CiteULike: heliopais's microrna_target_prediction

Identification of human microRNA targets from isolated argonaute protein complexes.

M Beitzinger — 2008-06-24T11:32:53-00:00

RNA biology, Vol. 4, No. 2. (June 2007), pp. 76-84.

MicroRNAs (miRNAs) constitute a class of small non-coding RNAs that regulate gene expression on the level of translation and/or mRNA stability. Mammalian miRNAs associate with members of the Argonaute (Ago) protein family and bind to partially complementary sequences in the 3' untranslated region (UTR) of specific target mRNAs. Computer algorithms based on factors such as free binding energy or sequence conservation have been used to predict miRNA target mRNAs. Based on such predictions, up to one third of all mammalian mRNAs seem to be under miRNA regulation. However, due to the low degree of complementarity between the miRNA and its target, such computer programs are often imprecise and therefore not very reliable. Here we report the first biochemical identification approach of miRNA targets from human cells. Using highly specific monoclonal antibodies against members of the Ago protein family, we co-immunoprecipitate Ago-bound mRNAs and identify them by cloning. Interestingly, most of the identified targets are also predicted by different computer programs. Moreover, we randomly analyzed six different target candidates and were able to experimentally validate five as miRNA targets. Our data clearly indicate that miRNA targets can be experimentally identified from Ago complexes and therefore provide a new tool to directly analyze miRNA function.

L(ou)sy miRNA targets?

Nikolaus Rajewsky — 2006-09-06T16:54:14-00:00

Nature Structural and Molecular Biology, Vol. 13, No. 9. (20 August 2006), pp. 754-755.

MicroRNAs (miRNAs) are noncoding small RNAs thought to post-transcriptionally regulate many metazoan genes by binding to partially complementary sites in target messenger RNAs. In this issue, Didiano and Hobert examine known and predicted targets of the nematode lsy-6 miRNA, question the general validity of previously proposed rules about miRNA-target interactions and suggest that many functional miRNA targets might be context dependent, as seen in metazoan gene regulation by transcription factors.

Serum response factor regulates a muscle-specific microRNA that targets Hand2 during cardiogenesis

Yong Zhao — 2005-06-12T20:39:42-00:00

Nature (12 June 2005)

On the relationship between GC content and the number of predicted microRNA binding sites by MicroInspector.

N Davis — 2008-05-21T15:47:35-00:00

Computational biology and chemistry, Vol. 32, No. 3. (June 2008), pp. 222-226.

MicroRNA GC content and length is believed to play a role in the prediction of putative microRNA targets. MicroInspector was evaluated to determine the extent to which these characteristics of microRNAs play a role in binding site predictive accuracy. A strong bias towards under predicting the number of expected bindings sites for low GC content sequences was observed, especially for microRNAs with <50% GC content. Researchers working with organisms with unusually low GC content should be aware of this bias.

Prediction of microRNA targets.

P Mazière — 2007-05-30T07:26:25-00:00

Drug Discov Today, Vol. 12, No. 11-12. (June 2007), pp. 452-458.

Recently, microRNAs (miRNAs) have been shown to be important regulators of genes in many organisms and have already been implicated in a growing number of diseases. MiRNAs are short (21-23 nucleotides) RNAs that bind to the 3' untranslated regions of target genes. This binding event causes translational repression of the target gene and, evidence now suggests, also stimulates rapid degradation of the target transcript. miRNAs represent a new species of regulator, controlling the levels of potentially large numbers of proteins, many of which might be important drug targets. The expression of miRNAs shows that they are highly differentially expressed, with specific miRNAs active in certain tissues at certain times. In many cancers, miRNA expression is significantly altered, and this has been shown to be a useful diagnostic tool. Several computational approaches have been developed for the prediction of miRNA targets.

A scoring matrix approach to detecting miRNA target sites

Simon Moxon — 2008-03-31T17:46:38-00:00

Algorithms for Molecular Biology, Vol. 3 (31 March 2008), 3.

The Effect of Central Loops in miRNA:MRE Duplexes on the Efficiency of miRNA-Mediated Gene Regulation.

W Ye — 2008-03-18T02:56:30-00:00

PLoS ONE, Vol. 3, No. 3. (2008)

MicroRNAs (miRNAs) guide posttranscriptional repression of mRNAs. Hundreds of miRNAs have been identified but the target identification of mammalian mRNAs is still a difficult task due to a poor understanding of the interaction between miRNAs and the miRNA recognizing element (MRE). In recent research, the importance of the 5' end of the miRNA:MRE duplex has been emphasized and the effect of the tail region addressed, but the role of the central loop has largely remained unexplored. Here we examined the effect of the loop region in miRNA:MRE duplexes and found that the location of the central loop is one of the important factors affecting the efficiency of gene regulation mediated by miRNAs. It was further determined that the addition of a loop score combining both location and size as a new criterion for predicting MREs and their cognate miRNAs significantly decreased the false positive rates and increased the specificity of MRE prediction.

MicroRNA-target pairs in the rat kidney identified by microRNA microarray, proteomic, and bioinformatic analysis.

Zhongmin Tian — 2008-02-01T07:56:51-00:00

Genome Research, Vol. 18, No. 3. (29 January 2008), pp. 404-411.

Mammalian genomes contain several hundred highly conserved genes encoding microRNAs. In silico analysis has predicted that a typical microRNA may regulate the expression of hundreds of target genes, suggesting miRNAs might have broad biological significance. A major challenge is to obtain experimental evidence for predicted microRNA-target pairs. We reasoned that reciprocal expression of a microRNA and a predicted target within a physiological context would support the presence and relevance of a microRNA-target pair. We used microRNA microarray and proteomic techniques to analyze the cortex and the medulla of rat kidneys. Of the 377 microRNAs analyzed, we identified 6 as enriched in the renal cortex and 11 in the renal medulla. From approximately 2100 detectable protein spots in two-dimensional gels, we identified 58 proteins as more abundant in the renal cortex and 72 in the renal medulla. The differential expression of several microRNAs and proteins was verified by real-time PCR and Western blot analyses, respectively. Several pairs of reciprocally expressed microRNAs and proteins were predicted to be microRNA-target pairs by TargetScan, PicTar, or miRanda. Seven pairs were predicted by two algorithms and two pairs by all three algorithms. The identification of reciprocal expression of microRNAs and their computationally predicted targets in the rat kidney provides a unique molecular basis for further exploring the biological role of microRNA. In addition, this study establishes a differential profile of microRNA expression between the renal cortex and the renal medulla and greatly expands the known differential proteome profiles between the two kidney regions.

Discovery of microRNA-mRNA modules via population-based probabilistic learning.

Je-Gun Joung — 2007-03-14T16:38:25-00:00

Bioinformatics, Vol. 23, No. 9. (9 March 2007), pp. 1141-1447.

MOTIVATION: MicroRNAs (miRNAs) and mRNAs constitute an important part of gen regulatory networks, influencing diverse biological phenomena. Elucidating closely related miRNAs and mRNAs can be an essential first step towards the discovery of their combinatorial effects on different cellular states. Here, we propose a probabilistic learning method to identify synergistic miRNAs involving regulation of their condition-specific target genes (mRNAs) from multiple information sources, i.e., computationally predicted target genes of miRNAs and their respective expression profiles. RESULTS: We used data sets consisting of miRNA-target gene binding information and expression profiles of miRNAs and mRNAs on human cancer samples. Our method allowed us to detect functionally correlated miRNA-mRNA modules involved in specific biological processes from multiple data sources by using a balanced fitness function and efficient searching over multiple populations. The proposed algorithm found two miRNA-mRNA modules, highly correlated with respect to their expression and biological function. Moreover, the mRNAs included in the same module showed much higher correlations when the related miRNAs were highly expressed, demonstrating our method's ability for finding coherent miRNA-mRNA modules. Most members of these modules have been reported to be closely related with cancer. Consequently, our method can provide a primary source of miRNA and target sets presumed to constitute closely related parts of gene regulatory pathways.

Bayesian Inference of MicroRNA Targets from Sequence and Expression Data.

Jim Huang — 2007-08-10T06:54:52-00:00

Journal of Computational Biology, Vol. 14, No. 5. (June 2007), pp. 550-563.

MicroRNAs (miRNAs) regulate a large proportion of mammalian genes by hybridizing to targeted messenger RNAs (mRNAs) and down-regulating their translation into protein. Although much work has been done in the genome-wide computational prediction of miRNA genes and their target mRNAs, an open question is how to efficiently obtain functional miRNA targets from a large number of candidate miRNA targets predicted by existing computational algorithms. In this paper, we propose a novel Bayesian model and learning algorithm, GenMiR++ (Generative model for miRNA regulation), that accounts for patterns of gene expression using miRNA expression data and a set of candidate miRNA targets. A set of high-confidence functional miRNA targets are then obtained from the data using a Bayesian learning algorithm. Our model scores 467 high-confidence miRNA targets out of 1,770 targets obtained from TargetScanS in mouse at a false detection rate of 2.5%: several confirmed miRNA targets appear in our high-confidence set, such as the interactions between miR-92 and the signal transduction gene MAP2K4, as well as the relationship between miR-16 and BCL2, an anti-apoptotic gene which has been implicated in chronic lymphocytic leukemia. We present results on the robustness of our model showing that our learning algorithm is not sensitive to various perturbations of the data. Our high-confidence targets represent a significant increase in the number of miRNA targets and represent a starting point for a global understanding of gene regulation.

miTarget: microRNA target-gene prediction using a Support Vector Machine

Sung-Kyu Kim — 2006-09-18T11:51:13-00:00

BMC Bioinformatics, Vol. 7 (18 September 2006), 411.

Prediction of both conserved and nonconserved microRNA targets in animals

Xiaowei Wang — 2007-12-03T10:15:08-00:00

Bioinformatics (29 November 2007), pp. 325-332.

Motivation: MicroRNAs (miRNAs) are involved in many diverse biological processes and they may potentially regulate the functions of thousands of genes. However, one major issue in miRNA studies is the lack of bioinformatics programs to accurately predict miRNA targets. Animal miRNAs have limited sequence complementarity to their gene targets, which makes it challenging to build target predic-tion models with high specificity. Results: Here we present a new miRNA target prediction program based on support vector machines (SVMs) and a large microarray training dataset. By systematically analyzing public microarray data, we have identified statistically significant features that are important to target downregulation. Heterogeneous prediction features have been non-linearly integrated in an SVM machine learning framework for the training of our target prediction model, MirTarget2. About half of the predicted miRNA target sites in human are not conserved in other organisms. Our prediction algorithm has been validated with independent experimental data for its improved performance on predicting a large number of miRNA downregulated gene targets. Availability: All the predicted targets were imported into an online database miRDB, which is freely accessible at http://mirdb.org. Contact: xwang@radonc.wustl.edu Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btm595

A biochemical approach to identifying microRNA targets

Fedor Karginov — 2007-11-30T08:58:31-00:00

Proceedings of the National Academy of Sciences (27 November 2007), 0709971104.

Identifying the downstream targets of microRNAs (miRNAs) is essential to understanding cellular regulatory networks. We devised a direct biochemical method for miRNA target discovery that combined RNA-induced silencing complex (RISC) purification with microarray analysis of bound mRNAs. Because targets of miR-124a have been analyzed, we chose it as our model. We honed our approach both by examining the determinants of stable binding between RISC and synthetic target RNAs in vitro and by determining the dependency of both repression and RISC coimmunoprecipitation on miR-124a seed sites in two of its well characterized targets in vivo. Examining the complete spectrum of miR-124 targets in 293 cells yielded both a set that were down-regulated at the mRNA level, as previously observed, and a set whose mRNA levels were unaffected by miR-124a. Reporter assays validated both classes, extending the spectrum of mRNA targets that can be experimentally linked to the miRNA pathway. 10.1073/pnas.0709971104

Experimental validation of miRNA targets

Donald Kuhn — 2008-01-02T07:04:09-00:00

Methods, Vol. 44, No. 1. (January 2008), pp. 47-54.

MicroRNAs are natural, single-stranded, small RNA molecules that regulate gene expression by binding to target mRNAs and suppress its translation or initiate its degradation. In contrast to the identification and validation of many miRNA genes is the lack of experimental evidence identifying their corresponding mRNA targets. The most fundamental challenge in miRNA biology is to define the rules of miRNA target recognition. This is critical since the biological role of individual miRNAs will be dictated by the mRNAs that they regulate. Therefore, only as target mRNAs are validated will it be possible to establish commonalities that will enable more precise predictions of miRNA/mRNA interactions. Currently there is no clear agreement as to what experimental procedures should be followed to demonstrate that a given mRNA is a target of a specific miRNA. Therefore, this review outlines several methods by which to validate miRNA targets. Additionally, we propose that multiple criteria should be met before miRNA target validation should be considered "confirmed."

Systematic identification of microRNA functions by combining target prediction and expression profiling.

Xiaowei Wang — 2006-04-12T04:48:43-00:00

Nucleic Acids Research, Vol. 34, No. 5. (20 March 2006), pp. 1646-1652.

Target predictions and validations are major obstacles facing microRNA (miRNA) researchers. Animal miRNA target prediction is challenging because of limited miRNA sequence complementarity to the targets. In addition, only a small number of predicted targets have been experimentally validated and the miRNA mechanism is poorly understood. Here we present a novel algorithm for animal miRNA target prediction. The algorithm combines relevant parameters for miRNA target recognition and heuristically assigns different weights to these parameters according to their relative importance. A score calculation scheme is introduced to reflect the strength of each parameter. We also performed microarray time course experiments to identify downregulated genes due to miRNA overexpression. The computational target prediction is combined with the miRNA transfection experiment to systematically identify the gene targets of human miR-124. miR-124 overexpression led to a significant downregulation of many cell cycle related genes. This may be the result of direct suppression of a few cell growth inhibitors at the early stage of miRNA overexpression, and these targeted genes were continuously suppressed over a long period of time. Our high-throughput approach can be generalized to globally identify the targets and functions of other miRNAs.

Prediction and verification of microRNA targets by MovingTargets, a highly adaptable prediction method.

C Burgler — 2005-11-17T11:55:36-00:00

BMC Genomics, Vol. 6, No. 1. (8 June 2005)

BACKGROUND: MicroRNAs (miRNAs) mediate a form of translational regulation in animals. Hundreds of animal miRNAs have been identified, but only a few of their targets are known. Prediction of miRNA targets for translational regulation is challenging, since the interaction with the target mRNA usually occurs via incomplete and interrupted base pairing. Moreover, the rules that govern such interactions are incompletely defined. RESULTS: MovingTargets is a software program that allows a researcher to predict a set of miRNA targets that satisfy an adjustable set of biological constraints. We used MovingTargets to identify a high-likelihood set of 83 miRNA targets in Drosophila, all of which adhere to strict biological constraints. We tested and verified 3 of these predictions in cultured cells, including a target for the Drosophila let-7 homolog. In addition, we utilized the flexibility of MovingTargets by relaxing the biological constraints to identify and validate miRNAs targeting tramtrack, a gene also known to be subject to translational control dependent on the RNA binding protein Musashi. CONCLUSION: MovingTargets is a flexible tool for the accurate prediction of miRNA targets in Drosophila. MovingTargets can be used to conduct a genome-wide search of miRNA targets using all Drosophila miRNAs and potential targets, or it can be used to conduct a focused search for miRNAs targeting a specific gene. In addition, the values for a set of biological constraints used to define a miRNA target are adjustable, allowing the software to incorporate the rules used to characterize a miRNA target as these rules are experimentally determined and interpreted.

Computational identification of microRNA targets

Nikolaus Rajewsky — 2007-11-23T11:06:12-00:00

Developmental Biology, Vol. 267, No. 2. (15 March 2004), pp. 529-535.

Recent experiments have shown that the genomes of organisms such as worm, fly, human, and mouse encode hundreds of microRNA genes. Many of these microRNAs are thought to regulate the translational expression of other genes by binding to partially complementary sites in messenger RNAs. Phenotypic and expression analysis suggests an important role of microRNAs during development. Therefore, it is of fundamental importance to identify microRNA targets. However, no experimental or computational high-throughput method for target site identification in animals has been published yet. Our main result is a new computational method that is designed to identify microRNA target sites. This method recovers with high specificity known microRNA target sites that have previously been defined experimentally. Based on these results, we present a simple model for the mechanism of microRNA target site recognition. Our model incorporates both kinetic and thermodynamic components of target recognition. When we applied our method to a set of 74 Drosophila melanogaster microRNAs, searching 3'UTR sequences of a predefined set of fly mRNAs for target sites which were evolutionary conserved between D. melanogaster and Drosophila pseudoobscura, we found that many key developmental body patterning genes such as hairy and fushi-tarazu are likely to be translationally regulated by microRNAs.

Architecture of a validated microRNA::target interaction.

MC Vella — 2005-09-26T22:59:33-00:00

Chem Biol, Vol. 11, No. 12. (December 2004), pp. 1619-1623.

MicroRNAs are small approximately 22 nucleotide regulators of numerous biological processes and bind target gene messenger RNAs to control gene expression. The C. elegans microRNA let-7 and its target lin-41 were the first microRNA::target interaction to be validated in vivo. let-7 molecules form imperfect duplexes with two required let-7 complementary sites in the lin-41 3' UTR. Here, we show that base pairing at both the 5' and 3' ends of the let-7 binding site, as well as the presence of unpaired RNA residues in the predicted duplexes, are required for lin-41 downregulation. In this study, our model for microRNA::target interactions also demonstrates that the context of a microRNA binding can be critical for function, revealing an unforeseen complexity in microRNA::target interactions.

A guide through present computational approaches for the identification of mammalian microRNA targets

Praveen Sethupathy — 2006-10-24T00:18:48-00:00

Nature Methods, Vol. 3, No. 11. (23 October 2006), pp. 881-886.

Perfect seed pairing is not a generally reliable predictor for miRNA-target interactions.

Dominic Didiano — 2006-08-22T18:04:28-00:00

Nature Structural and Molecular Biology, Vol. 13, No. 9. (20 August 2006), pp. 849-851.

We use Caenorhabditis elegans to test proposed general rules for microRNA (miRNA)-target interactions. We show that G.U base pairing is tolerated in the 'seed' region of the lsy-6 miRNA interaction with its in vivo target cog-1, and that 6- to 8-base-pair perfect seed pairing is not a generally reliable predictor for an interaction of lsy-6 with a 3' untranslated region (UTR). Rather, lsy-6 can functionally interact with its target site only in specific 3' UTR contexts. Our findings illustrate the difficulty of establishing generalizable rules of miRNA-target interactions.

Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms.

O Saetrom — 2005-11-17T11:57:28-00:00

RNA, Vol. 11, No. 7. (July 2005), pp. 995-1003.

We present a new microRNA target prediction algorithm called TargetBoost, and show that the algorithm is stable and identifies more true targets than do existing algorithms. TargetBoost uses machine learning on a set of validated microRNA targets in lower organisms to create weighted sequence motifs that capture the binding characteristics between microRNAs and their targets. Existing algorithms require candidates to have (1) near-perfect complementarity between microRNAs' 5' end and their targets; (2) relatively high thermodynamic duplex stability; (3) multiple target sites in the target's 3' UTR; and (4) evolutionary conservation of the target between species. Most algorithms use one of the two first requirements in a seeding step, and use the three others as filters to improve the method's specificity. The initial seeding step determines an algorithm's sensitivity and also influences its specificity. As all algorithms may add filters to increase the specificity, we propose that methods should be compared before such filtering. We show that TargetBoost's weighted sequence motif approach is favorable to using both the duplex stability and the sequence complementarity steps. (TargetBoost is available as a Web tool from http://www.interagon.com/demo/.).

Distance constraints between microRNA target sites dictate efficacy and cooperativity

Saetrom — 2007-06-16T17:03:45-00:00

Nucleic Acids Research, Vol. 35, No. 7. (April 2007), pp. 2333-2342.

Improving the prediction of human microRNA target genes by using ensemble algorithm.

X Yan — 2007-04-12T12:12:12-00:00

FEBS Lett, Vol. 581, No. 8. (17 April 2007), pp. 1587-1593.

MicroRNAs are a class of small endogenous noncoding RNAs which play important regulatory roles mainly by post-transcriptional depression. Finding miRNA target genes will help a lot to understand their biological functions. We developed an ensemble machine learning algorithm which helps to improve the prediction of miRNA targets. The performance was evaluated in the training set and in FMRP associated mRNAs. Moreover, using human mir-9 as a test case, our classification was validated in 9 of 15 transcripts tested. Finally, we applied our algorithm on the whole prediction data set provided by miRanda website. The results are available at http://www.biosino.org/~kanghu/mRTP/mRTP.html.

Predicting and validating microRNA targets

Eric Lai — 2007-10-08T08:50:00-00:00

Genome Biology, Vol. 5, No. 9. (2004)

Given that microRNAs select their targets by nucleotide base-pairing, it follows that it should be possible to find microRNA targets computationally. There has been considerable progress, but assessing success and biological significance requires a move into the 'wet' lab.

Identification of Drosophila MicroRNA targets.

A Stark — 2005-12-19T20:27:45-00:00

PLoS Biol, Vol. 1, No. 3. (December 2003)

MicroRNAs (miRNAs) are short RNA molecules that regulate gene expression by binding to target messenger RNAs and by controlling protein production or causing RNA cleavage. To date, functions have been assigned to only a few of the hundreds of identified miRNAs, in part because of the difficulty in identifying their targets. The short length of miRNAs and the fact that their complementarity to target sequences is imperfect mean that target identification in animal genomes is not possible by standard sequence comparison methods. Here we screen conserved 3' UTR sequences from the Drosophila melanogaster genome for potential miRNA targets. The screening procedure combines a sequence search with an evaluation of the predicted miRNA-target heteroduplex structures and energies. We show that this approach successfully identifies the five previously validated let-7, lin-4, and bantam targets from a large database and predict new targets for Drosophila miRNAs. Our target predictions reveal striking clusters of functionally related targets among the top predictions for specific miRNAs. These include Notch target genes for miR-7, proapoptotic genes for the miR-2 family, and enzymes from a metabolic pathway for miR-277. We experimentally verified three predicted targets each for miR-7 and the miR-2 family, doubling the number of validated targets for animal miRNAs. Statistical analysis indicates that the best single predicted target sites are at the border of significance; thus, target predictions should be considered as tentative until experimentally validated. We identify features shared by all validated targets that can be used to evaluate target predictions for animal miRNAs. Our initial evaluation and experimental validation of target predictions suggest functions for two miRNAs. For others, the screen suggests plausible functions, such as a role for miR-277 as a metabolic switch controlling amino acid catabolism. Cross-genome comparison proved essential, as it allows reduction of the sequence search space. Improvements in genome annotation and increased availability of cDNA sequences from other genomes will allow more sensitive screens. An increase in the number of confirmed targets is expected to reveal general structural features that can be used to improve their detection. While the screen is likely to miss some targets, our study shows that valid targets can be identified from sequence alone.

Spatial preferences of microRNA targets in 3' untranslated regions

William Majoros — 2007-06-07T20:23:08-00:00

BMC Genomics, Vol. 8 (07 June 2007), 152.

Inference of miRNA targets using evolutionary conservation and pathway analysis

Dimos Gaidatzis — 2007-03-02T13:02:08-00:00

BMC Bioinformatics, Vol. 8 (01 March 2007), 69.

Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets.

BP Lewis — 2006-01-04T15:30:57-00:00

Cell, Vol. 120, No. 1. (14 January 2005), pp. 15-20.

We predict regulatory targets of vertebrate microRNAs (miRNAs) by identifying mRNAs with conserved complementarity to the seed (nucleotides 2-7) of the miRNA. An overrepresentation of conserved adenosines flanking the seed complementary sites in mRNAs indicates that primary sequence determinants can supplement base pairing to specify miRNA target recognition. In a four-genome analysis of 3' UTRs, approximately 13,000 regulatory relationships were detected above the estimate of false-positive predictions, thereby implicating as miRNA targets more than 5300 human genes, which represented 30% of our gene set. Targeting was also detected in open reading frames. In sum, well over one third of human genes appear to be conserved miRNA targets.

Prediction of Mammalian MicroRNA Targets

Benjamin Lewis — 2007-10-24T09:53:44-00:00

Cell, Vol. 115, No. 7. (26 December 2003), pp. 787-798.

MicroRNAs (miRNAs) can play important gene regulatory roles in nematodes, insects, and plants by basepairing to mRNAs to specify posttranscriptional repression of these messages. However, the mRNAs regulated by vertebrate miRNAs are all unknown. Here we predict more than 400 regulatory target genes for the conserved vertebrate miRNAs by identifying mRNAs with conserved pairing to the 5' region of the miRNA and evaluating the number and quality of these complementary sites. Rigorous tests using shuffled miRNA controls supported a majority of these predictions, with the fraction of false positives estimated at 31% for targets identified in human, mouse, and rat and 22% for targets identified in pufferfish as well as mammals. Eleven predicted targets (out of 15 tested) were supported experimentally using a HeLa cell reporter system. The predicted regulatory targets of mammalian miRNAs were enriched for genes involved in transcriptional regulation but also encompassed an unexpectedly broad range of other functions.

Determinants of targeting by endogenous and exogenous microRNAs and siRNAs

Cydney Nielsen — 2007-10-23T08:10:15-00:00

RNA, Vol. 13, No. 11. (1 November 2007), pp. 1894-1910.

Vertebrate mRNAs are frequently targeted for post-transcriptional repression by microRNAs (miRNAs) through mechanisms involving pairing of 3' UTR seed matches to bases at the 5' end of miRNAs. Through analysis of expression array data following miRNA or siRNA overexpression or inhibition, we found that mRNA fold change increases multiplicatively (i.e., log-additively) with seed match count and that a single 8 mer seed match mediates down-regulation comparable to two 7 mer seed matches. We identified several targeting determinants that enhance seed match-associated mRNA repression, including the presence of adenosine opposite miRNA base 1 and of adenosine or uridine opposite miRNA base 9, independent of complementarity to the siRNA/miRNA. Increased sequence conservation in the [~]50 bases 5' and 3' of the seed match and increased AU content 3' of the seed match were each independently associated with increased mRNA down-regulation. All of these determinants are enriched in the vicinity of conserved miRNA seed matches, supporting their activity in endogenous miRNA targeting. Together, our results enable improved siRNA off-target prediction, allow integrated ranking of conserved and nonconserved miRNA targets, and show that targeting by endogenous and exogenous miRNAs/siRNAs involves similar or identical determinants. 10.1261/rna.768207

Combinatorial microRNA target predictions

Azra Krek — 2005-04-27T18:51:58-00:00

Nature Genetics, Vol. 37, No. 5. (03 April 2005), pp. 495-500.

Human MicroRNA targets.

B John — 2005-11-17T12:00:59-00:00

PLoS Biol, Vol. 2, No. 11. (November 2004)

MicroRNAs (miRNAs) interact with target mRNAs at specific sites to induce cleavage of the message or inhibit translation. The specific function of most mammalian miRNAs is unknown. We have predicted target sites on the 3' untranslated regions of human gene transcripts for all currently known 218 mammalian miRNAs to facilitate focused experiments. We report about 2,000 human genes with miRNA target sites conserved in mammals and about 250 human genes conserved as targets between mammals and fish. The prediction algorithm optimizes sequence complementarity using position-specific rules and relies on strict requirements of interspecies conservation. Experimental support for the validity of the method comes from known targets and from strong enrichment of predicted targets in mRNAs associated with the fragile X mental retardation protein in mammals. This is consistent with the hypothesis that miRNAs act as sequence-specific adaptors in the interaction of ribonuclear particles with translationally regulated messages. Overrepresented groups of targets include mRNAs coding for transcription factors, components of the miRNA machinery, and other proteins involved in translational regulation, as well as components of the ubiquitin machinery, representing novel feedback loops in gene regulation. Detailed information about target genes, target processes, and open-source software for target prediction (miRanda) is available at http://www.microrna.org. Our analysis suggests that miRNA genes, which are about 1% of all human genes, regulate protein production for 10% or more of all human genes.

Computational analysis of microRNA targets in Caenorhabditis elegans

Yuka Watanabe — 2007-10-03T10:16:02-00:00

Gene, Vol. 365 (Jan 2006), pp. 2-10.

Potent effect of target structure on microRNA function

Dang Long — 2007-10-03T10:16:02-00:00

Nature Structural and Molecular Biology, Vol. 14, No. 4. (1 April 2007), pp. 287-294.

MicroRNAs (miRNAs) are small noncoding RNAs that repress protein synthesis by binding to target messenger RNAs. We investigated the effect of target secondary structure on the efficacy of repression by miRNAs. Using structures predicted by the Sfold program, we model the interaction between an miRNA and a target as a two-step hybridization reaction: nucleation at an accessible target site followed by hybrid elongation to disrupt local target secondary structure and form the complete miRNA-target duplex. This model accurately accounts for the sensitivity to repression by let-7 of various mutant forms of the Caenorhabditis eleganslin-41 3' untranslated region and for other experimentally tested miRNA-target interactions in C. elegans and Drosophila melanogaster. These findings indicate a potent effect of target structure on target recognition by miRNAs and establish a structure-based framework for genome-wide identification of animal miRNA targets.

Fast and effective prediction of microRNA/target duplexes

Marc Rehmsmeier — 2007-10-03T10:16:02-00:00

RNA, Vol. 10, No. 10. (Oct 2004), pp. 1507-1517.

Specificity of microRNA target selection in translational repression

John Doench — 2007-10-03T10:16:02-00:00

Genes Dev, Vol. 18, No. 5. (Mar 2004), pp. 504-511.

Prediction of plant microRNA targets

Matthew Rhoades — 2007-10-03T10:16:02-00:00

Cell, Vol. 110, No. 4. (Aug 2002), pp. 513-520.

A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes

Kevin Miranda — 2007-10-03T10:16:02-00:00

Cell, Vol. 126, No. 6. (Sep 2006), pp. 1203-1217.

The role of site accessibility in microRNA target recognition

Michael Kertesz — 2007-09-27T00:12:34-00:00

Nature Genetics, Vol. 39, No. 10. (23 September 2007), pp. 1278-1284.

MicroRNAs are key regulators of gene expression1, 2, 3, 4, but the precise mechanisms underlying their interaction with their mRNA targets are still poorly understood. Here, we systematically investigate the role of target-site accessibility, as determined by base-pairing interactions within the mRNA, in microRNA target recognition. We experimentally show that mutations diminishing target accessibility substantially reduce microRNA-mediated translational repression, with effects comparable to those of mutations that disrupt sequence complementarity. We devise a parameter-free model for microRNA-target interaction that computes the difference between the free energy gained from the formation of the microRNA-target duplex and the energetic cost of unpairing the target to make it accessible to the microRNA. This model explains the variability in our experiments, predicts validated targets more accurately than existing algorithms, and shows that genomes accommodate site accessibility by preferentially positioning targets in highly accessible regions. Our study thus demonstrates that target accessibility is a critical factor in microRNA function