A bioinformatics method for predicting long noncoding RNAs associated with vascular disease

Li, Jianwei; Gao, Canzhu; Wang, YuChen; Ma, Wei; Tu, Jian; Wang, Junpei; Chen, Zhenzhen; Kong, Wei; Cui, Qinghua

doi:10.1007/s11427-014-4692-4

Cited by 48 publications

(31 citation statements)

References 27 publications

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“…This non-unique mapping problem also exists in RNAseq and MS. Research shows that similar sequences may have similar functions [37]. Other study shows that near transcripts in the genome tend to have similar functions [38]. Therefore, it is reasonable to think that the smORFs that match to the same probe may have similar functions, reducing the impact of quantitative inaccuracies caused by non-unique mapping.…”

Section: Discussionmentioning

confidence: 99%

smORFunction: A Tool for Predicting Functions of Small Open Reading Frames and Microproteins

Cui

2020

Preprint

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Background Small open reading frame (smORF) is open reading frame with a length of less than 100 codons. Microproteins, translated from smORFs, have been found to participate in a variety of biological processes such as muscle formation and contraction, cell proliferation, and immune activation. Although previous studies have collected and annotated a large abundance of smORFs, functions of the vast majority of smORFs are still unknown. It is thus increasingly important to develop computational methods to annotate the functions of these smORFs. Results In this study, we collected 617,462 unique smORFs from three studies. The expression of smORF RNAs was estimated by reannotated microarray probes. Using a speed-optimized correlation algorism, the functions of smORFs were predicted by their correlated genes with known functional annotations. After applying our method to 5 known microproteins from literatures, our method successfully predicted their functions. Further validation from the UniProt database showed that at least one function of 202 out of 270 microproteins was predicted. Conclusions We developed a method, smORFunction, to provide function predictions of smORFs/microproteins in at most 265 models generated from 173 datasets, including 48 tissues/cells, 82 diseases (and normal). The tool can be available at http://www.cuilab.cn/smorfunction.

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Section: Discussionmentioning

confidence: 99%

smORFunction: A Tool for Predicting Functions of Small Open Reading Frames and Microproteins

Cui

2020

Preprint

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“…According to the previous studies, there occur three complementary ways to predict the unknown function of lncRNA, including enrichment analysis of lncRNA's genomic neighbors, competing endogenous RNAs (ceRNAs) neighbors and gene co-expression neighbors. In this study, we chose the upstream and downstream 50kb flanking region of lncRNA gene as its neighbor region [23].…”

Section: Establishment Of Functional Neighbor Genes Of Asd-related Lnmentioning

confidence: 99%

“…Gene co-expression analysis and genomic neighbor region analysis are recognized as two traditional annotation ways of the biological functions and associated conditions of uncharacterized lncRNAs, which have a wide range of applications [22,23]. Competing endogenous RNA (ceRNA) crosstalk, which refers to a hypothesis that all RNA transcripts can communicate with and regulate each other through competing to bind shared miRNAs, has been increasingly recognized as an important way to study gene functions and associated biological mechanisms [24].…”

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confidence: 99%

Identification and functional analysis of long non-coding RNAs in autism spectrum disorders

Tong

Zhou

Wang

2020

Preprint

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BackgroundGenetic and environmental factors, alone or in combination, contribute to the pathogenesis of autism spectrum disorder (ASD). Although many protein-coding genes have now been identified as disease risk genes for ASD, a detailed illustration of long non-coding RNAs (lncRNAs) associated with ASD remains elusive. In this study, our aim was to identify ASD-related lncRNAs and explore their functions and associated biological pathways in autism. MethodsASD-related lncRNAs were identified based on genomic variant data of individuals with ASD from a twin study, and further validated using an independent copy number variant (CNV) dataset.The functions and associated biological pathways of ASD-related lncRNAs were explored by enrichment analysis of three different types of functional neighbor genes (i.e. genomic neighbors, competing endogenous RNA (ceRNA) neighbors and gene co-expression neighbors in the cortex).The differential functions of ASD-related lncRNAs in distinct brain regions were demonstrated by using gene co-expression network analysis based on tissue-specific gene expression profiles.Moreover, a functional network analysis were conducted for highly reliable functional neighbor genes of ASD-related lncRNAs. Finally, several potential drugs were predicted based on the enrichment of drug-induced pathway sets in ASD-altered biological pathway list. ResultsIn total, 532 ASD-related lncRNAs were identified, and 86.7% of these ASD-related lncRNAs 2 were further validated by a copy number variant (CNV) dataset. Most of functional neighbor genes of ASD-related lncRNAs were enriched in several functions and biological pathways, including nervous system development, inflammatory response and transcriptional regulation. As a set, ASD-related lncRNAs were mainly associated with nervous system development and dopaminergic synapse in the cortex, but associated with transcriptional regulation in the cerebellum. Moreover, all highly reliable functional neighbor genes were connected in a single functional network. Finally, several potential drugs were predicted and partly supported by the previous reports. ConclusionsWe concluded that ASD-related lncRNAs participate in the pathogenesis of ASD through various known biological pathways, which may be differential in distinct brain regions. And detailed investigation of ASD-related lncRNAs also provided clues for developing potential ASD diagnosis biomarker and therapy.

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“…However, this model cannot be used for diseases that have not any experiment-validated associated genes. Li et al proposed a genome location based model for screening human vascular disease-associated lncRNAs [26]. However, this model is invalid for lncRNAs that have no neighbour genes.…”

Section: Introductionmentioning

confidence: 99%

A random forest based computational model for predicting novel lncRNA-disease associations

Yao

Zhan

et al. 2020

BMC Bioinformatics

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Background: Accumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources. However, most of the existing models ignore the interference of noisy and redundancy information among these data resources. Results: To improve the ability of LDA prediction models, we implemented a random forest and feature selection based LDA prediction model (RFLDA in short). First, the RFLDA integrates the experiment-supported miRNA-disease associations (MDAs) and LDAs, the disease semantic similarity (DSS), the lncRNA functional similarity (LFS) and the lncRNA-miRNA interactions (LMI) as input features. Then, the RFLDA chooses the most useful features to train prediction model by feature selection based on the random forest variable importance score that takes into account not only the effect of individual feature on prediction results but also the joint effects of multiple features on prediction results. Finally, a random forest regression model is trained to score potential lncRNA-disease associations. In terms of the area under the receiver operating characteristic curve (AUC) of 0.976 and the area under the precision-recall curve (AUPR) of 0.779 under 5-fold cross-validation, the performance of the RFLDA is better than several state-of-the-art LDA prediction models. Moreover, case studies on three cancers demonstrate that 43 of the 45 lncRNAs predicted by the RFLDA are validated by experimental data, and the other two predicted lncRNAs are supported by other LDA prediction models. Conclusions: Cross-validation and case studies indicate that the RFLDA has excellent ability to identify potential disease-associated lncRNAs.

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A bioinformatics method for predicting long noncoding RNAs associated with vascular disease

Cited by 48 publications

References 27 publications

smORFunction: A Tool for Predicting Functions of Small Open Reading Frames and Microproteins

smORFunction: A Tool for Predicting Functions of Small Open Reading Frames and Microproteins

Identification and functional analysis of long non-coding RNAs in autism spectrum disorders

A random forest based computational model for predicting novel lncRNA-disease associations

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