INTRODUCTION:

In 1993 Lee and his co-workers gave some idea about microRNA but the term was given in 2001. lin-4 and let-7 were the first microRNA discovered in Caenorhabditis elegans [1,2]. microRNAs (miRNAs) are transcribed non coding RNA with a limited length of nearly 20 to 25 nucleotides found to be involved in numerous biological processes [3,4]. They affect the transcription and translation processes at gene level as they tend to bind to the 3' untranslated regions of mRNA targets causing gene regulation [5, 6] due to either mRNA destruction or translational inhibition. miRNAs were considered to have negative effect on gene expression but now they are found to be involved positively [7].They have functions both in plants and animals from leaf development to larval development. They are causative of many diseases affecting nearly every organ of human body.

They are tumor causing as well as tumor suppressor [8, 9] .And they are found to be involved in various processes at cellular level like apoptosis, hematopoietic differentiation, t cell differentiation. They are an important part in the scenario of cancer, as around 50% are found on cancer related genomic sites thus showing their involvement in the disease [10].Gene regulation by miRNAs is really noticeable as a single miRNA targets many genes.

microRNA Biogenesis:

miRNAs do not form protein but instead the primary transcribed form leads to a stem loop like structure called pre-miRNA ,thus forming the fully functional miRNA [4]. miRNA processing occurs in the following steps: First step of miRNA formation occurs in the nucleus with the formation of primary transcript called the pri-miRNAs [11] by the action of enzymes RNA polymerase II on the RNA precursors. The primary transcript is a 5' capped and 3' polyadenyl tailed structure. Primary transcripts formed are processed by the help of microprocessor complex. The complex consists of Pasha also known as DiGeorge syndrome critical region 8 (DGCR8) which combines with the RNase III enzyme Drosha to cut off the primary transcript leading to formation of an approximately 70 nuleotide long stem looped structure called pre-miRNA [12].The pre-miRNAs formed are thus transported to the cytoplasm by the help of karyopherin exportin 5 (Exp 5) in coordination with Ran-GTP complex [13]. A heterotrimer structure is thus formed by the unity of pre-miRNA, Exp 5 and Ran-GTP. The other to with the pre-miRNA helps to avoid any kind of exonucleolytic digestion.Now Dicer which is also a RNase III enzyme together with associated dsRNA binding domain (dsRBD) splits the stem loop structure to form an approximately 22 nucleotides lengthed miRNA duplex structure [13].The dsRNA structure is then recognized by the RISC complex (RNA induced silencing complex) which thus selects the strand with a lower stability at the 5'end of the duplex. The other unused strand is been degraded. RISC thus combines to this selected strand to form the full formed miRNA. Now this miRNA with the RISC associates to the mRNA molecule to cause its cleavage controling translation.

microRNAS - Their Functions And Human Diseases:

Over expression of mir-14 inhibits cell death in Drosophilla while its reduction may lead to reaper dependent cell death [15].lin-57 controls postembryonic development in C.elegans. miRNAs affect the nervous system causing schizophrenia and schizoaffective disorder. Syndromes like Tourette's [16] and Alzheimer's [17] are miRNAs related neurological diseases. Waisman syndrome and X linked mental retardation have their candiatate regions on hsa-miR-175 gene locus. hsa-miR-433 are involved in Parkinson disease. hsa-miR-9 and hsa-miR-9* are down regulated in Huntington’s disease [18]. hsa-miR-122 down regulation is a causative of hepatocellular carcinoma. Its inhibiton causes liver steatosis, lipid metabolism, and fatty acid metabolism and regulates cholesterol [19]. Antagomirs a new class of miRNAs are found to inhibit has-miR-122 expression [20]. hsa-miR-375 regulates insulin secretion and helps in diabetes treatment [21]. miRNA affects the cardiovascular system as it tends to regulate cardiogenesis and cause cardiomyopathy.mir-320 regulates cardiac ischemia. hsa-miR-21 is involved in early stage of myocardial infarction. hsa-let-7b, hsa-let-7c, hsa-miR-1, hsa-miR-103, hsa-miR-10b, hsa-miR-125b play important part in the development of cardiac hypertrophy. hsa-miR-133a controls cardiac hypertrophy. hsa-miR-133 is related to cardiac hypertrophy [22,23]. It is involved in DiGeorge syndrome due to deletion of some genes which includes DGCR8 .It shows disease symptoms like immunodeficiency, schizophrenia, heart disease, etc. Lung cancer is under control due to hsa-let-7.hsa-miR-125b, hsa-miR-145, hsa-miR-21, hsa-miR-155 all were under expressed in breast cancer. hsa-miR-143 and hsa-mir-145 helps in reduction of colorectal neoplasia [24]. Viral encoded miRNAs are seen to function in HIV HBV [25, 26]. Human cytomegalovirus uses miRNAs to control the viral regulation [27]. Epstein Barr virus regulates gene by the process of gene silencing. miRNAs are also found to be involved in simian virus-40 and Kaposi’s herpesvirus [28]. hsa-miR-210, hsa-miR-155, hsa-miR-106a, hsa-miR-17-5p were up regulated in DLBCL than in normal tissue while hsa-miR-150, hsa-miR-328, hsa-miR-139, hsa-miR-99a, hsa-miR-10a,hsa-miR-143,hsa-miR-145, hsa-miR-95, hsa-miR-149, hsa-miR-320, hsa-miR-151,hsa-let-7e were under expressed in DLBCL.

microRNA Gene Finding Approaches:

microRNA gene finding was a difficult process as they tend to escape the detection phenomenon. Mutagenesis and genetic techniques are not used due to their short length and redundant behavior [29].Annotation criteria for novel miRNA genes involve evidences about 22 nt RNA transcript or cDNA molecules. Mature miRNA should be part of phylogenetically conserved fold back precursor structure arm of a minimum free energy without any bulges or loops. Computational approaches are used for gene finding which includes: filter based methods that have distinguished initial candidates, structural criteria, conservation criteria and filters from the basic annotation criteria [30]. Homology method is there which obtains those stem loops that are identical previously obtained miRNAs stem loops. Target centered method uses miRNA targets to find miRNAs. Machine learning methods that do not obtain miRNA precursors from known miRNAs and negative sets of stem loops. Mixed approaches are there that includes computational tools as well as high throughput experiments.

microRNA's Gene Targets:

miRNA functions when it binds to mRNAs 3'UTR causing translation repression, and degradation. mRNA translational repression is caused by the imperfect complementarities of miRNA and formation of miRNA-RISC complex which act as inhibitory with various other translation factors. miRNA mediated mRNA decay occurs due to deadenylation and decapping process of mRNA. Animal targets are mainly located at 3'UTR while plant targets are located anywhere as degradation occurs from a single cleavage site. miRNA genes are mainly located in the proteins intronic regions or regions of exons or intergenes. miRNAs also play role in target prediction by pairing to the mRNAs 3'ends [31,32].

microRNA Prediction Tools:

miRNA precursors can be predicted by using following tools [32] :

Triplet SVM:

This program predicts sequences across miRNA hairpin structures which requires training of triplet elements of query sequence.

RNAmicro:

Uses multiple sequence alignment. Train miRNA precursor alignment returning probability for input being miRNA precursor.

MiRAlign:

It predicts those hairpins that have mature miRNAs.

miR-abela: It predicts miRNA gene.

MIRScan:

It assigns score for two hairpin input as it compares it with C.elegans/C.briggsae hairpin pairs.

microRNA's Target Prediction Tools:

There are many tools to predict miRNAs as following:

miRanda:

miRanda algorithm starts with matching of 3'UTRs of miRNA leading to thermodynamic stability and evolutionary conservation. It is used by microRNA.org [33, 34] which has target predictions for human, mouse and rat.

PicTar:

Target sites obtained for miRNAs are ranked by their scores which are calculated using PicTar algorithm which uses HMM to calculate maximum likelihood score. The final score is obtained by summing up the individual score of all the species. Predictions for mice, vertebrates, flies, worms are obtained. [33]

TargetScan:

It involves pairing followed by ranking resulting sites on basis of thermodynamic stability. Like PicTar it also involves integration of scores for different species. Predictions are available for human, mouse, cow, frog, etc. [33]

Rna22:

It involves sequence search at 3'UTR for miRNA patterns. These miRNA patterns clusters around particular UTR location creating a target island. Predictions for human, mouse, fly and worm can be obtained.

miRBase:

It includes integrated information about miRNAs and predicted gene targets. It is the primary online repository for miRNA sequences and it provides nomenclature to novel miRNAs with annotated information about the sequences and related gene targets. It contains about 9000 miRNA entries for sequences and genomic locations from 103 species. Target sites are identified by using miRanda algorithm [33]. It contains predictions for fly, worm and few vertebrates.

TarBase:

It provides knowledge about the functionality of miRNA target site with 1300 entries to test positivity or negativity.1000 entries for human genes from 200 papers are available. Positive interactions are supported by experiments for validations and ability of miRNA to cause translational repression or cleavage. [33]

miRGen –Targets:

Here multiple target prediction tools-miRanda (microrna.org), miRanda (miRBase), TargetScan, TarBase and PicTar combine at single interface [35]. Predictions are for human, rat, fly, mouse, worm and zebrafish [36].

GenMiR++:

Generative model for miRNA regulation or GenMiR++ integrates results from TargetScanS with miRNA mRNA expression profiles. Bayesian approach scores the miRNA mRNA pair which is low for over expressed miRNA mRNA and high for under expressed mRNAs-over expressed miRNAs. GenMir++ predicts those miRNA mRNA pairs that would lead to transcriptional degradation.

microRNA Databases:

CSRDB:

Cereal Small RNA Database is an integrated database for small RNAs including miRNAs for cereal crops. Plant small RNAs (smRNAs) includes microRNAs (miRNAs) which act as emerging as significant components of epigenetic processes and of gene networks involved in development and in homeostasis. CSRDB includes sequence datasets related to various cereal crops. The resource is available at http://sundarlab.ucdavis.edu/smrnas/.[37]

Dietary MicroRNA Database (DMD):

DMD is a database that achieves and acts as analytic tool for Food-Borne microRNAs. DMD is a database constructed as a source of those microRNAs which were discovered in dietary sources. The database contains microRNAs seen in almost 15 species of plants and animals like apple, grape, cow milk, cow fat, etc. Every annotated entry contains information regarding mature sequences, genome locations, hairpin structures of parental pre-microRNAs, cross-species sequence comparison, disease relevance, and the experimentally validated gene targets.DMD presents functional analysis for featured microRNAs including target prediction, pathway enrichment and gene network construction insights through viewing the functional pathways and building protein-protein interaction networks.DMD is helpful for researchers who aim to study microRNA under nutrition aspect. The database can be accessed at http://sbbi.unl.edu/dmd/.[38]

HMDD:

HMDD is a database for experimentally supported human microRNA and disease associations. The Human microRNA Disease Database is a database featuring human microRNAs and its associated diseases. MiRNA–disease association data from genetics, epigenetics, circulating miRNAs and miRNA–target interactions were integrated into the database .It is available via the Web site at http://cmbi.bjmu.edu.cn/hmdd.[39]

mESAdb:

microRNA Expression and Sequence Analysis Database is a database which features information associated with expression and analysis of microRNAs. It is available at http://konulab.fen.bilkent.edu.tr/mirna/.The database holds information about microRNAs and related target data with analysis modules including study and mining of expression datasets along with its association with annotation databases, HUGE Navigator, KEGG and GO. [40]

miR2Disease:

This database is a manually curated database having information related to microRNA deregulation in human disease. One seventh of these represent the pathogenic roles of deregulated microRNA in human disease. The database has information about microRNA, target genes and related disease which can be easily retrieved along with a submission page for researchers.miR2Disease is freely available at http://www.miR2Disease.org.[41]

miRCancer:

This database is created as an association to microRNA–cancer and is constructed by text mining on literature. The text mining is based on constructed rules which represent the common sentence structures typically used to state microRNA expressions in cancers. miRCancer is freely available on the web at http://mircancer.ecu.edu/.[42]

miRDB:

It is microRNA target prediction and functional annotation database. Unlike most other miRNA databases, miRNA functional annotations in miRDB are presented with a primary focus on mature miRNAs, which are the functional carriers of miRNA-mediated gene expression regulation. miRDB is freely accessible at http://mirdb.org.[43]

miRdSNP:

miRdSNP is a database incorporating three important areas of dSNPs, miRNA target sites, and diseases. It has disease-associated SNPs and microRNA target sites on human genes. miRdSNP is freely available on the web at http://mirdsnp.ccr.buffalo.edu.[44]

miRecords:

It is an integrated resource for animal microRNA–target interactions. The database consists of experimentally validated miRNA target interactions with detailed documentation for each interaction. It is useful resource for researchers and informatics scientists who are developing next generation miRNA target prediction programs. The miRecords is available at http://miRecords.umn.edu/miRecords.[45]

miR-EdiTar:

It is a database of predicted A-to-I edited miRNA target sites. A-to-I RNA editing is an important mechanism that consists of the conversion of specific adenosines into inosines in RNA molecules. Its dysregulation has been associated to several human diseases including cancer and has roles in miRNA - mediated gene expression regulation. The database contains predicted miRNA binding sites that could be affected by A-to-I editing and sites that could become miRNA binding sites as a result of A-to-I editing. miR-EdiTar is freely available online at http://microrna.osumc.edu/mireditar.[46]

miRGator:

It is a microRNA portal for deep sequencing, expression profiling and mRNA targeting information. The database in microRNA study includes biogenesis and molecular function with co expression analysis of miRNA and target mRNAs, based on miRNA-seq and RNA-seq data .It also includes heatmap and network views so that users can investigate the inverse correlation of gene expression and target relations, compiled from various databases of predicted and validated targets. The database is available at http://mirgator.kobic.re.kr.[47]

miRNEST:

It is a database about plant and animal microRNAs. It is an integrative resource for miRNAs with predictions from deep sequencing libraries, degradome analysis along with premiRNA classification. The database is available at http://mirnest.amu.edu.pl.[48]

miRo`:

It is a miRo` is a web-based knowledge base with information about miRNA and its phenotype associations in humans. It includes data from various online database sources with information regarding miRNAs, ontologies, diseases, and targets with data mining facilities along with annotated miRNA information. The miRo` web site is available at: http://ferrolab.dmi.unict.it/miro.[49]

miRPathDB:

It is database which links down the microRNAs and target pathways. The database contains a large number of miRNAs, different miRNA target sets (experimentally validated target genes as well as predicted targets genes) and a broad selection of functional biochemical categories (KEGG-,Wiki Pathways-, BioCarta-, SMPDB-, PID-, Reactome pathways, functional categories from gene ontology (GO), protein families from Pfam and chromosomal locations). miRNA Pathway Dictionary Database is accessible at https://mpd.bioinf.uni-sb.de/.[50]

PhenomiR:

It is a database for microRNA expression both in biological processes and diseases. It is a manually curated resource with 542 studies with investigates of deregulation of microRNA expression in diseases and biological processes. The database is available at http://mips.helmholtz-muenchen.de/phenomir.[51]

PMTED:

It is a plant microRNA target expression database. Plant MiRNA Target Expression Database is designed to retrieve and analyze expression profiles of miRNA targets as well as it provides a basic information query function for miRNAs and their target sequences, gene ontology, and differential expression profiles. It is available on http://pmted.agrinome.org.[52]

STarMirDB:

It is a database of microRNA binding sites. These small regulatory molecules are involved in diverse developmental, physiological and pathological processes. miRNAs target mRNAs (mRNAs) for translational repression and/or mRNA degradation. Predictions of miRNA binding sites facilitate experimental validation of miRNA targets. It provides a comprehensive list of sequence, thermodynamic and target structural features that are known to influence miRNA: target interaction. It is available at http://sfold.wadsworth.org/starmirDB.php.[53]

VIRmiRNA:

It is a database comprising experimentally validated viral miRNAs and their targets. Viral microRNAs (miRNAs) regulate gene expression of viral and/or host genes to benefit the virus, thus playing a key role in host–virus interactions and pathogenesis of viral diseases. This includes 1308 experimentally validated miRNA sequences with their isomiRs encoded by 44 viruses in viral miRNA ‘VIRmiRNA’ and 7283 of their target genes in ‘VIRmiRtar’. Additionally, there is information of 542 antiviral miRNAs encoded by the host against 24 viruses in antiviral miRNA ‘AVIRmir’. The database url: http://crdd.osdd.net/servers/virmirna.[54]

REFERENCES:

[1]. Lee RC, et al. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarily to lin-14. Cell. 1993;75:843–54.

[2]. Reinhart B, et al. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature. 2000;403:901–6.

[3]. Ambros V. The functions of animal microRNAs. Nature. 2004;431:350–5

[4]. Bartel D. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281–97.

[5]. Carthew, R.W. Gene regulation by microRNAs. Curr. Opin. Genet. Dev. 2006, 16: 203-8.

[6]. Guarnieri, D.J., DiLeone, R.J. MicroRNAs: a new class of gene regulators. Ann. Med. 2008, 40: 197-208.

[7]. Vasudevan, S., Tong, Y., Steitz, J.A. Switching from repression to activation: microRNAs can up-regulate translation. Science 2007,318: 1931-4.

[8]. Medina, P.P., Slack, F.J. microRNAs and cancer: An overview. Cell Cycle. 2008, 17: 7-16.

[9]. Zhang, B., Pan, X., Cobb, G.P., Anderson, T.A. microRNAs as oncogenes and tumor suppressors. Dev. Biol. 2007, 302: 1-12.

[10]. Calin, G.A., Sevignani, C., Dumitru, C.D., Hyslop, T., Noch, E., Yendamuri, S., Shimizu, M., Rattan, S., Bullrich, F., Negrini, M., Croce, C.M. Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc. Natl. Acad. Sci. USA 2004, 101: 2999-3004.

[11]. Ahmet M. Denli, Bastiaan B. J. Tops, Ronald H. A. Plasterk, René F. Ketting & Gregory J. Hannon, Processing of primary microRNAs by the Microprocessor complex Nature 432, 231-235 (11 November 2004).

[12]. Han J, Lee Y, Yeom KH, Nam JW, Heo I, Rhee JK, Sohn SY, Cho Y, Zhang BT, Kim VN. Molecular basis for the recognition of primary microRNAs by the Drosha-DGCR8 complex.Cell. 2006 Jun 2;125(5):887-901.

[13]. Lund, E., Dahlberg, J.E. Substrate selectivity of exportin 5 and Dicer in the biogenesis of microRNAs. Cold Spring Harb. Symp.Quant. Biol. 2006, 71: 59-66.

[14]. Roma Chandra. Study of miRNA mRNA Modules and their Relationship with Cancer Pathways.JPSR.2017,9(1):10-11.

[15]. Xu P, Vernooy SY, Guo M, Hay BA. The Drosophila microRNA Mir-14 suppresses cell death and is required for normal fat metabolism. Curr Biol. 2003 Apr 29;13(9):790-5.

[16]. Abelson, J.F., Kwan, K.Y., O'Roak, B.J., Baek, D.Y., Stillman,A.A., Morgan, T.M., Mathews, C.A., Pauls, D.L., Rasin, M.R.,Gunel, M., Davis, N.R., Ercan-Sencicek, A.G., Guez, D.H.,Spertus, J.A., Leckman, J.F., Dure, L.S. 4th., Kurlan, R., Singer,H.S., Gilbert, D.L., Farhi, A., Louvi, A., Lifton, R.P., Sestan, N.,State, M.W. Sequence variants in SLITRK1 are associated with Tourette's syndrome. Science 2005, 310: 317-20.

[17]. Hébert, S.S., Horré, K., Nicolaï, L., Papadopoulou, A.S., Mandemakers, W., Silahtaroglu, A.N., Kauppinen, S., Delacourte, A., De Strooper, B. Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer's disease correlates with increased BACE1/betasecretase expression. Proc. Natl. Acad. Sci. USA 2008, 105: 6415-20.

[18]. Mehler, M.F., Mattick, J.S. Non-coding RNAs in the nervous system.J. Physiol. 2006, 575: 333-41.

[19]. Esau, C., Davis, S., Murray, S.F., Yu, X.X., Pandey, S.K., Pear, M.,Watts, L., Booten, S.L., Graham, M., McKay, R., Subramaniam,A., Propp, S., Lollo, B.A., Freier, S., Bennett, C.F., Bhanot, S.,Monia, B.P. miR-122 regulation of lipid metabolism revealed by invivo antisense targeting. Cell Metab. 2006, 3: 87-98.

[20]. Krützfeldt, J., Rajewsky, N., Braich, R., Rajeev, K.G., Tuschl, T., Manoharan, M., Stoffel, M. Silencing of microRNAs in vivo with 'antagomirs'. Nature 2005, 438: 685-9.

[21]. Poy, M.N., Spranger, M., Stoffel, M. microRNAs and the regulation of glucose and lipid metabolism. Diabetes Obes. Metab. 2007,S2: 67-73.

[22]. Van Rooij, E., Olson, E.N. MicroRNAs: powerful new regulators of heart disease and provocative therapeutic targets. J. Clin. Invest.2007, 117: 2369-76.

[23]. Carè, A., Catalucci, D., Felicetti, F., Bonci, D., Addario, A., Gallo, P., Bang, M.L., Segnalini, P., Gu, Y., Dalton, N.D., Elia, L., Latronico, M.V., Høydal, M., Autore, C., Russo, M.A., Dorn, G.W. 2nd, Ellingsen, O., Ruiz-Lozano, P., Peterson, K.L., Croce, C.M., Peschle, C., Condorelli, G. MicroRNA-133 controls cardiac hypertrophy.Nat. Med. 2007, 13: 613-8.

[24]. Michael, M.Z., O' Connor, S.M., van Holst Pellekaan, N.G., Young, G.P., James, R.J. Reduced accumulation of specific microRNAs in colorectal neoplasia. Mol. Cancer Res. 2003, 1: 882-91.

[25]. Han, Y., Siliciano, R.F. Keeping quiet: microRNAs in HIV-1 latency. Nat. Med. 2007, 13: 1138-40.

[26]. Jin, W.B., Wu, F.L., Kong, D., Guo, A.G. HBV-encoded microRNA candidate and its target. Comput. Biol. Chem. 2007, 31:124-6.

[27]. Grey, F., Meyers, H., White, E.A., Spector, D.H., Nelson, J. A human cytomegalovirus-encoded microRNA regulates expression of multiple viral genes involved in replication. PLoS. Pathog. 2007,3: e163

[28]. Cai, X., Schäfer, A., Lu, S., Bilello, J.P., Desrosiers, R.C., Edwards, R., Raab-Traub, N., Cullen, B.R. Epstein-Barr virus microRNAs are evolutionarily conserved and differentially expressed. PLoS. Pathog. 2006, 2: e23.

[29]. Ambros, V, microRNAs : tiny regulators with great potential. Cell,2001,107:823-826.

[30]. Ambros V, Bartel B, Bartel DP, Burge CB, Carrington JC, Chen X, Dreyfuss G, Eddy SR, Griffiths-Jones S, Marshall M, et al. A uniform system for microRNA annotation. RNA (2003) 9:277–279

[31]. Krek A, Grun 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. 37:495-500 (2005)

[32]. Grimson A, Farh KK, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell. 27: 91-105 (2007)

[33]. John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS. Human MicroRNA targets. PLoS Biol. (2004) 2:e363.

[34]. Betel D, Wilson M, Gabow A, Marks DS, Sander C. The microRNAorg resource: targets and expression. Nucleic Acids Res. 36: D149-D153 (2008)

[35]. Lewis BP, Shih I-H, Jones-Rhoades MW, Bartel DP, Burge CB. Prediction of mammalian microRNA targets. Cell (2003) 115:787–798

[36]. Megraw M, Sethupathy P, Corda B, Hatzigeorgiou AG. miRGen: a database for the study of animal microRNA genomic organization and function. Nucleic Acids Res. 35: D149-D155 (2007).

[37]. Cameron Johnson, Lewis Bowman, Alex T. Adai, Vicki Vance and Venkatesan Sundaresan. CSRDB: a small RNA integrated database and browser resource for cereals. D829-D833, Nucleic Acids Research, 2006, Vol.35, Database issue.

[38]. Chiang K, Shu J, Zempleni J, Cui J .Dietary MicroRNA Database (DMD): An Archive Database and Analytic Tool for Food-Borne microRNAs. PLoS ONE 10(6): e0128089.doi:10.1371/journal.pone.0128089

[39]. Yang Li, Chengxiang Qiu, Jian Tu, Bin Geng, Jichun Yang, Tianzi Jiang, and Qinghua Cui. HMDD v2.0: a database for experimentally supported human microRNA and disease associations. D1070–D1074 Nucleic Acids Research, 2014, Vol. 42, Database issue.

[40]. Koray D. Kaya, Go Khan Karaku Lah, Cengiz M. Yakıcıer, Aybar C. Acar, and Ozlen Konu. mESAdb: microRNA Expression and Sequence Analysis Database. D170–D180 Nucleic Acids Research, 2011, Vol. 39, Database issue.

[41]. Qinghua Jiang, Yadong Wang, Yangyang Hao, Liran Juan, Mingxiang Teng, Xinjun Zhang, Meimei Li, Guohua Wang and Yunlong Liu.miR2Disease: a manually curated database for
microRNA deregulation in human disease.

[42]. Boya Xie, Qin Ding, Hongjin Han and Di Wu. miRCancer: a microRNA–cancer association database constructed by text mining on literature. Vol. 29 no. 5 2013, pages 638–644.doi:10.1093 /bioinformatics/btt014

[43]. Xiaowei Wang. miRDB: A microRNA target prediction and functional annotation database with a wiki interface.

[44]. Andrew E Bruno, Li Li, James L Kalabus, Yuzhuo Pan, Aiming Yu and Zihua Hu. miRdSNP: a database of disease-associated SNPs and microRNA target sites on 3’UTRs of human genes. BMC Genomics 2012, 13:44.

[45]. Feifei Xiao, Zhixiang Zuo, Guoshuai Cai, Shuli Kang, Xiaolian Gao and Tongbin Li. miRecords: an integrated resource for microRNA–target interactions. Nucleic Acids Research, 2009, Vol. 37, Database issue D105–D110.

[46]. Alessandro Lagana, Alessio Paone, Dario Veneziano, Luciano Cascione, Pierluigi Gasparini, Stefania Carasi, Francesco Russo, GiovanniNigita, Valentina Macca, Rosalba Giugno, Alfredo Pulvirenti, Dennis Shasha, Alfredo Ferro and Carlo M. Croce. miR-EdiTar: a database of predicted A-to-I edited miRNA target sites. Vol. 28 no. 23 2012, pages 3166–3168 Bioinformatics Applications Note Doi:10.1093/Bioinformatics/Bts589.

[47]. Soo young Cho, Insu Jang, Yukyung Jun, Suhyeon Yoon, Minjeong Ko, Yeajee Kwon, Ikjung Choi, Hyeshik Chang, Daeun Ryu, Byungwook Lee, V. Narry Kim, Wankyu Kim and Sanghyuk Lee. miRGator v3.0: a microRNA portal for deep sequencing, expression profiling and mRNA targeting. D252–D257 Nucleic Acids Research, 2013, Vol. 41, Database issue.

[48]. Michab W. Szczes niak and Izabela Makabowska. miRNEST 2.0: a database of plant and animal
microRNAs. D74–D77 Nucleic Acids Research, 2014, Vol. 42, Database issue.

[49]. Lagana, S. Forte, A. Giudice, M. R. Arena, P. L. Puglisi, R. Giugno, A. Pulvirenti, D. Shasha and A. Ferro. miRo` : a miRNA knowledge base. Database, Vol. 2009, Article ID bap008, doi:10.1093/database/bap008.

[50]. Christina Backes, Tim Kehl, Daniel Stockel, Tobias Fehlmann, Lara Schneider, Eckart Meese, Hans-Peter Lenhof and Andreas Keller. miRPathDB: a new dictionary on microRNAs and target pathways. D90–D96 Nucleic Acids Research, 2017, Vol. 45, Database issue.

[51]. Andreas Ruepp, Andreas Kowarsch, Daniel Schmidl, Felix Buggenthin, Barbara Brauner, Irmtraud Dunger, Gisela Fobo, Goar Frishman, Corinna Montrone, Fabian J Theis. PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes. Genome Biology 2010, 11:R6.

[52]. Xiuli Sun, Boquan Dong, Lingjie Yin, Rongzhi Zhang, Wei Du, Dongfeng Liu, Nan Shi, Aili Li, Yanchun Liang and Long Mao. PMTED: a plant microRNA target expression database.BMC Bioinformatics 2013, 14:174.

[53]. William Rennie, Shaveta Kanoria, Chaochun Liu, Bibekanand Mallick, Dang Long, Adam Wolenc, C. Steven Carmack, Jun Lu, and Ye Ding. STarMirDB: A database of microRNA binding sites. RNA BIOLOGY, 2016, VOL. 13, NO. 6, 554–560.

[54]. Abid Qureshi, Nishant Thakur, Isha Monga, Anamika Thakur and Manoj Kumar. VIRmiRNA: a comprehensive resource for experimentally validated viral miRNAs and their targets. Database, 2014, 1–10.

Received on 25.03.2017 Modified on 15.04.2017

Research J. Pharm. and Tech. 2017; 10(6): 1834-1839.

DOI: 10.5958/0974-360X.2017.00322.5