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      LncDisease: a sequence based bioinformatics tool for predicting lncRNA-disease associations

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          Abstract

          LncRNAs represent a large class of noncoding RNA molecules that have important functions and play key roles in a variety of human diseases. There is an urgent need to develop bioinformatics tools as to gain insight into lncRNAs. This study developed a sequence-based bioinformatics method, LncDisease, to predict the lncRNA-disease associations based on the crosstalk between lncRNAs and miRNAs. Using LncDisease, we predicted the lncRNAs associated with breast cancer and hypertension. The breast-cancer-associated lncRNAs were studied in two breast tumor cell lines, MCF-7 and MDA-MB-231. The qRT-PCR results showed that 11 (91.7%) of the 12 predicted lncRNAs could be validated in both breast cancer cell lines. The hypertension-associated lncRNAs were further evaluated in human vascular smooth muscle cells (VSMCs) stimulated with angiotensin II (Ang II). The qRT-PCR results showed that 3 (75.0%) of the 4 predicted lncRNAs could be validated in Ang II-treated human VSMCs. In addition, we predicted 6 diseases associated with the lncRNA GAS5 and validated 4 (66.7%) of them by literature mining. These results greatly support the specificity and efficacy of LncDisease in the study of lncRNAs in human diseases. The LncDisease software is freely available on the Software Page: http://www.cuilab.cn/.

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          Most cited references23

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          RNA maps reveal new RNA classes and a possible function for pervasive transcription.

          Significant fractions of eukaryotic genomes give rise to RNA, much of which is unannotated and has reduced protein-coding potential. The genomic origins and the associations of human nuclear and cytosolic polyadenylated RNAs longer than 200 nucleotides (nt) and whole-cell RNAs less than 200 nt were investigated in this genome-wide study. Subcellular addresses for nucleotides present in detected RNAs were assigned, and their potential processing into short RNAs was investigated. Taken together, these observations suggest a novel role for some unannotated RNAs as primary transcripts for the production of short RNAs. Three potentially functional classes of RNAs have been identified, two of which are syntenically conserved and correlate with the expression state of protein-coding genes. These data support a highly interleaved organization of the human transcriptome.
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            A coding-independent function of gene and pseudogene mRNAs regulates tumour biology

            The canonical role of messenger RNA (mRNA) is to deliver protein-coding information to sites of protein synthesis. However, given that microRNAs bind to RNAs, we hypothesized that RNAs possess a biological role in cancer cells that relies upon their ability to compete for microRNA binding and is independent of their protein-coding function. As a paradigm for the protein-coding-independent role of RNAs, we describe the functional relationship between the mRNAs produced by the PTEN tumour suppressor gene and its pseudogene (PTENP1) and the critical consequences of this interaction. We find that PTENP1 is biologically active as determined by its ability to regulate cellular levels of PTEN, and that it can exert a growth-suppressive role. We also show that PTENP1 locus is selectively lost in human cancer. We extend our analysis to other cancer-related genes that possess pseudogenes, such as oncogenic KRAS. Further, we demonstrate that the transcripts of protein coding genes such as PTEN are also biologically active. Together, these findings attribute a novel biological role to expressed pseudogenes, as they can regulate coding gene expression, and reveal a non-coding function for mRNAs.
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              Global identification of human transcribed sequences with genome tiling arrays.

              Elucidating the transcribed regions of the genome constitutes a fundamental aspect of human biology, yet this remains an outstanding problem. To comprehensively identify coding sequences, we constructed a series of high-density oligonucleotide tiling arrays representing sense and antisense strands of the entire nonrepetitive sequence of the human genome. Transcribed sequences were located across the genome via hybridization to complementary DNA samples, reverse-transcribed from polyadenylated RNA obtained from human liver tissue. In addition to identifying many known and predicted genes, we found 10,595 transcribed sequences not detected by other methods. A large fraction of these are located in intergenic regions distal from previously annotated genes and exhibit significant homology to other mammalian proteins.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                19 May 2016
                16 February 2016
                16 February 2016
                : 44
                : 9
                : e90
                Affiliations
                [1 ]Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing 100191, China
                [2 ]MOE Key Lab of Cardiovascular Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
                [3 ]Mitchell Cancer Institute, University of South Alabama, 1160 Springhill Ave, Mobile, AL 36604, USA
                [4 ]Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing 100191, China
                [5 ]Beijing Key Laboratory of Tumor Systems Biology, Peking University, 38 Xueyuan Road, Beijing 100191, China
                Author notes
                [* ]To whom the correspondence should be addressed. Tel: +86 10 82801585; Fax: +86 10 82801001; Email: cuiqinghua@ 123456bjmu.edu.cn
                Correspondence may also be addressed to Jichun Yang. Tel: +86 10 82805613; Fax: +86 10 82805613; Email: yangj@ 123456bjmu.edu.cn
                Correspondence may also be addressed to Yaguang Xi. Tel: +1 251 445 9857; Fax: +1 251 460 6994; Email: xi@ 123456health.southalabama.edu
                []These authors contributed equally to this work as the first authors.
                Article
                10.1093/nar/gkw093
                4872090
                26887819
                f0a38fa0-b550-4895-8f23-4d9d455d327d
                © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 06 February 2016
                : 04 February 2016
                : 02 November 2015
                Page count
                Pages: 8
                Categories
                Methods Online
                Custom metadata
                19 May 2016

                Genetics
                Genetics

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