iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition

By Pengmian Feng, Hao Lin, Kuo-Chen Chou. Nucleic Acids Research, 2013

598 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b5e0feaf92b7add01f3031cea5a5656271134837

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In Molecular Biology

1. Kuo-Chen Chou
2. Xuan Xiao
3. Hao Lin
4. Jianhua Jia
5. Dongsheng Cao
6. Ling-Yun Wu
7. Jun Ding
8. Yan Xu

Citations in the corpus

• iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. (2013, background): “(19), see a recent paper [52].”
• BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions (2016, methodology): “Thirdly, the adjacency list file and the molecular features in the above two steps are provided to calculate the final interaction features tasks such as SVM-Prot [61], Cell-Ploc [36], iGPCR-Drug [64], iRSpot-PseDNC [39], IDrug-Target [65] and iNuc-PseKNC [66].”
• iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition (2014, methodology): “However, among the three methods, the jackknife test is deemed the least arbitrary and most objective because it can always yield a unique outcome for a given benchmark data set as elucidated in (33) and demonstrated by the equations (28)–(32) therein.”
• iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition (2013, methodology): “The idea of PseAAC has been widely used in bioinformatics, proteomics, and system biology [25], such as predicting protein structural class [31], predicting metalloproteinase family [32], predicting protein subcellular localization [33], predicting DNA-binding proteins [21], identifying allergenic p”
• A Survey on ensemble learning under the era of deep learning (2021, methodology): “2008) and pseudo dinucleotide composition (PseDNC) (Chen et al. 2013) that reflect the characteristics of a DNA sequence from different angles.”
• PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. (2014, methodology): “Its optimal value is determined via an optimization procedure as described in our previous studies [25,26,28].”
• Impacts of bioinformatics to medicinal chemistry. (2015, methodology): “The PseKNC approach has been successfully used to identify recombination spots [29,122], identifying splicing sites [123], predicting nucleosome positioning in genomes [124], predicting translation initiation site in human genes [125], and identifying sigma-54 promoters [126].”