Inferring microRNA and transcription factor regulatory networks in heterogeneous data.
Inferring microRNA and transcription factor regulatory networks in heterogeneous data.
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DOI：
10.1186/1471-2105-14-92
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发表时间：
2013-03-11
影响因子：
3
通讯作者：
Li J
中科院分区：
文献类型：
--
作者：
Le TD;Liu L;Liu B;Tsykin A;Goodall GJ;Satou K;Li J
转录因子和微小RNA是后生动物的主要基因调控因子。这两种主要调节剂的调节机制引起了生物学家的极大兴趣，并可能为疾病的病因提供见解。然而，miRNAs和转录因子在调控网络中的相互作用仍然没有被发现。目前，在生物实验室中研究涉及miRNAs和TF的调控机制是非常困难的。即使在数据层面，涉及miRNA、TF和基因的网络也太复杂而难以实现。以前的研究主要是针对从数据中推断miRNA或TF调控网络。然而，涉及单一类型的调节剂的网络可能无法完全揭示复杂的基因调控机制，例如，TF通过miRNA间接调控基因的方式。我们提出了一个框架，从异质性数据中学习的三个组件的监管网络，与存在的miRNA，TF和mRNA。该方法首先利用贝叶斯网络结构学习从多个数据源构建调控网络：miRNAs、TF和mRNAs的基因表达谱，基于序列数据的靶标信息，以及样本类别。然后，为了产生更有意义的结果用于进一步的生物实验和研究，该方法搜索学习的网络以识别miRNA和TF之间的相互作用，并应用网络模体发现算法来进一步推断网络。我们将所提出的框架应用于上皮间质转化（EMT）的数据集。这些结果阐明了EMT复杂的基因调控机制，涉及TF和miRNA。一些发现的相互作用和分子功能已被文献证实。此外，许多其他发现的相互作用和生物标志物具有高度的统计学意义，因此可以是通过实验验证的良好候选者。此外，我们的方法产生的结果是紧凑的，涉及少量的相互作用，已被证明是高度相关的EMT。我们已经设计了一个框架，从多个数据源，包括基因表达数据，目标信息和样本类别，推断涉及TF和miRNA的基因调控网络。EMT数据集上的结果表明，所提出的方法能够产生与数据集的生物条件高度相关的紧凑且有意义的基因调控网络。该框架具有应用于其他异质数据集，以揭示复杂的基因调控关系的潜力。
Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA. We propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then, in order to produce more meaningful results for further biological experimentation and research, the method searches the learnt network to identify the interplay between miRNAs and TFs and applies a network motif finding algorithm to further infer the network. We apply the proposed framework to the data sets of epithelial-to-mesenchymal transition (EMT). The results elucidate the complex gene regulatory mechanism for EMT which involves both TFs and miRNAs. Several discovered interactions and molecular functions have been confirmed by literature. In addition, many other discovered interactions and bio-markers are of high statistical significance and thus can be good candidates for validation by experiments. Moreover, the results generated by our method are compact, involving a small number of interactions which have been proved highly relevant to EMT. We have designed a framework to infer gene regulatory networks involving both TFs and miRNAs from multiple sources of data, including gene expression data, target information, and sample categories. Results on the EMT data sets have shown that the proposed approach is able to produce compact and meaningful gene regulatory networks that are highly relevant to the biological conditions of the data sets. This framework has the potential for application to other heterogeneous datasets to reveal the complex gene regulatory relationships.
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DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
30.8
作者：
Chen, JF;Mandel, EM;Wang, DZ
通讯作者：
Wang, DZ
影响因子：
5.8
作者：
Audenaert, Pieter;Van Parys, Thomas;Michoel, Tom
通讯作者：
Michoel, Tom
影响因子：
30.8
作者：
Berezikov, Eugene;Cuppen, Edwin;Plasterk, Ronald H. A.
通讯作者：
Plasterk, Ronald H. A.
影响因子：
3
作者：
Le Béchec A;Portales-Casamar E;Vetter G;Moes M;Zindy PJ;Saumet A;Arenillas D;Theillet C;Wasserman WW;Lecellier CH;Friederich E
通讯作者：
Friederich E