iMRM: a platform for simultaneously identifying multiple kinds of RNA modifications

Empirical Comparison and Analysis of Web-Based DNA N 4-Methylcytosine Site Prediction Tools

DNA N⁴-methylcytosine (4mC) is a crucial epigenetic modification involved in various biological processes. Accurate genome-wide identification of these sites is critical for improving our understanding of their biological functions and mechanisms. As experimental methods for 4mC identification are tedious, expensive, and labor-intensive, several machine learning-based approaches have been developed for genome-wide detection of such sites in multiple species. However, the predictions projected by these tools are difficult to quantify and compare. To date, no systematic performance comparison of 4mC tools has been reported. The aim of this study was to compare and critically evaluate 12 publicly available 4mC site prediction tools according to species specificity, based on a huge independent validation dataset. The tools 4mCCNN (Escherichia coli), DNA4mC-LIP (Arabidopsis thaliana), iDNA-MS (Fragaria vesca), DNA4mC-LIP and 4mCCNN (Drosophila melanogaster), and four tools for Caenorhabditis elegans achieved excellent overall performance compared with their counterparts. However, none of the existing methods was suitable for Geoalkalibacter subterraneus, Geobacter pickeringii, and Mus musculus, thereby limiting their practical applicability. Model transferability to five species and non-transferability to three species are also discussed. The presented evaluation will assist researchers in selecting appropriate prediction tools that best suit their purpose and provide useful guidelines for the development of improved 4mC predictors in the future.

iPiDA-sHN: Identification of Piwi-interacting RNA-disease associations by selecting high quality negative samples

As a large group of small non-coding RNAs (ncRNAs), Piwi-interacting RNAs (piRNAs) have been detected to be associated with various diseases. Identifying disease associated piRNAs can provide promising candidate molecular targets to promote the drug design. Although, a few computational ensemble methods have been developed for identifying piRNA-disease associations, the low-quality negative associations even with positive associations used during the training process prevent the predictive performance improvement. In this study, we proposed a new computational predictor named iPiDA-sHN to predict potential piRNA-disease associations. iPiDA-sHN presented the piRNA-disease pairs by incorporating piRNA sequence information, the known piRNA-disease association network, and the disease semantic graph. High-level features of piRNA-disease associations were extracted by the Convolutional Neural Network (CNN). Two-step positive-unlabeled learning strategy based on Support Vector Machine (SVM) was employed to select the high quality negative samples from the unknown piRNA-disease pairs. Finally, the SVM predictor trained with the known piRNA-disease associations and the high quality negative associations was used to predict new piRNA-disease associations. The experimental results showed that iPiDA-sHN achieved superior predictive ability compared with other state-of-the-art predictors.

Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework

DNA N6-methyladenine (6mA) represents important epigenetic modifications, which are responsible for various cellular processes. The accurate identification of 6mA sites is one of the challenging tasks in genome analysis, which leads to an understanding of their biological functions. To date, several species-specific machine learning (ML)-based models have been proposed, but majority of them did not test their model to other species. Hence, their practical application to other plant species is quite limited. In this study, we explored 10 different feature encoding schemes, with the goal of capturing key characteristics around 6mA sites. We selected five feature encoding schemes based on physicochemical and position-specific information that possesses high discriminative capability. The resultant feature sets were inputted to six commonly used ML methods (random forest, support vector machine, extremely randomized tree, logistic regression, naïve Bayes and AdaBoost). The Rosaceae genome was employed to train the above classifiers, which generated 30 baseline models. To integrate their individual strength, Meta-i6mA was proposed that combined the baseline models using the meta-predictor approach. In extensive independent test, Meta-i6mA showed high Matthews correlation coefficient values of 0.918, 0.827 and 0.635 on Rosaceae, rice and Arabidopsis thaliana, respectively and outperformed the existing predictors. We anticipate that the Meta-i6mA can be applied across different plant species. Furthermore, we developed an online user-friendly web server, which is available at http://kurata14.bio.kyutech.ac.jp/Meta-i6mA/.

ncPro-ML: An integrated computational tool for identifying non-coding RNA promoters in multiple species

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A computational method for identifying non-coding promoters was proposed for the first time.

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A high-quality dataset was built to train and test the models for identifying non-coding promoters.

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A user-friendly web server was developed to recognize non-coding promoters.

The promoter is located near the transcription start sites and regulates transcription initiation of the gene. Accurate identification of promoters is essential for understanding the mechanism of gene regulation. Since experimental methods are costly and ineffective, developing efficient and accurate computational tools to identify promoters are necessary. Although a series of methods have been proposed for identifying promoters, none of them is able to identify the promoters of non-coding RNA (ncRNA). In the present work, a new method called ncPro-ML was proposed to identify the promoter of ncRNA in Homo sapiens and Mus musculus, in which different kinds of sequence encoding schemes were used to convert DNA sequences into feature vectors. To test the length effect, for each species, datasets including sequences with different lengths were built. The results demonstrated that ncPro-ML achieved the best performance based on the dataset with the sequence length of 221 nucleotides for human and mouse. The performances of ncPro-ML were also satisfying from both independent dataset test and cross-species test. The results indicate that the proposed predictor can server as a powerful tool for the discovery of ncRNA promoters. In addition, a web-server for ncPro-ML was developed, which can be freely accessed at http://www.bio-bigdata.cn/ncPro-ML/.

Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features

There are a lot of bacteria in the environment, and Gram-positive bacteria are the most common ones. Some Gram-positive bacteria are very harmful to the human body, so it is significant to predict Gram-positive bacterial protein subcellular location. And identification of Gram-positive bacterial protein subcellular location is important for developing effective drugs. In this paper, a new Gram-positive bacterial protein subcellular location dataset was established. The amino acid composition, the gene ontology annotation information, the hydropathy dipeptide composition information, the amino acid dipeptide composition information, and the autocovariance average chemical shift information were selected as characteristic parameters, then these parameters were combined. The locations of Gram-positive bacterial proteins were predicted by the Support Vector Machine (SVM) algorithm, and the overall accuracy (OA) reached 86.1% under the Jackknife test. The overall accuracy (OA) in our predictive model was higher than those in existing methods. This improved method may be helpful for protein function prediction.

im6A-TS-CNN: Identifying the N6-Methyladenine Site in Multiple Tissues by Using the Convolutional Neural Network

N⁶-methyladenosine (m⁶A) is the most abundant post-transcriptional modification and involves a series of important biological processes. Therefore, accurate detection of the m⁶A site is very important for revealing its biological functions and impacts on diseases. Although both experimental and computational methods have been proposed for identifying m⁶A sites, few of them are able to detect m⁶A sites in different tissues. With the consideration of the spatial specificity of m⁶A modification, it is necessary to develop methods able to detect the m⁶A site in different tissues. In this work, by using the convolutional neural network (CNN), we proposed a new method, called im6A-TS-CNN, that can identify m⁶A sites in brain, liver, kidney, heart, and testis of Homo sapiens, Mus musculus, and Rattus norvegicus. In im6A-TS-CNN, the samples were encoded by using the one-hot encoding scheme. The results from both a 5-fold cross-validation test and independent dataset test demonstrate that im6A-TS-CNN is better than the existing method for the same purpose. The command-line version of im6A-TS-CNN is available at https://github.com/liukeweiaway/DeepM6A_cnn.

Prediction of N7-methylguanosine sites in human RNA based on optimal sequence features

N-7 methylguanosine (m7G) modification is a ubiquitous post-transcriptional RNA modification which is vital for maintaining RNA function and protein translation. Developing computational tools will help us to easily predict the m7G sites in RNA sequence. In this work, we designed a sequence-based method to identify the modification site in human RNA sequences. At first, several kinds of sequence features were extracted to code m7G and non-m7G samples. Subsequently, we used mRMR, F-score, and Relief to obtain the optimal subset of features which could produce the maximum prediction accuracy. In 10-fold cross-validation, results showed that the highest accuracy is 94.67% achieved by support vector machine (SVM) for identifying m7G sites in human genome. In addition, we examined the performances of other algorithms and found that the SVM-based model outperformed others. The results indicated that the predictor could be a useful tool for studying m7G. A prediction model is available at https://github.com/MapFM/m7g_model.git.

Identifying Antioxidant Proteins by Combining Multiple Methods

Antioxidant proteins play important roles in preventing free radical oxidation from damaging cells and DNA. They have become ideal candidates of disease prevention and treatment. Therefore, it is urgent to identify antioxidants from natural compounds. Since experimental methods are still cost ineffective, a series of computational methods have been proposed to identify antioxidant proteins. However, the performance of the current methods are still not satisfactory. In this study, a support vector machine based method, called Vote9, was proposed to identify antioxidants, in which the sequences were encoded by using the features generated from 9 optimal individual models. Results from jackknife test demonstrated that Vote9 is comparable with the best one of the existing predictors for this task. We hope that Vote9 will become a useful tool or at least can play a complementary role to the existing methods for identifying antioxidants.

Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods

The G Protein-Coupled Receptor (GPCR) family consists of more than 800 different members. In this article, we attempt to use the physicochemical properties of Composition, Transition, Distribution (CTD) to represent GPCRs. The dimensionality reduction method of MRMD2.0 filters the physicochemical properties of GPCR redundancy. Matplotlib plots the coordinates to distinguish GPCRs from other protein sequences. The chart data show a clear distinction effect, and there is a well-defined boundary between the two. The experimental results show that our method can predict GPCRs.