Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions.
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ABSTRACT:
SUBMITTER: Chen L
PROVIDER: S-EPMC8776474 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:5-methylcytosine (m5C) is a common nucleobase modification, and recent investigations have indicated its prevalence in cellular RNAs including mRNA, tRNA and rRNA. With the rapid accumulation of m5C sites data, it becomes not only feasible but also important to build an accurate model to predict m5C sites in silico. For this purpose, here, we developed a web-server named RNAm5Cfinder based on RNA sequence features and machine learning method to predict RNA m5C sites in eight tissue/cell types from mouse and human. We confirmed the accuracy and usefulness of RNAm5Cfinder by independent tests, and the results show that the comprehensive and cell-specific predictors could pinpoint the generic or tissue-specific m5C sites with the Area Under Curve (AUC) no less than 0.77 and 0.87, respectively. RNAm5Cfinder web-server is freely available at http://www.rnanut.net/rnam5cfinder .
| S-EPMC6251864 | biostudies-literature
Project description:BackgroundSumoylation, which is a reversible and dynamic post-translational modification, is one of the vital processes in a cell. Before a protein matures to perform its function, sumoylation may alter its localization, interactions, and possibly structural conformation. Abberations in protein sumoylation has been linked with a variety of disorders and developmental anomalies. Experimental approaches to identification of sumoylation sites may not be effective due to the dynamic nature of sumoylation, laborsome experiments and their cost. Therefore, computational approaches may guide experimental identification of sumoylation sites and provide insights for further understanding sumoylation mechanism.ResultsIn this paper, the effectiveness of using various sequence properties in predicting sumoylation sites was investigated with statistical analyses and machine learning approach employing support vector machines. These sequence properties were derived from windows of size 7 including position-specific amino acid composition, hydrophobicity, estimated sub-window volumes, predicted disorder, and conformational flexibility. 5-fold cross-validation results on experimentally identified sumoylation sites revealed that our method successfully predicts sumoylation sites with a Matthew's correlation coefficient, sensitivity, specificity, and accuracy equal to 0.66, 73%, 98%, and 97%, respectively. Additionally, we have showed that our method compares favorably to the existing prediction methods and basic regular expressions scanner.ConclusionsBy using support vector machines, a new, robust method for sumoylation site prediction was introduced. Besides, the possible effects of predicted conformational flexibility and disorder on sumoylation site recognition were explored computationally for the first time to our knowledge as an additional parameter that could aid in sumoylation site prediction.
| S-EPMC4290605 | biostudies-literature
Project description:Membrane proteins (MPs) are involved in many essential biomolecule mechanisms as a pivotal factor in enabling the small molecule and signal transport between the two sides of the biological membrane; this is the reason that a large portion of modern medicinal drugs target MPs. Therefore, accurately identifying the membrane protein-ligand binding sites (MPLs) will significantly improve drug discovery. In this paper, we propose a sequence-based MPLs predictor called MPLs-Pred, where evolutionary profiles, topology structure, physicochemical properties, and primary sequence segment descriptors are combined as features applied to a random forest classifier, and an under-sampling scheme is used to enhance the classification capability with imbalanced samples. Additional ligand-specific models were taken into consideration in refining the prediction. The corresponding experimental results based on our method achieved an appreciable performance, with 0.63 MCC (Matthews correlation coefficient) as the overall prediction precision, and those values were 0.604, 0.7, and 0.692, respectively, for the three main types of ligands: drugs, metal ions, and biomacromolecules. MPLs-Pred is freely accessible at http://icdtools.nenu.edu.cn/.
| S-EPMC6651575 | biostudies-literature
Project description:Pseudouridine (Ψ) is the first discovered and the most prevalent posttranscriptional modification, which has been widely studied during the past decades. Pseudouridine was observed in almost all kinds of RNAs and shown to have important biological functions. Currently, the time-consuming and high-cost procedures of experimental approaches limit its uses in real-life Ψ site detection. Alternatively, by taking advantage of the explosive growth of Ψ sequencing data, the computational methods may provide a more cost-effective avenue. To date, the existing mouse Ψ site predictors were all developed based on sequence-derived features, and their performance can be further improved by adding the domain knowledge derived feature. Therefore, it is highly desirable to propose a genomic feature-based computational method to increase the accuracy and efficiency of the identification of Ψ RNA modification in the mouse transcriptome. In our study, a predictive framework PSI-MOUSE was built. Besides the conventional sequence-based features, PSI-MOUSE first introduced 38 additional genomic features derived from the mouse genome, which achieved a satisfactory improvement in the prediction performance, compared with other existing models. Moreover, PSI-MOUSE also features in automatically annotating the putative Ψ sites with diverse types of posttranscriptional regulations (RNA-binding protein [RBP]-binding regions, miRNA-RNA interactions, and splicing sites), which can serve as a useful research tool for the study of Ψ RNA modification in the mouse genome. Finally, 3282 experimentally validated mouse Ψ sites were also collected in a database with customized query functions. For the convenience of academic users, a website was built to provide a user-friendly interface for the query and analysis on the database. The website is freely accessible at www.xjtlu.edu.cn/biologicalsciences/psimouse and http://psimouse.rnamd.com. We introduced the genome-derived features to mouse for the first time, and we achieved a good performance in mouse Ψ site prediction. Compared with the existing state-of-art methods, our newly developed approach PSI-MOUSE obtained a substantial improvement in prediction accuracy, marking the reliable contributions of genomic features for the prediction of RNA modifications in a species other than human.
| S-EPMC7285933 | biostudies-literature
Project description:BackgroundDNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristics and machine learning algorithms have been explored to identify 4mC sites from DNA sequences. However, state-of-the-art methods have limited performance because of the lack of effective sequence features and the ad hoc choice of learning algorithms to cope with this problem. This paper is aimed to propose new sequence feature space and a machine learning algorithm with feature selection scheme to address the problem.ResultsThe feature importance score distributions in datasets of six species are firstly reported and analyzed. Then the impact of the feature selection on model performance is evaluated by independent testing on benchmark datasets, where ACC and MCC measurements on the performance after feature selection increase by 2.3% to 9.7% and 0.05 to 0.19, respectively. The proposed method is compared with three state-of-the-art predictors using independent test and 10-fold cross-validations, and our method outperforms in all datasets, especially improving the ACC by 3.02% to 7.89% and MCC by 0.06 to 0.15 in the independent test. Two detailed case studies by the proposed method have confirmed the excellent overall performance and correctly identified 24 of 26 4mC sites from the C.elegans gene, and 126 out of 137 4mC sites from the D.melanogaster gene.ConclusionsThe results show that the proposed feature space and learning algorithm with feature selection can improve the performance of DNA 4mC prediction on the benchmark datasets. The two case studies prove the effectiveness of our method in practical situations.
| S-EPMC7488740 | biostudies-literature
Project description:DNA N4-methylcytosine (4mC) is an important epigenetic modification that plays a vital role in regulating DNA replication and expression. However, it is challenging to detect 4mC sites through experimental methods, which are time-consuming and costly. Thus, computational tools that can identify 4mC sites would be very useful for understanding the mechanism of this important type of DNA modification. Several machine learning-based 4mC predictors have been proposed in the past 3 years, although their performance is unsatisfactory. Deep learning is a promising technique for the development of more accurate 4mC site predictions. In this work, we propose a deep learning-based approach, called DeepTorrent, for improved prediction of 4mC sites from DNA sequences. It combines four different feature encoding schemes to encode raw DNA sequences and employs multi-layer convolutional neural networks with an inception module integrated with bidirectional long short-term memory to effectively learn the higher-order feature representations. Dimension reduction and concatenated feature maps from the filters of different sizes are then applied to the inception module. In addition, an attention mechanism and transfer learning techniques are also employed to train the robust predictor. Extensive benchmarking experiments demonstrate that DeepTorrent significantly improves the performance of 4mC site prediction compared with several state-of-the-art methods.
| S-EPMC8599298 | biostudies-literature
Project description:BackgroundRNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions.ResultsWe propose RPISeq, a family of classifiers for predicting RNA-protein interactions using only sequence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of RPISeq are presented: RPISeq-SVM, which uses a Support Vector Machine (SVM) classifier and RPISeq-RF, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), RPISeq achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of RPISeq was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, RPISeq classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from E. coli, S. cerevisiae, D. melanogaster, M. musculus, and H. sapiens.ConclusionsOur experiments with RPISeq demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. RPISeq offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. RPISeq is freely available as a web-based server at http://pridb.gdcb.iastate.edu/RPISeq/.
| S-EPMC3322362 | biostudies-literature
Project description:Recent progress in deep learning has greatly improved the prediction of RNA splicing from DNA sequence. Here, we present Pangolin, a deep learning model to predict splice site strength in multiple tissues. Pangolin outperforms state-of-the-art methods for predicting RNA splicing on a variety of prediction tasks. Pangolin improves prediction of the impact of genetic variants on RNA splicing, including common, rare, and lineage-specific genetic variation. In addition, Pangolin identifies loss-of-function mutations with high accuracy and recall, particularly for mutations that are not missense or nonsense, demonstrating remarkable potential for identifying pathogenic variants.
| S-EPMC9022248 | biostudies-literature
Project description:Nitrotyrosine is a product of tyrosine nitration mediated by reactive nitrogen species. As an indicator of cell damage and inflammation, protein nitrotyrosine serves to reveal biological change associated with various diseases or oxidative stress. Accurate identification of nitrotyrosine site provides the important foundation for further elucidating the mechanism of protein nitrotyrosination. However, experimental identification of nitrotyrosine sites through traditional methods are laborious and expensive. In silico prediction of nitrotyrosine sites based on protein sequence information are thus highly desired. Here, we report a novel predictor, NTyroSite, for accurate prediction of nitrotyrosine sites using sequence evolutionary information. The generated features were optimized using a Wilcoxon-rank sum test. A random forest classifier was then trained using these features to build the predictor. The final NTyroSite predictor achieved an area under a receiver operating characteristics curve (AUC) score of 0.904 in a 10-fold cross-validation test. It also significantly outperformed other existing implementations in an independent test. Meanwhile, for a better understanding of our prediction model, the predominant rules and informative features were extracted from the NTyroSite model to explain the prediction results. We expect that the NTyroSite predictor may serve as a useful computational resource for high-throughput nitrotyrosine site prediction. The online interface of the software is publicly available at https://biocomputer.bio.cuhk.edu.hk/NTyroSite/.
| S-EPMC6099560 | biostudies-literature
Project description:MotivationRNA molecules become attractive small-molecule drug targets to treat disease in recent years. Computer-aided drug design can be facilitated by detecting the RNA sites that bind small molecules. However, very limited progress has been reported for the prediction of small molecule-RNA binding sites.ResultsWe developed a novel method RNAsite to predict small molecule-RNA binding sites using sequence profile- and structure-based descriptors. RNAsite was shown to be competitive with the state-of-the-art methods on the experimental structures of two independent test sets. When predicted structure models were used, RNAsite outperforms other methods by a large margin. The possibility of improving RNAsite by geometry-based binding pocket detection was investigated. The influence of RNA structure's flexibility and the conformational changes caused by ligand binding on RNAsite were also discussed. RNAsite is anticipated to be a useful tool for the design of RNA-targeting small molecule drugs.Availability and implementationhttp://yanglab.nankai.edu.cn/RNAsite.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8034527 | biostudies-literature