Pairing beyond the Seed Supports MicroRNA Targeting Specificity

Broughton, James P.; Lovci, Michael T.; Huang, Jessica L.; Yeo, G; Pasquinelli, Amy E.

doi:10.1016/j.molcel.2016.09.004

Cited by 389 publications

(404 citation statements)

References 52 publications

(112 reference statements)

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“…The performance 672 gap between miRAW and the descriptor-based approaches suggests that current 673 knowledge is still not sufficient to accurately capture all aspects of the miRNA targeting 674 process. This is consistent with recent studies, e.g., [3], [7] and [31], which 675 demonstrate that the whole miRNA can play a relevant role in many functional miRNA 676 targets. Based on these findings, we took advantage of deep learning methodology to 677 incorporate the whole miRNA sequence for target prediction.…”

supporting

confidence: 89%

“…This seed-centric 15 view has been supported by structural studies [5] and a widely cited report [6] that 16 investigated the importance of other (non-canonical) regions within a miRNA and 17 concluded their contributions had relatively low relevance compared to the (canonical) 18 seed region. However, more recent studies have revealed that many relevant targets are 19 implemented via non-canonical binding and involve nucleotides outside the seed region, 20 indicating that the entire miRNA should be considered in target prediction 21 algorithms [3,7,8]. This is also supported by the performance of target prediction tools 22 which typically identify approximately 80% of known miRNA targets, indicating the 23 mechanisms associated with the remaining 20% of non-canonical targets remain poorly 24 understood.…”

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confidence: 99%

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miRAW: A deep learning approach to predict miRNA targets by analyzing whole miRNA transcripts

Plà

Zhong

Rayner

2017

Preprint

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MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to partially complementary regions within the 3'UTR of their target genes. Computational methods play an important role in target prediction and assume that the miRNA "seed region" (nt 2 to 8) is required for functional targeting, but typically only identify 80% of known bindings. Recent studies have highlighted a role for the entire miRNA, suggesting that a more flexible methodology is needed.We present a novel approach for miRNA target prediction based on Deep Learning (DL) which, rather than incorporating any knowledge (such as seed regions), investigates the entire miRNA and 3'UTR mRNA nucleotides to learn a uninhibited set of feature descriptors related to the targeting process.We collected more than 150,000 experimentally validated homo sapiens miRNA:gene targets and cross referenced them with different CLIP-Seq, CLASH and iPAR-CLIP datasets to obtain 20,000 validated miRNA:gene exact target sites. Using this data, we implemented and trained a deep neural network -composed of autoencoders and a feed-forward network -able to automatically learn features describing miRNA-mRNA interactions and assess functionality. Predictions were then refined using information such as site location or site accessibility energy.In a comparison using independent datasets, our DL approach consistently outperformed existing prediction methods, recognizing the seed region as a common feature in the targeting process, but also identifying the role of pairings outside this region. Thermodynamic analysis also suggests that site accessibility plays a role in targeting but that it cannot be used as a sole indicator for functionality.Data and source code available at: https://bitbucket.org/account/user/bipous/projects/MIRAW Author summary microRNAs are small RNA molecules that regulate biological processes by binding to the 3'UTR of a gene and their dysregulation is associated with several diseases. Computationally predicting these targets remains a challenge as they only partially match their target and so there can be hundreds of targets for a single microRNA.Current tools assume that most of the knowledge defining a microRNA-gene interaction can be captured by analysing the binding produced in the seed region (≈ the first 8nt in the miRNA). However, recent studies show that the whole microRNA can be important and form non-canonical targets. Here, we use a target prediction methodology miRAW: deep learning for miRNA target prediction -Pla et al.

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supporting

confidence: 89%

mentioning

confidence: 99%

miRAW: A deep learning approach to predict miRNA targets by analyzing whole miRNA transcripts

Plà

Zhong

Rayner

2017

Preprint

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“…Thus, in this biological context, the miR-790/791 family members are not redundant. This in vivo example lends credence to recent biochemical studies that have shown specific target-binding activity by distinct miRNA family members (Moore et al 2015;Broughton et al 2016). The second implication of this study is that a single miRNA, even a poorly conserved one, can have a function essential to animal survival in the wild.…”

supporting

confidence: 73%

A sense-able microRNA

Pasquinelli

2016

Genes Dev.

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In this issue of Genes & Development, Drexel and colleagues (pp. 2042–2047) present a beautiful example of how microRNAs (miRNAs) can regulate tissue-specific gene expression in a biologically relevant setting. They found that miR-791 is expressed in only three types of carbon dioxide (CO2)-sensing neurons in Caenorhabditis elegans, and its primary function there seems to be repression of two target genes that interfere with the behavioral response to CO2. Interestingly, these two targets are broadly expressed across other tissues. Thus, restricted miRNA expression can lead to target repression in select tissues to promote distinct cellular physiologies.

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“…In a recent study, it was reported that the 3 0 region of miRNA contributes to the specific binding of miRNAs from the same family into target mRNAs. 25 However, this finding does not apply to our results, because the number of base-pairs between 3 0 region of miRNA and target mRNA was similar between the binding of miR-221 and miR-222 into CDKN1C (Fig. S2).…”

Section: Discussionmentioning

confidence: 57%

Knockout of miR-221 and miR-222 reveals common and specific targets for paralogous miRNAs

et al. 2017

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MicroRNAs (miRNAs) regulate the expression of mRNA through sequence-specific binding of the 3 0 untranslated region (UTR). The seed sequence of miRNAs is the key determinant for target site recognition. Paralogous miRNAs, which share the same seed sequences but differ in their 3 0 regions, are known to regulate largely overlapping groups of mRNAs. However, no study has analyzed functional differences between paralogous miRNAs with proper experimental methods. In this study, we compared the targets of paralogous miRNAs, miR-221 and miR-222. Using a nuclease-mediated genome engineering technique, we established knockout cell lines for these miRNAs, and precisely analyzed differences in target regulation. We found that miR-221 and miR-222 suppress the previously identified targets, CDKN1B and CDKN1C, differentially. Whereas both miRNAs suppressed CDKN1B, only miR-221 suppressed CDKN1C. From transcriptome analyses, we found that several different target mRNAs were regulated by each of miR-221 and miR-222 independently, although a large number of mRNAs responded commonly to miR-221 and miR-222. This is the first study to compare the mRNA regulations by paralogous miRNAs and illustrate that paralogous miRNAs with the same seed sequence also have difference in target regulation.

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Pairing beyond the Seed Supports MicroRNA Targeting Specificity

Cited by 389 publications

References 52 publications

miRAW: A deep learning approach to predict miRNA targets by analyzing whole miRNA transcripts

miRAW: A deep learning approach to predict miRNA targets by analyzing whole miRNA transcripts

A sense-able microRNA

Knockout of miR-221 and miR-222 reveals common and specific targets for paralogous miRNAs

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