MicroRNA and microRNA target database explained

This microRNA database and microRNA targets databases is a compilation of databases and web portals and servers used for microRNAs and their targets. MicroRNAs (miRNAs) represent an important class of small non-coding RNAs (ncRNAs) that regulate gene expression by targeting messenger RNAs.[1]

microRNA target gene databases

NameDescription typeLink References
StarBasestarBase is designed for decoding miRNA-lncRNA, miRNA-mRNA, miRNA-circRNA, miRNA-pseudogene, miRNA-sncRNA, protein-lncRNA, protein-sncRNA, protein-mRNA and protein-pseudogene interactions and ceRNA networks from 108 CLIP-Seq (HITS-CLIP, PAR-CLIP, iCLIP, CLASH) datasets. It also provides Pan-Cancer Analysis for microRNAs, lncRNAs, circRNAs and protein-coding genes from 6000 tumor samples. database website [2] [3]
StarScanStarScan is developed for scanning small RNA (miRNA, piRNA, siRNA) mediated RNA cleavage events in lncRNA, circRNA, mRNA and pseudo genes from degradome sequencing data. web-based software website [4]
CupidCupid is a method for simultaneous prediction of miRNA-target interactions and their mediated competing endogenous RNA (ceRNA) interactions. It is an integrative approach significantly improves on miRNA-target prediction accuracy as assessed by both mRNA and protein level measurements in breast cancer cell lines. Cupid is implemented in 3 steps: Step 1: re-evaluate candidate miRNA binding sites in 3' UTRs. Step2: interactions are predicted by integrating information about selected sites and the statistical dependency between the expression profiles of miRNA and putative targets. Step 3: Cupid assesses whether inferred targets compete for predicted miRNA regulators. * Only the source code for step 3 is provided. software (MATLAB) website [5]
TargetScanPredicts biological targets of miRNAs by searching for the presence of sites that match the seed region of each miRNA. In flies and nematodes, predictions are ranked based on the probability of their evolutionary conservation. In zebrafish, predictions are ranked based on site number, site type, and site context, which includes factors that influence target-site accessibility. In mammals, the user can choose whether the predictions should be ranked based on the probability of their conservation or on site number, type, and context. In mammals and nematodes, the user can choose to extend the predictions beyond conserved sites and consider all sites. database, webserver website [6] [7] [8] [9] [10] [11]
TarBaseA comprehensive database of experimentally supported animal microRNA targets database website [12]
Diana-microTDIANA-microT 3.0 is an algorithm based on several parameters calculated individually for each microRNA and it combines conserved and non-conserved microRNA recognition elements into a final prediction score.webserverwebserver [13]
miRecordsan integrated resource for microRNA-target interactions. database website [14]
PicTarPicTar is Combinatorial microRNA target predictions. database, webserver, predictions website [15]
PITAPITA, incorporates the role of target-site accessibility, as determined by base-pairing interactions within the mRNA, in microRNA target recognition.webserver, predictions predictions [16]
RepTarA database of inverse miRNA target predictions, based on the RepTar algorithm that is independent of evolutionary conservation considerations and is not limited to seed pairing sites. database website [17]
RNA22The first link (predictions) provides RNA22 predictions for all protein coding transcripts in human, mouse, roundworm, and fruit fly. It allows you to visualize the predictions within a cDNA map and also find transcripts where multiple miR's of interest target. The second web-site link (custom) first finds putative microRNA binding sites in the sequence of interest, then identifies the targeted microRNA. webserver, predictions predictions custom [18]
miRTarBaseThe experimentally validated microRNA-target interactions database. As a database, miRTarBase has accumulated more than three hundred and sixty thousand miRNA-target interactions (MTIs), which are collected by manually surveying pertinent literature after NLP of the text systematically to filter research articles related to functional studies of miRNAs. Generally, the collected MTIs are validated experimentally by reporter assay, western blot, microarray and next-generation sequencing experiments. While containing the largest amount of validated MTIs, the miRTarBase provides the most updated collection by comparing with other similar, previously developed databases.database website [19] [20] [21] [22]
miRwalkAggregates and compare results from other miRNA-to-mRNA databases database, webserver http://www.umm.uni-heidelberg.de/apps/zmf/mirwalk/[23]
MBSTARMultiple Instance approach for finding out true or functional microRNA binding sites. webserver, predictions predictions [24]
.

microRNA databases

NameDescription typeLink References
deepBasedeepBase is a database for annotating and discovering small and long ncRNAs (microRNAs, siRNAs, piRNAs...) from high-throughput deep sequencing data. website [25]
miRBasemiRBase database is a searchable database of published miRNA sequences and annotation. website [26]
microRNA.orgmicroRNA.org is a database for Experimentally observed microRNA expression patterns and predicted microRNA targets & target downregulation scores. website [27]
miRGen 4.0DIANA-miRGen v4: indexing promoters and regulators for more than 1500 microRNAs website [28]
MirGeneDBMirGeneDB is a database of manually curated microRNA genes that have been validated and annotated. MirGeneDB 2.1 includes more than 16,000 microRNA gene entries representing more than 1,500 miRNA families from 75 metazoan species. All microRNAs can be browsed, searched and downloaded.databasewebsite[29]
miRNAMapmiRNAMap: genomic maps of microRNA genes and their target genes in mammalian genomes website [30]
PMRDPMRD: plant microRNA database website [31]
TargetScanTargetScan7.0 classifies microRNAs according to their level of conservation (i.e., species-specific, conserved among mammals, or broadly conserved among vertebrates) and aggregates them into families based upon their seed sequence. It also annotates conserved isomiRs using small RNA sequencing datasets.databasewebsite
VIRmiRNAVIRmiRNA is the first dedicated resource on experimental viral miRNA and their targets. This resource also provides inclusive knowledge about anti-viral miRNAs known to play role in antiviral immunity of host.Databasewebsite[32]

Further reading

External links

Notes and References

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  2. Yang . J. -H. . Li . J. -H. . Shao . P. . Zhou . H. . Chen . Y. -Q. . Qu . L. -H. . 10.1093/nar/gkq1056 . StarBase: A database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data . Nucleic Acids Research . 39 . Database issue . D202–D209 . 2010 . 21037263. 3013664 .
  3. Li. JH. Liu, S . Zhou, H . Qu, LH . Yang, JH . starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data.. Nucleic Acids Research. Jan 1, 2014. 42. 1. D92-7. 24297251. 10.1093/nar/gkt1248 . 3964941.
  4. Liu. S. Li. JH. Wu. J. Zhou. KR. Zhou. H. Yang. JH. Qu. LH. StarScan: a web server for scanning small RNA targets from degradome sequencing data.. Nucleic Acids Research. 18 May 2015. 25990732. 10.1093/nar/gkv524. 43. W1. W480-6. 4489260.
  5. Chiu. Hua-Sheng. Llobet-Navas. David. Yang. Xuerui. Chung. Wei-Jen. Ambesi-Impiombato. Alberto. Iyer. Archana. Kim. Hyunjae "Ryan". Seviour. Elena G.. Luo. Zijun. Sehgal. Vasudha. Moss. Tyler. Lu. Yiling. Ram. Prahlad. Silva. José. Mills. Gordon B.. Califano. Andrea. Sumazin. Pavel. Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks. Genome Research. February 2015. 25. 2. 257–67. 10.1101/gr.178194.114. 25378249. 4315299.
  6. Lewis. BP. Burge CB . Bartel DP . Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. Jan 14, 2005. 120. 1. 15–20. 15652477. 10.1016/j.cell.2004.12.035. free.
  7. Grimson. A. Farh, KK . Johnston, WK . Garrett-Engele, P . Lim, LP . Bartel, DP . MicroRNA targeting specificity in mammals: determinants beyond seed pairing.. Molecular Cell. Jul 6, 2007. 27. 1. 91–105. 17612493. 10.1016/j.molcel.2007.06.017 . 3800283.
  8. Friedman. RC. Farh, KK . Burge, CB . Bartel, DP . Most mammalian mRNAs are conserved targets of microRNAs.. Genome Research. January 2009. 19. 1. 92–105. 10.1101/gr.082701.108. 18955434. 2612969.
  9. Garcia. DM. Baek, D . Shin, C . Bell, GW . Grimson, A . Bartel, DP . Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs.. Nature Structural & Molecular Biology. Sep 11, 2011. 18. 10. 1139–46. 21909094. 10.1038/nsmb.2115. 3190056.
  10. Predicting effective microRNA target sites in mammalian mRNAs . eLife . 2015-08-12 . 2050-084X . 4532895 . 26267216 . e05005 . 4 . 10.7554/eLife.05005 . Vikram . Agarwal . George W. . Bell . Jin-Wu . Nam . David P. . Bartel . free .
  11. Agarwal . V . Subtelny . AO . Thiru . P . Ulitsky . I . Bartel . DP . Predicting microRNA targeting efficacy in Drosophila. . Genome Biology . 4 October 2018 . 19 . 1 . 152 . 10.1186/s13059-018-1504-3 . 30286781. 6172730 . free .
  12. Sethupathy P, Corda B, Hatzigeorgiou AG . TarBase: A comprehensive database of experimentally supported animal microRNA targets. . RNA . 12 . 2 . 192–197 . 2006 . 16373484 . 10.1261/rna.2239606 . 1370898.
  13. Maragkakis M, Alexiou P, Papadopoulos GL, Reczko M, Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K, Simossis VA, Sethupathy P, Vergoulis T, Koziris N, Sellis T, Tsanakas P, Hatzigeorgiou AG . Accurate microRNA target prediction correlates with protein repression levels. . BMC Bioinformatics . 10 . 295 . 2009 . 19765283 . 10.1186/1471-2105-10-295. 2752464 . free .
  14. Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T . miRecords: an integrated resource for microRNA-target interactions. . Nucleic Acids Res. . 37 . Database issue . D105-110 . 2009 . 18996891 . 10.1093/nar/gkn851 . 2686554.
  15. Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, Rajewsky N . Combinatorial microRNA target predictions. . Nat Genet . 37 . 5 . 495–500 . 2005 . 15806104 . 10.1038/ng1536. 22672750 .
  16. Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E . The role of site accessibility in microRNA target recognition. . Nat Genet . 39 . 10 . 1278–84 . 2007 . 17893677 . 10.1038/ng2135. 1721807 .
  17. Elefant. Naama. Berger Amnon. Shein Harel. Hofree Matan. Margalit Hanah. Hanah Margalit. Altuvia Yael. Jan 2011. RepTar: a database of predicted cellular targets of host and viral miRNAs. Nucleic Acids Res.. England. 39. Database issue. D188-94. 10.1093/nar/gkq1233. 3013742. 21149264.
  18. Miranda KC, Huynh T, Tay Y, Ang YS, Tam WL, Thomson AM, Lim B, Rigoutsos I . A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. . Cell . 126 . 6 . 1203–17 . 2006 . 16990141 . 10.1016/j.cell.2006.07.031. 12749133 .
  19. Hsu SD, Lin FM, Wu WY, Liang C, Huang WC, Chan WL, Tsai WT, Chen GZ, Lee CJ, Chiu CM, Chien CH, Wu MC, Huang CY, Tsou AP, Huang HD . miRTarBase: a database curates experimentally validated microRNA-target interactions. . Nucleic Acids Research . 39 . Database issue . D163-9 . 2011 . 21071411 . 10.1093/nar/gkq1107 . 3013699.
  20. Hsu SD, Tseng YT, Shrestha S, Lin YL, Khaleel A, Chou CH, Chu CF, Huang HY, Lin CM, Ho SY, Jian TY, Lin FM, Chang TH, Weng SL, Liao KW, Liao IE, Liu CC, Huang HD . miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. . Nucleic Acids Research . 42 . Database issue . D78-85 . 2014 . 24304892 . 10.1093/nar/gkt1266 . 3965058.
  21. Chou CH, Chang NW, Shrestha S, Hsu SD, Lin YL, Lee WH, Yang CD, Hong HC, Wei TY, Tu SJ, Tsai TR, Ho SY, Jian TY, Wu HY, Chen PR, Lin NC, Huang HT, Yang TL, Pai CY, Tai CS, Chen WL, Huang CY, Liu CC, Weng SL, Liao KW, Hsu WL, Huang HD . miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database. . Nucleic Acids Research . 44 . Database issue . D239-47 . 2016 . 26590260 . 10.1093/nar/gkv1258 . 4702890.
  22. Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, Huang WC, Sun TH, Tu SJ, Lee WH, Chiew MY, Tai CS, Wei TY, Tsai TR, Huang HT, Wang CY, Wu HY, Ho SY, Chen PR, Chuang CH, Hsieh PJ, Wu YS, Chen WL, Li MJ, Wu YC, Huang XY, Ng FL, Buddhakosai W, Huang PC, Lan KC, Huang CY, Weng SL, Cheng YN, Liang C, Hsu WL, Huang HD . miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. . Nucleic Acids Research . 46 . Database issue . D296-302 . 2018 . 29126174 . 10.1093/nar/gkt1266 . 5753222 .
  23. Dweep H, Sticht C, Pandey P, Gretz N . miRWalk-database: prediction of possible miRNA binding sites by "walking" the genes of three genomes . Journal of Biomedical Informatics. 44. 5 . 839–47. 2011 . 21605702 . 10.1016/j.jbi.2011.05.002. free.
  24. Bandyopadhyay S, Ghosh D, Mitra R, Zhao Z . MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets. . Sci. Rep.. 5. 2015 . 25614300 . 10.1038/srep08004 . 8004 . 4648438. 2015NatSR...5E8004B.
  25. Yang JH, Shao P, Zhou H, Chen YQ, Qu LH . deepBase: a database for deeply annotating and mining deep sequencing data. . Nucleic Acids Res. . 38 . Database issue . D123-130 . 2010 . 19966272 . 10.1093/nar/gkp943 . 2808990.
  26. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ . miRBase: tools for microRNA genomics. . Nucleic Acids Res. . 36 . D154–D158 . 2008 . Database issue . 17991681 . 10.1093/nar/gkm952 . 2238936.
  27. Betel D, Wilson M, Gabow A, Marks DS, Sander C . The microRNA.org resource: targets and expression. . Nucleic Acids Res. . 36 . Database issue . D149-153 . 2007 . 18158296 . 10.1093/nar/gkm995 . 2238905.
  28. Perdikopanis N, Georgakilas GK, Grigoriadis D, Pierros V, Kavakiotis I, Alexiou P, Hatzigeorgiou A . DIANA-miRGen v4: indexing promoters and regulators for more than 1500 microRNAs . Nucleic Acids Res . 49 . D1 . D151-159 . 2021 . 33245765 . 10.1093/nar/gkaa1060 . free . 7778932.
  29. Fromm . Bastian . Høye . Eirik . Domanska . Diana . Zhong . Xiangfu . Aparicio-Puerta . Ernesto . Ovchinnikov . Vladimir . Umu . Sinan U . Chabot . Peter J . Kang . Wenjing . Aslanzadeh . Morteza . Tarbier . Marcel . Mármol-Sánchez . Emilio . Urgese . Gianvito . Johansen . Morten . Hovig . Eivind . Hackenberg . Michael . Friedländer . Marc R . Peterson . Kevin J . MirGeneDB 2.1: toward a complete sampling of all major animal phyla . Nucleic Acids Research . 50 . D1 . D204–D210 . November 2021 . 34850121 . 8728275 . 10.1093/nar/gkab1101 .
  30. Hsu PW, Huang HD, Hsu SD, Lin LZ, Tsou AP, Tseng CP, Stadler PF, Washietl S, Hofacker IL . miRNAMap: genomic maps of microRNA genes and their target genes in mammalian genomes. . Nucleic Acids Res. . 34 . Database issue . D135-139 . 2006 . 16381831 . 10.1093/nar/gkj135 . 1347497.
  31. Zhang Z, Yu J, Li D, Zhang Z, Liu F, Zhou X, Wang T, Ling Y, Su Z . PMRD: plant microRNA database. . Nucleic Acids Res. . 38 . Database issue . D806-813 . 2010 . 19808935 . 10.1093/nar/gkp818 . 2808885.
  32. Qureshi. Abid. Thakur. Nishant. Monga. Isha. Thakur. Anamika. Kumar. Manoj. 2014-01-01. VIRmiRNA: a comprehensive resource for experimentally validated viral miRNAs and their targets. Database: The Journal of Biological Databases and Curation. 2014. 10.1093/database/bau103. 1758-0463. 4224276. 25380780. bau103.

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