Abstract：Motivation: Coexisting in a DNA system, meiosis and recombination are two indispensible aspects for cell reproduction and growth. With the avalanche of genome sequences emerging in the post- genomic age, it is an urgent challenge to acquire the information of DNA recombination spots be- cause it can timely provide very useful insights into the mechanism of meiotic recombination and the process of genome evolution.
Results: To address such a challenge, we have developed a predictor, called iRSpot-EL, by fusing different modes of pseudo K-tuple nucleotide composition and mode of dinucleotide-based auto- cross covariance into an ensemble classifier of clustering approach. Five-fold cross tests on a widely used benchmark dataset have indicated that the new predictor remarkably outperforms its existing counterparts. Particularly, far beyond their reach, the new predictor can be easily used to conduct the genome-wide analysis and the results obtained are quite consistent with the experi- mental map.
Availability and Implementation: For the convenience of most experimental scientists, a user- friendly web-server for iRSpot-EL has been established at http://bioinformatics.hitsz.edu.cn/iRSpot- EL/, by which users can easily obtain their desired results without the need to go through the com- plicated mathematical equations involved.
Supplementary information: Supplementary data are available at Bioinformatics online.
1 Introduction
Recombination plays an important role in genetic evolution, which describes the exchange of genetic information during the period of each generation in diploid organisms. Recombination pro- vides many new combinations of genetic variations and is an import- ant source for biopersity, which can accelerate the procedure of biological evolution. Knowledge of recombination spots may also provide very useful information for in-depth understanding the re- production and growth of cells. Therefore, it is highly demanded to develop computational methods for predicting the recombination spots. Actually, many efforts have been made in this regard. For in- stance, based on the gapped dinucleotide composition features, Jiang et al. (2007) developed a predictor called RF-DYMHC to do the job. Liu et al. (2012), using the kmer approach and the incre- ment of persity combined with quadratic discriminant analysis, de- veloped the IDQD predictor for the same purpose. In the above two predictors, however, only the local DNA sequence information was utilized, and hence their prediction quality may be limited. To im- prove this situation, recently two new predictors, iRSpot-PseDNC (Chen et al., 2013) and iRSpot-TNCPseAAC (Qiu et al., 2014) were developed. The former was based on the DNA local structural VC The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, properties (Chen et al., 2012) and pseudo dinucleotide composition (Chen et al., 2014); while the latter based on the DNA trinucleotide composition (Chen et al., 2014) as well as the corresponding pseudo amino acid components (Chou, 2001).
Each of the aforementioned methods has its own advantage, and did play a role in stimulating the development of this important area. Meanwhile, they also have some disadvantages, as reflected by the following facts. (i) Although powerful predictors have been pro- posed, there is no efficient approach to combine them to further im- prove the predictive performance. (ii) None of these methods allows users to set the desired parameters for prediction, and hence it is dif- ficult for them to optimize the predictor system according to the need of their focus. (iii) Except the RF-DYMHC (Jiang et al., 2007), all the other predictors cannot be directly used for genome-wide analysis. Even for the RF-DYMHC predictor, its approach is not ac- curate because the window size therein is arbitrary. 减数分裂和基因重组英文文献和中文翻译:http://www.youerw.com/fanyi/lunwen_205051.html