徐炳祥 (2024-01-31 09:45):
#paper doi: 10.1038/s41588-020-00712-y Nature Genetics, 2020, CHESS enables quantitative comparison of chromatin contact data and automatic feature extraction。本文介绍了一种基于计算机视觉中结构相似度(SSIM)的Hi-C数据相似度度量和结构变化区域的搜索算法,通过在基因组上进行滑窗计算,该算法不仅能基于Hi-C数据计算出两个样品在全基因组水平下染色质空间构象的相似程度,更能通过局部计算寻找出存在显著染色质空间构象变异的区域。该算法不仅可以进行同一物种内的比较,也可以进行跨物种比较。且对测序深度不敏感。本文将计算机视觉中的很多降噪/特征提取/形态学处理技术引入到了Hi-C相互作用图谱的处理中,对计算机上视觉技术在染色质空间构象数据的分析中的应用有重要参考价值。
IF:31.700Q1 Nature genetics, 2020-11. DOI: 10.1038/s41588-020-00712-y PMID: 33077914
CHESS enables quantitative comparison of chromatin contact data and automatic feature extraction
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Abstract:
Dynamic changes in the three-dimensional (3D) organization of chromatin are associated with central biological processes, such as transcription, replication and development. Therefore, the comprehensive identification and quantification of these changes is fundamental to understanding of evolutionary and regulatory mechanisms. Here, we present Comparison of Hi-C Experiments using Structural Similarity (CHESS), an algorithm for the comparison of chromatin contact maps and automatic differential feature extraction. We demonstrate the robustness of CHESS to experimental variability and showcase its biological applications on (1) interspecies comparisons of syntenic regions in human and mouse models; (2) intraspecies identification of conformational changes in Zelda-depleted Drosophila embryos; (3) patient-specific aberrant chromatin conformation in a diffuse large B-cell lymphoma sample; and (4) the systematic identification of chromatin contact differences in high-resolution Capture-C data. In summary, CHESS is a computationally efficient method for the comparison and classification of changes in chromatin contact data.
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