Document Details
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Document Type |
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Article In Journal |
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Document Title |
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iRSpot-EL: identify recombination spots with an ensemble learning approach iRSpot-EL: identify recombination spots with an ensemble learning approach |
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Document Language |
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English |
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Abstract |
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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.
Availability and Implementation: For the convenience of most experimental scientists, a userfriendly
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. |
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ISSN |
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1367-4803 |
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Journal Name |
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Bioinformatics |
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Volume |
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33 |
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Issue Number |
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1 |
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Publishing Year |
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1438 AH
2017 AD |
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Article Type |
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Article |
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Added Date |
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Sunday, May 28, 2017 |
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Researchers
| Liu Bin | Bin, Liu | Investigator | Doctorate | |
| Wang Shanyi | Shanyi, Wang | Researcher | Doctorate | |
| Long Ren | Ren, Long | Researcher | Doctorate | |
| Chou Kuo-Chen | Kuo-Chen, Chou | Researcher | Doctorate | |
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