New 3D graphical representation for RNA structure analysis and its application in the pre-miRNA identification of plants

Fu, Xiquan; Liao, Bo; Zhu, Wen; Cai, Liang

doi:10.1039/c8ra04138e

Cited by 9 publications

(3 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pre-miRNA prediction is one of the hot topics in the field of miRNA research (Yue et al, 2014; Cheng et al, 2015; Liao et al, 2015a; Luo et al, 2017, 2018; Peng et al, 2017, 2018; Xiao et al, 2017; Fu et al, 2018). In recent years, machine learning-based miRNA precursor prediction methods have made great progress.…”

Section: Discussionmentioning

confidence: 99%

Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures

Zhu

Cai

et al. 2019

Front. Genet.

Self Cite

View full text Add to dashboard Cite

Playing critical roles as post-transcriptional regulators, microRNAs (miRNAs) are a family of short non-coding RNAs that are derived from longer transcripts called precursor miRNAs (pre-miRNAs). Experimental methods to identify pre-miRNAs are expensive and time-consuming, which presents the need for computational alternatives. In recent years, the accuracy of computational methods to predict pre-miRNAs has been increasing significantly. However, there are still several drawbacks. First, these methods usually only consider base frequencies or sequence information while ignoring the information between bases. Second, feature extraction methods based on secondary structures usually only consider the global characteristics while ignoring the mutual influence of the local structures. Third, methods integrating high-dimensional feature information is computationally inefficient. In this study, we have proposed a novel mutual information-based feature representation algorithm for pre-miRNA sequences and secondary structures, which is capable of catching the interactions between sequence bases and local features of the RNA secondary structure. In addition, the feature space is smaller than that of most popular methods, which makes our method computationally more efficient than the competitors. Finally, we applied these features to train a support vector machine model to predict pre-miRNAs and compared the results with other popular predictors. As a result, our method outperforms others based on both 5-fold cross-validation and the Jackknife test.

show abstract

Section: Discussionmentioning

confidence: 99%

Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures

Zhu

Cai

et al. 2019

Front. Genet.

Self Cite

View full text Add to dashboard Cite

show abstract

“…2D and 3D representations of RNA sequences create a data matrix based on the structural information. Although such representations have been used for measuring RNA similarities and classifying viruses (Yao et al, 2005;Li et al, 2012), they are rarely applied for pre-miRNA analysis (Fu et al, 2018). The workflow developed in this study is the first example of application of 3D representations of RNAs for ML-based miRNA prediction.…”

Section: Discussionmentioning

confidence: 99%

MicroRNA prediction based on 3D graphical representation of RNA secondary structures

Demirci¹

2019

Turk J Biol

View full text Add to dashboard Cite

MicroRNAs (miRNAs) are posttranscriptional regulators of gene expression. While a miRNA can target hundreds of messenger RNA (mRNAs), an mRNA can be targeted by different miRNAs, not to mention that a single miRNA might have various binding sites in an mRNA sequence. Therefore, it is quite involved to investigate miRNAs experimentally. Thus, machine learning (ML) is frequently used to overcome such challenges. The key parts of a ML analysis largely depend on the quality of input data and the capacity of the features describing the data. Previously, more than 1000 features were suggested for miRNAs. Here, it is shown that using 36 features representing the RNA secondary structure and its dynamic 3D graphical representation provides up to 98% accuracy values. In this study, a new approach for ML-based miRNA prediction is proposed. Thousands of models are generated through classification of known human miRNAs and pseudohairpins with 3 classifiers: decision tree, naïve Bayes, and random forest. Although the method is based on human data, the best model was able to correctly assign 96% of nonhuman hairpins from MirGeneDB, suggesting that this approach might be useful for the analysis of miRNAs from other species.

show abstract

“…The four classic metrics were used to quantify the performance of model predictions, including Accuracy (Acc), Recall (Re), Precision (Pre), and F1 measure (F1), de ned as [41][42][43][44][45]:…”

Section: Model Evaluationmentioning

confidence: 99%

NRTPredictor: identifying rice root cell state in single-cell RNA-seq via ensemble learning

Wang

Lin

Yan

et al. 2023

Preprint

View full text Add to dashboard Cite

Background Single-cell RNA sequencing (scRNA-seq) measurements of gene expression show great promise for studying cellular heterogeneity of rice root. How precisely annotating cell identity is a major unresolved problem in plant scRNA-seq analysis due to the inherent high dimensionality and sparsity.Results To address this challenge, we present NRTPredictor, an ensemble-learning system, to predict rice root cell stage and mine biomarkers through complete model interpretability. The performance of NRTPredictor was evaluated using an external dataset, with 98.01% accuracy and 95.45% recall. With the power of the interpretability provided by NRTPredictor, our model recognizes 110 important marker genes, partially involved in the phenylpropanoid biosynthesis, that domain knowledge does not consider. Expression patterns of rice root could be mapped by the above-mentioned candidate genes, showing the superiority of NRTPredictor. Integrative bulk RNA-seq analysis we revealed aberrant expression of Epidermis and Cortex cell subpopulations in flooding, Pi stress and salt stress.Conclusion Taken together, our results demonstrate that NRTPredictor is a useful tool for automated prediction of rice root cell stage and provides a valuable resource for deciphering the rice root cellular heterogeneity and the molecular mechanisms of flooding, Pi stress and salt stress. Based on the proposed model, a free webserver has been established, which is available at http://bio.germplasmai.com.

show abstract

New 3D graphical representation for RNA structure analysis and its application in the pre-miRNA identification of plants

Abstract: MicroRNAs (miRNAs) are a family of short non-coding RNAs that play significant roles as post-transcriptional regulators.

Cited by 9 publications

References 54 publications

Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures

Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures

MicroRNA prediction based on 3D graphical representation of RNA secondary structures

NRTPredictor: identifying rice root cell state in single-cell RNA-seq via ensemble learning

Contact Info

Product

Resources

About