背景:染色体是细胞生物学中最基本的部分之一,其中DNA拥有分层信息。DNA通过形成环来压缩其大小,这些区域容纳了各种蛋白质颗粒,包括CTCF,SMC3,H3组蛋白。众多的测序方法,比如Hi-C,ChIP-seq,和Micro-C,已经被开发来研究这些属性。利用这些数据,科学家已经开发了各种环路预测技术,这些技术极大地改进了表征环路预测和相关方面的方法。
结果:在这项研究中,我们对22种循环调用方法进行了分类,并对其中的11种方法进行了全面研究。此外,我们已经提供了对这些用于循环检测的算法的基础方法的详细见解,根据他们的基本方法将他们分为五个不同的组。此外,我们包括了关键信息,如分辨率,输入和输出格式,和参数。对于这个分析,我们使用了5KB的GM12878Hi-C数据集,10KB,100KB和250KB分辨率。我们的评估标准包括各种因素,包括记忆用法,运行时间,测序深度,和蛋白质特异性位点的恢复,如CTCF,H3K27ac,和RNAPII。
结论:此分析提供了对每种方法的循环检测过程的见解,以及每个人的优点和缺点,使读者能够有效地为他们的数据集选择合适的方法。我们评估了这些工具的功能,并介绍了一种新的生物,一致性,和计算稳健性得分(BCC得分)来衡量其整体稳健性,确保对其绩效进行全面评估。
BACKGROUND: Chromosome is one of the most fundamental part of cell biology where DNA holds the hierarchical information. DNA compacts its size by forming loops, and these regions house various protein particles, including CTCF, SMC3, H3 histone. Numerous sequencing methods, such as Hi-C, ChIP-seq, and Micro-C, have been developed to investigate these properties. Utilizing these data, scientists have developed a variety of
loop prediction techniques that have greatly improved their methods for characterizing
loop prediction and related aspects.
RESULTS: In this study, we categorized 22
loop calling methods and conducted a comprehensive study of 11 of them. Additionally, we have provided detailed insights into the methodologies underlying these algorithms for
loop detection, categorizing them into five distinct groups based on their fundamental approaches. Furthermore, we have included critical information such as resolution, input and output formats, and parameters. For this analysis, we utilized the GM12878 Hi-C datasets at 5 KB, 10 KB, 100 KB and 250 KB resolutions. Our evaluation criteria encompassed various factors, including memory usages, running time, sequencing depth, and recovery of protein-specific sites such as CTCF, H3K27ac, and RNAPII.
CONCLUSIONS: This analysis offers insights into the
loop detection processes of each method, along with the strengths and weaknesses of each, enabling readers to effectively choose suitable methods for their datasets. We evaluate the capabilities of these tools and introduce a novel Biological, Consistency, and Computational robustness score ( B C C score ) to measure their overall robustness ensuring a comprehensive evaluation of their performance.