Prediction of nucleosome DNA formation potential and nucleosome positioning using increment of diversity combined with quadratic discriminant analysis

Circ-LocNet: A Computational Framework for Circular RNA Sub-Cellular Localization Prediction

Circular ribonucleic acids (circRNAs) are novel non-coding RNAs that emanate from alternative splicing of precursor mRNA in reversed order across exons. Despite the abundant presence of circRNAs in human genes and their involvement in diverse physiological processes, the functionality of most circRNAs remains a mystery. Like other non-coding RNAs, sub-cellular localization knowledge of circRNAs has the aptitude to demystify the influence of circRNAs on protein synthesis, degradation, destination, their association with different diseases, and potential for drug development. To date, wet experimental approaches are being used to detect sub-cellular locations of circular RNAs. These approaches help to elucidate the role of circRNAs as protein scaffolds, RNA-binding protein (RBP) sponges, micro-RNA (miRNA) sponges, parental gene expression modifiers, alternative splicing regulators, and transcription regulators. To complement wet-lab experiments, considering the progress made by machine learning approaches for the determination of sub-cellular localization of other non-coding RNAs, the paper in hand develops a computational framework, Circ-LocNet, to precisely detect circRNA sub-cellular localization. Circ-LocNet performs comprehensive extrinsic evaluation of 7 residue frequency-based, residue order and frequency-based, and physio-chemical property-based sequence descriptors using the five most widely used machine learning classifiers. Further, it explores the performance impact of K-order sequence descriptor fusion where it ensembles similar as well dissimilar genres of statistical representation learning approaches to reap the combined benefits. Considering the diversity of statistical representation learning schemes, it assesses the performance of second-order, third-order, and going all the way up to seventh-order sequence descriptor fusion. A comprehensive empirical evaluation of Circ-LocNet over a newly developed benchmark dataset using different settings reveals that standalone residue frequency-based sequence descriptors and tree-based classifiers are more suitable to predict sub-cellular localization of circular RNAs. Further, K-order heterogeneous sequence descriptors fusion in combination with tree-based classifiers most accurately predict sub-cellular localization of circular RNAs. We anticipate this study will act as a rich baseline and push the development of robust computational methodologies for the accurate sub-cellular localization determination of novel circRNAs.

A deformation energy model reveals sequence-dependent property of nucleosome positioning

We present a deformation energy model for predicting nucleosome positioning, in which a position-dependent structural parameter set derived from crystal structures of nucleosomes was used to calculate the DNA deformation energy. The model is successful in predicting nucleosome occupancy genome-wide in budding yeast, nucleosome free energy, and rotational positioning of nucleosomes. Our model also indicates that the genomic regions underlying the MNase-sensitive nucleosomes in budding yeast have high deformation energy and, consequently, low nucleosome-forming ability, while the MNase-sensitive non-histone particles are characterized by much lower DNA deformation energy and high nucleosome preference. In addition, we also revealed that remodelers, SNF2 and RSC8, are likely to act in chromatin remodeling by binding to broad nucleosome-depleted regions that are intrinsically favorable for nucleosome positioning. Our data support the important role of position-dependent physical properties of DNA in nucleosome positioning.

Remarks on Computational Method for Identifying Acid and Alkaline Enzymes

The catalytic efficiency of the enzyme is thousands of times higher than that of ordinary catalysts. Thus, they are widely used in industrial and medical fields. However, enzymes with protein structure can be destroyed and inactivated in high temperature, over acid or over alkali environment. It is well known that most of enzymes work well in an environment with pH of 6-8, while some special enzymes remain active only in an alkaline environment with pH > 8 or an acidic environment with pH < 6. Therefore, the identification of acidic and alkaline enzymes has become a key task for industrial production. Because of the wide varieties of enzymes, it is hard work to determine the acidity and alkalinity of the enzyme by experimental methods, and even this task cannot be achieved. Converting protein sequences into digital features and building computational models can efficiently and accurately identify the acidity and alkalinity of enzymes. This review summarized the progress of the digital features to express proteins and computational methods to identify acidic and alkaline enzymes. We hope that this paper will provide more convenience, ideas, and guides for computationally classifying acid and alkaline enzymes.

Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features

Bacteriophage is a type of virus that could infect the host bacteria. They have been applied in the treatment of pathogenic bacterial infection. Phage enzymes and hydrolases play the most important role in the destruction of bacterial cells. Correctly identifying the hydrolases coded by phage is not only beneficial to their function study, but also conducive to antibacteria drug discovery. Thus, this work aims to recognize the enzymes and hydrolases in phage. A combination of different features was used to represent samples of phage and hydrolase. A feature selection technique called analysis of variance was developed to optimize features. The classification was performed by using support vector machine (SVM). The prediction process includes two steps. The first step is to identify phage enzymes. The second step is to determine whether a phage enzyme is hydrolase or not. The jackknife cross-validated results showed that our method could produce overall accuracies of 85.1 and 94.3%, respectively, for the two predictions, demonstrating that the proposed method is promising.

ZCMM: A Novel Method Using Z-Curve Theory- Based and Position Weight Matrix for Predicting Nucleosome Positioning

Nucleosomes are the basic units of eukaryotes. The accurate positioning of nucleosomes plays a significant role in understanding many biological processes such as transcriptional regulation mechanisms and DNA replication and repair. Here, we describe the development of a novel method, termed ZCMM, based on Z-curve theory and position weight matrix (PWM). The ZCMM was trained and tested using the nucleosomal and linker sequences determined by support vector machine (SVM) in Saccharomyces cerevisiae (S. cerevisiae), and experimental results showed that the sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews correlation coefficient (MCC) values for ZCMM were 91.40%, 96.56%, 96.75%, and 0.88, respectively, and the average area under the receiver operating characteristic curve (AUC) value was 0.972. A ZCMM predictor was developed to predict nucleosome positioning in Homo sapiens (H. sapiens), Caenorhabditis elegans (C. elegans), and Drosophila melanogaster (D. melanogaster) genomes, and the accuracy (Acc) values were 77.72%, 85.34%, and 93.62%, respectively. The maximum AUC values of the four species were 0.982, 0.861, 0.912 and 0.911, respectively. Another independent dataset for S. cerevisiae was used to predict nucleosome positioning. Compared with the results of Wu's method, it was found that the Sn, Sp, Acc, and MCC of ZCMM results for S. cerevisiae were all higher, reaching 96.72%, 96.54%, 94.10%, and 0.88. Compared with the Guo's method 'iNuc-PseKNC', the results of ZCMM for D. melanogaster were better. Meanwhile, the ZCMM was compared with some experimental data in vitro and in vivo for S. cerevisiae, and the results showed that the nucleosomes predicted by ZCMM were highly consistent with those confirmed by these experiments. Therefore, it was further confirmed that the ZCMM method has good accuracy and reliability in predicting nucleosome positioning.

DNA physical properties outperform sequence compositional information in classifying nucleosome-enriched and -depleted regions

The nucleosome is the fundamental structural unit of eukaryotic chromatin and plays an essential role in the epigenetic regulation of cellular processes, such as DNA replication, recombination, and transcription. Hence, it is important to identify nucleosome positions in the genome. Our previous model based on DNA deformation energy, in which a set of DNA physical descriptors was used, performed well in predicting nucleosome dyad positions and occupancy. In this study, we established a machine-learning model for predicting nucleosome occupancy in order to further verify the physical descriptors. Results showed that (1) our model outperformed several other sequence compositional information-based models, indicating a stronger dependence of nucleosome positioning on DNA physical properties; (2) nucleosome-enriched and -depleted regions have distinct features in terms of DNA physical descriptors like sequence-dependent flexibility and equilibrium structure parameters; (3) gene transcription start sites and termination sites can be well characterized with the distribution patterns of the physical descriptors, indicating the regulatory role of DNA physical properties in gene transcription. In addition, we developed a web server for the model, which is freely accessible at http://lin-group.cn/server/iNuc-force/.

NucPosPred: Predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC

The nucleosome is the basic structure of chromatin in eukaryotic cells, with essential roles in the regulation of many biological processes, such as DNA transcription, replication and repair, and RNA splicing. Because of the importance of nucleosomes, the factors that determine their positioning within genomes should be investigated. High-resolution nucleosome-positioning maps are now available for organisms including Saccharomyces cerevisiae, Drosophila melanogaster and Caenorhabditis elegans, enabling the identification of nucleosome positioning by application of computational tools. Here, we describe a novel predictor called NucPosPred, which was specifically designed for large-scale identification of nucleosome positioning in C. elegans and D. melanogaster genomes. NucPosPred was separately optimized for each species for four types of DNA sequence feature extraction, with consideration of two classification algorithms (gradient-boosting decision tree and support vector machine). The overall accuracy obtained with NucPosPred was 92.29% for C. elegans and 88.26% for D. melanogaster, outperforming previous methods and demonstrating the potential for species-specific prediction of nucleosome positioning. For the convenience of most experimental scientists, a web-server for the predictor NucPosPred is available at http://121.42.167.206/NucPosPred/index.jsp.

Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences

With the avalanche of DNA/RNA sequences generated in the post-genomic age, it is urgent to develop automated methods for analyzing the relationship between the sequences and their functions.

Nucleosome Positioning with Set of Key Positions and Nucleosome Affinity

The formation and precise positioning of nucleosome in chromatin occupies a very important role in studying life process. Today, there are many researchers who discovered that the positioning where the location of a DNA sequence fragment wraps around a histone octamer in genome is not random but regular. However, the positioning is closely relevant to the concrete sequence of core DNA. So in this paper, we analyzed the relation between the affinity and sequence structure of core DNA, and extracted the set of key positions. In these positions, the nucleotide sequences probably occupy mainly action in the binding. First, we simplified and formatted the experimental data with the affinity. Then, to find the key positions in the wrapping, we used neural network to analyze the positive and negative effects of nucleosome generation for each position in core DNA sequences. However, we reached a class of weights with every position to describe this effect. Finally, based on the positions with high weights, we analyzed the reason why the chosen positions are key positions, and used these positions to construct a model for nucleosome positioning prediction. Experimental results show the effectiveness of our method.

iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition

MOTIVATION

Nucleosome positioning participates in many cellular activities and plays significant roles in regulating cellular processes. With the avalanche of genome sequences generated in the post-genomic age, it is highly desired to develop automated methods for rapidly and effectively identifying nucleosome positioning. Although some computational methods were proposed, most of them were species specific and neglected the intrinsic local structural properties that might play important roles in determining the nucleosome positioning on a DNA sequence.

RESULTS

Here a predictor called 'iNuc-PseKNC' was developed for predicting nucleosome positioning in Homo sapiens, Caenorhabditis elegans and Drosophila melanogaster genomes, respectively. In the new predictor, the samples of DNA sequences were formulated by a novel feature-vector called 'pseudo k-tuple nucleotide composition', into which six DNA local structural properties were incorporated. It was observed by the rigorous cross-validation tests on the three stringent benchmark datasets that the overall success rates achieved by iNuc-PseKNC in predicting the nucleosome positioning of the aforementioned three genomes were 86.27%, 86.90% and 79.97%, respectively. Meanwhile, the results obtained by iNuc-PseKNC on various benchmark datasets used by the previous investigators for different genomes also indicated that the current predictor remarkably outperformed its counterparts.

AVAILABILITY

A user-friendly web-server, iNuc-PseKNC is freely accessible at http://lin.uestc.edu.cn/server/iNuc-PseKNC.

Predicting nucleosome positions in yeast: using the absolute frequency

Nucleosome is the basic structure of chromatin in eukaryotic cells, and they form the chromatin fiber interconnected by sections of linker DNA. Nucleosome positioning is of great significance for gene transcription regulation. In this paper, we consider the difference of absolute frequency of nucleotides between the nucleosome forming and nucleosome inhibiting sequences. Based on the 2-mer absolute frequency of nucleotides in genome, a new model is constructed to distinguish nucleosome DNA and linker DNA. When used to predict DNA potential for forming nucleosomes in S. cerevisiae, the model achieved a high accuracy of 96.05%. Thus, the model is very useful for predicting nucleosome positioning.

Structure-based identification of new high-affinity nucleosome binding sequences

The substrate for the proteins that express genetic information in the cell is not naked DNA but an assembly of nucleosomes, where the DNA is wrapped around histone proteins. The organization of these nucleosomes on genomic DNA is influenced by the DNA sequence. Here, we present a structure-based computational approach that translates sequence information into the energy required to bend DNA into a nucleosome-bound conformation. The calculations establish the relationship between DNA sequence and histone octamer binding affinity. In silico selection using this model identified several new DNA sequences, which were experimentally found to have histone octamer affinities comparable to the highest-affinity sequences known. The results provide insights into the molecular mechanism through which DNA sequence information encodes its organization. A quantitative appreciation of the thermodynamics of nucleosome positioning and rearrangement will be one of the key factors in understanding the regulation of transcription and in the design of new promoter architectures for the purposes of tuning gene expression dynamics.