iRSpot-EL: identify recombination spots with an ensemble learning approach

B Liu, S Wang, R Long, KC Chou - Bioinformatics, 2017 - academic.oup.com
B Liu, S Wang, R Long, KC Chou
Bioinformatics, 2017academic.oup.com
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 …
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.
Supplementary information
Supplementary data are available at Bioinformatics online.
Oxford University Press
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