Three-dimensional chromosomal structure plays an important role in gene regulation. Chromosome conformation capture techniques, especially the high-throughput, sequencing-based technique Hi-C, provide new insights on spatial architectures of chromosomes. However, Hi-C data contains artifacts and systemic biases that substantially influence subsequent analysis. Computational models have been developed to address these biases explicitly, however, it is difficult to enumerate and eliminate all the biases in models. Other models are designed to correct biases implicitly, but they will also be invalid in some situations such as copy number variations. New methods are required for better bias correction. We characterize a new kind of artifact in Hi-C data. We find that this artifact is caused by incorrect alignment of Hi-C reads against approximate repeat regions and can lead to erroneous chromatin contact signals. The artifact cannot be corrected by current Hi-C correction methods. We design a probabilistic method and develop a new Hi-C processing pipeline by integrating our probabilistic method with the HiC-Pro pipeline. We find that the new pipeline can remove this new artifact effectively, while preserving important features of the original Hi-C matrices.

中文翻译：

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概率方法纠正以前无法表征的Hi-C伪像

三维染色体结构在基因调控中起着重要作用。染色体构象捕获技术，尤其是基于序列的高通量技术Hi-C，为染色体的空间结构提供了新的见识。但是，Hi-C数据包含伪影和系统偏差，这些伪影和系统偏差会严重影响后续分析。已经开发出计算模型来明确解决这些偏差，但是，难以枚举和消除模型中的所有偏差。其他模型旨在隐式地纠正偏差，但在某些情况下（例如拷贝数变化）它们也将无效。需要新的方法来更好地校正偏差。我们在Hi-C数据中表征了一种新的工件。我们发现该伪像是由Hi-C读码与近似重复区域的错误对齐引起的，并且可能导致染色质接触信号错误。当前的Hi-C校正方法无法校正伪影。我们设计了一种概率方法，并通过将我们的概率方法与HiC-Pro流水线集成来开发新的Hi-C处理流水线。我们发现，新的管道可以有效地删除此新工件，同时保留原始Hi-C矩阵的重要功能。