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      lncRNAfunc: a knowledgebase of lncRNA function in human cancer

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          Abstract

          The long non-coding RNAs associating with other molecules can coordinate several physiological processes and their dysfunction can impact diverse human diseases. To date, systematic and intensive annotations on diverse interaction regulations of lncRNAs in human cancer were not available. Here, we built lncRNAfunc, a knowledgebase of lncRNA function in human cancer at https://ccsm.uth.edu/lncRNAfunc, aiming to provide a resource and reference for providing therapeutically targetable lncRNAs and intensive interaction regulations. To do this, we collected 15 900 lncRNAs across 33 cancer types from TCGA. For individual lncRNAs, we performed multiple interaction analyses of different biomolecules including DNA, RNA, and protein levels. Our intensive studies of lncRNAs provide diverse potential mechanisms of lncRNAs that regulate gene expression through binding enhancers and 3′-UTRs of genes, competing for miRNA binding sites with mRNAs, recruiting the transcription factors to gene promoters. Furthermore, we investigated lncRNAs that potentially affect the alternative splicing events through interacting with RNA binding Proteins. We also performed multiple functional annotations including cancer stage-associated lncRNAs, RNA A-to-I editing event-associated lncRNAs, and lncRNA expression quantitative trait loci. lncRNAfunc is a unique resource for cancer research communities to help better understand potential lncRNA regulations and therapeutic lncRNA targets.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

            Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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              starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data

              Although microRNAs (miRNAs), other non-coding RNAs (ncRNAs) (e.g. lncRNAs, pseudogenes and circRNAs) and competing endogenous RNAs (ceRNAs) have been implicated in cell-fate determination and in various human diseases, surprisingly little is known about the regulatory interaction networks among the multiple classes of RNAs. In this study, we developed starBase v2.0 (http://starbase.sysu.edu.cn/) to systematically identify the RNA–RNA and protein–RNA interaction networks from 108 CLIP-Seq (PAR-CLIP, HITS-CLIP, iCLIP, CLASH) data sets generated by 37 independent studies. By analyzing millions of RNA-binding protein binding sites, we identified ∼9000 miRNA-circRNA, 16 000 miRNA-pseudogene and 285 000 protein–RNA regulatory relationships. Moreover, starBase v2.0 has been updated to provide the most comprehensive CLIP-Seq experimentally supported miRNA-mRNA and miRNA-lncRNA interaction networks to date. We identified ∼10 000 ceRNA pairs from CLIP-supported miRNA target sites. By combining 13 functional genomic annotations, we developed miRFunction and ceRNAFunction web servers to predict the function of miRNAs and other ncRNAs from the miRNA-mediated regulatory networks. Finally, we developed interactive web implementations to provide visualization, analysis and downloading of the aforementioned large-scale data sets. This study will greatly expand our understanding of ncRNA functions and their coordinated regulatory networks.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                07 January 2022
                17 November 2021
                17 November 2021
                : 50
                : D1
                : D1295-D1306
                Affiliations
                West China Biomedical Big Data Center, West China Hospital, Sichuan University , Chengdu 610041, China
                Med-X Center for Informatics, Sichuan University , Chengdu 610041, China
                Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston , Houston, TX 77030, USA
                West China Biomedical Big Data Center, West China Hospital, Sichuan University , Chengdu 610041, China
                Med-X Center for Informatics, Sichuan University , Chengdu 610041, China
                Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston , Houston, TX 77030, USA
                College of Electronic and Information Engineering, Tongji University , Shanghai, Shanghai 201804, China
                School of Life Sciences and Technology, Xidian University , Xi’an 710126, China
                Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston , Houston, TX 77030, USA
                Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston , Houston, TX 77030, USA
                McGovern Medical School, The University of Texas Health Science Center at Houston , Houston, TX 77030, USA
                School of Dentistry, The University of Texas Health Science Center at Houston , Houston, TX 77030, USA
                Author notes
                To whom correspondence should be addressed. Tel: +1 713 500 3923 and 3636; Email: xiaobo.zhou@ 123456uth.tmc.edu
                Correspondence may also be addressed to Pora Kim. Email: Pora.Kim@ 123456uth.tmc.edu
                Correspondence may also be addressed to Mengyuan Yang. Email:  Mengyuan.Yang@ 123456uth.tmc.edu

                The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.

                Author information
                https://orcid.org/0000-0002-8321-6864
                https://orcid.org/0000-0001-7191-6495
                Article
                gkab1035
                10.1093/nar/gkab1035
                8728133
                34791419
                bb773844-db28-4cea-bab1-c74e73e96123
                © The Author(s) 2021. 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 ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 October 2021
                : 13 October 2021
                : 15 August 2021
                Page count
                Pages: 12
                Funding
                Funded by: Clinical Research Incubation;
                Award ID: 2019HXFH022
                Funded by: disciplines of excellence;
                Award ID: ZYJC18010
                Categories
                AcademicSubjects/SCI00010
                Database Issue

                Genetics
                Genetics

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