ArticleInternational journal of molecular sciences2014
iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components.
Article in International journal of molecular sciences, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed, 237 citations in OpenAlex.
- Predicting Polymerase Chain Reaction Success: Integrating theCombinatorial chemistry & high throughput screening · 2026Article
- LMFE: A Novel Method for Predicting Plant LncRNA Based on Multi-Feature Fusion and Ensemble Learning.Genes · 2025Article
- 6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site.BioData mining · 2023Article
- Circ-LocNet: A Computational Framework for Circular RNA Sub-Cellular Localization Prediction.International journal of molecular sciences · 2022Article
- Genome-Wide Scanning of Potential Hotspots for Adenosine Methylation: A Potential Path to Neuronal Development.Life (Basel, Switzerland) · 2021Article
- Deep4mC: systematic assessment and computational prediction for DNA N4-methylcytosine sites by deep learning.Briefings in bioinformatics · 2021Article
- Epigenetic Marks and Variation of Sequence-Based Information Along Genomic Regions Are Predictive of Recombination Hot/Cold Spots inFrontiers in genetics · 2021Article
- Identification of 4-carboxyglutamate residue sites based on position based statistical feature and multiple classification.Scientific reports · 2020Article
- Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach.Frontiers in genetics · 2020Article
- iN6-methylat (5-step): identifying DNA NMolecular genetics and genomics : MGG · 2019Article
- iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAACCurrent genomics · 2019Article
- iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical MomentsCurrent genomics · 2019Article
- iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou's PseAAC to pseudo-tri-nucleotide composition.Molecular genetics and genomics : MGG · 2019Article
- iRNA-3typeA: Identifying Three Types of Modification at RNA's Adenosine Sites.Molecular therapy. Nucleic acids · 2018Article
- iRSpot-Pse6NC: Identifying recombination spots inInternational journal of biological sciences · 2018Article
- nifPred: Proteome-Wide Identification and Categorization of Nitrogen-Fixation Proteins of Diaztrophs Based on Composition-Transition-Distribution Features Using Support Vector Machine.Frontiers in microbiology · 2018Article
- Article
- iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals.Oncotarget · 2017Article
- Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression.Oncotarget · 2017Article
- iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition.Oncotarget · 2017Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Meiosis and recombination are the two opposite aspects that coexist in a DNA system. As a driving force for evolution by generating natural genetic variations, meiotic recombination plays a very important role in the formation of eggs and sperm. Interestingly, the recombination does not occur randomly across a genome, but with higher probability in some genomic regions called "hotspots", while with lower probability in so-called "coldspots". With the ever-increasing amount of genome sequence data in the postgenomic era, computational methods for effectively identifying the hotspots and coldspots have become urgent as they can timely provide us with useful insights into the mechanism of meiotic recombination and the process of genome evolution as well. To meet the need, we developed a new predictor called "iRSpot-TNCPseAAC", in which a DNA sample was formulated by combining its trinucleotide composition (TNC) and the pseudo amino acid components (PseAAC) of the protein translated from the DNA sample according to its genetic codes. The former was used to incorporate its local or short-rage sequence order information; while the latter, its global and long-range one. Compared with the best existing predictor in this area, iRSpot-TNCPseAAC achieved higher rates in accuracy, Mathew's correlation coefficient, and sensitivity, indicating that the new predictor may become a useful tool for identifying the recombination hotspots and coldspots, or, at least, become a complementary tool to the existing methods. It has not escaped our notice that the aforementioned novel approach to incorporate the DNA sequence order information into a discrete model may also be used for many other genome analysis problems. The web-server for iRSpot-TNCPseAAC is available at http://www.jci-bioinfo.cn/iRSpot-TNCPseAAC. Furthermore, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the current web server to obtain their desired result without the need to follow the complicated mathematical equations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.