ArticleNucleic acids research2013
iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 183 papers.
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183 citing papers in PubMed.
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- Integrating LASSO Feature Selection and Soft Voting Classifier to Identify Origins of Replication Sites.Current genomics · 2022Article
- Deep learning identifies and quantifies recombination hotspot determinants.Bioinformatics (Oxford, England) · 2022Article
- ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with PseudoInternational journal of molecular sciences · 2022Article
- Identification of intelligence-related proteins through a robust two-layer predictor.Communicative & integrative biology · 2022Article
- Improved recognition of splice sites in3 Biotech · 2021Article
123 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Meiotic recombination is an important biological process. As a main driving force of evolution, recombination provides natural new combinations of genetic variations. Rather than randomly occurring across a genome, meiotic recombination takes place in some genomic regions (the so-called 'hotspots') with higher frequencies, and in the other regions (the so-called 'coldspots') with lower frequencies. Therefore, the information of the hotspots and coldspots would provide useful insights for in-depth studying of the mechanism of recombination and the genome evolution process as well. So far, the recombination regions have been mainly determined by experiments, which are both expensive and time-consuming. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapidly and effectively identifying the recombination regions. In this study, a predictor, called 'iRSpot-PseDNC', was developed for identifying the recombination hotspots and coldspots. In the new predictor, the samples of DNA sequences are formulated by a novel feature vector, the so-called 'pseudo dinucleotide composition' (PseDNC), into which six local DNA structural properties, i.e. three angular parameters (twist, tilt and roll) and three translational parameters (shift, slide and rise), are incorporated. It was observed by the rigorous jackknife test that the overall success rate achieved by iRSpot-PseDNC was >82% in identifying recombination spots in Saccharomyces cerevisiae, indicating the new predictor is promising or at least may become a complementary tool to the existing methods in this area. Although the benchmark data set used to train and test the current method was from S. cerevisiae, the basic approaches can also be extended to deal with all the other genomes. Particularly, it has not escaped our notice that the PseDNC approach can be also used to study many other DNA-related problems. As a user-friendly web-server, iRSpot-PseDNC is freely accessible at http://lin.uestc.edu.cn/server/iRSpot-PseDNC.
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