期刊
BIOINFORMATICS
卷 36, 期 3, 页码 666-675出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btz651
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资金
- National Natural Science Foundation of China [91749205, 91329302, 31210103916]
- China Ministry of Science and Technology [2015CB964803, 2016YFE0108700]
- Chinese Academy of Sciences [XDA01010303, YZ201243]
- Max Planck fellowship
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.
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