CoChat
Sign Up
Cited by 203

miRTargetLink 2.0—interactive miRNA target gene and target pathway networks

Kern, Fabian, Aparicio-Puerta, Ernesto, Li, Yongping, Fehlmann, Tobias, Kehl, Tim, Wagner, Viktoria, Ray, Kamalika, Ludwig, Nicole +3 more·2021·Nucleic Acids Research
DOI 10.1093/nar/gkab297PMC PMC8262750
Open this paper in CoChat — free account

Abstract

Which genes, gene sets or pathways are regulated by certain miRNAs? Which miRNAs regulate a particular target gene or target pathway in a certain physiological context? Answering such common research questions can be time consuming and labor intensive. Especially for researchers without computational experience, the integration of different data sources, selection of the right parameters and concise visualization can be demanding. A comprehensive analysis should be central to present adequate answers to complex biological questions. With miRTargetLink 2.0, we develop an all-in-one solution for human, mouse and rat miRNA networks. Users input in the unidirectional search mode either a single gene, gene set or gene pathway, alternatively a single miRNA, a set of miRNAs or an miRNA pathway. Moreover, genes and miRNAs can jointly be provided to the tool in the bidirectional search mode. For the selected entities, interaction graphs are generated from different data sources and dynamically presented. Connected application programming interfaces (APIs) to the tailored enrichment tools miEAA and GeneTrail facilitate downstream analysis of pathways and context-annotated categories of network nodes. MiRTargetLink 2.0 is freely accessible at https://www.ccb.uni-saarland.de/mirtargetlink2.

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/gkab297

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

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.

Cites

Flagged first, then by citations

Research works printed but not linked: 38

  1. [1]Kern F., Fehlmann T., Violich I., Alsop E., Hutchins E., Kahraman M., Grammes N.L., Guimarães P., Backes C., Poston K.L. et al. Deep sequencing of sncRNAs reveals hallmarks and regulatory modules of the transcriptome during Parkinson’s disease progression. Nat. Aging. 2021; 1: 309– 322.
  2. [3]Xu W., San Lucas A., Wang Z., Liu Y. Identifying microRNA targets in different gene regions. BMC Bioinform. 2014; 15: S4. PMC4110731 25077573
  3. [4]Loffreda A., Rigamonti A., Barabino S.M., Lenzken S.C. RNA-binding proteins in the regulation of miRNA activity: a focus on neuronal functions. Biomolecules. 2015; 5: 2363– 2387. 26437437 PMC4693239
  4. [6]Stavast C.J., Erkeland S.J. The non-canonical aspects of microRNAs: many roads to gene regulation. Cells. 2019; 8,: 1465. PMC6912820 31752361
  5. [7]Bartel D.P. Metazoan microRNAs. Cell. 2018; 173: 20– 51. 29570994 PMC6091663
  6. [8]Kern F., Krammes L., Danz K., Diener C., Kehl T., Kuchler O., Fehlmann T., Kahraman M., Rheinheimer S., Aparicio-Puerta E. et al. Validation of human microRNA target pathways enables evaluation of target prediction tools. Nucleic Acids Res. 2021; 49: 127– 144. 33305319 PMC7797041
  7. [9]Hamberg M., Backes C., Fehlmann T., Hart M., Meder B., Meese E., Keller A. miRTargetLink—miRNAs, genes and interaction networks. Int. J. Mol. Sci. 2016; 17: 564. 27089332 PMC4849020
  8. [10]Lukasik A., Wojcikowski M., Zielenkiewicz P. Tools4miRs: one place to gather all the tools for miRNA analysis. Bioinformatics. 2016; 32: 2722– 2724. 27153626 PMC5013900
  9. [11]Paraskevopoulou M.D., Georgakilas G., Kostoulas N., Vlachos I.S., Vergoulis T., Reczko M., Filippidis C., Dalamagas T., Hatzigeorgiou A.G. DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows. Nucleic Acids Res. 2013; 41: W169– W173. 23680784 PMC3692048
  10. [12]Vlachos I.S., Zagganas K., Paraskevopoulou M.D., Georgakilas G., Karagkouni D., Vergoulis T., Dalamagas T., Hatzigeorgiou A.G. DIANA-miRPath v3.0: deciphering microRNA function with experimental support. Nucleic Acids Res. 2015; 43: W460– W466. 25977294 PMC4489228
  11. [13]Jung D., Kim B., Freishtat R.J., Giri M., Hoffman E., Seo J. miRTarVis: an interactive visual analysis tool for microRNA–mRNA expression profile data. BMC Proc. 2015; 9: S2. PMC4547149 26361498
  12. [14]Licursi V., Conte F., Fiscon G., Paci P. MIENTURNET: an interactive web tool for microRNA-target enrichment and network-based analysis. BMC Bioinform. 2019; 20: 545. PMC6829817 31684860
  13. [15]Giroux P., Bhajun R., Segard S., Picquenot C., Charavay C., Desquilles L., Pinna G., Ginestier C., Denis J., Cherradi N. et al. miRViz: a novel webserver application to visualize and interpret microRNA datasets. Nucleic Acids Res. 2020; 48: W252– W261. 32319523 PMC7319447
  14. [16]Chang L., Zhou G., Soufan O., Xia J. miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology. Nucleic Acids Res. 2020; 48: W244– W251. 32484539 PMC7319552
  15. [17]Fan Y., Siklenka K., Arora S.K., Ribeiro P., Kimmins S., Xia J. miRNet: dissecting miRNA–target interactions and functional associations through network-based visual analysis. Nucleic Acids Res. 2016; 44: W135– W141. 27105848 PMC4987881
  16. [18]Kowarsch A., Preusse M., Marr C., Theis F.J. miTALOS: analyzing the tissue-specific regulation of signaling pathways by human and mouse microRNAs. RNA. 2011; 17: 809– 819. 21441347 PMC3078731
  17. [19]Preusse M., Theis F.J., Mueller N.S. miTALOS v2: analyzing tissue specific microRNA function. PLoS One. 2016; 11: e0151771. 26998997 PMC4801359
  18. [20]Nam S., Li M., Choi K., Balch C., Kim S., Nephew K.P. MicroRNA and mRNA integrated analysis (MMIA): a web tool for examining biological functions of microRNA expression. Nucleic Acids Res. 2009; 37: W356– W362. 19420067 PMC2703907
  19. [21]Muniategui A., Nogales-Cadenas R., Vazquez M., Aranguren X.L., Agirre X., Luttun A., Prosper F., Pascual-Montano A., Rubio A. Quantification of miRNA–mRNA interactions. PLoS One. 2012; 7: e30766. 22348024 PMC3279346
  20. [22]Laczny C., Leidinger P., Haas J., Ludwig N., Backes C., Gerasch A., Kaufmann M., Vogel B., Katus H.A., Meder B. et al. miRTrail–a comprehensive webserver for analyzing gene and miRNA patterns to enhance the understanding of regulatory mechanisms in diseases. BMC Bioinform. 2012; 13: 36. PMC3352041 22356618
  21. [23]Xie G.Y., Xia M., Miao Y.R., Luo M., Zhang Q., Guo A.Y. FFLtool: a web server for transcription factor and miRNA feed forward loop analysis in human. Bioinformatics. 2020; 36: 2605– 2607. 31830251
  22. [24]Kehl T., Kern F., Backes C., Fehlmann T., Stöckel D., Meese E., Lenhof H.-P., Keller A. miRPathDB 2.0: a novel release of the miRNA Pathway Dictionary Database. Nucleic Acids Res. 2019; 48: D142– D147. PMC7145528 31691816
  23. [25]Huang H.-Y., Lin Y.-C.-D., Li J., Huang K.-Y., Shrestha S., Hong H.-C., Tang Y., Chen Y.-G., Jin C.-N., Yu Y. et al. miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database. Nucleic Acids Res. 2019; 48: D148– D154. PMC7145596 31647101
  24. [26]Tokar T., Pastrello C., Rossos A.E.M., Abovsky M., Hauschild A.-C., Tsay M., Lu R., Jurisica I. mirDIP 4.1—integrative database of human microRNA target predictions. Nucleic Acids Res. 2017; 46: D360– D370. PMC5753284 29194489
  25. [28]Kozomara A., Birgaoanu M., Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019; 47: D155– D162. 30423142 PMC6323917
  26. [29]Kern F., Fehlmann T., Solomon J., Schwed L., Grammes N., Backes C., Van Keuren-Jensen K., Craig D.W., Meese E., Keller A. miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems. Nucleic Acids Res. 2020; 48: W521– W528. 32374865 PMC7319446
  27. [30]Gerstner N., Kehl T., Lenhof K., Muller A., Mayer C., Eckhart L., Grammes N.L., Diener C., Hart M., Hahn O. et al. GeneTrail 3: advanced high-throughput enrichment analysis. Nucleic Acids Res. 2020; 48: W515– W520. 32379325 PMC7319559
  28. [31]Xin J., Mark A., Afrasiabi C., Tsueng G., Juchler M., Gopal N., Stupp G.S., Putman T.E., Ainscough B.J., Griffith O.L. et al. High-performance web services for querying gene and variant annotation. Genome Biol. 2016; 17: 91. 27154141 PMC4858870
  29. [32]Franz M., Lopes C.T., Huck G., Dong Y., Sumer O., Bader G.D. Cytoscape.js: a graph theory library for visualisation and analysis. Bioinformatics. 2016; 32: 309– 311. 26415722 PMC4708103
  30. [33]Fehlmann T., Lehallier B., Schaum N., Hahn O., Kahraman M., Li Y., Grammes N., Geffers L., Backes C., Balling R. et al. Common diseases alter the physiological age-related blood microRNA profile. Nat. Commun. 2020; 11: 5958. 33235214 PMC7686493
  31. [34]Madrer N., Soreq H. Cholino-ncRNAs modulate sex-specific- and age-related acetylcholine signals. FEBS Lett. 2020; 594: 2185– 2198. 32330292 PMC7496432
  32. [36]Palmieri V., Backes C., Ludwig N., Fehlmann T., Kern F., Meese E., Keller A. IMOTA: an interactive multi-omics tissue atlas for the analysis of human miRNA-target interactions. Nucleic Acids Res. 2018; 46: D770– D775. 28977416 PMC5753248
  33. [37]Chen Z., Wang H., Zhong J., Yang J., Darwazeh R., Tian X., Huang Z., Jiang L., Cheng C., Wu Y. et al. Significant changes in circular RNA in the mouse cerebral cortex around an injury site after traumatic brain injury. Exp. Neurol. 2019; 313: 37– 48. 30529438
  34. [38]Martyniuk C.J., Martinez R., Kostyniuk D.J., Mennigen J.A., Zubcevic J. Genetic ablation of bone marrow beta-adrenergic receptors in mice modulates miRNA-transcriptome networks of neuroinflammation in the paraventricular nucleus. Physiol. Genomics. 2020; 52: 169– 177. 32089076 PMC7191424
  35. [39]Seifer B.J., Su D., Taylor H.S. Circulating miRNAs in murine experimental endometriosis. Reprod. Sci. 2017; 24: 376– 381. 27655771
  36. [40]Latta L., Ludwig N., Krammes L., Stachon T., Fries F.N., Mukwaya A., Szentmary N., Seitz B., Wowra B., Kahraman M. et al. Abnormal neovascular and proliferative conjunctival phenotype in limbal stem cell deficiency is associated with altered microRNA and gene expression modulated by PAX6 mutational status in congenital aniridia. Ocul. Surf. 2021; 19: 115– 127. 32422284
  37. [41]Fehlmann T., Backes C., Kahraman M., Haas J., Ludwig N., Posch A.E., Wurstle M.L., Hubenthal M., Franke A., Meder B. et al. Web-based NGS data analysis using miRMaster: a large-scale meta-analysis of human miRNAs. Nucleic Acids Res. 2017; 45: 8731– 8744. 28911107 PMC5587802
  38. [42]Winek K., Lobentanzer S., Nadorp B., Dubnov S., Dames C., Jagdmann S., Moshitzky G., Hotter B., Meisel C., Greenberg D.S. et al. Transfer RNA fragments replace microRNA regulators of the cholinergic poststroke immune blockade. Proc. Natl. Acad. Sci. USA. 2020; 117: 32606– 32616. 33288717 PMC7768686

Cited by

Sorted by citations

Authors
Kern, F., Aparicio-Puerta, E., Li, Y., Fehlmann, T., Kehl, T., Wagner, V., Ray, K., Ludwig, N., Lenhof, H.-P., Meese, E., & Keller, A.
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2021-05-01
Volume
49
Issue
W1
Pages
W409-W416
ISSN
  • 0305-1048
  • 1362-4962
DOI
10.1093/nar/gkab297
PMC
PMC8262750
Cited by
203
References
4
Language
en
Licence
creativecommons.org/licenses/by-nc/4.0/ (vor)
Funders
  • Saarland University
  • Instituto de Salud Carlos III (IFI16/00041, MV19/00058)

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