Suppr超能文献

调控区域的总结合亲和力图谱可预测人类细胞中的转录因子结合和基因表达。

Total Binding Affinity Profiles of Regulatory Regions Predict Transcription Factor Binding and Gene Expression in Human Cells.

作者信息

Grassi Elena, Zapparoli Ettore, Molineris Ivan, Provero Paolo

机构信息

Dept. of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy.

Center for Translational Genomics and Bioinformatics, San Raffaele Scientific Institute, Milan, Italy.

出版信息

PLoS One. 2015 Nov 24;10(11):e0143627. doi: 10.1371/journal.pone.0143627. eCollection 2015.

Abstract

Transcription factors regulate gene expression by binding regulatory DNA. Understanding the rules governing such binding is an essential step in describing the network of regulatory interactions, and its pathological alterations. We show that describing regulatory regions in terms of their profile of total binding affinities for transcription factors leads to increased predictive power compared to methods based on the identification of discrete binding sites. This applies both to the prediction of transcription factor binding as revealed by ChIP-seq experiments and to the prediction of gene expression through RNA-seq. Further significant improvements in predictive power are obtained when regulatory regions are defined based on chromatin states inferred from histone modification data.

摘要

转录因子通过结合调控性DNA来调节基因表达。理解此类结合的规则是描述调控相互作用网络及其病理改变的关键一步。我们发现,与基于离散结合位点识别的方法相比,根据转录因子的总结合亲和力谱来描述调控区域可提高预测能力。这既适用于ChIP-seq实验所揭示的转录因子结合预测,也适用于通过RNA-seq对基因表达的预测。当基于从组蛋白修饰数据推断出的染色质状态来定义调控区域时,预测能力会进一步显著提高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a413/4658012/96d7284fcd0e/pone.0143627.g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验