Journal
FRONTIERS IN GENETICS
Volume 11, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2020.539227
Keywords
DNA sequence; feature selection; deep neural network; classification; system biology; novel feature extraction
Categories
Funding
- Key Research Area Grant of the Ministry of Science and Technology of China [2016YFA0501703]
- National Natural Science Foundation of China [61832019, 61503244]
- Science and Technology Commission of Shanghai Municipality [19430750600]
- Natural Science Foundation of Henan Province [162300410060]
- Joint Research Funds for Medical and Engineering and Scientific Research at Shanghai Jiao Tong University [YG2017ZD14]
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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:.
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