Using weighted features to predict recombination hotspots in Saccharomyces cerevisiae

DNA N6-methyladenine (6mA) plays an important role in regulating the gene expression of eukaryotes. Accurate identification of 6mA sites may assist in understanding genomic 6mA distributions and biological functions. Various experimental methods have been applied to detect 6mA sites in a genome-wide scope, but they are too time-consuming and expensive. Developing computational methods to rapidly identify 6mA sites is needed. In this paper, a new machine learning-based method, i6mA-DNCP, was proposed for identifying 6mA sites in the rice genome. Dinucleotide composition and dinucleotide-based DNA properties were first employed to represent DNA sequences. After a specially designed DNA property selection process, a bagging classifier was used to build the prediction model. The jackknife test on a benchmark dataset demonstrated that i6mA-DNCP could obtain 84.43% sensitivity, 88.86% specificity, 86.65% accuracy, a 0.734 Matthew’s correlation coefficient (MCC), and a 0.926 area under the receiver operating characteristic curve (AUC). Moreover, three independent datasets were established to assess the generalization ability of our method. Extensive experiments validated the effectiveness of i6mA-DNCP.

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“…In this study, 15 DNA properties from [21] were used. These properties can be divided into two groups.…”

Section: Methodsmentioning

confidence: 99%

i6mA-DNCP: Computational Identification of DNA N6-Methyladenine Sites in the Rice Genome Using Optimized Dinucleotide-Based Features

Kong

Zhang

2019

Genes

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“…Liu et al . incorporated the weight of features into recombination hotspots prediction model 19 . A predictor called iRSpot-DACC was also presented to predict recombination hotspots and coldspots 20 .…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Meiotic recombination caused by meiotic double-strand DNA breaks. In some regions the frequency of DNA recombination is relatively higher, while in other regions the frequency is lower: the former is usually called “recombination hotspot”, while the latter the “recombination coldspot”. Information of the hot and cold spots may provide important clues for understanding the mechanism of genome revolution. Therefore, it is important to accurately predict these spots. In this study, we rebuilt the benchmark dataset by unifying its samples with a same length (131 bp). Based on such a foundation and using SVM (Support Vector Machine) classifier, a new predictor called “iRSpot-Pse6NC” was developed by incorporating the key hexamer features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach. It has been observed via rigorous cross-validations that the proposed predictor is superior to its counterparts in overall accuracy, stability, sensitivity and specificity. For the convenience of most experimental scientists, the web-server for iRSpot-Pse6NC has been established at http://lin-group.cn/server/iRSpot-Pse6NC, by which users can easily obtain their desired result without the need to go through the detailed mathematical equations involved.

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pDHS-ELM: computational predictor for plant DNase I hypersensitive sites based on extreme learning machines

et al. 2018

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DNase I hypersensitive sites (DHSs) are hallmarks of chromatin zones containing transcriptional regulatory elements, making them critical in understanding the regulatory mechanisms of gene expression. Although large amounts of DHSs in the plant genome have been identified by high-throughput techniques, current DHSs obtained from experimental methods cover only a fraction of plant species and cell processes. Furthermore, these experimental methods are both time-consuming and expensive. Hence, it is urgent to develop automated computational means to efficiently and accurately predict DHSs in the plant genome. Recently, several methods have been proposed to predict the DHSs. However, all these methods took a lot of time to build the model, making them inappropriate for data with massive volume. In the present work, a new ensemble extreme learning machine (ELM)-based model called pDHS-ELM was proposed to predict the DHSs in the plant genome by fusing two different modes of pseudo-nucleotide composition. Here, two kinds of features including reverse complement kmer and pseudo-nucleotide composition were used to represent the DHSs. The ELM model was used to build the base classifiers. Then, an ensemble framework was employed to combine the outputs of these base classifiers. When applied to DHSs in Arabidopsis thaliana and rice (Oryza sativa) genome, the proposed method could obtain accuracies up to 88.48 and 87.58%, respectively. Compared with the state-of-the-art techniques, pDHS-ELM achieved higher sensitivity, specificity, and Matthew's correlation coefficient with much less training and test time. By employing pDHS-ELM, we identified 42,370 and 103,979 DHSs in A. thaliana and rice genome, respectively. The predicted DHSs were depleted of bulk nucleosomes and were tightly associated with transcription factors. Approximately 90% of the predicted DHSs were overlapped with transcription factors. Meanwhile, we demonstrated that the predicted DHSs were also associated with DNA methylation, nucleosome positioning/occupancy, and histone modification. This result suggests that pDHS-ELM can be considered as a new promising and powerful tool for transcriptional regulatory elements analysis. Our pDHS-ELM tool is available from the following website https://github.com/shanxinzhang/pDHS-ELM/ .

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Using weighted features to predict recombination hotspots in Saccharomyces cerevisiae

Cited by 16 publications

References 80 publications

i6mA-DNCP: Computational Identification of DNA N6-Methyladenine Sites in the Rice Genome Using Optimized Dinucleotide-Based Features

i6mA-DNCP: Computational Identification of DNA N6-Methyladenine Sites in the Rice Genome Using Optimized Dinucleotide-Based Features

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

pDHS-ELM: computational predictor for plant DNase I hypersensitive sites based on extreme learning machines

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