Bin Liu1,2,3, Shanyi Wang1, Ren Long1, Kuo-Chen Chou3,4. 1. School of Computer Science and Technology. 2. Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China. 3. Gordon Life Science Institute, Belmont, MA 02478, USA. 4. Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.
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 because 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 experimental 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 complicated mathematical equations involved. CONTACT: bliu@gordonlifescience.org or bliu@insun.hit.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.
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 because 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 experimental 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 complicated mathematical equations involved. CONTACT: bliu@gordonlifescience.org or bliu@insun.hit.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.
Authors: Fuyi Li; Yanan Wang; Chen Li; Tatiana T Marquez-Lago; André Leier; Neil D Rawlings; Gholamreza Haffari; Jerico Revote; Tatsuya Akutsu; Kuo-Chen Chou; Anthony W Purcell; Robert N Pike; Geoffrey I Webb; A Ian Smith; Trevor Lithgow; Roger J Daly; James C Whisstock; Jiangning Song Journal: Brief Bioinform Date: 2019-11-27 Impact factor: 11.622
Authors: Fuyi Li; Chen Li; Tatiana T Marquez-Lago; André Leier; Tatsuya Akutsu; Anthony W Purcell; A Ian Smith; Trevor Lithgow; Roger J Daly; Jiangning Song; Kuo-Chen Chou Journal: Bioinformatics Date: 2018-12-15 Impact factor: 6.937
Authors: Rajesh B Patil; Euzebio G Barbosa; Jaiprakash N Sangshetti; Vishal P Zambre; Sanjay D Sawant Journal: Mol Divers Date: 2018-03-13 Impact factor: 2.943