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  • Oxford University Press (OUP)  (5)
  • Li, Chunquan  (5)
  • Pan, Qi  (5)
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  • Oxford University Press (OUP)  (5)
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  • 1
    Online Resource
    Online Resource
    LncSEA: a platform for long non-coding RNA related sets and enrichment analysis
    Oxford University Press (OUP) ; 2021
    In:  Nucleic Acids Research Vol. 49, No. D1 ( 2021-01-08), p. D969-D980
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 49, No. D1 ( 2021-01-08), p. D969-D980
    Abstract: Long non-coding RNAs (lncRNAs) have been proven to play important roles in transcriptional processes and various biological functions. Establishing a comprehensive collection of human lncRNA sets is urgent work at present. Using reference lncRNA sets, enrichment analyses will be useful for analyzing lncRNA lists of interest submitted by users. Therefore, we developed a human lncRNA sets database, called LncSEA, which aimed to document a large number of available resources for human lncRNA sets and provide annotation and enrichment analyses for lncRNAs. LncSEA supports & gt;40 000 lncRNA reference sets across 18 categories and 66 sub-categories, and covers over 50 000 lncRNAs. We not only collected lncRNA sets based on downstream regulatory data sources, but also identified a large number of lncRNA sets regulated by upstream transcription factors (TFs) and DNA regulatory elements by integrating TF ChIP-seq, DNase-seq, ATAC-seq and H3K27ac ChIP-seq data. Importantly, LncSEA provides annotation and enrichment analyses of lncRNA sets associated with upstream regulators and downstream targets. In summary, LncSEA is a powerful platform that provides a variety of types of lncRNA sets for users, and supports lncRNA annotations and enrichment analyses. The LncSEA database is freely accessible at http://bio.liclab.net/LncSEA/index.php.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkaa806
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1472175-2
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
  • 2
    Online Resource
    Online Resource
    ENdb: a manually curated database of experimentally supported enhancers for human and mouse
    Oxford University Press (OUP) ; 2019
    In:  Nucleic Acids Research ( 2019-10-29)
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), ( 2019-10-29)
    Abstract: Enhancers are a class of cis-regulatory elements that can increase gene transcription by forming loops in intergenic regions, introns and exons. Enhancers, as well as their associated target genes, and transcription factors (TFs) that bind to them, are highly associated with human disease and biological processes. Although some enhancer databases have been published, most only focus on enhancers identified by high-throughput experimental techniques. Therefore, it is highly desirable to construct a comprehensive resource of manually curated enhancers and their related information based on low-throughput experimental evidences. Here, we established a comprehensive manually-curated enhancer database for human and mouse, which provides a resource for experimentally supported enhancers, and to annotate the detailed information of enhancers. The current release of ENdb documents 737 experimentally validated enhancers and their related information, including 384 target genes, 263 TFs, 110 diseases and 153 functions in human and mouse. Moreover, the enhancer-related information was supported by experimental evidences, such as RNAi, in vitro knockdown, western blotting, qRT-PCR, luciferase reporter assay, chromatin conformation capture (3C) and chromosome conformation capture-on-chip (4C) assays. ENdb provides a user-friendly interface to query, browse and visualize the detailed information of enhancers. The database is available at http://www.licpathway.net/ENdb.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkz973
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 1472175-2
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
  • 3
    Online Resource
    Online Resource
    ATACdb: a comprehensive human chromatin accessibility database
    Oxford University Press (OUP) ; 2021
    In:  Nucleic Acids Research Vol. 49, No. D1 ( 2021-01-08), p. D55-D64
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 49, No. D1 ( 2021-01-08), p. D55-D64
    Abstract: Accessible chromatin is a highly informative structural feature for identifying regulatory elements, which provides a large amount of information about transcriptional activity and gene regulatory mechanisms. Human ATAC-seq datasets are accumulating rapidly, prompting an urgent need to comprehensively collect and effectively process these data. We developed a comprehensive human chromatin accessibility database (ATACdb, http://www.licpathway.net/ATACdb), with the aim of providing a large amount of publicly available resources on human chromatin accessibility data, and to annotate and illustrate potential roles in a tissue/cell type-specific manner. The current version of ATACdb documented a total of 52 078 883 regions from over 1400 ATAC-seq samples. These samples have been manually curated from over 2200 chromatin accessibility samples from NCBI GEO/SRA. To make these datasets more accessible to the research community, ATACdb provides a quality assurance process including four quality control (QC) metrics. ATACdb provides detailed (epi)genetic annotations in chromatin accessibility regions, including super-enhancers, typical enhancers, transcription factors (TFs), common single-nucleotide polymorphisms (SNPs), risk SNPs, eQTLs, LD SNPs, methylations, chromatin interactions and TADs. Especially, ATACdb provides accurate inference of TF footprints within chromatin accessibility regions. ATACdb is a powerful platform that provides the most comprehensive accessible chromatin data, QC, TF footprint and various other annotations.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkaa943
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1472175-2
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
  • 4
    Online Resource
    Online Resource
    TcoFBase: a comprehensive database for decoding the regulatory transcription co-factors in human and mouse
    Oxford University Press (OUP) ; 2022
    In:  Nucleic Acids Research Vol. 50, No. D1 ( 2022-01-07), p. D391-D401
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 50, No. D1 ( 2022-01-07), p. D391-D401
    Abstract: Transcription co-factors (TcoFs) play crucial roles in gene expression regulation by communicating regulatory cues from enhancers to promoters. With the rapid accumulation of TcoF associated chromatin immunoprecipitation sequencing (ChIP-seq) data, the comprehensive collection and integrative analyses of these data are urgently required. Here, we developed the TcoFBase database (http://tcof.liclab.net/TcoFbase), which aimed to document a large number of available resources for mammalian TcoFs and provided annotations and enrichment analyses of TcoFs. TcoFBase curated 2322 TcoFs and 6759 TcoFs associated ChIP-seq data from over 500 tissues/cell types in human and mouse. Importantly, TcoFBase provided detailed and abundant (epi) genetic annotations of ChIP-seq based TcoF binding regions. Furthermore, TcoFBase supported regulatory annotation information and various functional annotations for TcoFs. Meanwhile, TcoFBase embedded five types of TcoF regulatory analyses for users, including TcoF gene set enrichment, TcoF binding genomic region annotation, TcoF regulatory network analysis, TcoF-TF co-occupancy analysis and TcoF regulatory axis analysis. TcoFBase was designed to be a useful resource that will help reveal the potential biological effects of TcoFs and elucidate TcoF-related regulatory mechanisms.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkab950
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1472175-2
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
  • 5
    Online Resource
    Online Resource
    HiFreSP: A novel high-frequency sub-pathway mining approach to identify robust prognostic gene signatures
    Oxford University Press (OUP) ; 2020
    In:  Briefings in Bioinformatics Vol. 21, No. 4 ( 2020-07-15), p. 1411-1424
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 21, No. 4 ( 2020-07-15), p. 1411-1424
    Abstract: With the increasing awareness of heterogeneity in cancers, better prediction of cancer prognosis is much needed for more personalized treatment. Recently, extensive efforts have been made to explore the variations in gene expression for better prognosis. However, the prognostic gene signatures predicted by most existing methods have little robustness among different datasets of the same cancer. To improve the robustness of the gene signatures, we propose a novel high-frequency sub-pathways mining approach (HiFreSP), integrating a randomization strategy with gene interaction pathways. We identified a six-gene signature (CCND1, CSF3R, E2F2, JUP, RARA and TCF7) in esophageal squamous cell carcinoma (ESCC) by HiFreSP. This signature displayed a strong ability to predict the clinical outcome of ESCC patients in two independent datasets (log-rank test, P = 0.0045 and 0.0087). To further show the predictive performance of HiFreSP, we applied it to two other cancers: pancreatic adenocarcinoma and breast cancer. The identified signatures show high predictive power in all testing datasets of the two cancers. Furthermore, compared with the two popular prognosis signature predicting methods, the least absolute shrinkage and selection operator penalized Cox proportional hazards model and the random survival forest, HiFreSP showed better predictive accuracy and generalization across all testing datasets of the above three cancers. Lastly, we applied HiFreSP to 8137 patients involving 20 cancer types in the TCGA database and found high-frequency prognosis-associated pathways in many cancers. Taken together, HiFreSP shows higher prognostic capability and greater robustness, and the identified signatures provide clinical guidance for cancer prognosis. HiFreSP is freely available via GitHub: https://github.com/chunquanlipathway/HiFreSP.
    Type of Medium: Online Resource
    ISSN: 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbz078
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2036055-1
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