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      LncTarD 2.0: an updated comprehensive database for experimentally-supported functional lncRNA–target regulations in human diseases

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          Abstract

          An updated LncTarD 2.0 database provides a comprehensive resource on key lncRNA–target regulations, their influenced functions and lncRNA-mediated regulatory mechanisms in human diseases. LncTarD 2.0 is freely available at ( http://bio-bigdata.hrbmu.edu.cn/LncTarD or https://lnctard.bio-database.com/). LncTarD 2.0 was updated with several new features, including (i) an increased number of disease-associated lncRNA entries, where the current release provides 8360 key lncRNA–target regulations, with 419 disease subtypes and 1355 lncRNAs; (ii) predicted 3312 out of 8360 lncRNA–target regulations as potential diagnostic or therapeutic biomarkers in circulating tumor cells (CTCs); (iii) addition of 536 new, experimentally supported lncRNA–target regulations that modulate properties of cancer stem cells; (iv) addition of an experimentally supported clinical application section of 2894 lncRNA–target regulations for potential clinical application. Importantly, LncTarD 2.0 provides RNA-seq/microarray and single-cell web tools for customizable analysis and visualization of lncRNA–target regulations in diseases. RNA-seq/microarray web tool was used to mining lncRNA–target regulations in both disease tissue samples and CTCs blood samples. The single-cell web tools provide single-cell lncRNA–target annotation from the perspectives of pan-cancer analysis and cancer-specific analysis at the single-cell level. LncTarD 2.0 will be a useful resource and mining tool for the investigation of the functions and mechanisms of lncRNA deregulation in human disease.

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          Most cited references50

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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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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              NCBI GEO: archive for functional genomics data sets—update

              The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.
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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
                06 January 2023
                02 November 2022
                02 November 2022
                : 51
                : D1
                : D199-D207
                Affiliations
                College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081, China
                College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081, China
                College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081, China
                College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081, China
                College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081, China
                College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081, China
                College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081, China
                College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081, China
                College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081, China
                College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081, China
                College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081, China
                College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081, China
                College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081, China
                College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081, China
                Author notes
                To whom correspondence should be addressed. Tel: +86 451 86615922; Email: wangli@ 123456hrbmu.edu.cn
                Correspondence may also be addressed to Yunpeng Zhang. Tel: +86 451 86615922; Email: zhangyp@ 123456hrbmu.edu.cn
                Correspondence may also be addressed to Shangwei Ning. Tel: +86 451 86615922; Email: ningsw@ 123456ems.hrbmu.edu.cn

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

                Author information
                https://orcid.org/0000-0003-4079-8945
                https://orcid.org/0000-0002-1936-8513
                Article
                gkac984
                10.1093/nar/gkac984
                9825480
                36321659
                9c619c59-15ea-4575-90ea-56fdc1792c20
                © The Author(s) 2022. 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-NonCommercial License ( https://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
                : 31 October 2022
                : 05 October 2022
                : 08 September 2022
                Page count
                Pages: 9
                Funding
                Funded by: University Nursing Program for Young Scholar with Creative Talents in Heilongjiang Province, DOI 10.13039/501100012676;
                Award ID: UNPYSCT-2020174
                Funded by: Excellent Youth Project of Provincial scientific research Institute;
                Award ID: CZKYF2022-1-C006
                Funded by: Hei Long Jiang Postdoctoral Special Foundation;
                Award ID: LBH-TZ1018
                Categories
                AcademicSubjects/SCI00010
                Database Issue

                Genetics
                Genetics

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