NPInter v4.0: an integrated database of ncRNA interactions

Teng, Xueyi; Chen, Xiaomin; Hou, Xue; Tang, Yiheng; Zhang, Peng; Kang, Quan; Hao, Yajing; Chen, Runsheng; Zhao, Yi; He, Shunmin

doi:10.1093/nar/gkz969

Cited by 115 publications

(122 citation statements)

References 57 publications

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“…Finally, when comparing other characteristics of the lists of DE lncRNAs with GENCODE v24 lncRNAs [ 3 ], we found that the three lists were significantly enriched in functionally validated and disease associated lncRNAs, lncRNAs containing retroviral elements repeats (ERVL-MaLR,ERV1), lncRNAs genes with longer total and exonic lengths, lncRNAs with protein-coding genes upstream in the same strand, and with lower distances to the closest protein coding gene (adjusted p -value < 0.05, Table S4 ). We searched for experimentally validated interactions of DE lncRNAs in the NPInter v4.0 database [ 52 ], and found that 82, 62, and 29 lncRNAs in the FFPE, TCGA, and overlap lists, respectively, interacted with RNAs or proteins ( Table S5 ). We performed a functional enrichment analysis of the set of interacting proteins and found significant enrichment in several biological processes (adjusted p -value < 0.05), which are summarized in Figure S4 and Table S6 .…”

Section: Resultsmentioning

confidence: 99%

“…Interestingly, some of the genes with interactions had no functional annotation in the databases, for instance, ENSG00000231881 and ENSG00000233858 ). For ENSG00000231881 , we found a recent study suggesting that it regulates the VEGF signaling pathway by binding to miR-133b, thereby promoting metastasis in CRC cells [ 52 , 53 ]. This notion is consistent with our results showing that ENSG00000231881 is up-regulated in CRC tumors.…”

Section: Resultsmentioning

confidence: 99%

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Target Enrichment Enables the Discovery of lncRNAs with Somatic Mutations or Altered Expression in Paraffin-Embedded Colorectal Cancer Samples

Iraola-Guzmán

Brunet-Vega

Pegueroles

et al. 2020

Cancers

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Long non-coding RNAs (lncRNAs) play important roles in cancer and are potential new biomarkers or targets for therapy. However, given the low and tissue-specific expression of lncRNAs, linking these molecules to particular cancer types and processes through transcriptional profiling is challenging. Formalin-fixed, paraffin-embedded (FFPE) tissues are abundant resources for research but are prone to nucleic acid degradation, thereby complicating the study of lncRNAs. Here, we designed and validated a probe-based enrichment strategy to efficiently profile lncRNA expression in FFPE samples, and we applied it for the detection of lncRNAs associated with colorectal cancer (CRC). Our approach efficiently enriched targeted lncRNAs from FFPE samples, while preserving their relative abundance, and enabled the detection of tumor-specific mutations. We identified 379 lncRNAs differentially expressed between CRC tumors and matched healthy tissues and found tumor-specific lncRNA variants. Our results show that numerous lncRNAs are differentially expressed and/or accumulate variants in CRC tumors, thereby suggesting a role in CRC progression. More generally, our approach unlocks the study of lncRNAs in FFPE samples, thus enabling the retrospective use of abundant, well documented material available in hospital biobanks.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Target Enrichment Enables the Discovery of lncRNAs with Somatic Mutations or Altered Expression in Paraffin-Embedded Colorectal Cancer Samples

Iraola-Guzmán

Brunet-Vega

Pegueroles

et al. 2020

Cancers

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“…For putative miRNA–mRNA interactions, we have obtained 762,540 unique interactions between 2,600 miRNAs and 21,538 mRNAs from miRTarBase v8.0 ( Chou et al, 2018 ) and TarBase v8.0 ( Karagkouni et al, 2018 ) databases. By combining the interactions from LncBase v2.0 ( Paraskevopoulou et al, 2016 ) and NPInter v4.0 ( Teng et al, 2020 ) databases, we have collected 138,951 unique miRNA–lncRNA interactions between 1,044 miRNAs and 13,243 lncRNAs. The obtained miRNA–target interactions could be seen in Supplementary Table 2 .…”

Section: Methodsmentioning

confidence: 99%

ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder

et al. 2020

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Autism spectrum disorder (ASD) is a class of neurodevelopmental disorders characterized by genetic and environmental risk factors. The pathogenesis of ASD has a strong genetic basis, consisting of rare de novo or inherited variants among a variety of multiple molecules. Previous studies have shown that microRNAs (miRNAs) are involved in neurogenesis and brain development and are closely associated with the pathogenesis of ASD. However, the regulatory mechanisms of miRNAs in ASD are largely unclear. In this work, we present a stepwise method, ASDmiR, for the identification of underlying pathogenic genes, networks, and modules associated with ASD. First, we conduct a comparison study on 12 miRNA target prediction methods by using the matched miRNA, lncRNA, and mRNA expression data in ASD. In terms of the number of experimentally confirmed miRNA–target interactions predicted by each method, we choose the best method for identifying miRNA–target regulatory network. Based on the miRNA–target interaction network identified by the best method, we further infer miRNA–target regulatory bicliques or modules. In addition, by integrating high-confidence miRNA–target interactions and gene expression data, we identify three types of networks, including lncRNA–lncRNA, lncRNA–mRNA, and mRNA–mRNA related miRNA sponge interaction networks. To reveal the community of miRNA sponges, we further infer miRNA sponge modules from the identified miRNA sponge interaction network. Functional analysis results show that the identified hub genes, as well as miRNA-associated networks and modules, are closely linked with ASD. ASDmiR is freely available at https://github.com/chenchenxiong/ASDmiR .

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“…Fifteen out of 20 interactions in Table 2 had been verified by CLIP-seq (Ule et al, 2005) in RAID v2.0. Two of the remaining five had been verified by eCLIP (Van Nostrand et al, 2016) in NPInter database (Teng et al, 2019). Since our method does not require any prior knowledge of direct lncRNA-protein interactions, and all data in our method came only from the RAID v2.0database,ourmethodshouldhaveagoodperformance.Although other three interactions are not verified, it is possible that they are undiscovered interactions under certain conditions.…”

Section: Prediction Of Novel Interactionsmentioning

confidence: 99%

Predicting lncRNA–Protein Interactions With miRNAs as Mediators in a Heterogeneous Network Model

Zhou

Shen

et al. 2020

Front. Genet.

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Long non-coding RNAs (lncRNAs) play important roles in various biological processes, where lncRNA-protein interactions are usually involved. Therefore, identifying lncRNAprotein interactions is of great significance to understand the molecular functions of lncRNAs. Since the experiments to identify lncRNA-protein interactions are always costly and time consuming, computational methods are developed as alternative approaches. However, existing lncRNA-protein interaction predictors usually require prior knowledge of lncRNA-protein interactions with experimental evidences. Their performances are limited due to the number of known lncRNA-protein interactions. In this paper, we explored a novel way to predict lncRNA-protein interactions without direct prior knowledge. MiRNAs were picked up as mediators to estimate potential interactions between lncRNAs and proteins. By validating our results based on known lncRNA-protein interactions, our method achieved an AUROC (Area Under Receiver Operating Curve) of 0.821, which is comparable to the state-of-the-art methods. Moreover, our method achieved an improved AUROC of 0.852 by further expanding the training dataset. We believe that our method can be a useful supplement to the existing methods, as it provides an alternative way to estimate lncRNA-protein interactions in a heterogeneous network without direct prior knowledge. All data and codes of this work can be downloaded from GitHub (https://github.com/zyk2118216069/LncRNA-protein-interactions-prediction).

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NPInter v4.0: an integrated database of ncRNA interactions

Cited by 115 publications

References 57 publications

Target Enrichment Enables the Discovery of lncRNAs with Somatic Mutations or Altered Expression in Paraffin-Embedded Colorectal Cancer Samples

Target Enrichment Enables the Discovery of lncRNAs with Somatic Mutations or Altered Expression in Paraffin-Embedded Colorectal Cancer Samples

ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder

Predicting lncRNA–Protein Interactions With miRNAs as Mediators in a Heterogeneous Network Model

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