Journal
BIOINFORMATICS
Volume 33, Issue 1, Pages 35-41Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btw539
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Funding
- National High Technology Research and Development Program of China (863 Program) [2015AA015405]
- National Natural Science Foundation of China [61672184, 61300112, 61573118, 61272383]
- Natural Science Foundation of Guangdong Province [2014A030313695]
- Guangdong Natural Science Funds for Distinguished Yong Scholars [2016A030306008]
- Scientific Research Foundation in Shenzhen [JCYJ20150626110425228]
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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 postgenomic 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 autocross 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.
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