Pseudo Trinucleotide Composition Research Articles

CRBSP:Prediction of CircRNA-RBP Binding Sites Based on Multimodal Intermediate Fusion.

Circular RNA (CircRNA) is widely expressed and has physiological and pathological significance, regulating post-transcriptional processes via its protein-binding activity. However, whereas much work has been done on linear RNA and RNA binding protein (RBP), little is known about the binding sites of CircRNA. The current report is on the development of a medium-term multimodal data fusion strategy, CRBSP, to predict CircRNA-RBP binding sites. CRBSP represents the CircRNA trinucleotide semantic, location, composition and frequency information as the corresponding coding methods of Word to vector (Word2vec), Position-specific trinucleotide propensity (PSTNP), Pseudo trinucleotide composition (PseTNC) and Trinucleotide nucleotide composition (TNC), respectively. CNN (Convolution Neural Networks) was used to extract global information and BiLSTM (bidirectional Long- and Short-Term Memory network) encoder and LSTM (Long- and Short-Term Memory network) decoder for local sequence information. Enhancement of the contributions of key features by the self-attention mechanism was followed by mid-term fusion of the four enhanced features. Logistic Regression (LR) classifier showed that CRBSP gives a mean AUC value of 0.9362 through 5-fold Cross Validation of all 37 datasets, a performance which is superior to five current state-of-the-art models. Similar evaluation of linear RNA-RBP binding sites gave an AUC value of 0.7615 which is also higher than other prediction methods, demonstrating the robustness of CRBSP.

M6Aminer: Predicting the m6Am Sites on mRNA by Fusing Multiple Sequence-Derived Features into a CatBoost-Based Classifier.

As one of the most important post-transcriptional modifications, m6Am plays a fairly important role in conferring mRNA stability and in the progression of cancers. The accurate identification of the m6Am sites is critical for explaining its biological significance and developing its application in the medical field. However, conventional experimental approaches are time-consuming and expensive, making them unsuitable for the large-scale identification of the m6Am sites. To address this challenge, we exploit a CatBoost-based method, m6Aminer, to identify the m6Am sites on mRNA. For feature extraction, nine different feature-encoding schemes (pseudo electron-ion interaction potential, hash decimal conversion method, dinucleotide binary encoding, nucleotide chemical properties, pseudo k-tuple composition, dinucleotide numerical mapping, K monomeric units, series correlation pseudo trinucleotide composition, and K-spaced nucleotide pair frequency) were utilized to form the initial feature space. To obtain the optimized feature subset, the ExtraTreesClassifier algorithm was adopted to perform feature importance ranking, and the top 300 features were selected as the optimal feature subset. With different performance assessment methods, 10-fold cross-validation and independent test, m6Aminer achieved average AUC of 0.913 and 0.754, demonstrating a competitive performance with the state-of-the-art models m6AmPred (0.905 and 0.735) and DLm6Am (0.897 and 0.730). The prediction model developed in this study can be used to identify the m6Am sites in the whole transcriptome, laying a foundation for the functional research of m6Am.

Sigma70Pred: A highly accurate method for predicting sigma70 promoter in Escherichia coli K-12 strains.

Sigma70 factor plays a crucial role in prokaryotes and regulates the transcription of most of the housekeeping genes. One of the major challenges is to predict the sigma70 promoter or sigma70 factor binding site with high precision. In this study, we trained and evaluate our models on a dataset consists of 741 sigma70 promoters and 1,400 non-promoters. We have generated a wide range of features around 8,000, which includes Dinucleotide Auto-Correlation, Dinucleotide Cross-Correlation, Dinucleotide Auto Cross-Correlation, Moran Auto-Correlation, Normalized Moreau-Broto Auto-Correlation, Parallel Correlation Pseudo Tri-Nucleotide Composition, etc. Our SVM based model achieved maximum accuracy 97.38% with AUROC 0.99 on training dataset, using 200 most relevant features. In order to check the robustness of the model, we have tested our model on the independent dataset made by using RegulonDB10.8, which included 1,134 sigma70 and 638 non-promoters, and able to achieve accuracy of 90.41% with AUROC of 0.95. Our model successfully predicted constitutive promoters with accuracy of 81.46% on an independent dataset. We have developed a method, Sigma70Pred, which is available as webserver and standalone packages at https://webs.iiitd.edu.in/raghava/sigma70pred/. The services are freely accessible.

IRNA-PseTNC: identification of RNA 5-methylcytosine sites using hybrid vector space of pseudo nucleotide composition

RNA 5-methylcytosine (m5C) sites perform a major role in numerous biological processes and commonly reported in both DNA and RNA cellular. The enzymatic mechanism and biological functions of m5C sites in DNA remain the focusing area of researchers for last few decades. Likewise, the investigators also targeted m5C sites in RNA due to its cellular functions, positioning and formation mechanism. Currently, several rudimentary roles of the m5C in RNA have been explored, but a lot of improvements are still under consideration. Initially, the identification of RNA methylcytosine sites was carried out via experimental methods, which were very hard, erroneous and time consuming owing to partial availability of recognized structures. Looking at the significance of m5C role in RNA, scientists have diverted their attention from structure to sequence-based prediction. In this regards, an intelligent computational model is proposed in order to identify m5C sites in RNA with high precision. Three RNA sequences formulation methods namely: pseudo dinucleotide composition,pseudo trinucleotide composition and pseudo tetra nucleotide composition are applied to extract variant and high profound numerical features. In a sequel, the vector spaces are fused to build a hybrid space in order to compensate the weakness of each other. Various learning hypotheses are examined to select the best operational engine, which can truly identify the pattern of the target class. The strength and generalization of the proposed model are measured using two different cross validation tests. The reported outcomes reveal that the proposed model achieved 3% better accuracy than that of the highest present approach in the literature so far.

INuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou's PseAAC to pseudo-tri-nucleotide composition.

Nucleosome is a central element of eukaryotic chromatin, which composes of histone proteins and DNA molecules. It performs vital roles in many eukaryotic intra-nuclear processes, for instance, chromatin structure and transcriptional regulation formation. Identification of nucleosome positioning via wet lab is difficult; so, the attention is diverted towards the accurate intelligent automated prediction. In this regard, a novel intelligent automated model "iNuc-ext-PseTNC" is developed to identify the nucleosome positioning in genomes accurately. In this predictor, the sequences of DNA are mathematically represented by two different discrete feature extraction techniques, namely pseudo-tri-nucleotide composition (PseTNC) and pseudo-di-nucleotide composition. Several contemporary machine learning algorithms were examined. Further, the predictions of individual classifiers were integrated through an evolutionary genetic algorithm. The success rates of the ensemble model are higher than individual classifiers. After analyzing the prediction results, it is noticed that iNuc-ext-PseTNC model has achieved better performance in combination with PseTNC feature space, which are 94.3%, 93.14%, and 88.60% of accuracies using six-fold cross-validation test for the three benchmark datasets S1, S2, and S3, respectively. The achieved outcomes exposed that the results of iNuc-ext-PseTNC model are prominent compared to the existing methods so far notifiable in the literature. It is ascertained that the proposed model might be more fruitful and a practical tool for rudimentary academia and research.

IRSpotH-TNCPseAAC: Identifying Recombination Spots in Human by Using Pseudo Trinucleotide Composition With an Ensemble of Support Vector Machine Classifiers

Background: For the formation of human gametes, meiotic recombination is crucial. Meanwhile, it has played an important role in the process that generates genetic diversity for that it is a defining event in the formation of human sperm and eggs. However, the recombination isn't a random occurrence across a genome, it usually occurs in some genomic regions, the so-called “hotspots”, with higher probability, while in the so-called “coldspots” with lower probability. Research has shown that new combinations of genetic variations can be provided by recombination. Therefore, the useful insights for in-depth studying of the genome evolution process and the mechanism of recombination would be provided based on the information of the coldspots and hotspots. Currently, the recombination regions would be determined by experiments, but it's a tedious job, which generally requires precious instruments and takes a long time. So in the study the work is starting to be studied by computational predicting models to address the above problems. Method: In this paper, a new predictor, called ‘iRSpotH-TNCPseAAC’ was developed to identify the human recombination coldspots and hotspots. In the new discrete predictive model, a feature vector called ‘pseudo trinucleotide composition’ or PseTNC is proposed to formulate the given DNA segment with its sequence-order information as complete as possible. Results: In this study, based on the rigorous jackknife test the overall success rate obtained by iRSpotH- TNCPseAAC is higher than 93% in identifying human’s recombination spots, and with mean success rate is 76.07% of the concerned 18 chromosomes. It means that our predictor can become a useful complementary tool in this area. Not only that, the PseTNC method can be used to further explore many other DNA-related problems. Finally, a web- server called iRSpotH-TNCPseAAC, which has the advantages of easy operation and convenient for using, is built and freely accessible at http://www.jci-bioinfo.cn/iRSpotH-TNCPseAAC. Conclusion: To timely acquire the information of recombination spots in DNA sequence is very significant to make in-depth study on epigenetic inheritance and analyze human diseases. Furthermore, it will facilitate drug development. A certain conclusion is that the iRSpotH-TNCPseAAC predictor may become a very practical online predictive high throughput tools in identifying recombination spots. Keywords: Pseudo amino acid composition, support vector machine, web-server, iRSpotH-TNCPseAAC, meiosis, coldspots, hotspots.

Predicting DNase I hypersensitive sites via un-biased pseudo trinucleotide composition

DNase I Hypersensitive sites (DHS) are the regions that are sensitive to cleavage by the DNase I enzyme. Knowledge regarding these sites is helpful for decryption of the functions of non-coding genomic regions. Various biological processes need its intervention. Traditional techniques are laborious and time-consuming to predict DHS sites. Particularly, with the avalanche of DNA sequences generated in the post-genomic era, the development of computational approaches is highly essential to precisely and timely predict DHS sites in DNA sequences. The existing feature encoding schemes such as pseudo dinucleotide composition, pseudo trinucleotide composition etc. cannot effectively express features from DHS sequences. In the current study, we proposed a new computational technique to predict DHS sites which uses Un-biased Pseudo Trinucleotide Composition (Unb-PseTNC) strategy to extract nominal descriptors from the DHS benchmark dataset and avoid biasness among the classes during the classification phase. Several classification algorithm including Support vector machine (SVM), probabilistic neural network and k-nearest neighbor are employed to classify extracted features. It was observed that SVM in conjunction with Unb-PseTNC outperforms other techniques. By comparing with other existing predictors, it was perceived that our proposed method achieved higher prediction rates by applying rigorous jackknife test. This indicates that the proposed model will become a useful tool to predict DHS sites and can also be utilized for in-depth study of DNA and genome analysis.

“iSS-Hyb-mRMR”: Identification of splicing sites using hybrid space of pseudo trinucleotide and pseudo tetranucleotide composition

Background and objectivesGene splicing is a vital source of protein diversity. Perfectly eradication of introns and joining exons is the prominent task in eukaryotic gene expression, as exons are usually interrupted by introns. Identification of splicing sites through experimental techniques is complicated and time-consuming task. With the avalanche of genome sequences generated in the post genomic age, it remains a complicated and challenging task to develop an automatic, robust and reliable computational method for fast and effective identification of splicing sites. MethodsIn this study, a hybrid model “iSS-Hyb-mRMR” is proposed for quickly and accurately identification of splicing sites. Two sample representation methods namely; pseudo trinucleotide composition (PseTNC) and pseudo tetranucleotide composition (PseTetraNC) were used to extract numerical descriptors from DNA sequences. Hybrid model was developed by concatenating PseTNC and PseTetraNC. In order to select high discriminative features, minimum redundancy maximum relevance algorithm was applied on the hybrid feature space. The performance of these feature representation methods was tested using various classification algorithms including K-nearest neighbor, probabilistic neural network, general regression neural network, and fitting network. Jackknife test was used for evaluation of its performance on two benchmark datasets S1 and S2, respectively. ResultsThe predictor, proposed in the current study achieved an accuracy of 93.26%, sensitivity of 88.77%, and specificity of 97.78% for S1, and the accuracy of 94.12%, sensitivity of 87.14%, and specificity of 98.64% for S2, respectively. ConclusionIt is observed, that the performance of proposed model is higher than the existing methods in the literature so for; and will be fruitful in the mechanism of RNA splicing, and other research academia.

Benchmark data for identifying DNA methylation sites via pseudo trinucleotide composition.

This data article contains three benchmark datasets for training and testing iDNA-Methyl, a web-server predictor for identifying DNA methylation sites [Liu et al. Anal. Biochem. 474 (2015) 69–79].

IDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition

Predominantly occurring on cytosine, DNA methylation is a process by which cells can modify their DNAs to change the expression of gene products. It plays very important roles in life development but also in forming nearly all types of cancer. Therefore, knowledge of DNA methylation sites is significant for both basic research and drug development. Given an uncharacterized DNA sequence containing many cytosine residues, which one can be methylated and which one cannot? With the avalanche of DNA sequences generated during the postgenomic age, it is highly desired to develop computational methods for accurately identifying the methylation sites in DNA. Using the trinucleotide composition, pseudo amino acid components, and a dataset-optimizing technique, we have developed a new predictor called “iDNA-Methyl” that has achieved remarkably higher success rates in identifying the DNA methylation sites than the existing predictors. A user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/iDNA-Methyl, where users can easily get their desired results. We anticipate that the web-server predictor will become a very useful high-throughput tool for basic research and drug development and that the novel approach and technique can also be used to investigate many other DNA-related problems and genome analysis.

Prediction of CpG island methylation status by integrating DNA physicochemical properties

As an inheritable epigenetic modification, DNA methylation plays important roles in many biological processes. The non-uniform distribution of DNA methylation across the genome implies that characterizing genome-wide DNA methylation patterns is necessary to better understand the regulatory mechanisms of DNA methylation. Although a series of experimental technologies have been proposed, they are cost-ineffective for DNA methylation status detection. As complements to experimental techniques, computational methods will facilitate the identification of DNA methylation status. In the present study, we proposed a Naïve Bayes model to predict CpG island methylation status. In this model, DNA sequences are formulated by “pseudo trinucleotide composition” into which three DNA physicochemical properties were incorporated. It was observed by the jack-knife test that the overall success rate achieved by the proposed model in predicting the DNA methylation status was 88.22%. This result indicates that the proposed model is a useful tool for DNA methylation status prediction.

ITIS-PseTNC: A sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition

Translation is a key process for gene expression. Timely identification of the translation initiation site (TIS) is very important for conducting in-depth genome analysis. With the avalanche of genome sequences generated in the postgenomic age, it is highly desirable to develop automated methods for rapidly and effectively identifying TIS. Although some computational methods were proposed in this regard, none of them considered the global or long-range sequence-order effects of DNA, and hence their prediction quality was limited. To count this kind of effects, a new predictor, called “iTIS-PseTNC,” was developed by incorporating the physicochemical properties into the pseudo trinucleotide composition, quite similar to the PseAAC (pseudo amino acid composition) approach widely used in computational proteomics. It was observed by the rigorous cross-validation test on the benchmark dataset that the overall success rate achieved by the new predictor in identifying TIS locations was over 97%. As a web server, iTIS-PseTNC is freely accessible at http://lin.uestc.edu.cn/server/iTIS-PseTNC. To maximize the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web server to obtain the desired results without the need to go through detailed mathematical equations, which are presented in this paper just for the integrity of the new prection method.

Predicting promoters by pseudo-trinucleotide compositions based on discrete wavelets transform

In this paper, the discrete wavelet transform was introduced into the trinucleotide compositions and a novel DNA sequence representation technique, namely pseudo-trinucleotide compositions was proposed. The pseudo-trinucleotide compositions based on discrete wavelets transform were then employed to model support vector machines (SVM) for the prediction of promoters. The model was evaluated on the genie dataset, and the overall prediction accuracy (ACC) by jackknife validation for the classification of promoters, introns and exons was 82.46%, while the ACC for the classification of promoters and unpromoters was 82.18%, which was far better than the previous results. The satisfied prediction result revealed that the pseudo-trinucleotide composition based on discrete wavelet transform was an effective representation method for DNA sequence, and plays a very important role in the prediction of DNA function.

Predicting methylation status of human DNA sequences by pseudo-trinucleotide composition

DNA methylation plays a key role in the regulation of gene expression. The most common type of DNA modification consists of the methylation of cytosine in the CpG dinucleotide. The detections of DNA methylation have been determined mostly by experimental methods; however, these methods were time-consuming, expensive, and difficult to meet the requirements of modern large-scale sequencing technology. Accordingly, it is necessary to develop automatic and reliable prediction methods for DNA methylation. In this study, the pseudo-trinucleotide composition was proposed, and a novel method was developed by support vector machine (SVM) with the pseudo-trinucleotide composition as input parameter to represent DNA sequence for DNA methylation prediction. The model was evaluated on two datasets, including a dataset of Rollins (dataset_1) and a dataset collected healthy human records from the MethDB database (dataset_2). For dataset_1, the Matthews correlation coefficient ( MCC) and accuracy ( ACC) by jackknife validation were 0.8051 and 0.6098, respectively. For dataset_2, the MCC and ACC were 0.8500 and 0.7203, respectively. The good prediction results reveal that the pseudo-trinucleotide composition is an effective representation method for DNA sequence and plays a very important role in the prediction of DNA function.