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RAIN:RNA-蛋白质关联与相互作用网络

RAIN: RNA-protein Association and Interaction Networks.

作者信息

Junge Alexander, Refsgaard Jan C, Garde Christian, Pan Xiaoyong, Santos Alberto, Alkan Ferhat, Anthon Christian, von Mering Christian, Workman Christopher T, Jensen Lars Juhl, Gorodkin Jan

机构信息

Center for Non-coding RNA in Technology and Health, University of Copenhagen, Copenhagen,, Groennegaardsvej 3, DK-1870 Frederiksberg C, Denmark.

Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Groennegaardsvej 3, DK-1870 Frederiksberg C, Denmark.

出版信息

Database (Oxford). 2017 Jan 10;2017. doi: 10.1093/database/baw167. Print 2017.

Abstract

Protein association networks can be inferred from a range of resources including experimental data, literature mining and computational predictions. These types of evidence are emerging for non-coding RNAs (ncRNAs) as well. However, integration of ncRNAs into protein association networks is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data, interaction predictions and automatic literature mining. RAIN uses an integrative scoring scheme to assign a confidence score to each interaction. We demonstrate that RAIN outperforms the underlying microRNA-target predictions in inferring ncRNA interactions. RAIN can be operated through an easily accessible web interface and all interaction data can be downloaded.Database URL: http://rth.dk/resources/rain.

摘要

蛋白质关联网络可以从一系列资源中推断出来,包括实验数据、文献挖掘和计算预测。这类证据在非编码RNA(ncRNA)中也不断涌现。然而,由于数据的异质性,将ncRNA整合到蛋白质关联网络中具有挑战性。在此,我们展示了一个ncRNA-RNA和ncRNA-蛋白质相互作用的数据库,以及它与蛋白质-蛋白质相互作用的STRING数据库的整合。这些ncRNA关联涵盖了四种生物,并且是根据经过整理的实例、实验数据、相互作用预测和自动文献挖掘建立的。RAIN使用一种综合评分方案为每个相互作用分配一个置信度分数。我们证明,在推断ncRNA相互作用方面,RAIN优于潜在的微小RNA靶标预测。RAIN可以通过易于访问的网页界面进行操作,所有相互作用数据均可下载。数据库网址:http://rth.dk/resources/rain

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