NPInter v2.0: an updated database of ncRNA interactions

Yuan, Jiao; Wu, Wei; Xie, Chaoyong; Zhao, Guoguang; Zhao, Yi; Chen, Runsheng

doi:10.1093/nar/gkt1057

Cited by 145 publications

(102 citation statements)

References 42 publications

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“…Cross-linking immunoprecipitation (CLIP) based experiments were retrieved from StarBase (9) and supplemented by interactions identified in CLASH and CRAC experiments (21–23). Furthermore, experimentally supported interactions were extracted from miRTarBase (6) and NPInter (5) and redundancy between the databases was removed. The confidence of the experimental evidence was based on the number of experiments supporting a given interaction.…”

Section: Methodsmentioning

confidence: 99%

“…The interactions involve 171 miRNAs and 437 mRNAs. We defined our gold standard based on the curated miRNA–mRNA interactions from Croft et al (3) as well as miRNA–mRNA interactions from miRTarBase (6) and NPInter (5) that were supported by at least two low-throughput experiments. We defined a low-throughput experiment as one that reports less than five miRNA interactions.…”

Section: Methodsmentioning

confidence: 99%

“…This led to the generation of databases storing ncRNA interactions such as miRTarBase (6) and TarBase (7) containing microRNA (miRNA)–target interactions. NPInter (5), RAID (8) and StarBase (9) are examples of databases collecting interactions between ncRNAs and proteins.…”

Section: Introductionmentioning

confidence: 99%

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RAIN: RNA–protein Association and Interaction Networks

et al. 2017

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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

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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RAIN: RNA–protein Association and Interaction Networks

et al. 2017

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“…Many online databases are available for lncRNA annotation, some of which include predicted or experimentally validated lncRNA functions [6,[70][71][72][73][74][75]. Identifying lncRNA functions is especially challenging because of the large number of lncRNAs and the variety of mechanisms driving their functions.…”

Section: Lncrna Annotation and Function Predictionmentioning

confidence: 99%

Noncoding RNA Expression During Viral Infection: The Long and the Short of It

Josset

Tisoncik-Go

Katze

2014

MicroRNAs and Other Non-Coding RNAs in Inflammation

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New technologies have expanded our view of viral-host interactions with the growing identification of non-coding RNAs (ncRNAs) that act as key regulators of viral infection. In this chapter, we explore novel genomics-based approaches used to characterize ncRNAs involved in viral infection, focusing mainly on microRNAs and long noncoding RNAs. We present recent evidence implicating virally encoded and host-derived ncRNAs in viral replication and pathogenesis regulation, focusing on four different viral diseases (IAV, KHSV, HIV, and HBV). Finally, we discuss the current knowledge of ncRNAs modulation of innate and adaptive immune responses to viral infection. These findings highlight the complexity of host-pathogen networks determining the outcome of viral disease. Understanding the role of ncRNAs in these networks may offer novel antiviral therapy and diagnostic tools.

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“…The prior knowledge of the coding gene-disease associations pops up a question: is there any possibility that there exists lncRNA-gene regulation in order to perform a particular function and/or infer a disorder in the biological system? In fact this has been puzzled quite a few researchers and apparently there exists such lncRNA-gene relationship network inside cell (Li et al 2013;Yuan et al 2014). It is yet to explore the mysterious modular organization of the lncRNAs and coding genes, and understanding of their complex phenomena that are causing human diseases.…”

Section: Introductionmentioning

confidence: 99%

Inferring disease associations of the long non-coding RNAs through non-negative matrix factorization

Biswas

Kang

Kim³

et al. 2015

Netw Model Anal Health Inform Bioinforma

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Long non-coding RNAs (lncRNAs) have been implicated in various biological processes, and are linked in many dysregulations. Over the past decade, researchers reported a large number of human disease associations with the lncRNAs, both intergenic lncRNAs (lincRNAs) and non-intergenic lncRNAs. Thanks to the next generation sequencing platform, RNA-seq, through which researchers also were able to quantify expression profiles of each of the lncRNAs in human tissue samples. In this article we adapted the non-negative matrix factorization method to develop a low-rank computational model that can describe the existing knowledge about both non-intergenic and intergenic lncRNA-disease associations represented in a two dimensional association matrix as well as convey a way of ranking disease causing lncRNAs. We proposed several NMF formulations for the problem and we found that the sparsity-constrained NMF obtained the best model among all the other models. By exploiting the inherent biclustering ability of the NMF models, we extracted several lncRNA groups and disease groups that possess biological significance. Moreover, we proposed an integrative NMF formulation where we incorporated along with the coding gene and lincRNA disease association data, prior knowledge about relationship networks among the coding genes and lincRNAs, and the RNA-seq expression profile data to identify potential lincRNA-coding gene co-modules with which we further enhanced the lincRNA-disease associations and untangled mysteries about functional chemistry of the intergenic lncRNAs. Experimental results show the superiority of our proposed method over two state-of-theart clustering algorithms-k-means and hierarchical clustering.

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NPInter v2.0: an updated database of ncRNA interactions

Cited by 145 publications

References 42 publications

RAIN: RNA–protein Association and Interaction Networks

RAIN: RNA–protein Association and Interaction Networks

Noncoding RNA Expression During Viral Infection: The Long and the Short of It

Inferring disease associations of the long non-coding RNAs through non-negative matrix factorization

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