MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model.
MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model.
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DOI：
10.1186/1471-2105-12-67
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发表时间：
2011-03-04
影响因子：
3
通讯作者：
Friederich E
中科院分区：
文献类型：
--
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
为了理解生物过程和疾病，解开转录因子（TF），microRNAs（miRNAs）及其靶标在调控网络和基本子网络中的协调相互作用至关重要。一个综合的计算资源，产生这些调控分子相互作用的全面看法，在全基因组范围内的生物学家将极大的兴趣，但目前还没有。为了识别和分析分子相互作用网络，我们开发了MIR@NT@N，这是一种基于元调控网络模型和大规模数据库的综合方法。MIR@NT@N使用基于图的方法来预测跨多个调节过程的新分子作用物（即作用于蛋白质编码或miRNA基因的TF，或作用于信使RNA的miRNA）。利用这些预测，用户可以生成网络并进一步分析它们以识别子网络，包括诸如反馈和前馈环路（FBL和FFL）等基元。此外，网络可以建立从分子演员与先验的作用，在一个给定的生物过程中预测新的和意外的相互作用的列表。分析可以通过整合额外的信息（如微阵列表达数据）进行情境化和过滤。所有结果，包括生成的图形，都可以可视化，保存和导出为各种格式。MIR@NT@N性能已使用已发表的数据进行了评估，然后应用于上皮细胞向间充质细胞转化（EMT）的调控程序，EMT是一种涉及胚胎发育和疾病的进化保守过程。MIR@NT@N是一种有效的计算方法，用于识别新的分子调控和预测基因调控网络和子网络，包括在给定的生物背景下的保守基序。利用M@IA环境，MIR@NT@N是一个用户友好的网络资源，可在http://mironton.uni.lu上免费获得，并将定期更新。
To understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs), microRNAs (miRNAs) and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date. To identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs). Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL). In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT), an evolutionary-conserved process which is implicated in embryonic development and disease. MIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http://mironton.uni.lu which will be updated on a regular basis.
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DOI：
10.1007/978-1-59745-524-4_4
发表时间：
2009-01-01
期刊：
BIOMEDICAL INFORMATICS
影响因子：
--
作者：
Baitaluk, Michael
通讯作者：
Baitaluk, Michael
影响因子：
11.2
作者：
Bracken, Cameron P.;Gregory, Philip A.;Goodall, Gregory J.
通讯作者：
Goodall, Gregory J.
影响因子：
64.5
作者：
Marson A;Levine SS;Cole MF;Frampton GM;Brambrink T;Johnstone S;Guenther MG;Johnston WK;Wernig M;Newman J;Calabrese JM;Dennis LM;Volkert TL;Gupta S;Love J;Hannett N;Sharp PA;Bartel DP;Jaenisch R;Young RA
通讯作者：
Young RA
Mirbase：MicroRNA基因组学的工具。
影响因子：
14.9
作者：
Griffiths-Jones, Sam;Saini, Harpreet Kaur;van Dongen, Stijn;Enright, Anton J.
通讯作者：
Enright, Anton J.
影响因子：
14.9
作者：
Betel D;Wilson M;Gabow A;Marks DS;Sander C
通讯作者：
Sander C