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

Abstract

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.

Comparative analysis and prediction of nucleosome positioning using integrative feature representation and machine learning algorithms

“…Obviously, choosing an appropriate K value will have a greater impact on the classification effect of each classifier. Some studies have combined DNA sequence features [ 22 , 23 , 27 , 28 ]. Similarly, FCGR can also use different combinations of K nucleotide values as feature vectors.…”

“…To verify the effectiveness of our method, we compared the prediction results of the optimal performing predictors in Tables 1 , 2 , 3 and 4 with other models using the same datasets. DLNN-5 [ 24 ] is a deep learning model with a convolution kernel size of 5, and ZCMM [ 23 ] is based on SVM. Tables 8 , 9 , 10 and 11 shows that our methods perform prominently on H. sapiens and S. cerevisiae datasets.…”

“…It can also handle nonlinear classification problems through nuclear techniques. Thus, support vector machines have also been widely used in the field of bioinformatics [ 19 , 21 , 23 ]. Its basic idea is to map the sample from the original low-dimensional space to a high-dimensional space, so that the sample can find a partitioning hyperplane with the largest interval in the feature space, and separate samples of different categories.…”

“…3LS model used similar methods and combined the distribution of different numbers of nucleotide combinations in the sequence to further improve the prediction accuracy [ 22 ]. ZCMM model based on the Z-curve (z-curve) theory and the position weight matrix (PWM), the prediction performance is excellent on D. melanogaster [ 23 ].…”