iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition

ORI-Explorer: a unified cell-specific tool for origin of replication sites prediction by feature fusion

MOTIVATION

The origins of replication sites (ORIs) are precise regions inside the DNA sequence where the replication process begins. These locations are critical for preserving the genome's integrity during cell division and guaranteeing the faithful transfer of genetic data from generation to generation. The advent of experimental techniques has aided in the discovery of ORIs in many species. Experimentation, on the other hand, is often more time-consuming and pricey than computational approaches, and it necessitates specific equipment and knowledge. Recently, ORI sites have been predicted using computational techniques like motif-based searches and artificial intelligence algorithms based on sequence characteristics and chromatin states.

RESULTS

In this article, we developed ORI-Explorer, a unique artificial intelligence-based technique that combines multiple feature engineering techniques to train CatBoost Classifier for recognizing ORIs from four distinct eukaryotic species. ORI-Explorer was created by utilizing a unique combination of three traditional feature-encoding techniques and a feature set obtained from a deep-learning neural network model. The ORI-Explorer has significantly outperformed current predictors on the testing dataset. Furthermore, by employing the sophisticated SHapley Additive exPlanation method, we give crucial insights that aid in comprehending model success, highlighting the most relevant features vital for forecasting cell-specific ORIs. ORI-Explorer is also intended to aid community-wide attempts in discovering potential ORIs and developing innovative verifiable biological hypotheses.

AVAILABILITY AND IMPLEMENTATION

The used datasets along with the source code are made available through https://github.com/Z-Abbas/ORI-Explorer and https://zenodo.org/record/8358679.

Hemolytic-Pred: A machine learning-based predictor for hemolytic proteins using position and composition-based features

Objective

The objective of this study is to propose a novel in-silico method called Hemolytic-Pred for identifying hemolytic proteins based on their sequences, using statistical moment-based features, along with position-relative and frequency-relative information.

Methods

Primary sequences were transformed into feature vectors using statistical and position-relative moment-based features. Varying machine learning algorithms were employed for classification. Computational models were rigorously evaluated using four different validation. The Hemolytic-Pred webserver is available for further analysis at http://ec2-54-160-229-10.compute-1.amazonaws.com/.

Results

XGBoost outperformed the other six classifiers with an accuracy value of 0.99, 0.98, 0.97, and 0.98 for self-consistency test, 10-fold cross-validation, Jackknife test, and independent set test, respectively. The proposed method with the XGBoost classifier is a workable and robust solution for predicting hemolytic proteins efficiently and accurately.

Conclusions

The proposed method of Hemolytic-Pred with XGBoost classifier is a reliable tool for the timely identification of hemolytic cells and diagnosis of various related severe disorders. The application of Hemolytic-Pred can yield profound benefits in the medical field.

Accurate Identification of DNA Replication Origin by Fusing Epigenomics and Chromatin Interaction Information

DNA replication initiation is a complex process involving various genetic and epigenomic signatures. The correct identification of replication origins (ORIs) could provide important clues for the study of a variety of diseases caused by replication. Here, we design a computational approach named iORI-Epi to recognize ORIs by incorporating epigenome-based features, sequence-based features, and 3D genome-based features. The iORI-Epi displays excellent robustness and generalization ability on both training datasets and independent datasets of K562 cell line. Further experiments confirm that iORI-Epi is highly scalable in other cell lines (MCF7 and HCT116). We also analyze and clarify the regulatory role of epigenomic marks, DNA motifs, and chromatin interaction in DNA replication initiation of eukaryotic genomes. Finally, we discuss gene enrichment pathways from the perspective of ORIs in different replication timing states and heuristically dissect the effect of promoters on replication initiation. Our computational methodology is worth extending to ORI identification in other eukaryotic species.

ORI-Deep: improving the accuracy for predicting origin of replication sites by using a blend of features and long short-term memory network

Replication of DNA is an important process for the cell division cycle, gene expression regulation and other biological evolution processes. It also has a crucial role in a living organism's physical growth and structure. Replication of DNA comprises of three stages known as initiation, elongation and termination, whereas the origin of replication sites (ORI) is the location of initiation of the DNA replication process. There exist various methodologies to identify ORIs in the genomic sequences, however, these methods have used either extensive computations for execution, or have limited optimization for the large datasets. Herein, a model called ORI-Deep is proposed to identify ORIs from the multiple cell type genomic sequence benchmark data. An efficient method is proposed using a deep neural network to identify ORIs for four different eukaryotic species. For better representation of data, a feature vector is constructed using statistical moments for the training and testing of data and is further fed to a long short-term memory (LSTM) network. To prove the effectiveness of the proposed model, we applied several validation techniques at different levels to obtain seven accuracy metrics, and the accuracy score for self-consistency, 10-fold cross-validation, jackknife and the independent set test is observed to be 0.977, 0.948, 0.976 and 0.977, respectively. Based on the results, it can be concluded that ORI-Deep can efficiently predict the sites of origin replication in DNA sequence with high accuracy. Webserver for ORI-Deep is available at (https://share.streamlit.io/waqarhusain/orideep/main/app.py), whereas source code is available at (https://github.com/WaqarHusain/OriDeep).

Deep neural learning based protein function prediction

It is vital for the annotation of uncharacterized proteins by protein function prediction. At present, Deep Neural Network based protein function prediction is mainly carried out for dataset of small scale proteins or Gene Ontology, and usually explore the relationships between single protein feature and function tags. The practical methods for large-scale multi-features protein prediction still need to be studied in depth. This paper proposes a DNN based protein function prediction approach IGP-DNN. This method uses Grasshopper Optimization Algorithm (GOA) and Intuitionistic Fuzzy c-Means clustering (IFCM) based protein function modules extracting algorithm to extract the features of protein modules, utilizing Kernel Principal Component Analysis (KPCA) method to reduce the dimensionality of the protein attribute information, and integrating module features and attribute features. Inputting integrated data into DNN through multiple hidden layers to classify proteins and predict protein functions. In the experiments, the F-measure value of IGP-DNN on the DIP dataset reaches 0.4436, which shows better performance.

Using multi-layer perceptron to identify origins of replication in eukaryotes via informative features

BACKGROUND

The origin is the starting site of DNA replication, an extremely vital part of the informational inheritance between parents and children. More importantly, accurately identifying the origin of replication has great application value in the diagnosis and treatment of diseases related to genetic information errors, while the traditional biological experimental methods are time-consuming and laborious.

RESULTS

We carried out research on the origin of replication in a variety of eukaryotes and proposed a unique prediction method for each species. Throughout the experiment, we collected data from 7 species, including Homo sapiens, Mus musculus, Drosophila melanogaster, Arabidopsis thaliana, Kluyveromyces lactis, Pichia pastoris and Schizosaccharomyces pombe. In addition to the commonly used sequence feature extraction methods PseKNC-II and Base-content, we designed a feature extraction method based on TF-IDF. Then the two-step method was utilized for feature selection. After comparing a variety of traditional machine learning classification models, the multi-layer perceptron was employed as the classification algorithm. Ultimately, the data and codes involved in the experiment are available at https://github.com/Sarahyouzi/EukOriginPredict .

CONCLUSIONS

The prediction accuracy of the training set of the above-mentioned seven species after 100 times fivefold cross validation reach 92.60%, 90.80%, 91.22%, 96.15%, 96.72%, 99.86%, 96.72%, respectively. It denotes that compared with other methods, the methods we designed could accomplish superior performance. In addition, our experiments reveals that the models of multiple species could predict each other with high accuracy, and the results of STREME shows that they have a certain common motif.

In silico screening of ssDNA aptamer against Escherichia coli O157:H7: A machine learning and the Pseudo K-tuple nucleotide composition based approach

This study was planned to in silico screening of ssDNA aptamer against Escherichia coli O157:H7 by combination of machine learning and the PseKNC approach. For this, firstly a total numbers of 47 validated ssDNA aptamers as well as 498 random DNA sequences were considered as positive and negative training data respectively. The sequences then converted to numerical vectors using PseKNC method through Pse-in-one 2.0 web server. After that, the numerical vectors were subjected to classification by the SVM, ANN and RF algorithms available in Orange 3.2.0 software. The performances of the tested models were evaluated using cross-validation, random sampling and ROC curve analyzes. The primary results demonstrated that the ANN and RF algorithms have appropriate performances for the data classification. To improve the performances of mentioned classifiers the positive training data was triplicated and re-training process was also performed. The results confirmed that data size improvement had significant effect on the accuracy of data classification especially about RF model. Subsequently, the RF algorithm with accuracy of 98% was selected for aptamer screening. The thermodynamics details of folding process as well as secondary structures of the screened aptamers were also considered as final evaluations. The results confirmed that the selected aptamers by the proposed method had appropriate structure properties and there is no thermodynamics limit for the aptamers folding.

iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou's PseAAC

Sumoylation is the post-translational modification that is involved in the adaption of the cells and the functional properties of a large number of proteins. Sumoylation has key importance in subcellular concentration, transcriptional synchronization, chromatin remodeling, response to stress, and regulation of mitosis. Sumoylation is associated with developmental defects in many human diseases such as cancer, Huntington's, Alzheimer's, Parkinson's, Spin cerebellar ataxia 1, and amyotrophic lateral sclerosis. The covalent bonding of Sumoylation is essential to inheriting part of the operative characteristics of some other proteins. For that reason, the prediction of the Sumoylation site has significance in the scientific community. A novel and efficient technique is proposed to predict the Sumoylation sites in proteins by incorporating Chou's Pseudo Amino Acid Composition (PseAAC) with statistical moments-based features. The outcomes from the proposed system using 10 fold cross-validation testing are 94.51%, 94.24%, 94.79% and 0.8903% accuracy, sensitivity, specificity and MCC, respectively. The performance of the proposed system is so far the best in comparison to the other state-of-the-art methods. The codes for the current study are available on the GitHub repository using the link: https://github.com/csbioinfopk/iSumoK-PseAAC.

iRspot-DCC: Recombination hot/ cold spots identification based on dinucleotide-based correlation coefficient and convolutional neural network

The correct identification of gene recombination cold/hot spots is of great significance for studying meiotic recombination and genetic evolution. However, most of the existing recombination spots recognition methods ignore the global sequence information hidden in the DNA sequence, resulting in their low recognition accuracy. A computational predictor called iRSpot-DCC was proposed in this paper to improve the accuracy of cold/hot spots identification. In this approach, we propose a feature extraction method based on dinucleotide correlation coefficients that focus more on extracting potential DNA global sequence information. Then, 234 representative features vectors are filtered by SVM weight calculation. Finally, a convolutional neural network with better performance than SVM is selected as a classifier. The experimental results of 5-fold cross-validation test on two standard benchmark datasets showed that the prediction accuracy of our recognition method reached 95.11%, and the Mathew correlation coefficient (MCC) reaches 90.04%, outperforming most other methods. Therefore, iRspot-DCC is a high-precision cold/hot spots identification method for gene recombination, which effectively extracts potential global sequence information from DNA sequences.

Evaluating machine learning methodologies for identification of cancer driver genes

Cancer is driven by distinctive sorts of changes and basic variations in genes. Recognizing cancer driver genes is basic for accurate oncological analysis. Numerous methodologies to distinguish and identify drivers presently exist, but efficient tools to combine and optimize them on huge datasets are few. Most strategies for prioritizing transformations depend basically on frequency-based criteria. Strategies are required to dependably prioritize organically dynamic driver changes over inert passengers in high-throughput sequencing cancer information sets. This study proposes a model namely PCDG-Pred which works as a utility capable of distinguishing cancer driver and passenger attributes of genes based on sequencing data. Keeping in view the significance of the cancer driver genes an efficient method is proposed to identify the cancer driver genes. Further, various validation techniques are applied at different levels to establish the effectiveness of the model and to obtain metrics like accuracy, Mathew's correlation coefficient, sensitivity, and specificity. The results of the study strongly indicate that the proposed strategy provides a fundamental functional advantage over other existing strategies for cancer driver genes identification. Subsequently, careful experiments exhibit that the accuracy metrics obtained for self-consistency, independent set, and cross-validation tests are 91.08%., 87.26%, and 92.48% respectively.

iDRP-PseAAC: Identification of DNA Replication Proteins Using General PseAAC and Position Dependent Features

DNA replication is one of the specific processes to be considered in all the living organisms, specifically eukaryotes. The prevalence of DNA replication is significant for an evolutionary transition at the beginning of life. DNA replication proteins are those proteins which support the process of replication and are also reported to be important in drug design and discovery. This information depicts that DNA replication proteins have a very important role in human bodies, however, to study their mechanism, their identification is necessary. Thus, it is a very important task but, in any case, an experimental identification is time-consuming, highly-costly and laborious. To cope with this issue, a computational methodology is required for prediction of these proteins, however, no prior method exists. This study comprehends the construction of novel prediction model to serve the proposed purpose. The prediction model is developed based on the artificial neural network by integrating the position relative features and sequence statistical moments in PseAAC for training neural networks. Highest overall accuracy has been achieved through tenfold cross-validation and Jackknife testing that was computed to be 96.22% and 98.56%, respectively. Our astonishing experimental results demonstrated that the proposed predictor surpass the existing models that can be served as a time and cost-effective stratagem for designing novel drugs to strike the contemporary bacterial infection.

Sequence-based Identification of Allergen Proteins Developed by Integration of PseAAC and Statistical Moments via 5-Step Rule

Background:

Allergens are antigens that can stimulate an atopic type I human hypersensitivity reaction by an immunoglobulin E (IgE) reaction. Some proteins are naturally allergenic than others. The challenge for toxicologists is to identify properties that allow proteins to cause allergic sensitization and allergic diseases. The identification of allergen proteins is a very critical and pivotal task. The experimental identification of protein functions is a hectic, laborious and costly task; therefore, computer scientists have proposed various methods in the field of computational biology and bioinformatics using various data science approaches. Objectives: Herein, we report a novel predictor for the identification of allergen proteins.

Methods:

For feature extraction, statistical moments and various position-based features have been incorporated into Chou’s pseudo amino acid composition (PseAAC), and are used for training of a neural network.

Results:

The predictor is validated through 10-fold cross-validation and Jackknife testing, which gave 99.43% and 99.87% accurate results.

Conclusions:

Thus, the proposed predictor can help in predicting the Allergen proteins in an efficient and accurate way and can provide baseline data for the discovery of new drugs and biomarkers.

A deep learning framework combined with word embedding to identify DNA replication origins

The DNA replication influences the inheritance of genetic information in the DNA life cycle. As the distribution of replication origins (ORIs) is the major determinant to precisely regulate the replication process, the correct identification of ORIs is significant in giving an insightful understanding of DNA replication mechanisms and the regulatory mechanisms of genetic expressions. For eukaryotes in particular, multiple ORIs exist in each of their gene sequences to complete the replication in a reasonable period of time. To simplify the identification process of eukaryote's ORIs, most of existing methods are developed by traditional machine learning algorithms, and target to the gene sequences with a fixed length. Consequently, the identification results are not satisfying, i.e. there is still great room for improvement. To break through the limitations in previous studies, this paper develops sequence segmentation methods, and employs the word embedding technique, 'Word2vec', to convert gene sequences into word vectors, thereby grasping the inner correlations of gene sequences with different lengths. Then, a deep learning framework to perform the ORI identification task is constructed by a convolutional neural network with an embedding layer. On the basis of the analysis of similarity reduction dimensionality diagram, Word2vec can effectively transform the inner relationship among words into numerical feature. For four species in this study, the best models are obtained with the overall accuracy of 0.975, 0.765, 0.885, 0.967, the Matthew's correlation coefficient of 0.940, 0.530, 0.771, 0.934, and the AUC of 0.975, 0.800, 0.888, 0.981, which indicate that the proposed predictor has a stable ability and provide a high confidence coefficient to classify both of ORIs and non-ORIs. Compared with state-of-the-art methods, the proposed predictor can achieve ORI identification with significant improvement. It is therefore reasonable to anticipate that the proposed method will make a useful high throughput tool for genome analysis.

iDHS-DASTS: identifying DNase I hypersensitive sites based on LASSO and stacking learning

The DNase I hypersensitivity site is an important marker of the DNA regulatory region, and its identification in the DNA sequence is of great significance for biomedical research. However, traditional identification methods are extremely time-consuming and can not obtain an accurate result. In this paper, we proposed a predictor called iDHS-DASTS to identify the DHS based on benchmark datasets. First, we adopt a feature extraction method called PseDNC which can incorporate the original DNA properties and spatial information of the DNA sequence. Then we use a method called LASSO to reduce the dimensions of the original data. Finally, we utilize stacking learning as a classifier, which includes Adaboost, random forest, gradient boosting, extra trees and SVM. Before we train the classifier, we use SMOTE-Tomek to overcome the imbalance of the datasets. In the experiment, our iDHS-DASTS achieves remarkable performance on three benchmark datasets. We achieve state-of-the-art results with over 92.06%, 91.06% and 90.72% accuracy for datasets [Doublestruck S]1, [Doublestruck S]2 and [Doublestruck S]3, respectively. To verify the validation and transferability of our model, we establish another independent dataset [Doublestruck S]4, for which the accuracy can reach 90.31%. Furthermore, we used the proposed model to construct a user friendly web server called iDHS-DASTS, which is available at http://www.xdu-duan.cn/.

Computational prediction of species-specific yeast DNA replication origin via iterative feature representation

Deoxyribonucleic acid replication is one of the most crucial tasks taking place in the cell, and it has to be precisely regulated. This process is initiated in the replication origins (ORIs), and thus it is essential to identify such sites for a deeper understanding of the cellular processes and functions related to the regulation of gene expression. Considering the important tasks performed by ORIs, several experimental and computational approaches have been developed in the prediction of such sites. However, existing computational predictors for ORIs have certain curbs, such as building only single-feature encoding models, limited systematic feature engineering efforts and failure to validate model robustness. Hence, we developed a novel species-specific yeast predictor called yORIpred that accurately identify ORIs in the yeast genomes. To develop yORIpred, we first constructed optimal 40 baseline models by exploring eight different sequence-based encodings and five different machine learning classifiers. Subsequently, the predicted probability of 40 models was considered as the novel feature vector and carried out iterative feature learning approach independently using five different classifiers. Our systematic analysis revealed that the feature representation learned by the support vector machine algorithm (yORIpred) could well discriminate the distribution characteristics between ORIs and non-ORIs when compared with the other four algorithms. Comprehensive benchmarking experiments showed that yORIpred achieved superior and stable performance when compared with the existing predictors on the same training datasets. Furthermore, independent evaluation showcased the best and accurate performance of yORIpred thus underscoring the significance of iterative feature representation. To facilitate the users in obtaining their desired results without undergoing any mathematical, statistical or computational hassles, we developed a web server for the yORIpred predictor, which is available at: http://thegleelab.org/yORIpred.

Predicting LncRNA Subcellular Localization Using Unbalanced Pseudo-k Nucleotide Compositions

Background:

Long non-coding RNAs (lncRNAs) are transcripts with a length more than 200 nucleotides, functioning in the regulation of gene expression. More evidence has shown that the biological functions of lncRNAs are intimately related to their subcellular localizations. Therefore, it is very important to confirm the lncRNA subcellular localization.

Methods:

In this paper, we proposed a novel method to predict the subcellular localization of lncRNAs. To more comprehensively utilize lncRNA sequence information, we exploited both kmer nucleotide composition and sequence order correlated factors of lncRNA to formulate lncRNA sequences. Meanwhile, a feature selection technique which was based on the Analysis Of Variance (ANOVA) was applied to obtain the optimal feature subset. Finally, we used the support vector machine (SVM) to perform the prediction.

Results:

The AUC value of the proposed method can reach 0.9695, which indicated the proposed predictor is an efficient and reliable tool for determining lncRNA subcellular localization. Furthermore, the predictor can reach the maximum overall accuracy of 90.37% in leave-one-out cross validation, which clearly outperforms the existing state-of- the-art method.

Conclusion:

It is demonstrated that the proposed predictor is feasible and powerful for the prediction of lncRNA subcellular. To facilitate subsequent genetic sequence research, we shared the source code at https://github.com/NicoleYXF/lncRNA.

Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework

Origins of replication sites (ORIs), which refers to the initiative locations of genomic DNA replication, play essential roles in DNA replication process. Detection of ORIs' distribution in genome scale is one of key steps to in-depth understanding their regulation mechanisms. In this study, we presented a novel machine learning-based approach called Stack-ORI encompassing 10 cell-specific prediction models for identifying ORIs from four different eukaryotic species (Homo sapiens, Mus musculus, Drosophila melanogaster and Arabidopsis thaliana). For each cell-specific model, we employed 12 feature encoding schemes that cover nucleic acid composition, position-specific and physicochemical properties information. The optimal feature set was identified from each encoding individually and developed their respective baseline models using the eXtreme Gradient Boosting (XGBoost) classifier. Subsequently, the predicted scores of 12 baseline models are integrated as a novel feature vector to train XGBoost and develop the final model. Extensive experimental results show that Stack-ORI achieves significantly better performance as compared with their baseline models on both training and independent datasets. Interestingly, Stack-ORI consistently outperforms existing predictor in all cell-specific models, not only on training but also on independent test. Moreover, our novel approach provides necessary interpretations that help understanding model success by leveraging the powerful SHapley Additive exPlanation algorithm, thus underlining the most important feature encoding schemes significant for predicting cell-specific ORIs.

Identification of Lysine Carboxylation Sites in Proteins by Integrating Statistical Moments and Position Relative Features via General PseAAC

Background:

Carboxylation is one of the most biologically important post-translational modifications and occurs on lysine, arginine, and glutamine residues of a protein. Among all these three, the covalent attachment of the carboxyl group with the lysine side chain is the most frequent and biologically important type of carboxylation. For studying such biological functions, it is essential to correctly determine the lysine sites sensitive to carboxylation.

Objective:

Herein, we present a computational model for the prediction of the carboxylysine site which is based on machine learning.

Methods:

Various position and composition relative features have been incorporated into the Pse- AAC for construction of feature vectors and a neural network is employed as a classifier. The model is validated by jackknife, cross-validation, self-consistency, and independent testing.

Results:

The results of the self-consistency test elaborated that model has 99.76% Acc, 99.76% Sp, 99.76% Sp, and 0.99 MCC. Using the jackknife method, prediction model validation gave 97.07% Acc, while for 10-fold cross-validation, prediction model validation gave 95.16% Acc.

Conclusion:

The results of independent dataset testing were 94.3% which illustrated that the proposed model has better performance as compared to the existing model PreLysCar; however, the accuracy can be improved further, in the future, due to the increasing number of carboxylysine sites in proteins.

Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach

Meiotic recombination is the driving force of evolutionary development and an important source of genetic variation. The meiotic recombination does not take place randomly in a chromosome but occurs in some regions of the chromosome. A region in chromosomes with higher rate of meiotic recombination events are considered as hotspots and a region where frequencies of the recombination events are lower are called coldspots. Prediction of meiotic recombination spots provides useful information about the basic functionality of inheritance and genome diversity. This study proposes an intelligent computational predictor called iRSpots-DNN for the identification of recombination spots. The proposed predictor is based on a novel feature extraction method and an optimized deep neural network (DNN). The DNN was employed as a classification engine whereas, the novel features extraction method was developed to extract meaningful features for the identification of hotspots and coldspots across the yeast genome. Unlike previous algorithms, the proposed feature extraction avoids bias among different selected features and preserved the sequence discriminant properties along with the sequence-structure information simultaneously. This study also considered other effective classifiers named support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF) to predict recombination spots. Experimental results on a benchmark dataset with 10-fold cross-validation showed that iRSpots-DNN achieved the highest accuracy, i.e., 95.81%. Additionally, the performance of the proposed iRSpots-DNN is significantly better than the existing predictors on a benchmark dataset. The relevant benchmark dataset and source code are freely available at: https://github.com/Fatima-Khan12/iRspot_DNN/tree/master/iRspot_DNN.

DeepPred-SubMito: A Novel Submitochondrial Localization Predictor Based on Multi-Channel Convolutional Neural Network and Dataset Balancing Treatment

Mitochondrial proteins are physiologically active in different compartments, and their abnormal location will trigger the pathogenesis of human mitochondrial pathologies. Correctly identifying submitochondrial locations can provide information for disease pathogenesis and drug design. A mitochondrion has four submitochondrial compartments, the matrix, the outer membrane, the inner membrane, and the intermembrane space, but various existing studies ignored the intermembrane space. The majority of researchers used traditional machine learning methods for predicting mitochondrial protein localization. Those predictors required expert-level knowledge of biology to be encoded as features rather than allowing the underlying predictor to extract features through a data-driven procedure. Besides, few researchers have considered the imbalance in datasets. In this paper, we propose a novel end-to-end predictor employing deep neural networks, DeepPred-SubMito, for protein submitochondrial location prediction. First, we utilize random over-sampling to decrease the influence caused by unbalanced datasets. Next, we train a multi-channel bilayer convolutional neural network for multiple subsequences to learn high-level features. Third, the prediction result is outputted through the fully connected layer. The performance of the predictor is measured by 10-fold cross-validation and 5-fold cross-validation on the SM424-18 dataset and the SubMitoPred dataset, respectively. Experimental results show that the predictor outperforms state-of-the-art predictors. In addition, the prediction of results in the M983 dataset also confirmed its effectiveness in predicting submitochondrial locations.

Recent Development of Computational Predicting Bioluminescent Proteins

Bioluminescent Proteins (BLPs) are widely distributed in many living organisms that act as a key role of light emission in bioluminescence. Bioluminescence serves various functions in finding food and protecting the organisms from predators. With the routine biotechnological application of bioluminescence, it is recognized to be essential for many medical, commercial and other general technological advances. Therefore, the prediction and characterization of BLPs are significant and can help to explore more secrets about bioluminescence and promote the development of application of bioluminescence. Since the experimental methods are money and time-consuming for BLPs identification, bioinformatics tools have played important role in fast and accurate prediction of BLPs by combining their sequences information with machine learning methods. In this review, we summarized and compared the application of machine learning methods in the prediction of BLPs from different aspects. We wish that this review will provide insights and inspirations for researches on BLPs.

Delving into Eukaryotic Origins of Replication Using DNA Structural Features

DNA replication in eukaryotes is an intricate process, which is precisely synchronized by a set of regulatory proteins, and the replication fork emanates from discrete sites on chromatin called origins of replication (Oris). These spots are considered as the gateway to chromosomal replication and are stereotyped by sequence motifs. The cognate sequences are noticeable in a small group of entire origin regions or totally absent across different metazoans. Alternatively, the use of DNA secondary structural features can provide additional information compared to the primary sequence. In this article, we report the trends in DNA sequence-based structural properties of origin sequences in nine eukaryotic systems representing different families of life. Biologically relevant DNA secondary structural properties, namely, stability, propeller twist, flexibility, and minor groove shape were studied in the sequences flanking replication start sites. Results indicate that Oris in yeasts show lower stability, more rigidity, and narrow minor groove preferences compared to genomic sequences surrounding them. Yeast Oris also show preference for A-tracts and the promoter element TATA box in the vicinity of replication start sites. On the contrary, Drosophila melanogaster, humans, and Arabidopsis thaliana do not have such features in their Oris, and instead, they show high preponderance of G-rich sequence motifs such as putative G-quadruplexes or i-motifs and CpG islands. Our extensive study applies the DNA structural feature computation to delve into origins of replication across organisms ranging from yeasts to mammals and including a plant. Insights from this study would be significant in understanding origin architecture and help in designing new algorithms for predicting DNA trans-acting factor recognition events.

Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection

The largest family of drug targets in clinical trials constitute of GPCRs (G-protein coupled receptors) which accounts for about 34% of FDA (Food and Drug Administration) approved drugs acting on 108 unique GPCRs. Factors such as readily identifiable conserved motif in structures, 127 orphan GPCRs despite various de-orphaning techniques, directed functional antibodies for validation as drug targets, etc. has widened their therapeutic windows. The availability of 44 crystal structures of unique receptors, unexplored non-olfactory GPCRs (encoded by 50% of the human genome) and 205 ligand receptor complexes now present a strong foundation for structure-based drug discovery and design. The growing impact of polypharmacology for complex diseases like schizophrenia, cancer etc. warrants the need for novel targets and considering the undiscriminating and selectivity of GPCRs, they can fulfill this purpose. Again, natural genetic variations within the human genome sometimes delude the therapeutic expectations of some drugs, resulting in medication response differences and ADRs (adverse drug reactions). Around ~30 billion US dollars are dumped annually for poor accounting of ADRs in the US alone. To curb such undesirable reactions, the knowledge of established and currently in clinical trials GPCRs families can offer huge understanding towards the drug designing prospects including "off-target" effects reducing economical resource and time. The druggability of GPCR protein families and critical roles played by them in complex diseases are explained. Class A, class B1, class C and class F are generally established family and GPCRs in phase I (19%), phase II(29%), phase III(52%) studies are also reviewed. From the phase I studies, frizzled receptors accounted for the highest in trial targets, neuropeptides in phase II and melanocortin in phase III studies. Also, the bioapplications for nanoparticles along with future prospects for both nanomedicine and GPCR drug industry are discussed. Further, the use of computational techniques and methods employed for different target validations are also reviewed along with their future potential for the GPCR based drug discovery.

iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators

MOTIVATION

Transcription termination is an important regulatory step of gene expression. If there is no terminator in gene, transcription could not stop, which will result in abnormal gene expression. Detecting such terminators can determine the operon structure in bacterial organisms and improve genome annotation. Thus, accurate identification of transcriptional terminators is essential and extremely important in the research of transcription regulations.

RESULTS

In this study, we developed a new predictor called 'iTerm-PseKNC' based on support vector machine to identify transcription terminators. The binomial distribution approach was used to pick out the optimal feature subset derived from pseudo k-tuple nucleotide composition (PseKNC). The 5-fold cross-validation test results showed that our proposed method achieved an accuracy of 95%. To further evaluate the generalization ability of 'iTerm-PseKNC', the model was examined on independent datasets which are experimentally confirmed Rho-independent terminators in Escherichia coli and Bacillus subtilis genomes. As a result, all the terminators in E. coli and 87.5% of the terminators in B. subtilis were correctly identified, suggesting that the proposed model could become a powerful tool for bacterial terminator recognition.

AVAILABILITY AND IMPLEMENTATION

For the convenience of most of wet-experimental researchers, the web-server for 'iTerm-PseKNC' was established at http://lin-group.cn/server/iTerm-PseKNC/, by which users can easily obtain their desired result without the need to go through the detailed mathematical equations involved.

A Novel Amino Acid Properties Selection Method for Protein Fold Classification

BACKGROUND

Amino acid physicochemical properties encoded in protein primary structure play a crucial role in protein folding. However, it is not yet clear which of the properties are the most suitable for protein fold classification.

OBJECTIVE

To avoid exhaustively searching the total properties space, an amino acid properties selection method was proposed in this study to rapidly obtain a suitable properties combination for protein fold classification.

METHODS

The proposed amino acid properties selection method was based on sequential floating forward selection strategy. Beginning with an empty set, variable number of features were added iteratively until achieving the iteration termination condition.

RESULTS

The experimental results indicate that the proposed method improved prediction accuracies by 0.26-5% on a widely used benchmark dataset with appropriately selected amino acid properties.

CONCLUSION

The proposed properties selection method can be extended to other biomolecule property related classification problems in bioinformatics.

A computational platform to identify origins of replication sites in eukaryotes

The locations of the initiation of genomic DNA replication are defined as origins of replication sites (ORIs), which regulate the onset of DNA replication and play significant roles in the DNA replication process. The study of ORIs is essential for understanding the cell-division cycle and gene expression regulation. Accurate identification of ORIs will provide important clues for DNA replication research and drug development by developing computational methods. In this paper, the first integrated predictor named iORI-Euk was built to identify ORIs in multiple eukaryotes and multiple cell types. In the predictor, seven eukaryotic (Homo sapiens, Mus musculus, Drosophila melanogaster, Arabidopsis thaliana, Pichia pastoris, Schizosaccharomyces pombe and Kluyveromyces lactis) ORI data was collected from public database to construct benchmark datasets. Subsequently, three feature extraction strategies which are k-mer, binary encoding and combination of k-mer and binary were used to formulate DNA sequence samples. We also compared the different classification algorithms' performance. As a result, the best results were obtained by using support vector machine in 5-fold cross-validation test and independent dataset test. Based on the optimal model, an online web server called iORI-Euk (http://lin-group.cn/server/iORI-Euk/) was established for the novel ORI identification.

iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites

Regulation of proteolysis plays a critical role in a myriad of important cellular processes. The key to better understanding the mechanisms that control this process is to identify the specific substrates that each protease targets. To address this, we have developed iProt-Sub, a powerful bioinformatics tool for the accurate prediction of protease-specific substrates and their cleavage sites. Importantly, iProt-Sub represents a significantly advanced version of its successful predecessor, PROSPER. It provides optimized cleavage site prediction models with better prediction performance and coverage for more species-specific proteases (4 major protease families and 38 different proteases). iProt-Sub integrates heterogeneous sequence and structural features and uses a two-step feature selection procedure to further remove redundant and irrelevant features in an effort to improve the cleavage site prediction accuracy. Features used by iProt-Sub are encoded by 11 different sequence encoding schemes, including local amino acid sequence profile, secondary structure, solvent accessibility and native disorder, which will allow a more accurate representation of the protease specificity of approximately 38 proteases and training of the prediction models. Benchmarking experiments using cross-validation and independent tests showed that iProt-Sub is able to achieve a better performance than several existing generic tools. We anticipate that iProt-Sub will be a powerful tool for proteome-wide prediction of protease-specific substrates and their cleavage sites, and will facilitate hypothesis-driven functional interrogation of protease-specific substrate cleavage and proteolytic events.

Using extreme gradient boosting to identify origin of replication in Saccharomyces cerevisiae via hybrid features

DNA replication is a fundamental task that plays a crucial role in the propagation of all living things on earth. Hence, the accurate identification of its origin could be the key to giving an insightful understanding of the regulatory mechanism of gene expression. Indeed, with the robust development of computational techniques and the abundant biological sequencing data, it has become possible for scientists to identify the origin of replication accurately and promptly. This growing concern has drawn a lot of attention among experts in this field. However, to gain better outcomes, more work is required. Therefore, this study is designed to explore the combination of state-of-the-art features and extreme gradient boosting learning system in classifying DNA sequences. Our hybrid approach is able to identify the origin of DNA replication with achieved sensitivity of 85.19%, specificity of 93.83%, accuracy of 89.51%, and MCC of 0.7931. Evidence is presented to show that our proposed method is superior to the state-of-the-art methods on the same benchmark dataset. Moreover, the research results represent a further step towards developing the prediction models for DNA replication in particular and DNA sequences in general.

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties

Understanding of quorum-sensing peptides (QSPs) in their functional mechanism plays an essential role in finding new opportunities to combat bacterial infections by designing drugs. With the avalanche of the newly available peptide sequences in the post-genomic age, it is highly desirable to develop a computational model for efficient, rapid and high-throughput QSP identification purely based on the peptide sequence information alone. Although, few methods have been developed for predicting QSPs, their prediction accuracy and interpretability still requires further improvements. Thus, in this work, we proposed an accurate sequence-based predictor (called iQSP) and a set of interpretable rules (called IR-QSP) for predicting and analyzing QSPs. In iQSP, we utilized a powerful support vector machine (SVM) cooperating with 18 informative features from physicochemical properties (PCPs). Rigorous independent validation test showed that iQSP achieved maximum accuracy and MCC of 93.00% and 0.86, respectively. Furthermore, a set of interpretable rules IR-QSP was extracted by using random forest model and the 18 informative PCPs. Finally, for the convenience of experimental scientists, the iQSP web server was established and made freely available online. It is anticipated that iQSP will become a useful tool or at least as a complementary existing method for predicting and analyzing QSPs.

Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches

BACKGROUND

Targeting critical viral-host Protein-Protein Interactions (PPIs) has enormous application prospects for therapeutics. Using experimental methods to evaluate all possible virus-host PPIs is labor-intensive and time-consuming. Recent growth in computational identification of virus-host PPIs provides new opportunities for gaining biological insights, including applications in disease control. We provide an overview of recent computational approaches for studying virus-host PPI interactions.

METHODS

In this review, a variety of computational methods for virus-host PPIs prediction have been surveyed. These methods are categorized based on the features they utilize and different machine learning algorithms including classical and novel methods.

RESULTS

We describe the pivotal and representative features extracted from relevant sources of biological data, mainly include sequence signatures, known domain interactions, protein motifs and protein structure information. We focus on state-of-the-art machine learning algorithms that are used to build binary prediction models for the classification of virus-host protein pairs and discuss their abilities, weakness and future directions.

CONCLUSION

The findings of this review confirm the importance of computational methods for finding the potential protein-protein interactions between virus and host. Although there has been significant progress in the prediction of virus-host PPIs in recent years, there is a lot of room for improvement in virus-host PPI prediction.

Biological Production of (S)-acetoin: A State-of-the-Art Review

Acetoin is an important four-carbon compound that has many applications in foods, chemical synthesis, cosmetics, cigarettes, soaps, and detergents. Its stereoisomer (S)-acetoin, a high-value chiral compound, can also be used to synthesize optically active drugs, which could enhance targeting properties and reduce side effects. Recently, considerable progress has been made in the development of biotechnological routes for (S)-acetoin production. In this review, various strategies for biological (S)- acetoin production are summarized, and their constraints and possible solutions are described. Furthermore, future prospects of biological production of (S)-acetoin are discussed.

iProtease-PseAAC(2L): A two-layer predictor for identifying proteases and their types using Chou's 5-step-rule and general PseAAC

Proteases are a type of enzymes, which perform the process of proteolysis. Proteolysis normally refers to protein and peptide degradation which is crucial for the survival, growth and wellbeing of a cell. Moreover, proteases have a strong association with therapeutics and drug development. The proteases are classified into five different types according to their nature and physiochemical characteristics. Mostly the methods used to differentiate protease from other proteins and identify their class requires a clinical test which is usually time-consuming and operator dependent. Herein, we report a classifier named iProtease-PseAAC (2L) for identifying proteases and their classes. The predictor is developed employing the flow of 5-step rule, initiating from the collection of benchmark dataset and terminating at the development of predictor. Rigorous verification and validation tests are performed and metrics are collected to calculate the authenticity of the trained model. The self-consistency validation gives the 98.32% accuracy, for cross-validation the accuracy is 90.71% and jackknife gives 96.07% accuracy. The average accuracy for level-2 i.e. protease classification is 95.77%. Based on the above-mentioned results, it is concluded that iProtease-PseAAC (2L) has the great ability to identify the proteases and their classes using a given protein sequence.

Identifying Cancer Targets Based on Machine Learning Methods via Chou's 5-steps Rule and General Pseudo Components

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of 'big data' derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

iRO-PsekGCC: Identify DNA Replication Origins Based on Pseudo k-Tuple GC Composition

Identification of replication origins is playing a key role in understanding the mechanism of DNA replication. This task is of great significance in DNA sequence analysis. Because of its importance, some computational approaches have been introduced. Among these predictors, the iRO-3wPseKNC predictor is the first discriminative method that is able to correctly identify the entire replication origins. For further improving its predictive performance, we proposed the Pseudo k-tuple GC Composition (PsekGCC) approach to capture the "GC asymmetry bias" of yeast species by considering both the GC skew and the sequence order effects of k-tuple GC Composition (k-GCC) in this study. Based on PseKGCC, we proposed a new predictor called iRO-PsekGCC to identify the DNA replication origins. Rigorous jackknife test on two yeast species benchmark datasets (Saccharomyces cerevisiae, Pichia pastoris) indicated that iRO-PsekGCC outperformed iRO-3wPseKNC. It can be anticipated that iRO-PsekGCC will be a useful tool for DNA replication origin identification. Availability and implementation: The web-server for the iRO-PsekGCC predictor was established, and it can be accessed at http://bliulab.net/iRO-PsekGCC/.

iDNA6mA-Rice: A Computational Tool for Detecting N6-Methyladenine Sites in Rice

DNA N6-methyladenine (6mA) is a dominant DNA modification form and involved in many biological functions. The accurate genome-wide identification of 6mA sites may increase understanding of its biological functions. Experimental methods for 6mA detection in eukaryotes genome are laborious and expensive. Therefore, it is necessary to develop computational methods to identify 6mA sites on a genomic scale, especially for plant genomes. Based on this consideration, the study aims to develop a machine learning-based method of predicting 6mA sites in the rice genome. We initially used mono-nucleotide binary encoding to formulate positive and negative samples. Subsequently, the machine learning algorithm named Random Forest was utilized to perform the classification for identifying 6mA sites. Our proposed method could produce an area under the receiver operating characteristic curve of 0.964 with an overall accuracy of 0.917, as indicated by the fivefold cross-validation test. Furthermore, an independent dataset was established to assess the generalization ability of our method. Finally, an area under the receiver operating characteristic curve of 0.981 was obtained, suggesting that the proposed method had good performance of predicting 6mA sites in the rice genome. For the convenience of retrieving 6mA sites, on the basis of the computational method, we built a freely accessible web server named iDNA6mA-Rice at http://lin-group.cn/server/iDNA6mA-Rice.

The Inhibition of Polysialyltranseferase ST8SiaIV Through Heparin Binding to Polysialyltransferase Domain (PSTD)

BACKGROUND

The polysialic acid (polySia) is a unique carbohydrate polymer produced on the surface Of Neuronal Cell Adhesion Molecule (NCAM) in a number of cancer cells, and strongly correlates with the migration and invasion of tumor cells and with aggressive, metastatic disease and poor clinical prognosis in the clinic. Its synthesis is catalyzed by two polysialyltransferases (polySTs), ST8SiaIV (PST) and ST8SiaII (STX). Selective inhibition of polySTs, therefore, presents a therapeutic opportunity to inhibit tumor invasion and metastasis due to NCAM polysialylation. Heparin has been found to be effective in inhibiting the ST8Sia IV activity, but no clear molecular rationale. It has been found that polysialyltransferase domain (PSTD) in polyST plays a significant role in influencing polyST activity, and thus it is critical for NCAM polysialylation based on the previous studies.

OBJECTIVE

To determine whether the three different types of heparin (unfractionated hepain (UFH), low molecular heparin (LMWH) and heparin tetrasaccharide (DP4)) is bound to the PSTD; and if so, what are the critical residues of the PSTD for these binding complexes?

METHODS

Fluorescence quenching analysis, the Circular Dichroism (CD) spectroscopy, and NMR spectroscopy were used to determine and analyze interactions of PSTD-UFH, PSTD-LMWH, and PSTD-DP4.

RESULTS

The fluorescence quenching analysis indicates that the PSTD-UFH binding is the strongest and the PSTD-DP4 binding is the weakest among these three types of the binding; the CD spectra showed that mainly the PSTD-heparin interactions caused a reduction in signal intensity but not marked decrease in α-helix content; the NMR data of the PSTD-DP4 and the PSTDLMWH interactions showed that the different types of heparin shared 12 common binding sites at N247, V251, R252, T253, S257, R265, Y267, W268, L269, V273, I275, and K276, which were mainly distributed in the long α-helix of the PSTD and the short 3-residue loop of the C-terminal PSTD. In addition, three residues K246, K250 and A254 were bound to the LMWH, but not to DP4. This suggests that the PSTD-LMWH binding is stronger than the PSTD-DP4 binding, and the LMWH is a more effective inhibitor than DP4.

CONCLUSION

The findings in the present study demonstrate that PSTD domain is a potential target of heparin and may provide new insights into the molecular rationale of heparin-inhibiting NCAM polysialylation.

A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction

Intrinsically disordered proteins and regions are widely distributed in proteins, which are associated with many biological processes and diseases. Accurate prediction of intrinsically disordered proteins and regions is critical for both basic research (such as protein structure and function prediction) and practical applications (such as drug development). During the past decades, many computational approaches have been proposed, which have greatly facilitated the development of this important field. Therefore, a comprehensive and updated review is highly required. In this regard, we give a review on the computational methods for intrinsically disordered protein and region prediction, especially focusing on the recent development in this field. These computational approaches are divided into four categories based on their methodologies, including physicochemical-based method, machine-learning-based method, template-based method and meta method. Furthermore, their advantages and disadvantages are also discussed. The performance of 40 state-of-the-art predictors is directly compared on the target proteins in the task of disordered region prediction in the 10th Critical Assessment of protein Structure Prediction. A more comprehensive performance comparison of 45 different predictors is conducted based on seven widely used benchmark data sets. Finally, some open problems and perspectives are discussed.

pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset

Background/Objective:

Knowledge of protein subcellular localization is vitally important for both basic research and drug development. Facing the avalanche of protein sequences emerging in the post-genomic age, it is urgent to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called “pLoc-mVirus” was developed for identifying the subcellular localization of virus proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, known as “multiplex proteins”, may simultaneously occur in, or move between two or more subcellular location sites. Despite the fact that it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mVirus was trained by an extremely skewed dataset in which some subset was over 10 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset.

Methods:

Using the Chou's general PseAAC (Pseudo Amino Acid Composition) approach and the IHTS (Inserting Hypothetical Training Samples) treatment to balance out the training dataset, we have developed a new predictor called “pLoc_bal-mVirus” for predicting the subcellular localization of multi-label virus proteins.

Results:

Cross-validation tests on exactly the same experiment-confirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mVirus, the existing state-of-theart predictor for the same purpose.

Conclusion:

Its user-friendly web-server is available at http://www.jci-bioinfo.cn/pLoc_balmVirus/, by which the majority of experimental scientists can easily get their desired results without the need to go through the detailed complicated mathematics. Accordingly, pLoc_bal-mVirus will become a very useful tool for designing multi-target drugs and in-depth understanding of the biological process in a cell.

Identifying Sigma70 Promoters with Novel Pseudo Nucleotide Composition

Promoters are DNA regulatory elements located directly upstream or at the 5' end of the transcription initiation site (TSS), which are in charge of gene transcription initiation. With the completion of a large number of microorganism genomics, it is urgent to predict promoters accurately in bacteria by using the computational method. In this work, a sequence-based predictor named "iPro70-PseZNC" was designed for identifying sigma70 promoters in prokaryote. In the predictor, the samples of DNA sequences are formulated by a novel pseudo nucleotide composition, called PseZNC, into which the multi-window Z-curve composition and six local DNA structural properties are incorporated. In the 5-fold cross-validation, the area under the curve of receiver operating characteristic of 0.909 was obtained on our benchmark dataset, indicating that the proposed predictor is promising and will provide an important guide in this area. Further studies showed that the performance of PseZNC is better than it of multi-window Z-curve composition. For the sake of convenience for researchers, a user-friendly online service was established and can be freely accessible at http://lin.uestc.edu.cn/server/iPro70-PseZNC. The PseZNC approach can be also extended to other DNA-related problems.

Fast Prediction of Protein Methylation Sites Using a Sequence-Based Feature Selection Technique

Protein methylation, an important post-translational modification, plays crucial roles in many cellular processes. The accurate prediction of protein methylation sites is fundamentally important for revealing the molecular mechanisms undergoing methylation. In recent years, computational prediction based on machine learning algorithms has emerged as a powerful and robust approach for identifying methylation sites, and much progress has been made in predictive performance improvement. However, the predictive performance of existing methods is not satisfactory in terms of overall accuracy. Motivated by this, we propose a novel random-forest-based predictor called MePred-RF, integrating several discriminative sequence-based feature descriptors and improving feature representation capability using a powerful feature selection technique. Importantly, unlike other methods based on multiple, complex information inputs, our proposed MePred-RF is based on sequence information alone. Comparative studies on benchmark datasets via vigorous jackknife tests indicate that our proposed MePred-RF method remarkably outperforms other state-of-the-art predictors, leading by a 4.5 percent average in terms of overall accuracy. A user-friendly webserver that implements the proposed method has been established for researchers' convenience, and is now freely available for public use through http://server.malab.cn/MePred-RF. We anticipate our research tool to be useful for the large-scale prediction and analysis of protein methylation sites.

sefOri: selecting the best-engineered sequence features to predict DNA replication origins

Motivation

Cell divisions start from replicating the double-stranded DNA, and the DNA replication process needs to be precisely regulated both spatially and temporally. The DNA is replicated starting from the DNA replication origins. A few successful prediction models were generated based on the assumption that the DNA replication origin regions have sequence level features like physicochemical properties significantly different from the other DNA regions.

Results

This study proposed a feature selection procedure to further refine the classification model of the DNA replication origins. The experimental data demonstrated that as large as 26% improvement in the prediction accuracy may be achieved on the yeast Saccharomyces cerevisiae. Moreover, the prediction accuracies of the DNA replication origins were improved for all the four yeast genomes investigated in this study.

Availability and implementation

The software sefOri version 1.0 was available at http://www.healthinformaticslab.org/supp/resources.php. An online server was also provided for the convenience of the users, and its web link may be found in the above-mentioned web page.

Supplementary information

Supplementary data are available at Bioinformatics online.