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      DeepLGP: a novel deep learning method for prioritizing lncRNA target genes

      1 , 2 , 3 , 2 , 4
      Bioinformatics
      Oxford University Press (OUP)

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          Abstract

          Motivation

          Although long non-coding RNAs (lncRNAs) have limited capacity for encoding proteins, they have been verified as biomarkers in the occurrence and development of complex diseases. Recent wet-lab experiments have shown that lncRNAs function by regulating the expression of protein-coding genes (PCGs), which could also be the mechanism responsible for causing diseases. Currently, lncRNA-related biological data are increasing rapidly. Whereas, no computational methods have been designed for predicting the novel target genes of lncRNA.

          Results

          In this study, we present a graph convolutional network (GCN) based method, named DeepLGP, for prioritizing target PCGs of lncRNA. First, gene and lncRNA features were selected, these included their location in the genome, expression in 13 tissues and miRNA-mediated lncRNA–gene pairs. Next, GCN was applied to convolve a gene interaction network for encoding the features of genes and lncRNAs. Then, these features were used by the convolutional neural network for prioritizing target genes of lncRNAs. In 10-cross validations on two independent datasets, DeepLGP obtained high area under curves (0.90–0.98) and area under precision-recall curves (0.91–0.98). We found that lncRNA pairs with high similarity had more overlapped target genes. Further experiments showed that genes targeted by the same lncRNA sets had a strong likelihood of causing the same diseases, which could help in identifying disease-causing PCGs.

          Availability and implementation

          https://github.com/zty2009/LncRNA-target-gene.

          Supplementary information

          Supplementary data are available at Bioinformatics online.

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          Most cited references36

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          Unique features of long non-coding RNA biogenesis and function.

          Long non-coding RNAs (lncRNAs) are a diverse class of RNAs that engage in numerous biological processes across every branch of life. Although initially discovered as mRNA-like transcripts that do not encode proteins, recent studies have revealed features of lncRNAs that further distinguish them from mRNAs. In this Review, we describe special events in the lifetimes of lncRNAs - before, during and after transcription - and discuss how these events ultimately shape the unique characteristics and functional roles of lncRNAs.
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            Local regulation of gene expression by lncRNA promoters, transcription and splicing.

            Mammalian genomes are pervasively transcribed to produce thousands of long non-coding RNAs (lncRNAs). A few of these lncRNAs have been shown to recruit regulatory complexes through RNA-protein interactions to influence the expression of nearby genes, and it has been suggested that many other lncRNAs can also act as local regulators. Such local functions could explain the observation that lncRNA expression is often correlated with the expression of nearby genes. However, these correlations have been challenging to dissect and could alternatively result from processes that are not mediated by the lncRNA transcripts themselves. For example, some gene promoters have been proposed to have dual functions as enhancers, and the process of transcription itself may contribute to gene regulation by recruiting activating factors or remodelling nucleosomes. Here we use genetic manipulation in mouse cell lines to dissect 12 genomic loci that produce lncRNAs and find that 5 of these loci influence the expression of a neighbouring gene in cis. Notably, none of these effects requires the specific lncRNA transcripts themselves and instead involves general processes associated with their production, including enhancer-like activity of gene promoters, the process of transcription, and the splicing of the transcript. Furthermore, such effects are not limited to lncRNA loci: we find that four out of six protein-coding loci also influence the expression of a neighbour. These results demonstrate that cross-talk among neighbouring genes is a prevalent phenomenon that can involve multiple mechanisms and cis-regulatory signals, including a role for RNA splice sites. These mechanisms may explain the function and evolution of some genomic loci that produce lncRNAs and broadly contribute to the regulation of both coding and non-coding genes.
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              A long noncoding RNA maintains active chromatin to coordinate homeotic gene expression.

              The genome is extensively transcribed into long intergenic noncoding RNAs (lincRNAs), many of which are implicated in gene silencing. Potential roles of lincRNAs in gene activation are much less understood. Development and homeostasis require coordinate regulation of neighbouring genes through a process termed locus control. Some locus control elements and enhancers transcribe lincRNAs, hinting at possible roles in long-range control. In vertebrates, 39 Hox genes, encoding homeodomain transcription factors critical for positional identity, are clustered in four chromosomal loci; the Hox genes are expressed in nested anterior-posterior and proximal-distal patterns colinear with their genomic position from 3' to 5'of the cluster. Here we identify HOTTIP, a lincRNA transcribed from the 5' tip of the HOXA locus that coordinates the activation of several 5' HOXA genes in vivo. Chromosomal looping brings HOTTIP into close proximity to its target genes. HOTTIP RNA binds the adaptor protein WDR5 directly and targets WDR5/MLL complexes across HOXA, driving histone H3 lysine 4 trimethylation and gene transcription. Induced proximity is necessary and sufficient for HOTTIP RNA activation of its target genes. Thus, by serving as key intermediates that transmit information from higher order chromosomal looping into chromatin modifications, lincRNAs may organize chromatin domains to coordinate long-range gene activation. ©2011 Macmillan Publishers Limited. All rights reserved
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                Author and article information

                Contributors
                Journal
                Bioinformatics
                Oxford University Press (OUP)
                1367-4803
                1460-2059
                August 15 2020
                August 15 2020
                May 29 2020
                August 15 2020
                August 15 2020
                May 29 2020
                : 36
                : 16
                : 4466-4472
                Affiliations
                [1 ]College of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
                [2 ]College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
                [3 ]School of Computer Science, Northwestern Polytechnical University, Xian, Shanxi 710072, China
                [4 ]NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Harbin Medical University, Harbin, Heilongjiang 150028, China
                Article
                10.1093/bioinformatics/btaa428
                32467970
                b2f3bd93-c65f-486b-aeee-554fa55f907a
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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