NSDNA: a manually curated database of experimentally supported ncRNAs associated with nervous system diseases

Wang, Jianjian; Cao, Yuze; Zhang, Huixue; Wang, Tianfeng; Tian, Qinghua; Lu, Xiaoyu; Lu, Xiaoyan; Kong, Xiaotong; Liu, Zhaojun; Wang, Ning; Zhang, Shuai; Ma, Heping; Ning, Shangwei; Wang, Lihua

doi:10.1093/nar/gkw1038

Cited by 39 publications

(32 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To begin with, literature published prior to March 1st, 2017 was manually searched using the protocols described in our previous study (7); the keywords 'miRNA' and 'myasthenia gravis' or 'microRNA' and 'myasthenia gravis' or 'miR' and 'myasthenia gravis' were searched in PubMed. Additionally, MG risk miRNAs were downloaded from The Nervous System Disease NcRNAome Atlas database (http://www.bio-bigdata.net/nsdna/) (19). Validated human miRNA target data was obtained from the miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/php/index.php) (20).…”

Section: Methodsmentioning

confidence: 99%

Global pathway view analysis of microRNA clusters in myasthenia gravis

Wang

Zhang

et al. 2019

Mol Med Report

Self Cite

View full text Add to dashboard Cite

The significant roles of microRNAs (miRNAs) in the pathogenesis of myasthenia gravis (MG) have been observed in numerous previous studies. The impact of miRNA clusters on immunity has been demonstrated in previous years; however, the regulation of miRNA clusters in MG remains to be elucidated. In the present study, 245 MG risk genes were collected and 99 MG risk pathways enriched by these genes were identified. A catalog of 126 MG risk miRNAs was then created; the MG risk miRNAs were located on each chromosome and a miRNA cluster was defined as a number of miRNAs with a relative distance of <6 kb on the same sub-band, same band, same region and same chromosome. Furthermore, enrichment analyses were performed using the target genes of the MG risk miRNA clusters, and a number of risk pathways of each miRNA clusters were identified. As a result, 15 significant miRNA clusters associated with MG were identified. Additionally, the most significant pathways of the miRNA clusters were identified to be enriched on chromosomes 9, 19 and 22, characterized by immunity, infection and carcinoma, suggesting that the mechanism of MG may be associated with certain abnormalities of miRNA clusters on chromosomes 9, 19 and 22. The present study provides novel insight into a global pathway view of miRNA clusters in the pathogenesis of MG.

show abstract

Section: Methodsmentioning

confidence: 99%

Global pathway view analysis of microRNA clusters in myasthenia gravis

Wang

Zhang

et al. 2019

Mol Med Report

Self Cite

View full text Add to dashboard Cite

show abstract

“…These associations are supported by 10 experiments and 4 predictive algorithms. The experimental repositories include Lnc2Cancer (Gao et al, 2018), dbDEMC ), LncRNADisease (Bao et al, 2018), MNDR (Wang et al, 2013), HMDD (Huang et al, 2018b), NSDNA (Wang et al, 2016a), LincSNP (Ning et al, 2016), miRCancer (Xie et al, 2013), PhenomiR (Ruepp et al, 2012), and miR2Disease (Jiang et al, 2008). The four prediction algorithms are LDAP (Lan et al, 2016), miRDP (Mørk et al, 2013) LncDisease (Wang et al, 2016b), and PBMDA (You et al, 2017).…”

Section: Mndrmentioning

confidence: 99%

Probing lncRNA–Protein Interactions: Data Repositories, Models, and Algorithms

Peng

Liu

Yang³

et al. 2020

Front. Genet.

View full text Add to dashboard Cite

Identifying lncRNA-protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance.Keywords: lncRNA-protein interaction, computational method, network-based method, machine learning-based method, data repositories

show abstract

“…35 DIANA-LncBase 36 was used to verify lncRNA-mRNA interactions. miRNAs potentially relevant to MG were identified using the nervous system disease NcRNAome atlas (NSDNA) prioritization tool.…”

Section: Bioinformatic Analysismentioning

confidence: 99%

“…We identified 113 miRNAs and mRNAs associated with MALAT-1 from a Starbase v2.0, 34 and 106 miRNAs associated with MG were obtained from NSDNA. 35 From the intersection of these two sets of miRNAs ( Figure 1A), we selected miR-338-3p and MSL2 for follow-up experiments. Targets were predicted using DIANA-LncBase.…”

Section: Mir-338-3p Potentially Interacts With Malat-1mentioning

confidence: 99%

The long noncoding RNA MALAT‐1 functions as a competing endogenous RNA to regulate MSL2 expression by sponging miR‐338‐3p in myasthenia gravis

Kong

Wang

Cao

et al. 2018

J of Cellular Biochemistry

Self Cite

View full text Add to dashboard Cite

Myasthenia gravis (MG) is a cell‐dependent autoimmune disease commonly associated with thymic pathology. Metastasis‐associated lung adenocarcinoma transcript 1 (MALAT‐1) has been associated with gene regulation and alternative splicing. It has shown relationship with proliferation, apoptosis, migration, and invasion. In this study, we found that MALAT‐1 expression was downregulated in MG. The level of the miR‐338‐3p was increased in peripheral blood mononuclear cells from MG patients compared with those from control subjects. MALAT‐1 competed for binding to miR‐338‐3p with male‐specific lethal 2 (MSL2) in luciferase reporter assays. We confirmed the MALAT‐1‐miR‐338‐3p‐MSL2 interaction network in MG in vitro. Thus, MALAT‐1 directly induced MSL2 expression in MG by acting as a competing endogenous RNA for miR‐338‐3p, suggesting that it may serve as a therapeutic target for MG treatment.

show abstract

NSDNA: a manually curated database of experimentally supported ncRNAs associated with nervous system diseases

Cited by 39 publications

References 31 publications

Global pathway view analysis of microRNA clusters in myasthenia gravis

Global pathway view analysis of microRNA clusters in myasthenia gravis

Probing lncRNA–Protein Interactions: Data Repositories, Models, and Algorithms

The long noncoding RNA MALAT‐1 functions as a competing endogenous RNA to regulate MSL2 expression by sponging miR‐338‐3p in myasthenia gravis

Contact Info

Product

Resources

About