· About miRecords

miRecords is a resource for animal miRNA-target interactions developed at Biolead.org. miRecords consists of two components. The Validated Targets component is a large, high-quality database of experimentally validated miRNA targets resulting from meticulous literature curation. The Predicted Targets component of miRecords is an integration of predicted miRNA targets produced by 11 established miRNA target prediction programs.  

· Current status of miRecords 

As of May 5, 2010, the Validated Targets component of miRecords hosts the 1597 records of 1529 interactions between 384 miRNAs and 1097 target genes in 9 animal species. Among these records, 1015 were curated from "low throughput" experiments.

The Predicted Targets component of mIRecords integrates the predicted targets of the following miRNA target prediction tools: DIANA-microT, MicroInspector, miRanda, MirTarget2, miTarget, NBmiRTar, PicTar, PITA, RNA22, RNAhybrid, and TargetScan. 

· Utility

In the Validated Targets section, the user can first select a species, then choose from a list of miRNAs for which experimentally validated targets have been documented. Optionally, the user can provide the RefSeq accession, the Entrez Gene ID or the gene name of the target gene, and initiate a search. The result of the search is presented as a list of miRNA-target interactions. For each miRNA-target interaction, the information about the miRNA and about the target gene are displayed together with the prediction results of 11 established miRNA target prediction programs. Positive predictions are indicated by lit-up symbols. The user can click a lit-up symbol to obtain more information about the prediction.

When the user clicks the “Click for detail” link displayed in the “Target Interaction” column, detailed information about a validated miRNA-target interaction is presented. The detailed information about the miRNA includes the Stemloop ID and accession number, mature miRNA ID and accession number, and the sequence of the miRNA. The detailed information about the target gene includes the RefSeq accession number, name, synonyms and the description of the gene.

Underneath the general information about the miRNA and the target gene, all records, or literature accounts about the miRNA-target interaction are presented. On the top of each record is the citation information, underneath which is the experimental support of the miRNA-target interaction, listed in the order of the target gene level evidence, followed by the target region level evidence, then by the target site level evidence. In each of the three levels of evidence, endogenous miRNA experiments (when available) are described first, followed by summaries of exogenous miRNA experiments. The summary of a target gene level, exogenous miRNA experiment includes the method of manipulating the miRNA level (e.g., “overexpression by mature miRNA transfection”, or “underexpression by siRNA knock-down”), reporter assay performed (e.g., Luciferase assay), mRNA level measurement performed (e.g., RT-PCR, or 5’RACE), mRNA level measurement result (e.g., “down-regulated”), protein level measurement performed (e.g., Western blot), and protein-level measurement result. The user can click the “Detail” button for each individual experiment, and the description of the experiment from the original publication is displayed.

The summary of target gene level or target site level evidence also includes the section of the mRNA that was used in the fusion reporter assay, and about whether point mutation experiments were performed. When available, a drawing of the alignment between the miRNA and the putative target site is also presented.

The Predicted Targets section is organized similarly to the Validated Targets section. When the user submits a query, all putative targets (including unvalidated ones) predicted by one or more of the established miRNA target prediction programs are presented.

· Endogenous miRNAs vs. exogenous miRNAs

A majority of studies of miRNA-target interactions were performed by experimental manipulations of the level of a miRNA (either by overexpression/misexpression or by underexpression) in a cell line or a tissue, followed by examination of changes in expression of the putative targets. These experiments could be generally classified as “exogenous miRNA experiments”.

Concerns have been raised, however, over how many of these miRNA-target interactions actually take place in endogenous, physiological conditions (Didiano and Hobert, 2006; Rajewsky, 2006). Similarly to the gene regulation by transcription factors, endogenous miRNA may require favorable cellular context to bind and regulate their targets, which cannot be replicated in exogenous miRNA experiments.

An increasing number of studies have provided evidence about endogenous miRNA-target interactions. For example, in (Johnson et al., 2005), when investigating whether let-60 was a target of the miRNA let-7 in the hypodermal seam cells in C. elegans, the authors fused let-60 3’UTR behind the E. Coli lacZ gene. They discovered that the reporter activity was downregulated at the L4 stage of the development, when let-7 was known to be expressed in the seam cells.  In contrast, the same reporter gene fused to an irrelevant control 3’UTR was expressed at all stages. As another example, in (Didiano and Hobert, 2006), the targeting of cog-1 by the miRNA lsy-6 was studied in the ASEL and ASER, two closely related bilaterally symmetric neurons in C. elegans. Only the ASEL but not the ASER neuron expresses endogenous lsy-6. The authors fused the cog-1 3’UTR to a GFP sensor construct, and found that this fusion was effectively downregulated in ASEL but not in ASER. In several other studies, the endogenous levels of miRNAs and the protein expression levels of their potential targets were measured simultaneously across several cell lines or specimens, and inverse correlations were observed between them (Asangani et al., 2008; Park et al., 2008; Wang et al., 2008).

In miRecords, we make a clear distinction between endogenous miRNA experiments and exogenous miRNA experiments. For each study involving endogenous miRNA experiments, we provide a brief summary about the rationale of the experiments as well as an explanation of the results.

· Target genes, target regions and target sites

We classify any experimental evidence about miRNA-target interaction as belonging to one of the three levels: the target gene level, the target region level, and the target site level. When the evidence indicates that the level of the full-length gene product (mRNA or protein) of a putative target has reduced following over- or misexpression of a miRNA, or that the full-length gene product has accumulated following underexpression of the miRNA, it is considered as target gene level evidence. The target gene level evidence also includes endogenous miRNA experiments leading to the finding of inverse correlations between the endogenous miRNA levels and the full-length protein products of the putative target genes. The target gene level experiments are often regarded as indirect support of the miRNA-target interactions, because the level of the gene product may change due to other reasons, e.g., a change in expression of another protein (which is a true target of the miRNA) that it interacts with.

When the experimental evidence indicates that a region of the mRNA of the putative target (shorter than the full-length transcript) is responsible for the miRNA-target interaction, it is documented as target region level evidence. Most target region level experiments were conducted with fusion of the 3’UTR of the putative target gene (or a section of the 3’UTR) to a reporter construct (e.g., Luciferase or GFP), followed by observations that the reporter expression is down-regulated (or up-regulated) in response to overexpression/misexpression (or underexpression) of the miRNA.

When an experiment points to a very short section of the mRNA (whose length is comparable with that of the mRNA) as being responsible for the miRNA-target interaction, it is classified as target site level evidence. The target site level experiments include reporter assays with fusion constructs made with short target sites, and target site mutation experiments (discussed below).

· Over- or misexpression and underexpression of miRNAs

The exogenous miRNA experiments can be broadly classified into two categories based on the methods by which the miRNA levels are manipulated: miRNA overexpression or misexpression experiments, and miRNA underexpression experiments. The methods commonly applied to over- or misexpress miRNAs include mature miRNA transfection (Kawahara et al., 2007), miRNA precursor transfection (Fukuda et al., 2005), and indirectly induced miRNA overexpression (using the DNA demethylatingn agent 5-Aza-Deoxycytidine (Lujambio et al., 2007), or the histone deacetylase inhibitor phenylbutyrate (PBA) (Saito et al., 2006)). The techniques applied to underexpress miRNAs include miRNA knock-down by siRNAs (Nakamoto et al., 2005), miRNA knock-down by antisense modified oligonucleotides, e.g. morpholinos (Woltering and Durston, 2008), locked nucleic acids (LNAs) (Elmen et al., 2008), or 2’-O-Me oligonucleotides (Leaman et al., 2005), and knock-out of the miRNA gene (Vigorito et al., 2007).

· Reporter assays, mRNA- and protein-level measurements

The means by which putative target expression levels are examined can be classified into four categories: reporter assays, mRNA-level measurements, protein-level measurements, and “others”. In a reporter assay, the putative target region or target site is fused with a reporter vector, and the expression level of the putative target region or target site is quantified by measuring the reporter’s activity. Commonly used reporters include Luciferase (Krek et al., 2005), GFP (Visvanathan et al., 2007), YFP (Woltering and Durston, 2008) and the lacZ / β-galactosidase reporter (Grosshans et al., 2005). Several methods of measuring mRNA levels of putative targets can be applied. They include RT-PCR (Zhao et al., 2005), Northern blot (Hossain et al., 2006), 5’RACE (Davis et al., 2005), DNA microarrays (Lim et al., 2005), ribonuclease protection assay (Yekta et al., 2004) and branched DNA probe assay (Akinc et al., 2008). Commonly applied protein-level measuring methods include Western blot (Luo et al., 2007), ELISA (Ye et al., 2008) and immunocytochemistry (Lee et al., 2007). The “others” category includes rare target expression analyses that do not belong to other categories, e.g., phenotype analysis (Woltering and Durston, 2008).

· Target site mutations

In a target site mutation experiment, point mutations are introduced to the putative target site. If the introduced point mutations lead to abolishment of the miRNA-mediated downregulation, the site is convincingly verified as a true target site. Besides validating miRNA target sites, target site mutation experiments are frequently conducted in studies investigating general features that influence the miRNA targeting, e.g. it was applied to study the importance of the 5’ seed region for miRNA-target interactions (Doench and Sharp, 2004), and of target accessibility features in assisting miRNA target discovery (Kertesz et al., 2007). 

· The 11 miRNA target prediction programs

Name	Version or Release Date	Description	References
DIANA-microT	May 15, 2004	DIANA-microT identifies mRNA targets for animal miRNAs and predicts mRNA targets, bearing single MREs, for human and mouse miRNAs.	Kiriakidou, M. et al. A combined computational-experimental approach predicts human microRNA targets. Genes Dev. 18, 1165-1178 (2004).[PubMed]
MicroInspector	Version 1.5	MicroInspector will analyse a user-defined RNA sequence, which is typically an mRNA or a part of an mRNA, for the occurrence of binding sites for known and registered miRNAs. The program allows variation of temperature, the setting of energy values as well as the selection of different miRNA databases to identify miRNA-binding sites of different strength.	Rusinov, V., Baev, V., Minkov, I.N. & Tabler, M. MicroInspector: a web tool for detection of miRNA binding sites in an RNA sequence. Nucleic Acids Res. 33 (Web server issue), W696-700 (2005).[PubMed]
miRanda	Version 1.9	A computational method for whole-genome prediction of miRNA target genes, For each miRNA, target genes are selected on the basis of three properties: sequence complementarity using a position-weighted local alignment algorithm, free energies of RNA-RNA duplexes, and conservation of target sites in related genomes. Application to the D. melanogaster, Drosophila pseudoobscura and Anopheles gambiae genomes identifies several hundred target genes potentially regulated by one or more known miRNAs.	A.J. Enright, B. John, U. Gaul, T. Tuschl, C. Sander, D.S.Marks; MicroRNA targets in Drosophila; Genome Biology 5(1):R1. (2003)[PubMed]
MirTarget2	Version 2.0	A miRNA target prediction program based on support vector machines (SVMs) and a large microarray training dataset.	Wang X, El Naqa IM. Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics. 2008 Feb 1;24(3):325-32. Epub (2007 Nov 29).[PubMed]
miTarget	Sep 18, 2006	miTarget is a Support Vector Machine (SVM) classifier for miRNA target-gene prediction. It uses a radial basis function kernel as a similarity measure for SVM features, which are categorized by structural, thermodynamic, and position-based features. Position-based features are introduced in this study for the first time and reflect the mechanism of miRNA binding. The SVM classifier produces high performance with a biologically relevant data set gained from the literature when compared with previous tools.	S.-K Kim*, J.-W Nam*, J.-K Rhee, W.-J Lee, B.-T. Zhang. miTarget: microRNA target-gene prediction using a Support Vector Machinen. BMC Bioinformatics , 7:411, (2006).[PubMed]
NBmiRTar	Version 1.0 Beta	A target prediction method (NBmiRTar) that does not require sequence conservation, using instead, machine learning by a naive Bayes classifier.	Malik Yousef, Segun Jung, Andrew V. Kossenkov, Louise C. Showe and Michael K. Showe Naive Bayes for MicroRNA Target Prediction Bioinformatics. 2007 Nov 15;23(22):2987-92. Epub (2007 Oct 8).[PubMed]
PicTar	March 26, 2007	PicTar, a computational method for identifying common targets of microRNAs. Statistical tests using genome-wide alignments of eight vertebrate genomes, PicTar's ability to specifically recover published microRNA targets, and experimental validation of seven predicted targets suggest that PicTar has an excellent success rate in predicting targets for single microRNAs and for combinations of microRNAs.	Krek, A. et al. Combinatorial microRNA target predictions. Nat. Genet. 37, 495-500 (2005).[PubMed]
PITA	Version 6 (Aug 31, 2008)	PITA follow standard seed parameter settings and consider seeds of length 6-8 bases, beginning at position 2 of the microRNA. No mismatches or loops are allowed, but a single G:U wobble is allowed in 7- or 8-mers.	Kertesz et al., The role of site accessibility in microRNA target recognition, Nature Genetics 2007 .[PubMed]
RNA22	May 20, 2008	A pattern-based method for the identification of microRNA-target sites and their corresponding RNA/RNA complexes .	T. Huynh, K. Miranda, Y. Tay, Y.-S. Ang, W.-L. Tam, A. M. Thomson, B. Lim, I. A pattern-based method for the identification of microRNA-target sites and their corresponding RNA/RNA complexes Rigoutsos Cell, Vol 126, 1203-1217, 22 September 2006.[PubMed]
RNAhybrid	Version 2.2	RNAhybrid is a tool for finding the minimum free energy hybridisation of a long and a short RNA. The hybridisation is performed in a kind of domain mode, ie. the short sequence is hybridised to the best fitting part of the long one. The tool is primarily meant as a means for microRNA target prediction.	Marc Rehmsmeier *, Peter Steffen, Matthias Hochsmann, Robert Giegerich Fast and effective prediction of microRNA/target duplexes RNA, 10:1507-1517, 2004.[PubMed]
TargetScan/ TargetScanS	Version 4.1	TargetScan/TargetScanS predicts biological targets of miRNAs by searching for the presence of conserved 8mer and 7mer sites	Lewis, B.P., Shih, I.H., Jones-Rhoades, M.W., Bartel, D.P. & Burge, C.B.Prediction of mammalian microRNA targets. Cell 115, 787-798 (2003).[PubMed]

· Compilation of data for predicted target interaction data

The targets predicted by DIANA-microT were obtained by submitting queries to the DIANA-microT server (http://www.diana.pcbi.upenn.edu/cgi-bin/micro_t.cgi).

The targets predicted by MicroInspector were obtained by submitting queries to the MicroInspector web server (http://mirna.imbb.forth.gr/microinspector/).

The targets predicted by miRanda were obtained by running a local implementation of the miRanda algorithm provided by the authors.

The targets predicted by MirTarget2 were downloaded from the MirTarget2 web site (http://mirdb.org) as a pre-compiled dataset.

The targets predicted by miTarget were obtained by submitting queries to the miTarget server (http://www.diana.pcbi.upenn.edu/cgi-bin/micro_t.cgi).

The targets predicted by NBmiRTar were obtained by submitting queries to the NBmiRTar server (http://wotan.wistar.upenn.edu/NBmiRTar/).

The targets predicted by PicTar were downloaded as precompiled tables from the UCSC genome browser web site (http://genome.ucsc.edu/). WormBase ID and FlyBase ID were tanslated to Refseq accession number with the mapping files provided by WormBase and FlyBase.

The targets predicted by PITA were downloaded from the PITA web site (http://genie.weizmann.ac.il/pubs/mir07/mir07_data.html) as a pre-compiled dataset.

The targets predicted by RNA22 were obtained by submitting queries to the RNA22 server (http://cbcsrv.watson.ibm.com/rna22.html).

The targets predicted by RNAhybrid were obtained by running a local implementation of the RNAhybrid algorithm provided by the authors.

The targets predicted by TargetScan/TargetScanS were downloaded from the TargetScan/TargetScanS web site (http://www.targetscan.org/) as a pre-compiled dataset. The Locus link IDs were converted to RefSeq Accessions.

· Summary of targets predicted by the 11 prediction programs (being updated, please check back later for more complete summary)

Program Number of Confirmed Predictions Number of predictions per miRNA DIANA-microT 22 414.7 MicroInspector 300 10689.7 miRanda 504 3005.5 MirTarget2 231 255.3 miTarget 118 8607.3 NBmiRTar 314 3234.3 PicTar 240 200.0 PITA 684 3956.3 RNA22 400 1884.3 RNAhybrid 838 10958.0 TargetScan/ TargetScanS 243 685.9

· The list of predicted targets is incomplete and being updated. Several of the existing miRNA predicting tools run very slowly. It takes a high-power computer several weeks or even longer to complete the prediction of all miRNAs for a single species. Please bear with us when we actively work on making the list more complete. Meanwhile, please be aware that if you specify the RefSeq accession of individual candiate target gene, the prediction is made by on the fly, i.e. by submitting the prediction request to each of the original predicting servers. The predictions made this way is always complete (unless one or more original servers are down which is indicated by red symbols showing in the table).

· References 

Akinc A, Zumbuehl A, Goldberg M, Leshchiner ES, Busini V, Hossain N, Bacallado SA, Nguyen DN, Fuller J, Alvarez R, Borodovsky A, Borland T, Constien R, de Fougerolles A, Dorkin JR, Narayanannair Jayaprakash K, Jayaraman M, John M, Koteliansky V, Manoharan M, Nechev L, Qin J, Racie T, Raitcheva D, Rajeev KG, Sah DW, Soutschek J, Toudjarska I, Vornlocher HP, Zimmermann TS, Langer R, Anderson DG (2008) A combinatorial library of lipid-like materials for delivery of RNAi therapeutics. Nat Biotechnol 26:561-569.

Asangani IA, Rasheed SA, Nikolova DA, Leupold JH, Colburn NH, Post S, Allgayer H (2008) MicroRNA-21 (miR-21) post-transcriptionally downregulates tumor suppressor Pdcd4 and stimulates invasion, intravasation and metastasis in colorectal cancer. Oncogene 27:2128-2136.

Davis E, Caiment F, Tordoir X, Cavaille J, Ferguson-Smith A, Cockett N, Georges M, Charlier C (2005) RNAi-mediated allelic trans-interaction at the imprinted Rtl1/Peg11 locus. Curr Biol 15:743-749.

Didiano D, Hobert O (2006) Perfect seed pairing is not a generally reliable predictor for miRNA-target interactions. Nat Struct Mol Biol 13:849-851.

Doench JG, Sharp PA (2004) Specificity of microRNA target selection in translational repression. Genes Dev 18:504-511.

Elmen J, Lindow M, Silahtaroglu A, Bak M, Christensen M, Lind-Thomsen A, Hedtjarn M, Hansen JB, Hansen HF, Straarup EM, McCullagh K, Kearney P, Kauppinen S (2008) Antagonism of microRNA-122 in mice by systemically administered LNA-antimiR leads to up-regulation of a large set of predicted target mRNAs in the liver. Nucleic Acids Res 36:1153-1162.

Fukuda Y, Kawasaki H, Taira K (2005) Exploration of human miRNA target genes in neuronal differentiation. Nucleic Acids Symp Ser (Oxf):341-342.

Grosshans H, Johnson T, Reinert KL, Gerstein M, Slack FJ (2005) The temporal patterning microRNA let-7 regulates several transcription factors at the larval to adult transition in C. elegans. Dev Cell 8:321-330.

Hossain A, Kuo MT, Saunders GF (2006) Mir-17-5p regulates breast cancer cell proliferation by inhibiting translation of AIB1 mRNA. Mol Cell Biol 26:8191-8201.

Johnson SM, Grosshans H, Shingara J, Byrom M, Jarvis R, Cheng A, Labourier E, Reinert KL, Brown D, Slack FJ (2005) RAS is regulated by the let-7 microRNA family. Cell 120:635-647.

Kawahara Y, Zinshteyn B, Sethupathy P, Iizasa H, Hatzigeorgiou AG, Nishikura K (2007) Redirection of silencing targets by adenosine-to-inosine editing of miRNAs. Science 315:1137-1140.

Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39:1278-1284.

Krek A, Grun 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 37:495-500.

Leaman D, Chen PY, Fak J, Yalcin A, Pearce M, Unnerstall U, Marks DS, Sander C, Tuschl T, Gaul U (2005) Antisense-mediated depletion reveals essential and specific functions of microRNAs in Drosophila development. Cell 121:1097-1108.

Lee DY, Deng Z, Wang CH, Yang BB (2007) MicroRNA-378 promotes cell survival, tumor growth, and angiogenesis by targeting SuFu and Fus-1 expression. Proc Natl Acad Sci U S A 104:20350-20355.

Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, Castle J, Bartel DP, Linsley PS, Johnson JM (2005) Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433:769-773.

Lujambio A, Ropero S, Ballestar E, Fraga MF, Cerrato C, Setien F, Casado S, Suarez-Gauthier A, Sanchez-Cespedes M, Gitt A, Spiteri I, Das PP, Caldas C, Miska E, Esteller M (2007) Genetic Unmasking of an Epigenetically Silenced microRNA in Human Cancer Cells. Cancer Res 67:1424-1429.

Luo X, Xiao J, Lin H, Li B, Lu Y, Yang B, Wang Z (2007) Transcriptional activation by stimulating protein 1 and post-transcriptional repression by muscle-specific microRNAs of IKs-encoding genes and potential implications in regional heterogeneity of their expressions. J Cell Physiol 212:358-367.

Nakamoto M, Jin P, O'Donnell WT, Warren ST (2005) Physiological identification of human transcripts translationally regulated by a specific microRNA. Hum Mol Genet 14:3813-3821.

Park SM, Gaur AB, Lengyel E, Peter ME (2008) The miR-200 family determines the epithelial phenotype of cancer cells by targeting the E-cadherin repressors ZEB1 and ZEB2. Genes Dev 22:894-907.

Rajewsky N (2006) L(ou)sy miRNA targets? Nat Struct Mol Biol 13:754-755.

Saito Y, Liang G, Egger G, Friedman JM, Chuang JC, Coetzee GA, Jones PA (2006) Specific activation of microRNA-127 with downregulation of the proto-oncogene BCL6 by chromatin-modifying drugs in human cancer cells. Cancer Cell 9:435-443.

Vigorito E, Perks KL, Abreu-Goodger C, Bunting S, Xiang Z, Kohlhaas S, Das PP, Miska EA, Rodriguez A, Bradley A, Smith KG, Rada C, Enright AJ, Toellner KM, Maclennan IC, Turner M (2007) microRNA-155 regulates the generation of immunoglobulin class-switched plasma cells. Immunity 27:847-859.

Visvanathan J, Lee S, Lee B, Lee JW, Lee SK (2007) The microRNA miR-124 antagonizes the anti-neural REST/SCP1 pathway during embryonic CNS development. Genes Dev 21:744-749.

Wang WX, Rajeev BW, Stromberg AJ, Ren N, Tang G, Huang Q, Rigoutsos I, Nelson PT (2008) The expression of microRNA miR-107 decreases early in Alzheimer's disease and may accelerate disease progression through regulation of beta-site amyloid precursor protein-cleaving enzyme 1. J Neurosci 28:1213-1223.

Woltering JM, Durston AJ (2008) MiR-10 represses HoxB1a and HoxB3a in zebrafish. PLoS ONE 3:e1396.

Ye W, Lv Q, Wong CK, Hu S, Fu C, Hua Z, Cai G, Li G, Yang BB, Zhang Y (2008) The effect of central loops in miRNA:MRE duplexes on the efficiency of miRNA-mediated gene regulation. PLoS ONE 3:e1719.

Yekta S, Shih IH, Bartel DP (2004) MicroRNA-directed cleavage of HOXB8 mRNA. Science 304:594-596.

Zhao Y, Samal E, Srivastava D (2005) Serum response factor regulates a muscle-specific microRNA that targets Hand2 during cardiogenesis. Nature 436:214-220.