Accurate loop calling for 3D genomic data with cLoops
Accurate loop calling for 3D genomic data with cLoops
复制标题
使用 cLoops 准确循环调用 3D 基因组数据
DOI：
10.1093/bioinformatics/btz651
复制
发表时间：
2020-02-01
期刊：
影响因子：
5.8
通讯作者：
Han, Jing-Dong J.
中科院分区：
文献类型：
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作者：
Cao, Yaqiang;Chen, Zhaoxiong;Han, Jing-Dong J.
动机：基于测序的3D基因组作图技术可以识别由数百个碱基之间的调节元件之间的相互作用形成的环。现有的循环调用工具大多局限于单一的数据类型，准确性取决于预定义的分辨率接触矩阵或所谓的peaks，并且可能具有高昂的硬件成本。结果：在这里，我们引入cLoops（'see loops'）来解决这些限制。cLoops基于聚类算法cDBSCAN，该算法直接分析配对末端标签（PET）以找到候选循环，并使用置换的局部背景来估计统计显著性。这两个独立于数据类型的过程使得能够可靠地识别尖锐和宽峰数据的循环，包括但不限于ChIA-PET、Hi-C、HiChIP和Trac-looping数据。与现有工具相比，cLoops识别的环显示出更少的距离依赖性偏差和相对于局部区域的更高富集。总而言之，cLoops提高了从测序数据检测3D基因组环的准确性，是通用的，灵活的，高效的，并且具有适度的硬件要求。
Motivation: Sequencing-based 3D genome mapping technologies can identify loops formed by interactions between regulatory elements hundreds of kilobases apart. Existing loop-calling tools are mostly restricted to a single data type, with accuracy dependent on a predefined resolution contact matrix or called peaks, and can have prohibitive hardware costs.Results: Here, we introduce cLoops ('see loops') to address these limitations. cLoops is based on the clustering algorithm cDBSCAN that directly analyzes the paired-end tags (PETs) to find candidate loops and uses a permuted local background to estimate statistical significance. These two data-type-independent processes enable loops to be reliably identified for both sharp and broad peak data, including but not limited to ChIA-PET, Hi-C, HiChIP and Trac-looping data. Loops identified by cLoops showed much less distance-dependent bias and higher enrichment relative to local regions than existing tools. Altogether, cLoops improves accuracy of detecting of 3D-genomic loops from sequencing data, is versatile, flexible, efficient, and has modest hardware requirements.