MicroRNA and microRNA target database

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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]

Contents

microRNA target gene databases

NameDescriptiontypeLinkReferences
StarBase starBase 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]
StarScan StarScan 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]
Cupid Cupid 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]
TargetScan Predicts 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]
TarBase A comprehensive database of experimentally supported animal microRNA targetsdatabase website [12]
Diana-microT DIANA-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.webserver webserver [13]
miRecords an integrated resource for microRNA-target interactions.database website [14]
PicTar PicTar is Combinatorial microRNA target predictions.database, webserver, predictions website [15]
PITA PITA, incorporates the role of target-site accessibility, as determined by base-pairing interactions within the mRNA, in microRNA target recognition.webserver, predictions predictions [16]
RepTar A 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]
RNA22 The 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]
miRTarBase The 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]
miRwalk Aggregates and compare results from other miRNA-to-mRNA databasesdatabase, webserver [23]
MBSTAR Multiple Instance approach for finding out true or functional microRNA binding sites.webserver, predictions predictions [24]
.

microRNA databases

NameDescriptiontypeLinkReferences
deepBase deepBase is a database for annotating and discovering small and long ncRNAs (microRNAs, siRNAs, piRNAs...) from high-throughput deep sequencing data. database website [25]
miRBase miRBase database is a searchable database of published miRNA sequences and annotation. database website [26]
microRNA.org microRNA.org is a database for Experimentally observed microRNA expression patterns and predicted microRNA targets & target downregulation scores. database website [27]
miRGen 4.0 DIANA-miRGen v4: indexing promoters and regulators for more than 1500 microRNAs database website [28]
MirGeneDB MirGeneDB 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. database website [29]
miRNAMap miRNAMap: genomic maps of microRNA genes and their target genes in mammalian genomes database website [30]
PMRD PMRD: plant microRNA database database website [31]
TargetScan TargetScan7.0 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. [10] database website [10]
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.Database website [32]

Related Research Articles

microRNA Small non-coding ribonucleic acid molecule

MicroRNA (miRNA) are small, single-stranded, non-coding RNA molecules containing 21 to 23 nucleotides. Found in plants, animals and some viruses, miRNAs are involved in RNA silencing and post-transcriptional regulation of gene expression. miRNAs base-pair to complementary sequences in mRNA molecules, then silence said mRNA molecules by one or more of the following processes:

  1. Cleavage of the mRNA strand into two pieces,
  2. Destabilization of the mRNA by shortening its poly(A) tail, or
  3. Reducing translation of the mRNA into proteins.

RNA activation (RNAa) is a small RNA-guided and Argonaute (Ago)-dependent gene regulation phenomenon in which promoter-targeted short double-stranded RNAs (dsRNAs) induce target gene expression at the transcriptional/epigenetic level. RNAa was first reported in a 2006 PNAS paper by Li et al. who also coined the term "RNAa" as a contrast to RNA interference (RNAi) to describe such gene activation phenomenon. dsRNAs that trigger RNAa have been termed small activating RNA (saRNA). Since the initial discovery of RNAa in human cells, many other groups have made similar observations in different mammalian species including human, non-human primates, rat and mice, plant and C. elegans, suggesting that RNAa is an evolutionarily conserved mechanism of gene regulation.

mir-9/mir-79 microRNA precursor family

The miR-9 microRNA, is a short non-coding RNA gene involved in gene regulation. The mature ~21nt miRNAs are processed from hairpin precursor sequences by the Dicer enzyme. The dominant mature miRNA sequence is processed from the 5' arm of the mir-9 precursor, and from the 3' arm of the mir-79 precursor. The mature products are thought to have regulatory roles through complementarity to mRNA. In vertebrates, miR-9 is highly expressed in the brain, and is suggested to regulate neuronal differentiation. A number of specific targets of miR-9 have been proposed, including the transcription factor REST and its partner CoREST.

mir-129 microRNA precursor family

The miR-129 microRNA precursor is a small non-coding RNA molecule that regulates gene expression. This microRNA was first experimentally characterised in mouse and homologues have since been discovered in several other species, such as humans, rats and zebrafish. The mature sequence is excised by the Dicer enzyme from the 5' arm of the hairpin. It was elucidated by Calin et al. that miR-129-1 is located in a fragile site region of the human genome near a specific site, FRA7H in chromosome 7q32, which is a site commonly deleted in many cancers. miR-129-2 is located in 11p11.2.

<span class="mw-page-title-main">Therapeutic Targets Database</span> Database of protein targets in drug design

Therapeutic Target Database (TTD) is a pharmaceutical and medical repository constructed by the Innovative Drug Research and Bioinformatics Group (IDRB) at Zhejiang University, China and the Bioinformatics and Drug Design Group at the National University of Singapore. It provides information about known and explored therapeutic protein and nucleic acid targets, the targeted disease, pathway information and the corresponding drugs directed at each of these targets. Detailed knowledge about target function, sequence, 3D structure, ligand binding properties, enzyme nomenclature and drug structure, therapeutic class, and clinical development status. TTD is freely accessible without any login requirement at https://idrblab.org/ttd/.

Degradome sequencing (Degradome-Seq), also referred to as parallel analysis of RNA ends (PARE), is a modified version of 5'-Rapid Amplification of cDNA Ends (RACE) using high-throughput, deep sequencing methods such as Illumina's SBS technology. The degradome encompasses the entire set of proteases that are expressed at a specific time in a given biological material, including tissues, cells, organisms, and biofluids. Thus, sequencing this degradome offers a method for studying and researching the process of RNA degradation. This process is used to identify and quantify RNA degradation products, or fragments, present in any given biological sample. This approach allows for the systematic identification of targets of RNA decay and provides insight into the dynamics of transcriptional and post-transcriptional gene regulation.

PAR-CLIP is a biochemical method for identifying the binding sites of cellular RNA-binding proteins (RBPs) and microRNA-containing ribonucleoprotein complexes (miRNPs). The method relies on the incorporation of ribonucleoside analogs that are photoreactive, such as 4-thiouridine (4-SU) and 6-thioguanosine (6-SG), into nascent RNA transcripts by living cells. Irradiation of the cells by ultraviolet light of 365 nm wavelength induces efficient crosslinking of photoreactive nucleoside–labeled cellular RNAs to interacting RBPs. Immunoprecipitation of the RBP of interest is followed by isolation of the crosslinked and coimmunoprecipitated RNA. The isolated RNA is converted into a cDNA library and is deep sequenced using next-generation sequencing technology.

<span class="mw-page-title-main">MiRTarBase</span> Database of MicroRNA-Target Interactions

miRTarBase is a curated database of MicroRNA-Target Interactions. As a database, miRTarBase has accumulated more than fifty thousand miRNA-target interactions (MTIs), which are collected by manually surveying pertinent literature after data mining 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.

StarBase is a database for decoding miRNA-mRNA, miRNA-lncRNA, miRNA-sncRNA, miRNA-circRNA, miRNA-pseudogene, protein-lncRNA, protein-ncRNA, protein-mRNA interactions, and ceRNA networks from CLIP-Seq and degradome sequencing data. StarBase provides miRFunction and ceRNAFunction web tools to predict the function of ncRNAs and protein-coding genes from the miRNA and ceRNA regulatory networks. StarBase also developed Pan-Cancer Analysis Platform to decipher Pan-Cancer Analysis Networks of lncRNAs, miRNAs, ceRNAs, and RNA-binding proteins (RBPs) by mining clinical and expression profiles of 14 cancer types from The Cancer Genome Atlas (TCGA) Data Portal.

High-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP) is a variant of CLIP for genome-wide mapping protein–RNA binding sites or RNA modification sites in vivo. HITS-CLIP was originally used to generate genome-wide protein-RNA interaction maps for the neuron-specific RNA-binding protein and splicing factor NOVA1 and NOVA2; since then a number of other splicing factor maps have been generated, including those for PTB, RbFox2, SFRS1, hnRNP C, and even N6-Methyladenosine (m6A) mRNA modifications.

In molecular biology, competing endogenous RNAs regulate other RNA transcripts by competing for shared microRNAs (miRNAs). Models for ceRNA regulation describe how changes in the expression of one or multiple miRNA targets alter the number of unbound miRNAs and lead to observable changes in miRNA activity - i.e., the abundance of other miRNA targets. Models of ceRNA regulation differ greatly. Some describe the kinetics of target-miRNA-target interactions, where changes in the expression of one target species sequester one miRNA species and lead to changes in the dysregulation of the other target species. Others attempt to model more realistic cellular scenarios, where multiple RNA targets are affecting multiple miRNAs and where each target pair is co-regulated by multiple miRNA species. Some models focus on mRNA 3' UTRs as targets, and others consider long non-coding RNA targets as well. It's evident that our molecular-biochemical understanding of ceRNA regulation remains incomplete.

In molecular biology mir-351 microRNA is a short RNA molecule. MicroRNAs function to regulate the expression levels of other genes by several mechanisms.

In molecular biology mir-23 microRNA is a short RNA molecule. MicroRNAs function to regulate the expression levels of other genes by several mechanisms.

In molecular biology mir-390 microRNA is a short RNA molecule. MicroRNAs function to regulate the expression levels of other genes by several mechanisms.

Competing endogenous RNAs hypothesis: ceRNAs regulate other RNA transcripts by competing for shared microRNAs. They are playing important roles in developmental, physiological and pathological processes, such as cancer. Multiple classes of ncRNAs and protein-coding mRNAs function as key ceRNAs (sponges) and to regulate the expression of mRNAs in plants and mammalian cells.

RNA Modification Base (RMBase) is designed for decoding the landscape of RNA modifications identified from high-throughput sequencing data. It contains ~124200 N6-Methyladenosines (m6A), ~9500 pseudouridine (Ψ) modifications, ~1000 5-methylcytosine (m5C) modifications, ~1210 2′-O-methylations (2′-O-Me) and ~3130 other types of RNA modifications. RMBase demonstrated thousands of RNA modifications located within mRNAs, regulatory ncRNAs, miRNA target sites and disease-related SNPs.

Transcription factors are proteins that bind genomic regulatory sites. Identification of genomic regulatory elements is essential for understanding the dynamics of developmental, physiological and pathological processes. Recent advances in chromatin immunoprecipitation followed by sequencing (ChIP-seq) have provided powerful ways to identify genome-wide profiling of DNA-binding proteins and histone modifications. The application of ChIP-seq methods has reliably discovered transcription factor binding sites and histone modification sites.

References

  1. ↑ Bartel, D. P. (2009). "MicroRNAs: Target Recognition and Regulatory Functions". Cell. 136 (2): 215–233. doi:10.1016/j.cell.2009.01.002. PMC   3794896 . PMID   19167326.
  2. ↑ Yang, J. -H.; Li, J. -H.; Shao, P.; Zhou, H.; Chen, Y. -Q.; Qu, L. -H. (2010). "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. doi:10.1093/nar/gkq1056. PMC   3013664 . PMID   21037263.
  3. ↑ Li, JH; Liu, S; Zhou, H; Qu, LH; Yang, JH (Jan 1, 2014). "starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data". Nucleic Acids Research. 42 (1): D92-7. doi:10.1093/nar/gkt1248. PMC   3964941 . PMID   24297251.
  4. ↑ Liu, S; Li, JH; Wu, J; Zhou, KR; Zhou, H; Yang, JH; Qu, LH (18 May 2015). "StarScan: a web server for scanning small RNA targets from degradome sequencing data". Nucleic Acids Research. 43 (W1): W480-6. doi:10.1093/nar/gkv524. PMC   4489260 . PMID   25990732.
  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 (February 2015). "Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks". Genome Research. 25 (2): 257–67. doi:10.1101/gr.178194.114. PMC   4315299 . PMID   25378249.
  6. ↑ Lewis, BP; Burge CB; Bartel DP (Jan 14, 2005). "Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets". Cell. 120 (1): 15–20. doi: 10.1016/j.cell.2004.12.035 . PMID   15652477.
  7. ↑ Grimson, A; Farh, KK; Johnston, WK; Garrett-Engele, P; Lim, LP; Bartel, DP (Jul 6, 2007). "MicroRNA targeting specificity in mammals: determinants beyond seed pairing". Molecular Cell. 27 (1): 91–105. doi:10.1016/j.molcel.2007.06.017. PMC   3800283 . PMID   17612493.
  8. ↑ Friedman, RC; Farh, KK; Burge, CB; Bartel, DP (January 2009). "Most mammalian mRNAs are conserved targets of microRNAs". Genome Research. 19 (1): 92–105. doi:10.1101/gr.082701.108. PMC   2612969 . PMID   18955434.
  9. ↑ Garcia, DM; Baek, D; Shin, C; Bell, GW; Grimson, A; Bartel, DP (Sep 11, 2011). "Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs" (PDF). Nature Structural & Molecular Biology. 18 (10): 1139–46. doi:10.1038/nsmb.2115. PMC   3190056 . PMID   21909094.
  10. 1 2 3 Agarwal, Vikram; Bell, George W.; Nam, Jin-Wu; Bartel, David P. (2015-08-12). "Predicting effective microRNA target sites in mammalian mRNAs". eLife. 4: e05005. doi: 10.7554/eLife.05005 . ISSN   2050-084X. PMC   4532895 . PMID   26267216.
  11. ↑ Agarwal, V; Subtelny, AO; Thiru, P; Ulitsky, I; Bartel, DP (4 October 2018). "Predicting microRNA targeting efficacy in Drosophila". Genome Biology. 19 (1): 152. doi: 10.1186/s13059-018-1504-3 . PMC   6172730 . PMID   30286781.
  12. ↑ Sethupathy P, Corda B, Hatzigeorgiou AG (2006). "TarBase: A comprehensive database of experimentally supported animal microRNA targets". RNA. 12 (2): 192–197. doi:10.1261/rna.2239606. PMC   1370898 . PMID   16373484.
  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 (2009). "Accurate microRNA target prediction correlates with protein repression levels". BMC Bioinformatics. 10: 295. doi: 10.1186/1471-2105-10-295 . PMC   2752464 . PMID   19765283.
  14. ↑ Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T (2009). "miRecords: an integrated resource for microRNA-target interactions". Nucleic Acids Res. 37 (Database issue): D105-110. doi:10.1093/nar/gkn851. PMC   2686554 . PMID   18996891.
  15. ↑ Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, Rajewsky N (2005). "Combinatorial microRNA target predictions". Nat Genet. 37 (5): 495–500. doi:10.1038/ng1536. PMID   15806104. S2CID   22672750.
  16. ↑ Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E (2007). "The role of site accessibility in microRNA target recognition". Nat Genet. 39 (10): 1278–84. doi:10.1038/ng2135. PMID   17893677. S2CID   1721807.
  17. ↑ Elefant, Naama; Berger Amnon; Shein Harel; Hofree Matan; Margalit Hanah; Altuvia Yael (Jan 2011). "RepTar: a database of predicted cellular targets of host and viral miRNAs". Nucleic Acids Res. 39 (Database issue). England: D188-94. doi:10.1093/nar/gkq1233. PMC   3013742 . PMID   21149264.
  18. ↑ Miranda KC, Huynh T, Tay Y, Ang YS, Tam WL, Thomson AM, Lim B, Rigoutsos I (2006). "A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes" (PDF). Cell. 126 (6): 1203–17. doi:10.1016/j.cell.2006.07.031. PMID   16990141. S2CID   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 (2011). "miRTarBase: a database curates experimentally validated microRNA-target interactions". Nucleic Acids Research. 39 (Database issue): D163-9. doi:10.1093/nar/gkq1107. PMC   3013699 . PMID   21071411.
  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 (2014). "miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions". Nucleic Acids Research. 42 (Database issue): D78-85. doi:10.1093/nar/gkt1266. PMC   3965058 . PMID   24304892.
  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 (2016). "miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database". Nucleic Acids Research. 44 (Database issue): D239-47. doi:10.1093/nar/gkv1258. PMC   4702890 . PMID   26590260.
  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 (2018). "miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions". Nucleic Acids Research. 46 (Database issue): D296-302. doi:10.1093/nar/gkt1266. PMC   5753222 . PMID   29126174.
  23. ↑ Dweep H, Sticht C, Pandey P, Gretz N (2011). "miRWalk-database: prediction of possible miRNA binding sites by "walking" the genes of three genomes". Journal of Biomedical Informatics. 44 (5): 839–47. doi: 10.1016/j.jbi.2011.05.002 . PMID   21605702.
  24. ↑ Bandyopadhyay S, Ghosh D, Mitra R, Zhao Z (2015). "MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets". Sci. Rep. 5: 8004. Bibcode:2015NatSR...5E8004B. doi:10.1038/srep08004. PMC   4648438 . PMID   25614300.
  25. ↑ Yang JH, Shao P, Zhou H, Chen YQ, Qu LH (2010). "deepBase: a database for deeply annotating and mining deep sequencing data". Nucleic Acids Res. 38 (Database issue): D123-130. doi:10.1093/nar/gkp943. PMC   2808990 . PMID   19966272.
  26. ↑ Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ (2008). "miRBase: tools for microRNA genomics". Nucleic Acids Res. 36 (Database issue): D154–D158. doi:10.1093/nar/gkm952. PMC   2238936 . PMID   17991681.
  27. ↑ Betel D, Wilson M, Gabow A, Marks DS, Sander C (2007). "The microRNA.org resource: targets and expression". Nucleic Acids Res. 36 (Database issue): D149-153. doi:10.1093/nar/gkm995. PMC   2238905 . PMID   18158296.
  28. ↑ Perdikopanis N, Georgakilas GK, Grigoriadis D, Pierros V, Kavakiotis I, Alexiou P, Hatzigeorgiou A (2021). "DIANA-miRGen v4: indexing promoters and regulators for more than 1500 microRNAs". Nucleic Acids Res. 49 (D1): D151-159. doi: 10.1093/nar/gkaa1060 . PMC   7778932 . PMID   33245765.
  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 (November 2021). "MirGeneDB 2.1: toward a complete sampling of all major animal phyla". Nucleic Acids Research. 50 (D1): D204–D210. doi:10.1093/nar/gkab1101. PMC   8728275 . PMID   34850121.
  30. ↑ Hsu PW, Huang HD, Hsu SD, Lin LZ, Tsou AP, Tseng CP, Stadler PF, Washietl S, Hofacker IL (2006). "miRNAMap: genomic maps of microRNA genes and their target genes in mammalian genomes". Nucleic Acids Res. 34 (Database issue): D135-139. doi:10.1093/nar/gkj135. PMC   1347497 . PMID   16381831.
  31. ↑ Zhang Z, Yu J, Li D, Zhang Z, Liu F, Zhou X, Wang T, Ling Y, Su Z (2010). "PMRD: plant microRNA database". Nucleic Acids Res. 38 (Database issue): D806-813. doi:10.1093/nar/gkp818. PMC   2808885 . PMID   19808935.
  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: bau103. doi:10.1093/database/bau103. ISSN   1758-0463. PMC   4224276 . PMID   25380780.

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