CoChat
Sign Up
Cited by 134MicroRNA in disease regulationGenetic Associations and EpidemiologyRNA Research and Splicing

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

miRNASNP-v3: a comprehensive database for SNPs and disease-related variations in miRNAs and miRNA targets

Open access
Liu, Chun-Jie, Fu, Xin, Xia, Mengxuan, Zhang, Qiong, Gu, Zhifeng, Guo, An-Yuan·2021·Nucleic Acids Research
DOI 10.1093/nar/gkaa783PMC PMC7778889
Open this paper in CoChat — free accountOpen PDF

Abstract

MicroRNAs (miRNAs) related single-nucleotide variations (SNVs), including single-nucleotide polymorphisms (SNPs) and disease-related variations (DRVs) in miRNAs and miRNA-target binding sites, can affect miRNA functions and/or biogenesis, thus to impact on phenotypes. miRNASNP is a widely used database for miRNA-related SNPs and their effects. Here, we updated it to miRNASNP-v3 (http://bioinfo.life.hust.edu.cn/miRNASNP/) with tremendous number of SNVs and new features, especially the DRVs data. We analyzed the effects of 7 161 741 SNPs and 505 417 DRVs on 1897 pre-miRNAs (2630 mature miRNAs) and 3′UTRs of 18 152 genes. miRNASNP-v3 provides a one-stop resource for miRNA-related SNVs research with the following functions: (i) explore associations between miRNA-related SNPs/DRVs and diseases; (ii) browse the effects of SNPs/DRVs on miRNA-target binding; (iii) functional enrichment analysis of miRNA target gain/loss caused by SNPs/DRVs; (iv) investigate correlations between drug sensitivity and miRNA expression; (v) inquire expression profiles of miRNAs and their targets in cancers; (vi) browse the effects of SNPs/DRVs on pre-miRNA secondary structure changes; and (vii) predict the effects of user-defined variations on miRNA-target binding or pre-miRNA secondary structure. miRNASNP-v3 is a valuable and long-term supported resource in functional variation screening and miRNA function studies.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/nar/gkaa783

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 32

  1. Bartel, 2018, Metazoan MicroRNAs, Cell, 173, 20, 10.1016/j.cell.2018.03.006 · DOI · Paper page
  2. Kozomara, 2019, miRBase: from microRNA sequences to function, Nucleic Acids Res., 47, D155, 10.1093/nar/gky1141 · DOI · Paper page
  3. Cammaerts, 2015, Genetic variants in microRNA genes: impact on microRNA expression, function, and disease, Front. Genet., 6, 186, 10.3389/fgene.2015.00186 · DOI
  4. Ghanbari, 2017, Genetic variants in microRNAs and their binding sites within gene 3′UTRs associate with susceptibility to age-related macular degeneration, Hum. Mutat., 38, 827, 10.1002/humu.23226 · DOI
  5. Zhang, 2016, A single nucleotide polymorphism in the 3′-untranslated region of the KRAS gene disrupts the interaction with let-7a and enhances the metastatic potential of osteosarcoma cells, Int. J. Mol. Med., 38, 919, 10.3892/ijmm.2016.2661 · DOI
  6. Tang, 2015, The polymorphic terminal-loop of pre-miR-1307 binding with MBNL1 contributes to colorectal carcinogenesis via interference with Dicer1 recruitment, Carcinogenesis, 36, 867, 10.1093/carcin/bgv066 · DOI
  7. Bruno, 2012, miRdSNP: a database of disease-associated SNPs and microRNA target sites on 3′UTRs of human genes, BMC Genomics, 13, 44, 10.1186/1471-2164-13-44 · DOI
  8. Li, 2020, dbMTS: A comprehensive database of putative human microRNA target site SNVs and their functional predictions, Hum. Mutat., 41, 1123, 10.1002/humu.24020 · DOI
  9. Bhattacharya, 2014, PolymiRTS Database 3.0: linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways, Nucleic Acids Res., 42, D86, 10.1093/nar/gkt1028 · DOI
  10. Bhattacharya, 2016, SomamiR 2.0: a database of cancer somatic mutations altering microRNA-ceRNA interactions, Nucleic Acids Res., 44, D1005, 10.1093/nar/gkv1220 · DOI
  11. Yue, 2018, MSDD: a manually curated database of experimentally supported associations among miRNAs, SNPs and human diseases, Nucleic Acids Res., 46, D181, 10.1093/nar/gkx1035 · DOI
  12. Gong, 2012, Genome-wide identification of SNPs in microRNA genes and the SNP effects on microRNA target binding and biogenesis, Hum. Mutat., 33, 254, 10.1002/humu.21641 · DOI
  13. Gong, 2015, An update of miRNASNP database for better SNP selection by GWAS data, miRNA expression and online tools, Database, 2015, bav029, 10.1093/database/bav029 · DOI
  14. Fehlmann, 2019, A review of databases predicting the effects of SNPs in miRNA genes or miRNA-binding sites, Brief. Bioinform., 20, 1011, 10.1093/bib/bbx155 · DOI
  15. Sherry, 2001, dbSNP: the NCBI database of genetic variation, Nucleic Acids Res., 29, 308, 10.1093/nar/29.1.308 · DOI · Paper page
  16. Buniello, 2019, The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019, Nucleic Acids Res., 47, D1005, 10.1093/nar/gky1120 · DOI · Paper page
  17. Landrum, 2020, ClinVar: improvements to accessing data, Nucleic Acids Res., 48, D835, 10.1093/nar/gkz972 · DOI · Paper page
  18. Tate, 2019, COSMIC: the catalogue of somatic mutations in cancer, Nucleic Acids Res., 47, D941, 10.1093/nar/gky1015 · DOI · Paper page
  19. Karagkouni, 2018, DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions, Nucleic Acids Res., 46, D239, 10.1093/nar/gkx1141 · DOI · Paper page
  20. Xiao, 2009, miRecords: an integrated resource for microRNA-target interactions, Nucleic Acids Res., 37, D105, 10.1093/nar/gkn851 · DOI
  21. Huang, 2020, miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database, Nucleic Acids Res., 48, D148
  22. Jiang, 2009, miR2Disease: a manually curated database for microRNA deregulation in human disease, Nucleic Acids Res., 37, D98, 10.1093/nar/gkn714 · DOI
  23. Li, 2014, starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data, Nucleic Acids Res., 42, D92, 10.1093/nar/gkt1248 · DOI · Paper page
  24. Agarwal, 2015, Predicting effective microRNA target sites in mammalian mRNAs, eLife, 4, e05005, 10.7554/eLife.05005 · DOI · Paper page
  25. Vejnar, 2013, miRmap web: comprehensive microRNA target prediction online, Nucleic Acids Res., 41, W165, 10.1093/nar/gkt430 · DOI
  26. Faiza, 2019, Comprehensive overview and assessment of miRNA target prediction tools in human and drosophila melanogaster, Curr. Bioinforma., 14, 432, 10.2174/1574893614666190103101033 · DOI
  27. Liu, 2018, GSCALite: a web server for gene set cancer analysis, Bioinformatics, 34, 3771, 10.1093/bioinformatics/bty411 · DOI
  28. Yu, 2012, clusterProfiler: an R package for comparing biological themes among gene clusters, OMICS, 16, 284, 10.1089/omi.2011.0118 · DOI · Paper page
  29. Astle, 2016, The allelic landscape of human blood cell trait variation and links to common complex disease, Cell, 167, 1415, 10.1016/j.cell.2016.10.042 · DOI · Paper page
  30. Rajapakse, 2018, CellMinerCDB for integrative Cross-Database genomics and pharmacogenomics analyses of cancer cell lines, iScience, 10, 247, 10.1016/j.isci.2018.11.029 · DOI
  31. Lorenz, 2011, ViennaRNA Package 2.0, Algorithms Mol Biol, 6, 26, 10.1186/1748-7188-6-26 · DOI · Paper page
  32. Kichaev, 2019, Leveraging polygenic functional enrichment to improve GWAS power, Am. J. Hum. Genet., 104, 65, 10.1016/j.ajhg.2018.11.008 · DOI · Paper page

Cited by

Sorted by citations

Metadata

Authors
Liu, C.-J., Fu, X., Xia, M., Zhang, Q., Gu, Z., & Guo, A.-Y.
Affiliations
  • Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China
  • Huazhong University of Science and Technology (CN)
  • Affiliated Hospital of Nantong University (CN)
Corresponding author
  • Gu, Zhifeng
  • Guo, An-Yuan
ORCID
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2020-09-29
Volume
49
Issue
D1
Pages
D1276-D1281
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkaa783
PMC
PMC7778889
OpenAlex
W3090496870
Cited by
134
References
1
Citations by year
2026: 9 · 2025: 15 · 2024: 27 · 2023: 30 · 2022: 35 · 2021: 17 · 2020: 1
FWCI
7.11
Type
journal article
Language
en
Open access
Licence
Locations
  • Nucleic Acids Research (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
  • National Natural Science Foundation of China (31822030, 31801113, 31801154, 31771458)
  • China Postdoctoral Science Foundation (2019M652623, 2018M632830)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
Keywords
microRNA, single nucleotide polymorphisms, 3' untranslated region, microRNA biogenesis, microRNA function, drug sensitivity, cancer gene expression, functional enrichment, database updates
MeSH terms
Binding Sites, Disease, Drug Resistance, Gene Expression Regulation, Humans, Nucleic Acid Conformation, RNA Precursors, Software, Internet, 3' Untranslated Regions, Polymorphism, Single Nucleotide, Databases, Genetic, MicroRNAs, Prescription Drugs

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review