Hi-C 染色质相互作用网络中小群内的转录枢纽

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-05-20 DOI:10.1089/cmb.2024.0515
Gatis Melkus, Andrejs Sizovs, Peteris Rucevskis, Sandra Silina
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引用次数: 0

摘要

染色质构象捕获技术允许以各种分辨率研究全基因组范围的染色质空间组织。尽管高通量染色质构象捕获(Hi-C)方法的精确度和分辨率不断提高,但要将转录活动与空间组织现象确凿地联系起来仍具有挑战性。我们开发了一种基于集群的方法来分析 Hi-C 数据,这种方法有助于识别染色体热点,这些热点的特点是染色质注释对转录起始位点有相当大的富集作用,并且在以前发表的工作基础上,我们发现这些染色体热点不仅在 ENCODE 项目确定的 RNA 聚合酶 II 结合位点中有显著富集,而且在我们确定的集群内,各种组织类型的 FANTOM5 和 GTEx 转录也有明显增加。根据所获得的数据,我们推测我们的集群是在 Hi-C 数据中识别转录工厂的一种合适方法,并概述了该方法的进一步扩展,这可能会使它在无法获得深度表达或聚合酶数据的数据集中用于定位转录活动增加的区域。
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Transcriptional Hubs Within Cliques in Ensemble Hi-C Chromatin Interaction Networks.

Chromatin conformation capture technologies permit the study of chromatin spatial organization on a genome-wide scale at a variety of resolutions. Despite the increasing precision and resolution of high-throughput chromatin conformation capture (Hi-C) methods, it remains challenging to conclusively link transcriptional activity to spatial organizational phenomena. We have developed a clique-based approach for analyzing Hi-C data that helps identify chromosomal hotspots that feature considerable enrichment of chromatin annotations for transcriptional start sites and, building on previously published work, show that these chromosomal hotspots are not only significantly enriched in RNA polymerase II binding sites as identified by the ENCODE project, but also identify a noticeable increase in FANTOM5 and GTEx transcription within our identified cliques across a variety of tissue types. From the obtained data, we surmise that our cliques are a suitable method for identifying transcription factories in Hi-C data, and outline further extensions to the method that may make it useful for locating regions of increased transcriptional activity in datasets where in-depth expression or polymerase data may not be available.

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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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