Prediction of miRNA targets by learning from interaction sequences

Zheng, Xueming; Chen, Long; Li, Xiuming; Zhang, Ying; Xu, Shengli; Huang, Xinxiang

doi:10.1371/journal.pone.0232578

Cited by 15 publications

(18 citation statements)

References 54 publications

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“…The search of expression-changing candidate miRNAs should be based on two main criteria: (1) calculation of thermodynamic properties (implemented in, for instance, the Vienna RNA package v2. * [31], which is incorporated in the IntaRNA [30] algorithm), and (2) classification of targets as effective and ineffective, which may come from definitions of "canonical" and "non-canonical" seeds or from applying deep learning methods [73]. That is why the algorithms of MicroSNiPer (a "canonical" interaction predictor) and IntaRNA (an extended flexible RNA-RNA binding analyzer) were incorporated into the proposed pipeline in order to evaluate a wide spectrum of miRNA targeting sites.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Effect of 3′-UTR Variants in DICER1 and DROSHA on Their Tissue-Specific Expression by miRNA Target Prediction

Bug

Тишков

Моисеев

et al. 2021

CIMB

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Untranslated gene regions (UTRs) play an important role in controlling gene expression. 3′-UTRs are primarily targeted by microRNA (miRNA) molecules that form complex gene regulatory networks. Cancer genomes are replete with non-coding mutations, many of which are connected to changes in tumor gene expression that accompany the development of cancer and are associated with resistance to therapy. Therefore, variants that occurred in 3′-UTR under cancer progression should be analysed to predict their phenotypic effect on gene expression, e.g., by evaluating their impact on miRNA target sites. Here, we analyze 3′-UTR variants in DICER1 and DROSHA genes in the context of myelodysplastic syndrome (MDS) development. The key features of this analysis include an assessment of both “canonical” and “non-canonical” types of mRNA-miRNA binding and tissue-specific profiling of miRNA interactions with wild-type and mutated genes. As a result, we obtained a list of DICER1 and DROSHA variants likely altering the miRNA sites and, therefore, potentially leading to the observed tissue-specific gene downregulation. All identified variants have low population frequency consistent with their potential association with pathology progression.

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Section: Discussionmentioning

confidence: 99%

Evaluating the Effect of 3′-UTR Variants in DICER1 and DROSHA on Their Tissue-Specific Expression by miRNA Target Prediction

Bug

Тишков

Моисеев

et al. 2021

CIMB

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“…Predictive models were used to assess the influence of mutations spanned by the top ten ranked features of each cancer type (whether they are of low, medium or high resolution) on splice sites (using SpliceAI 85 ), miRNA binding sites (using cnnMirTarget 86 ), mRNA expression levels (using Xpresso 87 ), polyadenylation (using SANPolyA 88 ), 3D folding (using Akita 89 ) and several protein-mRNA binding sites (using DeepCLIP 90 ).…”

Section: Methodsmentioning

confidence: 99%

Estimating the predictive power of silent mutations on cancer classification and prognosis

et al. 2021

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In recent years it has been shown that silent mutations, in and out of the coding region, can affect gene expression and may be related to tumorigenesis and cancer cell fitness. However, the predictive ability of these mutations for cancer type diagnosis and prognosis has not been evaluated yet. In the current study, based on the analysis of 9,915 cancer genomes and approximately three million mutations, we provide a comprehensive quantitative evaluation of the predictive power of various types of silent and non-silent mutations over cancer classification and prognosis. The results indicate that silent-mutation models outperform the equivalent null models in classifying all examined cancer types and in estimating the probability of survival 10 years after the initial diagnosis. Additionally, combining both non-silent and silent mutations achieved the best classification results for 68% of the cancer types and the best survival estimation results for up to nine years after the diagnosis. Thus, silent mutations hold considerable predictive power over both cancer classification and prognosis, most likely due to their effect on gene expression. It is highly advised that silent mutations are integrated in cancer research in order to unravel the full genomic landscape of cancer and its ramifications on cancer fitness.

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“…Deepmirtar [9] uses 750 manually extracted features in 7 categories, using a stack denoising autoencoder as a model, and achieves 93% accuracy at the site level. Xueming [10] uses a multi-layer convolutional neural network(CNN) stack structure, processing site, and gene rank prediction, and the model can use full-length mRNAs as input. However, even though these deep learning methods have achieved good results, there are still some problems that need to be improved.…”

Section: A Mirna Target Prediction Model Based On Distributed Representation Learning and Deep Learningmentioning

confidence: 99%

A MiRNA Target Prediction Model based on Distributed Representation Learning and Deep Learning

Sun¹,

Xiong²,

Sun³

et al. 2021

Preprint

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Background: MicroRNAs (miRNAs) are a kind of non-coding RNA, which plays an essential role in gene regulation by binding to messenger RNAs(mRNAs). Accurate and rapid identification of miRNA target genes is helpful to reveal the mechanism of transcriptome regulation, which is of great significance for the study of cancer and other diseases. Many bioinformatics methods have been proposed to solve this problem, but the previous research did not further study the encoding of the base sequence. Results: In this study, we developed a novel method combining word embedding and deep learning for human miRNA targets at the site level prediction, which is inspired by the similarity between natural language and biological sequences. First, the wor2vec model was used to mine the distribution representation of miRNAs and mRNAs. Then, the data features are fully extracted automatically from temporal and spatial via the stacked Bidirectional Long short-term memory(BiLSTM) network. We compare the effects of different embedding methods on model accuracy in different deep learning models, and the results prove that using word2vec can improve the accuracy of deep learning models. In addition, we performed visual analysis on the distributed represented sequences and found hidden similarity relationships between bases. Finally, compared with different advanced methods and data sets, the results show that our proposed method has gotten better performance in multiple evaluation aspects. Conclusions: We present a novel method for predicting miRNA target sites consisting of word2vec and the BiLSTM model and demonstrate that this method can realize automatic feature extraction and has higher accuracy. Furthermore, we process miRNA and mRNA as two languages for the first time and explore their biological significance through visual analysis.

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Prediction of miRNA targets by learning from interaction sequences

Cited by 15 publications

References 54 publications

Evaluating the Effect of 3′-UTR Variants in DICER1 and DROSHA on Their Tissue-Specific Expression by miRNA Target Prediction

Evaluating the Effect of 3′-UTR Variants in DICER1 and DROSHA on Their Tissue-Specific Expression by miRNA Target Prediction

Estimating the predictive power of silent mutations on cancer classification and prognosis

A MiRNA Target Prediction Model based on Distributed Representation Learning and Deep Learning

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