SEgene identifies links between super enhancers and gene expression across cell types.

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Norio Shinkai, Ken Asada, Hidenori Machino, Ken Takasawa, Satoshi Takahashi, Nobuji Kouno, Masaaki Komatsu, Ryuji Hamamoto, Syuzo Kaneko
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引用次数: 0

Abstract

Enhancers are non-coding DNA regions that facilitate gene transcription, with a specialized subset, super-enhancers, known to exert exceptionally strong transcriptional activation effects. Super-enhancers have been implicated in oncogenesis, and their identification is achievable through histone mark chromatin immunoprecipitation followed by sequencing data using existing analytical tools. However, conventional super-enhancer detection methodologies often do not accurately reflect actual gene expression levels, and the large volume of identified super-enhancers complicates comprehensive analysis. To address these limitations, we developed the super-enhancer to gene links (SE-to-gene Links) analysis, a platform named "SEgene" which incorporates the peak-to-gene links approach-a statistical method designed to reveal correlations between genes and peak regions ( https://github.com/hamamoto-lab/SEgene ). This platform enables a targeted evaluation of super-enhancer regions in relation to gene expression, facilitating the identification of super-enhancers that are functionally linked to transcriptional activity. Here, we demonstrate the application of SE-to-gene Links analysis to public datasets, confirming its efficacy in accurately detecting super-enhancers and identifying functionally associated genes. Additionally, SE-to-gene Links analysis identified ERBB2 as a significant gene of interest in the lung adenocarcinoma dataset from the National Cancer Center Japan cohort, suggesting a potential impact across multiple patient samples. Thus, the SE-to-gene Links analysis provides an analytical tool for evaluating super-enhancers as potential therapeutic targets, supporting the identification of clinically significant super-enhancer regions and their functionally associated genes.

SEgene识别超级增强子和跨细胞类型的基因表达之间的联系。
增强子是促进基因转录的非编码DNA区域,有一个特殊的子集,超级增强子,已知能发挥特别强的转录激活作用。超级增强子与肿瘤发生有关,它们的鉴定可以通过组蛋白标记染色质免疫沉淀以及使用现有分析工具的测序数据来实现。然而,传统的超增强子检测方法往往不能准确反映实际的基因表达水平,而且大量已识别的超增强子使综合分析复杂化。为了解决这些限制,我们开发了超级增强子基因链接(SE-to-gene links)分析,这是一个名为“SEgene”的平台,它结合了峰-基因链接方法——一种旨在揭示基因和峰区之间相关性的统计方法(https://github.com/hamamoto-lab/SEgene)。该平台能够有针对性地评估与基因表达相关的超增强子区域,促进识别与转录活性功能相关的超增强子。在这里,我们展示了将SE-to-gene Links分析应用于公共数据集,证实了其在准确检测超级增强子和识别功能相关基因方面的有效性。此外,SE-to-gene Links分析发现ERBB2是来自日本国家癌症中心队列的肺腺癌数据集中的一个重要基因,表明对多个患者样本有潜在影响。因此,SE-to-gene Links分析为评估超增强子作为潜在治疗靶点提供了一种分析工具,支持鉴定具有临床意义的超增强子区域及其功能相关基因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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