- Open Access
Identifying microRNA targets in different gene regions
© Xu et al.; licensee BioMed Central Ltd. 2014
- Published: 28 May 2014
Currently available microRNA (miRNA) target prediction algorithms require the presence of a conserved seed match to the 5' end of the miRNA and limit the target sites to the 3' untranslated regions of mRNAs. However, it has been noted that these requirements may be too stringent, leading to a substantial number of missing targets.
We have developed TargetS, a novel computational approach for predicting miRNA targets with the target sites located along entire gene sequences, which permits finding additional targets that are not located in the 3' un-translated regions. Our model is based on both canonical seed matching and non-canonical seed pairing, which discovers targets that allow one bit GU wobble. It does not rely on evolutionary conservation, so it allows the detection of species-specific miRNA-mRNA interactions and makes it suitable for analyzing un-conserved gene sequences. To test the performance of our approach, we have imported the widely used benchmark dataset revealing fold-changes in protein production corresponding to each of the five selected microRNAs. Compared to well-known miRNA target prediction tools, including TargetScanS, PicTar and MicroT_CDS, our method yields the highest sensitivity, while achieving a comparable level of accuracy. Human miRNA target predictions using our computational approach are available online at http://liubioinfolab.org/targetS/mirna.html
A simple but powerful computational miRNA target prediction method is developed that is solely based on canonical and non-canonical seed matches without requiring evolutionary conservation of the target sites. Our method also expands the target search space to different gene regions, rather than limiting to 3'UTR only. This improves the sensitivity of miRNA target identification, while achieving a comparable accuracy with existing methods.
- Random Forest
- miRNA Target
- miRNA Target Site
- miRNA Target Prediction
- Seed Match
MicroRNAs (miRNAs) are endogenous approximately 22 nucleotide RNA molecules that play important gene-regulatory roles in plants and animals . These small RNA molecules exert their regulatory effects on target gene mRNAs by inhibiting protein translation and/or promoting mRNA degradation. They are one of the most abundant classes of gene-regulatory molecules in mammals , with more than two thousand distinct miRNAs having been confidently identified in human . It has been estimated that at least 30% and perhaps as many as 60% of mRNAs are subject to post-transcription miRNA-mediated regulation . It has also been shown that a single miRNA can modulate the expression levels of several hundred to thousands of different mRNAs . Therefore, to fully understand the roles miRNA play in regulating different biological processes, one essential step is to determine which mRNAs are targeted for miRNA regulation.
In the past decade, dozens of miRNA target prediction programs for mammalian genomes have been developed, including TargetScanS [4, 6–8], PicTar , MicroT_CDS [10, 11], miRanda [12, 13], RNAhybrid , MirTarget2 , TargetMiner  and others [17–21]. The majority of these algorithms are based on the assumption that miRNAs target recognition requires conserved Waston-Crick pairing to the 5' region of the miRNA centered on nucleotides 2-7, which is known as the miRNA "seed". This notion has resulted in a biased focus on special types of seed-matched sites within the 3' untranslated regions (3'UTRs) of targeted mRNAs [22, 23]. However, many experimental results show that some "non-seed" miRNA target sites are highly biologically functional [24–26]. These non-seed sites contain single mismatches, GU wobbles, insertions or deletions in the seed-match regions. Besides the seed match "rule", most of the existing computational methods rely on evolutionarily conservation of putative target sites for target identification. However, there is no evidence showing that target sites must be evolutionarily conserved . Identification of mRNAs and proteins that are upregulated upon inhibition or the removal of an endogenous miRNA demonstrate that non-conserved targeting is even more widespread than conserved targeting [5, 27]. In addition, we note that most investigations into metazoan miRNA regulation have been focusing on searching for target sites in 3'UTRs. However, experiments have shown that targeting can occur in the 5' untranslated regions (5' UTRs) and the open reading frame (ORF) as well . Recently, Hafner et al. found that of the exonic target regions, 50% of target sites correspond to coding sequences (CDSs), compared with only 46% to 3'UTRs . Chi et al. also applied a high-throughput approach for isolating Argonaute-bound target sites, indicating that target sites in CDSs are as numerous as those located in 3'UTRs .
In this article, we introduce a simple but powerful miRNA target prediction method that is solely based on canonical seed pairing and non-canonical seed matches. Our method does not require stringent seed pairing or evolutionary conservation in searching for human miRNA target sites. In addition, we perform our search on the entire gene sequence (including promoters, 5'UTRs, CDSs, and 3'UTRs) rather than limiting the search space to the 3'UTRs only. We assessed our method based on a set of miRNA targets identified by the pSILAC method . It is found that, without applying complicated scoring schemes and considering evolutionary conservation of the target sites, our method successfully yielded the largest number of true targets while achieving a comparable level of accuracy, among all the methods we compared.
Comparison of signal-to-noise ratios
Signal-to-noise ratio, weight and proportion of different types of seed matches in different regions.
Number of matches (miRWalk)
Number of matches (Average of 50 times random shuffle)
Proportion (Average of 50 times shuffle)
In CDSs, type 2t8 has the most significant signal-to-noise ratio, while the ratios for seed type 2t8 > 2t7A1 > 2t7, which is similar as those calculated for 3'UTRs. However, type 2t8 is more significant than 2t8A1 and 1t8GU is more significant than 2t7A1, deviated from what we have seen in 3'UTR. These results together demonstrate that the mechanism underlying miRNA target recognition and regulation in CDSs may be different from that in 3'UTRs.
In 5'UTRs, type 1t8GU has the most significant signal-to-noise ratio, while the order of other types of seed matches is similar as that in CDSs. This indicates that the GU wobble pair may play a much more important role in 5'UTRs relative to its effects in other gene regions.
For promoters, the order of the signal-to-noise ratios of four different canonical seed matches is similar to that in 3'UTRs, while 1t8GU type has the second highest ratio. Type 2t7 has the lowest ratio close to 1. These show that promoters are the least effective regions, but they cannot be ignored .
A recent study has shown that miRNA binding sites in CDSs mediate smaller regulation than 3'UTRs binding, and there may be possible interactions between targets sites in CDSs and 3'UTRs . Another recent research study has also demonstrated that miRNA targets sites in CDSs can effectively inhibit translation while sites located in 3'UTRs are more efficient at triggering mRNAs degradation .
The proportion of five types of seed matches in each of the four gene regions are given in Table I. For the 50 random shuffled mRNA sequences, the distributions of the seed matches are similar among different regions, whereas, the proportion of seed match type 2t8A1 in 3'UTRs is much higher than that in other regions, based on the miRWalk data. Since type 2t8A1 is the most rigorous seed match type, it suggests that miRNA targets in 3'UTRs have more selection pressure.
Comparison with other computational target identification methods
To verify the robustness of our method, we applied it on an independent benchmark dataset obtained by the pSILAC method  and evaluated how our target predictions correlate with the results in the pSILAC dataset. To achieve a comparable predicted number of targets with other well-known methods such as TargetScanS and PicTar, we set the cut-off values and , respectively.
It has been shown previously that evolutionary conservation of target sites is a very important feature for improving the accuracy of target identification. To evaluate the effect of this feature in miRNA target prediction, we simply imported the conservation score of different seed match types calculated by phastCons  and set a cutoff value to identify the miRNA targets. The incorporation of target site conservation information indeed improved the accuracy of our method with an overlap of 65% (396/605) in pSILAC dataset, which is the highest accuracy among the state-of-the-art algorithms we investigated in our study. However it missed many true targets, no matter how we relaxed the stringency of the cutoff values for other features, namely and . Therefore, we chose not to incorporate the evolutionary conservation information in our method to achieve high prediction coverage.
Importance of different features.
number of all kinds of seed matches
frequency of outseed A composition
frequency of outseed C composition
frequency of outseed G composition
frequency of outseed U composition
frequency of outseed AA composition
frequency of outseed AC composition
frequency of outseed AG composition
frequency of outseed AU composition
frequency of outseed CA composition
frequency of outseed CC composition
frequency of outseed CG composition
frequency of outseed CU composition
frequency of outseed GA composition
frequency of outseed GC composition
frequency of outseed GG composition
frequency of outseed GU composition
frequency of outseed UA composition
frequency of outseed UC composition
frequency of outseed UG composition
frequency of outseed UU composition
frequency of seed A composition
frequency of seed C composition
frequency of seed G composition
frequency of seed U composition
frequency of seed AA composition
frequency of seed AC composition
frequency of seed AG composition
frequency of seed AU composition
frequency of seed CA composition
frequency of seed CC composition
frequency of seed CG composition
frequency of seed CU composition
frequency of seed GA composition
frequency of seed GC composition
frequency of seed GG composition
frequency of seed GU composition
frequency of seed UA composition
frequency of seed UC composition
frequency of seed UG composition
frequency of seed UU composition
frequency of seed AU nucleotide base pairing
frequency of seed UA nucleotide base pairing
frequency of seed GC nucleotide base pairing
frequency of seed CG nucleotide base pairing
frequency of seed GU nucleotide base pairing
frequency of seed UG nucleotide base pairing
Frequency of seed AU-AU Bi-Di-nucleotide base pairing
Frequency of seed AU-UA Bi-Di-nucleotide base pairing
Frequency of seed AU-GC Bi-Di-nucleotide base pairing
Frequency of seed AU-CG Bi-Di-nucleotide base pairing
Frequency of seed AU-GU Bi-Di-nucleotide base pairing
Frequency of seed AU-UG Bi-Di-nucleotide base pairing
Frequency of seed UA-AU Bi-Di-nucleotide base pairing
Frequency of seed UA-UA Bi-Di-nucleotide base pairing
Frequency of seed UA-GC Bi-Di-nucleotide base pairing
Frequency of seed UA-CG Bi-Di-nucleotide base pairing
Frequency of seed UA-GU Bi-Di-nucleotide base pairing
Frequency of seed UA-UG Bi-Di-nucleotide base pairing
Frequency of seed GC-AU Bi-Di-nucleotide base pairing
Frequency of seed GC-UA Bi-Di-nucleotide base pairing
Frequency of seed GC-GC Bi-Di-nucleotide base pairing
Frequency of seed GC-CG Bi-Di-nucleotide base pairing
Frequency of seed GC-GU Bi-Di-nucleotide base pairing
Frequency of seed GC-UG Bi-Di-nucleotide base pairing
Frequency of seed CG-AU Bi-Di-nucleotide base pairing
Frequency of seed CG-UA Bi-Di-nucleotide base pairing
Frequency of seed CG-GC Bi-Di-nucleotide base pairing
Frequency of seed CG-CG Bi-Di-nucleotide base pairing
Frequency of seed CG-GU Bi-Di-nucleotide base pairing
Frequency of seed CG-UG Bi-Di-nucleotide base pairing
Frequency of seed GU-AU Bi-Di-nucleotide base pairing
Frequency of seed GU-UA Bi-Di-nucleotide base pairing
Frequency of seed GU-GC Bi-Di-nucleotide base pairing
Frequency of seed GU-CG Bi-Di-nucleotide base pairing
Frequency of seed UG-AU Bi-Di-nucleotide base pairing
Frequency of seed UG-UA Bi-Di-nucleotide base pairing
Frequency of seed UG-GC Bi-Di-nucleotide base pairing
Frequency of seed UG-CG Bi-Di-nucleotide base pairing
In this paper, we have proposed a simple and novel computational method for miRNA target prediction (TargetS), which searches for miRNA target sites in either the 3'UTRs, CDSs, 5'UTRs or promoters. As mentioned, our method does not rely on evolutionary conservation, thus allowing the detection of species-specific interactions and making it suitable for analyzing un-conserved genomic sequences. We also include a non-canonical seed pairing type, namely the GU wobble pair as an alternative targeting criterion. The comparison results of TargetS with other methods were based on the independent pSILAC dataset, indicating that TargetS finds a significantly larger number of true miRNA targets at an accuracy level comparable with TargetScanS, PicTar and MicroT_CDS. We have developed a web-based tool that can easily access the human miRNA target prediction results from our TargetS method, with the miRNA name and/or gene name as the input. The user-friendly website is now available at: http://liubioinfolab.org/targetS/mirna.html. With the advent of large-scale sequencing and new crosslinking methods, more direct information of miRNAs and their targets' regulation will be obtained. Together with the information obtained from reliable computational prediction methods, the mechanism of miRNAs and their roles in regulating different important biological processes and molecular pathways can be further investigated. We hope such mechanistic insights will help us understand the progression of different types of diseases, and will lead to novel therapeutic strategies associated with miRNAs and their targets' regulation.
miRBase: The mature miRNAs sequences are downloaded from miRBase database . There are more than 30,000 reported miRNAs entries, including 2,557 entries for human in the latest version (Release 20, 2013).
miRWalk: This dataset hosts experimentally verified miRNA-mRNA interactions as well as the information of genes, pathways, organs, diseases, cell lines, OMIM disorders and literature on miRNAs . It includes 60,269 verified pairs of human miRNA-gene interactions that consist of 655 unique miRNAs and 3,028 unique genes.
pSILAC: A set of miRNA target genes identified by pSILAC (pulsed stable isotope labeling with amino acids in cell culture) method . It measured changes in synthesis of several thousand proteins in response to miRNA transfection or endogenous miRNA knockdown for five miRNAs (hsa-miR-1, hsa-miR-16, hsa-miR-155, hsa-miR-30a and hsa-let-7b). This dataset has been widely used as a benchmark for evaluating computational miRNA target prediction programs and can be downloaded from http://psilac.mdc-berlin.de
The sequences of the promoters, 5'UTRs, CDSs and 3'UTRs for each gene in human have been downloaded from the UCSC Genomes database  using the UCSC Table Brower, version GRCh37/hg19. When there are multiple sequences available for a single gene (e.g. multiple UCSC IDs corresponding to a single gene name), the longest sequence was chosen for further analysis.
Parameters considered in miRNA target prediction
Previously published methods [1, 19, 23] have shown that the most important features for miRNA target genes are 5′ seed matches of miRNA and thermodynamic stability of the miRNA-target duplex. We considered both features in our method when scoring each miRNA-mRNA pair.
For the first important feature, the types of canonical seed matches include 2t8A1 (requires Watson-Crick pairing to the 5' region of the miRNA on nucleotides 2 to 8 and the first nucleotide of target mRNAs being adenine), 2t8 (seed paring from position 2 to 8 in the 5' region of the miRNA), 2t7A1 (seed paring from position 2 to 7 with position 1 of target mRNA being adenine) and 2t7 (seed matches from position 2 to 7). However, many experimental results have shown that some 'non-seed' target sites such as single mismatches, GU wobbles, insertions or deletions in the seed-match regions are highly biologically functional as well [20, 24, 31]. Since insertions or deletions do not have a fixed format and it's hard to measure the significance of the signal, we just considered one non-canonical type of seed match, namely 1t8GU type (seed paring on positions 1 to 8 while allowing 1 GU wobble pair). So we have included five types of seed matches: 2t8A1, 2t8, 2t7A1, 2t7 and 1t8GU in our method (Figure 1).
The second important feature of targeting is thermodynamic stability. The binding energy between miRNA and the target mRNAs gained to form the miRNA-target duplex, is an important base measurement of duplex stability. The lower the free energy gained from the formation of miRNA-target duplex, the stronger the binding structure is and the more likely it suggests a true target binding. Kertesz et al. (2007) also found that the accessibility energy, , which is the difference between the free energy, , and the free energy required to unpair the target-site nucleotides to make the target accessible to the miRNA, , has a strong correlation with the measured degree of miRNA-mediated translational repression . So we took both the and to measure thermodynamic stability of target binding in developing our method. The binding energy was calculated by RNAhybrid . For each miRNA-mRNA pair, we calculated using the miRNA sequence and 58 nucleotides flanking the seed match sites in the mRNA sequences, including the seed match sites, the 30 and 20 nucleotides immediately connected to the 5' and 3' of seed match, respectively, while was calculated based on the 58 nucleotides in the mRNA sequence. We calculated the and for all seed matches found in each miRNA-mRNA pair.
Summarizing the free energy and the accessibility energy for each miRNA-mRNA pair
Where is the binding energy of a miRNA-mRNA duplex. A weight of is assigned if the pair contains the seed match of type i and the seed match is located in the gene region j. In our method, we considered five types of seed matches in each of the four gene regions (the promoter, 5'UTR, CDS, 3'UTR), so we have n = 5 and m = 4. Similarly, the accessibility energy () gained for seed match of type i located in the region j is assigned the weight of as well.
Then we set two cutoff values, and . When the summarized and are both less than their respective cutoff values, we label the mRNA as a putative target of the miRNA.
This work is supported in part by NIH grant R01 LM010022 and the seed grant from the University of Texas Health Science Center at Houston.
The authors declare that all page charges for this article will be paid using the NIH grant R01 LM010022.
This article has been published as part of BMC Bioinformatics Volume 15 Supplement 7, 2014: Selected articles from the 10th Annual Biotechnology and Bioinformatics Symposium (BIOT 2013). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/15/S7
- Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004, 116 (2): 281-297. 10.1016/S0092-8674(04)00045-5.View ArticlePubMedGoogle Scholar
- Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, Aravin A, Pfeffer S, Rice A, Kamphorst AO, Landthaler M: A mammalian microRNA expression atlas based on small RNA library sequencing. Cell. 2007, 129 (7): 1401-1414. 10.1016/j.cell.2007.04.040.PubMed CentralView ArticlePubMedGoogle Scholar
- Kozomara A, Griffiths-Jones S: miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic acids research. 2011, 39 (Database): D152-157. 10.1093/nar/gkq1027.PubMed CentralView ArticlePubMedGoogle Scholar
- Friedman RC, Farh KK-H, Burge CB, Bartel DP: Most mammalian mRNAs are conserved targets of microRNAs. Genome research. 2009, 19 (1): 92-105.PubMed CentralView ArticlePubMedGoogle Scholar
- Selbach M, Schwanhäusser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N: Widespread changes in protein synthesis induced by microRNAs. Nature. 2008, 455 (7209): 58-63. 10.1038/nature07228.View ArticlePubMedGoogle Scholar
- Lewis BP, Shih I-h, Jones-Rhoades MW, Bartel DP, Burge CB: Prediction of mammalian microRNA targets. Cell. 2003, 115 (7): 787-798. 10.1016/S0092-8674(03)01018-3.View ArticlePubMedGoogle Scholar
- Lewis BP, Burge CB, Bartel DP: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005, 120 (1): 15-20. 10.1016/j.cell.2004.12.035.View ArticlePubMedGoogle Scholar
- Grimson A, Farh KK-H, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP: MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Molecular cell. 2007, 27 (1): 91-105. 10.1016/j.molcel.2007.06.017.PubMed CentralView ArticlePubMedGoogle Scholar
- Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M: Combinatorial microRNA target predictions. Nature genetics. 2005, 37 (5): 495-500. 10.1038/ng1536.View ArticlePubMedGoogle Scholar
- Maragkakis M, Alexiou P, Papadopoulos GL, Reczko M, Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K, Simossis VA: Accurate microRNA target prediction correlates with protein repression levels. BMC bioinformatics. 2009, 10 (1): 295-10.1186/1471-2105-10-295.PubMed CentralView ArticlePubMedGoogle Scholar
- Reczko M, Maragkakis M, Alexiou P, Grosse I, Hatzigeorgiou AG: Functional microRNA targets in protein coding sequences. Bioinformatics. 2012, 28 (6): 771-776. 10.1093/bioinformatics/bts043.View ArticlePubMedGoogle Scholar
- Betel D, Wilson M, Gabow A, Marks DS, Sander C: The microRNA. org resource: targets and expression. Nucleic acids research. 2008, 36 (suppl 1): D149-D153.PubMed CentralPubMedGoogle Scholar
- John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS: Human microRNA targets. PLoS biology. 2004, 2 (11): e363-10.1371/journal.pbio.0020363.PubMed CentralView ArticlePubMedGoogle Scholar
- Krüger J, Rehmsmeier M: RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic acids research. 2006, 34 (suppl 2): W451-W454.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang X, El Naqa IM: Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics. 2008, 24 (3): 325-332. 10.1093/bioinformatics/btm595.View ArticlePubMedGoogle Scholar
- Bandyopadhyay S, Mitra R: TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples. Bioinformatics. 2009, 25 (20): 2625-2631. 10.1093/bioinformatics/btp503.View ArticlePubMedGoogle Scholar
- Liu H, Yue D, Chen Y, Gao S-J, Huang Y: Improving performance of mammalian microRNA target prediction. BMC bioinformatics. 2010, 11 (1): 476-10.1186/1471-2105-11-476.PubMed CentralView ArticlePubMedGoogle Scholar
- Wu Y, Wei B, Liu H, Li T, Rayner S: MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences. BMC bioinformatics. 2011, 12 (1): 107-10.1186/1471-2105-12-107.PubMed CentralView ArticlePubMedGoogle Scholar
- Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E: The role of site accessibility in microRNA target recognition. Nature genetics. 2007, 39 (10): 1278-1284. 10.1038/ng2135.View ArticlePubMedGoogle Scholar
- Hammell M, Long D, Zhang L, Lee A, Carmack CS, Han M, Ding Y, Ambros V: mirWIP: microRNA target prediction based on microRNA-containing ribonucleoprotein-enriched transcripts. Nature methods. 2008, 5 (9): 813-819. 10.1038/nmeth.1247.PubMed CentralView ArticlePubMedGoogle Scholar
- Mendes N, Freitas AT, Sagot M-F: Current tools for the identification of miRNA genes and their targets. Nucleic acids research. 2009, 37 (8): 2419-2433. 10.1093/nar/gkp145.PubMed CentralView ArticlePubMedGoogle Scholar
- Peter M: Targeting of mRNAs by multiple miRNAs: the next step. Oncogene. 2010, 29 (15): 2161-2164. 10.1038/onc.2010.59.View ArticlePubMedGoogle Scholar
- Bartel DP: MicroRNAs: target recognition and regulatory functions. Cell. 2009, 136 (2): 215-233. 10.1016/j.cell.2009.01.002.PubMed CentralView ArticlePubMedGoogle Scholar
- Brodersen P, Voinnet O: Revisiting the principles of microRNA target recognition and mode of action. Nature reviews Molecular cell biology. 2009, 10 (2): 141-148. 10.1038/nrm2619.View ArticlePubMedGoogle Scholar
- Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, Horvitz HR, Ruvkun G: The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature. 2000, 403 (6772): 901-906. 10.1038/35002607.View ArticlePubMedGoogle Scholar
- Brennecke J, Stark A, Russell RB, Cohen SM: Principles of microRNA-target recognition. PLoS biology. 2005, 3 (3): e85-10.1371/journal.pbio.0030085.PubMed CentralView ArticlePubMedGoogle Scholar
- Baek D, Villén J, Shin C, Camargo FD, Gygi SP, Bartel DP: The impact of microRNAs on protein output. Nature. 2008, 455 (7209): 64-71. 10.1038/nature07242.PubMed CentralView ArticlePubMedGoogle Scholar
- Lytle JR, Yario TA, Steitz JA: Target mRNAs are repressed as efficiently by microRNA-binding sites in the 5′ UTR as in the 3′ UTR. Proceedings of the National Academy of Sciences. 2007, 104 (23): 9667-9672. 10.1073/pnas.0703820104.View ArticleGoogle Scholar
- Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, Rothballer A, Ascano M, Jungkamp A-C, Munschauer M: Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell. 2010, 141 (1): 129-141. 10.1016/j.cell.2010.03.009.PubMed CentralView ArticlePubMedGoogle Scholar
- Chi SW, Zang JB, Mele A, Darnell RB: Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature. 2009, 460 (7254): 479-486.PubMed CentralPubMedGoogle Scholar
- Lee I, Ajay SS, Yook JI, Kim HS, Hong SH, Kim NH, Dhanasekaran SM, Chinnaiyan AM, Athey BD: New class of microRNA targets containing simultaneous 5′-UTR and 3′-UTR interaction sites. Genome research. 2009, 19 (7): 1175-1183. 10.1101/gr.089367.108.PubMed CentralView ArticlePubMedGoogle Scholar
- Piriyapongsa J, Bootchai C, Ngamphiw C, Tongsima S: MicroPIR: an integrated database of microRNA target sites within human promoter sequences. PloS one. 2012, 7 (3): e33888-10.1371/journal.pone.0033888.PubMed CentralView ArticlePubMedGoogle Scholar
- Fang Z, Rajewsky N: The impact of miRNA target sites in coding sequences and in 3′ UTRs. PloS one. 2011, 6 (3): e18067-10.1371/journal.pone.0018067.PubMed CentralView ArticlePubMedGoogle Scholar
- Hausser J, Syed AP, Bilen B, Zavolan M: Analysis of CDS-located miRNA target sites suggests that they can effectively inhibit translation. Genome research. 2013, 23 (4): 604-615. 10.1101/gr.139758.112.PubMed CentralView ArticlePubMedGoogle Scholar
- Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier LW, Richards S: Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome research. 2005, 15 (8): 1034-1050. 10.1101/gr.3715005.PubMed CentralView ArticlePubMedGoogle Scholar
- Yang Y, Wang Y-P, Li K-B: MiRTif: a support vector machine-based microRNA target interaction filter. BMC bioinformatics. 2008, 9 (Suppl 12): S4-10.1186/1471-2105-9-S12-S4.PubMed CentralView ArticlePubMedGoogle Scholar
- Maziere P, Enright AJ: Prediction of microRNA targets. Drug discovery today. 2007, 12 (11): 452-458.View ArticlePubMedGoogle Scholar
- Breiman L: Random forests. Machine learning. 2001, 45 (1): 5-32. 10.1023/A:1010933404324.View ArticleGoogle Scholar
- Shahi P, Loukianiouk S, Bohne-Lang A, Kenzelmann M, Küffer S, Maertens S, Eils R, Gröne H-J, Gretz N, Brors B: Argonaute--a database for gene regulation by mammalian microRNAs. Nucleic acids research. 2006, 34 (suppl 1): D115-D118.PubMed CentralView ArticlePubMedGoogle Scholar
- Karolchik D, Baertsch R, Diekhans M, Furey TS, Hinrichs A, Lu Y, Roskin KM, Schwartz M, Sugnet CW, Thomas DJ: The UCSC genome browser database. Nucleic acids research. 2003, 31 (1): 51-54. 10.1093/nar/gkg129.PubMed CentralView ArticlePubMedGoogle Scholar
- Rehmsmeier M, Steffen P, Höchsmann M, Giegerich R: Fast and effective prediction of microRNA/target duplexes. Rna. 2004, 10 (10): 1507-1517. 10.1261/rna.5248604.PubMed CentralView ArticlePubMedGoogle Scholar
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