title: iRSpot-EL: identify recombination spots with an ensemble learning approach.
abstract: 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 because it can timely provide very useful insights into the mechanism of meiotic recombination and the process of genome evolution.
  
  
  
  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 experimental map.
  
  
  
  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 complicated mathematical equations involved.
  
  
  
  bliu@gordonlifescience.org or bliu@insun.hit.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.
authors.affiliation: School of Computer Science and Technology.|Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.|Gordon Life Science Institute, Belmont, MA 02478, USA.
authors.forename: Bin
authors.initials: B
authors.lastname: Liu
authors.affiliation: School of Computer Science and Technology.
authors.forename: Shanyi
authors.initials: S
authors.lastname: Wang
authors.affiliation: School of Computer Science and Technology.
authors.forename: Ren
authors.initials: R
authors.lastname: Long
authors.affiliation: Gordon Life Science Institute, Belmont, MA 02478, USA.|Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D015825:Chromosomes, Fungal; D023281:Genomics / Q000379:methods; D008540:Meiosis; D011995:Recombination, Genetic; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D015003:Yeasts / Q000235:genetics