Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots

Abstract

Pseudo dinucleotide composition (PseDNC) and Z curve showed excellent performance in the classification issues of nucleotide sequences in bioinformatics. Inspired by the principle of Z curve theory, we improved PseDNC to give the phase-specific PseDNC (psPseDNC). In this study, we used the prediction of recombination spots as a case to illustrate the capability of psPseDNC and also PseDNC fused with Z curve theory based on a novel machine learning method named large margin distribution machine (LDM). We verified that combining the two widely used approaches could generate better performance compared to only using PseDNC with a support vector machine based (SVM-based) model. The best Mathew's correlation coefficient (MCC) achieved by our LDM-based model was 0.7037 through the rigorous jackknife test and improved by ∼6.6%, ∼3.2%, and ∼2.4% compared with three previous studies. Similarly, the accuracy was improved by 3.2% compared with our previous iRSpot-PseDNC web server through an independent data test. These results demonstrate that the joint use of PseDNC and Z curve enhances performance and can extract more information from a biological sequence. To facilitate research in this area, we constructed a user-friendly web server for predicting hot/cold spots, HcsPredictor, which can be freely accessed from . In summary, we provided a united algorithm by integrating Z curve with PseDNC. We hope this united algorithm could be extended to other classification issues in DNA elements.

Accurate prediction of human essential genes using only nucleotide composition and association information

“…The Z-curve has been widely used in the field of bioinformatics for tasks such as protein coding gene identification (Zhang and Wang 2000;Chen et al 2003;Guo et al 2003;Guo and Zhang 2006;Hua et al 2015), promoter recognition (Yang et al 2008), translation start recognition (Ou et al 2004), recombination spots recognition (Dong et al 2016), and nucleosome position mapping ). However, correlation and λ -interval nucleotide composition have not been incorporated into the Z-curve method.…”

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

“…The meiotic recombination does not take place randomly in a chromosome but occurs in some regions of a chromosome. In general, the region that exhibits a high frequency of recombination is considered as hotspots, whereas the region that exhibits low frequency of recombination is considered as coldspots (Liu et al, 2012 ; Dong et al, 2016 ). The study of recombination spots provides useful information about the basic functionality of inheritance and genome diversity.…”

iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC

“…A predictor called iRSpot-DACC was also presented to predict recombination hotspots and coldspots 20 . Recently, the same problem was further investigated by including the Z curve approach 21 , and the ensemble learning approach 22 .…”

“…Therefore, most of those models are quite limited for practical applications. (ii) Some works 13 ; 14 ; 21 ; 24 used codon composition or coding region information to formulate DNA samples. However, recombination spots are not always located in coding regions.…”