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基于网络的方法利用推断的转录因子活性来分析遗传变异对基因表达的影响。

Network-based approaches that exploit inferred transcription factor activity to analyze the impact of genetic variation on gene expression.

作者信息

Bussemaker Harmen J, Causton Helen C, Fazlollahi Mina, Lee Eunjee, Muroff Ivor

机构信息

Department of Biological Sciences, Columbia University, New York, NY 10027.

Department of Systems Biology, Columbia University, New York, NY 10032.

出版信息

Curr Opin Syst Biol. 2017 Apr;2:98-102. doi: 10.1016/j.coisb.2017.04.002. Epub 2017 Apr 17.

DOI:10.1016/j.coisb.2017.04.002
PMID:28691107
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5499699/
Abstract

Over the past decade, a number of methods have emerged for inferring protein-level transcription factor activities in individual samples based on prior information about the structure of the gene regulatory network. We discuss how this has enabled new methods for dissecting trans-acting mechanisms that underpin genetic variation in gene expression.

摘要

在过去十年中，基于基因调控网络结构的先验信息，出现了许多用于推断单个样本中蛋白质水平转录因子活性的方法。我们将讨论这如何催生了剖析支撑基因表达遗传变异的反式作用机制的新方法。

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Proc Natl Acad Sci U S A. 2017 Feb 28;114(9):2301-2306. doi: 10.1073/pnas.1621192114. Epub 2017 Feb 13.
2
Promoter shape varies across populations and affects promoter evolution and expression noise.启动子形状在不同群体中存在差异，会影响启动子进化和表达噪声。
Nat Genet. 2017 Apr;49(4):550-558. doi: 10.1038/ng.3791. Epub 2017 Feb 13.
3
SMiLE-seq identifies binding motifs of single and dimeric transcription factors.
使用基因调控网络模型估计细胞状态转变的驱动因素。
BMC Syst Biol. 2017 Dec 13;11(1):139. doi: 10.1186/s12918-017-0517-y.
SMiLE-seq 鉴定了单个和二聚体转录因子的结合基序。
Nat Methods. 2017 Mar;14(3):316-322. doi: 10.1038/nmeth.4143. Epub 2017 Jan 16.
4
The Genetics of Transcription Factor DNA Binding Variation.转录因子DNA结合变异的遗传学
Cell. 2016 Jul 28;166(3):538-554. doi: 10.1016/j.cell.2016.07.012.
5
Functional characterization of somatic mutations in cancer using network-based inference of protein activity.利用基于网络的蛋白质活性推断对癌症中的体细胞突变进行功能表征。
Nat Genet. 2016 Aug;48(8):838-47. doi: 10.1038/ng.3593. Epub 2016 Jun 20.
6
Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks.巴塞特：利用深度卷积神经网络学习可及基因组的调控密码。
Genome Res. 2016 Jul;26(7):990-9. doi: 10.1101/gr.200535.115. Epub 2016 May 3.
7
Survey of variation in human transcription factors reveals prevalent DNA binding changes.人类转录因子变异调查揭示了普遍存在的DNA结合变化。
Science. 2016 Mar 25;351(6280):1450-1454. doi: 10.1126/science.aad2257. Epub 2016 Mar 24.
8
Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets.识别转录因子与其转录靶点之间连接性的基因调控因子。
Proc Natl Acad Sci U S A. 2016 Mar 29;113(13):E1835-43. doi: 10.1073/pnas.1517140113. Epub 2016 Mar 10.
9
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Bioinformatics. 2016 Jan 1;32(1):96-105. doi: 10.1093/bioinformatics/btv531. Epub 2015 Sep 10.
10
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