Electron-ion Interaction Pseudopotentials Research Articles

A novel deep learning identifier for promoters and their strength using heterogeneous features

Promoters, which are short (50–1500 base-pair) in DNA regions, have emerged to play a critical role in the regulation of gene transcription. Numerous dangerous diseases, likewise cancer, cardiovascular, and inflammatory bowel diseases, are caused by genetic variations in promoters. Consequently, the correct identification and characterization of promoters are significant for the discovery of drugs. However, experimental approaches to recognizing promoters and their strengths are challenging in terms of cost, time, and resources. Therefore, computational techniques are highly desirable for the correct characterization of promoters from unannotated genomic data. Here, we designed a powerful bi-layer deep-learning based predictor named “PROCABLES“, which discriminates DNA samples as promoters in the first-phase and strong or weak promoters in the second-phase respectively. The proposed method utilizes five distinct features, such as word2vec, k-spaced nucleotide pairs, trinucleotide propensity-based features, trinucleotide composition, and electron–ion interaction pseudopotentials, to extract the hidden patterns from the DNA sequence. Afterwards, a stacked framework is formed by integrating a convolutional neural network (CNN) with bidirectional long-short-term memory (LSTM) using multi-view attributes to train the proposed model. The PROCABLES model achieved an accuracy of 0.971 and 0.920 and the MCC 0.940 and 0.840 for the first and second-layer using the ten-fold cross-validation test, respectively. The predicted results anticipate that the proposed PROCABLES protocol outperformed the advanced computational predictors targeting promoters and their types. In summary, this research will provide useful hints for the recognition of large-scale promoters in particular and other DNA problems in general.

DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction

Human leukocyte antigen (HLA) is closely involved in regulating the human immune system. Despite great advance in detecting classical HLA Class I binders, there are few methods or toolkits for recognizing non-classical HLA Class I binders. To fill in this gap, we have developed a deep learning-based tool called DeepHLAPred. The DeepHLAPred used electron-ion interaction pseudo potential, integer numerical mapping and accumulated amino acid frequency as initial representation of non-classical HLA binder sequence. The deep learning module was used to further refine high-level representations. The deep learning module comprised two parallel convolutional neural networks, each followed by maximum pooling layer, dropout layer, and bi-directional long short-term memory network. The experimental results showed that the DeepHLAPred reached the state-of-the-art performanceson the cross-validation test and the independent test. The extensive test demonstrated the rationality of the DeepHLAPred. We further analyzed sequence pattern of non-classical HLA class I binders by information entropy. The information entropy of non-classical HLA binder sequence implied sequence pattern to a certain extent. In addition, we have developed a user-friendly webserver for convenient use, which is available at http://www.biolscience.cn/DeepHLApred/. The tool and the analysis is helpful to detect non-classical HLA Class I binder. The source code and data is available at https://github.com/tangxingyu0/DeepHLApred.

4mCBERT: A computing tool for the identification of DNA N4-methylcytosine sites by sequence- and chemical-derived information based on ensemble learning strategies

N4-methylcytosine (4mC) is an important DNA chemical modification pattern which is a new methylation modification discovered in recent years and plays critical roles in gene expression regulation, defense against invading genetic elements, genomic imprinting, and so on. Identifying 4mC site from DNA sequence segment contributes to discovering more novel modification patterns. In this paper, we present a model called 4mCBERT that encodes DNA sequence segments by sequence characteristics including one-hot, electron-ion interaction pseudopotential, nucleotide chemical property, word2vec and chemical information containing physicochemical properties (PCP), chemical bidirectional encoder representations from transformers (chemical BERT) and employs ensemble learning framework to develop a prediction model. PCP and chemical BERT features are firstly constructed and applied to predict 4mC sites and show positive contributions to identifying 4mC. For the Matthew's Correlation Coefficient, 4mCBERT significantly outperformed other state-of-the-art models on six independent benchmark datasets including A. thaliana, C. elegans, D. melanogaster, E. coli, G. Pickering, and G. subterraneous by 4.32 % to 24.39 %, 2.52 % to 31.65 %, 2 % to 16.49 %, 6.63 % to 35.15, 8.59 % to 61.85 %, and 8.45 % to 34.45 %. Moreover, 4mCBERT is designed to allow users to predict 4mC sites and retrain 4mC prediction models. In brief, 4mCBERT shows higher performance on six benchmark datasets by incorporating sequence- and chemical-driven information and is available at http://cczubio.top/4mCBERT and https://github.com/abcair/4mCBERT.

Automated detection of colon cancer using genomic signal processing

BackgroundDisorders in deoxyribonucleic acid (DNA) mutations are the common cause of colon cancer. Detection of these mutations is the first step in colon cancer diagnosis. Differentiation among normal and cancerous colon gene sequences is a method used for mutation identification. Early detection of this type of disease can avoid complications that can lead to death. In this study, 55 healthy and 55 cancerous genes for colon cells obtained from the national center for biotechnology information GenBank are used. After applying the electron–ion interaction pseudopotential (EIIP) numbering representation method for the sequences, single-level discrete wavelet transform (DWT) is applied using Haar wavelet. Then, some statistical features are obtained from the wavelet domain. These features are mean, variance, standard deviation, autocorrelation, entropy, skewness, and kurtosis. The resulting values are applied to the k-nearest neighbor (KNN) and support vector machine (SVM) algorithms to obtain satisfactory classification results.ResultsFour important parameters are calculated to evaluate the performance of the classifiers. Accuracy (ACC), F1 score, and Matthews correlation coefficient (MCC) are 95%, 94.74%, and 0.9045%, respectively, for SVM and 97.5%, 97.44%, and 0.9512%, respectively, for KNN.ConclusionThis study has created a novel successful system for colorectal cancer classification and detection with the well-satisfied results. The K-nearest network results are the best with low error for the generated classification system, even though the results of the SVM network are acceptable.

Analysis of the correlation between key gene mutations and breast cancer

The purpose of this study is to investigate whether mutations in key genes affect the prognosis of breast cancer patients, and base mutations is discussed by combining the physical properties of the electron–ion interaction pseudopotential. We study 994 breast cancer mutation samples, including 15,015 mutation genes, and use social network algorithm to screen key genes. In order to analyze the relationship between electron–ion interaction pseudopotential and mutation of key genes, this paper proposes the [Formula: see text] difference formula. It not only analyses gene mutation at the physical level, but also explains the mutation rule of single base. Simultaneously, we use the Kaplan–Meier to analyze the overall survival rate of breast cancer samples and use Tarone-Ware to test. The results showed that mutations in GATA3, PIK3CA, and TP53 are significant difference in overall survival rate of breast cancer ([Formula: see text], [Formula: see text]). Proteins often interact to form protein complexes, and driving protein function. Therefore, by constructing protein–protein interaction network and finding modular proteins of key genes, we can find genes closely related to key genes. Finally, the results of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology function enrichment analysis show that key genes played an important role in metabolic pathways and biological functions. In clinical medicine, screening for key gene mutations may help predict survival in breast cancer patients.

XG-ac4C: identification of N4-acetylcytidine (ac4C) in mRNA using eXtreme gradient boosting with electron-ion interaction pseudopotentials

N4-acetylcytidine (ac4C) is a post-transcriptional modification in mRNA which plays a major role in the stability and regulation of mRNA translation. The working mechanism of ac4C modification in mRNA is still unclear and traditional laboratory experiments are time-consuming and expensive. Therefore, we propose an XG-ac4C machine learning model based on the eXtreme Gradient Boost classifier for the identification of ac4C sites. The XG-ac4C model uses a combination of electron-ion interaction pseudopotentials and electron-ion interaction pseudopotentials of trinucleotide of the nucleotides in ac4C sites. Moreover, Shapley additive explanations and local interpretable model-agnostic explanations are applied to understand the importance of features and their contribution to the final prediction outcome. The obtained results demonstrate that XG-ac4C outperforms existing state-of-the-art methods. In more detail, the proposed model improves the area under the precision-recall curve by 9.4% and 9.6% in cross-validation and independent tests, respectively. Finally, a user-friendly web server based on the proposed model for ac4C site identification is made freely available at http://nsclbio.jbnu.ac.kr/tools/xgac4c/.

Moment invariants for cancer classification based on electron–ion interaction pseudo potentials (EIIP)

Cancer is considered one of the most dangerous diseases leading to imminent death during recent decades. It spreads quickly at a frightening pace to all parts of the body and early detection of such diseases may help reduce the risks. Many methods have been used to classify and predict the outcome of cancer, such as image processing techniques, artificial intelligence, and nanotechnology. Out of these methods, artificial intelligence techniques have played an essential role and have provided satisfying results from different investigations in various fields. Considering that cancer is a genetic disease, electron–ion interaction pseudo potential can be used to convert DNA sequences from string shape into number values so that genomic signal processing can be applied for the feature extraction step. In this paper, 51 healthy and 51 cancer genes from multiple cells obtained from the National Center for Biotechnology Information Genbank were used for analysis. Then, 7 invariant moments were extracted into 2 types of learning machine methods: supervised and unsupervised learning. Finally, their results were compared using Receiver Operating Characteristic parameters. From these results, the best accuracy was obtained from the trainable cascade-forward backpropagation network. The calculated parameters were 0.3333, 0.3158, 0.6842, 0.6667, and 67.5% for the Kohonen network, 0.1053, 0.1429, 0.8571, 0.8947, and 87.5% for the feed-forward network, and 0.0000, 0.0909, 0.9091, 1.0000, and 95% for the cascade-forward network for FPR, FNR, TPR, TNR, and accuracy respectively.

Prediction of m5C Modifications in RNA Sequences by Combining Multiple Sequence Features

5-Methylcytosine (m5C) is a well-known post-transcriptional modification that plays significant roles in biological processes, such as RNA metabolism, tRNA recognition, and stress responses. Traditional high-throughput techniques on identification of m5C sites are usually time consuming and expensive. In addition, the number of RNA sequences shows explosive growth in the post-genomic era. Thus, machine-learning-based methods are urgently requested to quickly predict RNA m5C modifications with high accuracy. Here, we propose a noval support-vector-machine (SVM)-based tool, called iRNA-m5C_SVM, by combining multiple sequence features to identify m5C sites in Arabidopsis thaliana. Eight kinds of popular feature-extraction methods were first investigated systematically. Then, four well-performing features were incorporated to construct a comprehensive model, including position-specific propensity (PSP) (PSNP, PSDP, and PSTP, associated with frequencies of nucleotides, dinucleotides, and trinucleotides, respectively), nucleotide composition (nucleic acid, di-nucleotide, and tri-nucleotide compositions; NAC, DNC, and TNC, respectively), electron-ion interaction pseudopotentials of trinucleotide (PseEIIPs), and general parallel correlation pseudo-dinucleotide composition (PC-PseDNC-general). Evaluated accuracies over 10-fold cross-validation and independent tests achieved 73.06% and 80.15%, respectively, which showed the best predictive performances in A. thaliana among existing models. It is believed that the proposed model in this work can be a promising alternative for further research on m5C modification sites in plant.

I4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes

N4-methylcytosine (4mC) is one of the most important DNA modifications and involved in regulating cell differentiations and gene expressions. The accurate identification of 4mC sites is necessary to understand various biological functions. In this work, we developed a new computational predictor called i4mC-Mouse to identify 4mC sites in the mouse genome. Herein, six encoding schemes of k-space nucleotide composition (KSNC), k-mer nucleotide composition (Kmer), mono nucleotide binary encoding (MBE), dinucleotide binary encoding, electron–ion interaction pseudo potentials (EIIP) and dinucleotide physicochemical composition were explored that cover different characteristics of DNA sequence information. Subsequently, we built six RF-based encoding models and then linearly combined their probability scores to construct the final predictor. Among the six RF-based models, the Kmer, KSNC, MBE, and EIIP encodings are sufficient, which contributed to 10%, 45%, 25%, and 20% of the prediction performance, respectively. On the independent test the i4mC-Mouse predicted the 4mC sites with accuracy and MCC of 0.816 and 0.633, respectively, which were approximately 2.5% and 5% higher than those of the existing method (4mCpred-EL). For experimental biologists, a freely available web application was implemented at http://kurata14.bio.kyutech.ac.jp/i4mC-Mouse/.

IRNA-m5C_NB: A Novel Predictor to Identify RNA 5-Methylcytosine Sites Based on the Naive Bayes Classifier

As one of the widespread RNA post-transcriptional modifications (PTCMs), 5-Methylcytosine (m5C) plays vital roles in better understanding of basic biological mechanisms and major disease treatments. In experiments, traditional high-throughput approaches to find m5C sites are usually expensive and laborious. Additionally, facing with a large number of RNA sequences, developing accurate computational methods to distinguish m5C and non-m5C sites is an efficient solution. Here we introduced a novel predictor, called iRNA-m5C_NB, to identify m5C sites in Home sapiens using Naive Bayes (NB) algorithm. In this method, unbalanced dataset Met935 is firstly analyzed using efficient hybrid-sampling strategy SMOTEEEN. Then top 57 features are selected by the ANOVA F-value from four kinds of well-performance feature extraction techniques, including Bi-profile Bayes (BPB), enhanced Nucleic Acid Composition (ENAC), electron-ion interaction pseudopotentials (EIIP) and mMGap_1. Based on the jackknife test, the evaluated recall for the unbalanced training dataset Met935 is up to 82.81% with MCC of 0.63. And for the independent dataset Test1157, the predictor still shows high recall of 70.06% and MCC of 0.34. It is the first m5C predictor constructed using the unbalanced dataset, and the recall scores are increased by 19.82% and 59.23% for jackknife and independent tests compared with the latest tool RNAm5CPred, respectively. We demonstrate that the proposed predictor iRNA-m5C_NB outperforms other state-of-art models, which hopes to be an efficient and reliable method to identify m5C sites.

A Study of the Potential of EIIP Mapping Method in Exon Prediction Using the Frequency Domain Techniques

Recently, a number of numerical DNA sequence representations have evolved in order to transform the DNA sequence analysis problems from the traditional string processing domain to the discrete signal processing domain. On the other hand, the coding regions (exons) detection problem has received a special attention due to the 3-base periodicity property of exons which can be easily detected using simple discrete signal processing techniques. The 3-base periodicity in the nucleotide arrangement is evidenced as a sharp peak at frequency f =1/3 in the frequency domain power spectrum. In this paper, we exploit the 3-base periodicity property of a set of the Electron-Ion Interaction Pseudopotential (EIIP) coded DNA sequences by employing a frequency domain power spectrum estimation techniques as Short Time Fourier Transform (STFT),Auto Regressive (AR), Singular Vector Decomposition (SVD) and Digital filtering methods. Also, we give a brief comparison of these methods In order to enhance the coding prediction performance as well as the computa- tional complexity. Results provided that both STFT and digital filtering techniques for EIIP coded sequences performs with highest accuracy compared with AR and SVD methods.

On the quasiparticle spectrum and maxima of the dynamic ion structure factor in liquid metals

The ion density-density retarded Green function of an elementary liquid metal has been investigated in terms of the electron-ion interaction pseudopotential. This notion has been used for studying the spectrum of quasiparticles due to ion excitations, omega i(k), and its relation to the position of the maximum omega max(k) of the dynamic ion structure factor. Numerical calculations for liquid rubidium show that the quasiparticles are well defined within the region of k<or approximately=1 AA-1, where deviations are found from the acoustic-dispersion curve.