy can be used to enhance Hi-C data by utilizing multi-mapping reads.