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iRSpot-EL: identify recombination spots with an ensemble learning approach
- Source :
- Bioinformatics (Oxford, England). 33(1)
- Publication Year :
- 2016
-
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. Supplementary information Supplementary data are available at Bioinformatics online.
- Subjects :
- 0301 basic medicine
Statistics and Probability
Genome evolution
Computer science
Machine learning
computer.software_genre
Biochemistry
Genome
Nucleotide composition
law.invention
03 medical and health sciences
Meiosis
law
Yeasts
A-DNA
Cluster analysis
Molecular Biology
Recombination, Genetic
business.industry
Cell growth
Genomics
Sequence Analysis, DNA
Covariance
Ensemble learning
Computer Science Applications
Computational Mathematics
030104 developmental biology
Computational Theory and Mathematics
Recombinant DNA
Artificial intelligence
Chromosomes, Fungal
Homologous recombination
business
computer
Classifier (UML)
Recombination
Software
Subjects
Details
- ISSN :
- 13674811
- Volume :
- 33
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Bioinformatics (Oxford, England)
- Accession number :
- edsair.doi.dedup.....45eef1bad7a4b6666f6f2e11f3424047