Qin, S.; Fan, Y.; Hu, S.; Wang, Y.; Wang, Z.; Cao, Y.; Liu, Q.; Tan, S.; Dai, Z.; Zhou, W. iPReditor-CMG: Improving Predictive RNA Editor for Crop Mitochondrial Genomes Using Genomic Sequence Features and Optimal Support Vector Machine. Preprints2021, 2021100332. https://doi.org/10.20944/preprints202110.0332.v1
APA Style
Qin, S., Fan, Y., Hu, S., Wang, Y., Wang, Z., Cao, Y., Liu, Q., Tan, S., Dai, Z., & Zhou, W. (2021). iPReditor-CMG: Improving Predictive RNA Editor for Crop Mitochondrial Genomes Using Genomic Sequence Features and Optimal Support Vector Machine. Preprints. https://doi.org/10.20944/preprints202110.0332.v1
Chicago/Turabian Style
Qin, S., Zhijun Dai and Wei Zhou. 2021 "iPReditor-CMG: Improving Predictive RNA Editor for Crop Mitochondrial Genomes Using Genomic Sequence Features and Optimal Support Vector Machine" Preprints. https://doi.org/10.20944/preprints202110.0332.v1
Abstract
Cytosine (C) to uracil (U) RNA editing is one of the most important post-transcriptional processes, however exploring C-to-U editing events efficiently within the crop mitochondrial genome remains a challenge. An improving predictive RNA editor for crop mitochondrial genomes, iPReditor-CMG, was proposed, which was based on SVM, three common crop mitochondrial genomes and self-sequenced tobacco mitochondrial ATPase. After multi-combination feature extracting, high-dimension feature screening and multi-test independent predicting, the results showed that the average accuracy of intraspecific prediction was 0.85, and the highest value even up to 0.91, which outperformed the previous reference models. While the prediction accuracies were 0.78 between dicotyledons and no more than 0.56 between dicotyledons and monocotyledons, implying a possible similarity in C-to-U editing mechanisms among close relatives. The best model was finally identified with an independent test accuracy of 0.91 and an area under the curve of 0.88, and further suggested that five unreported feature sequences TGACA, ACAAC, GTAGA, CCGTT and TAACA were closely associated with the editing phenomenon. Multiple evaluation findings supported that the iPReditor-CMG could be effectively applied to predict crop mitochondrial editing sites, which may contribute to insight into their recognition mechanisms and even other post-transcriptional events in crop mitochondria.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The commenter has declared there is no conflict of interests.
Comment:
Qin S, Fan Y, Hu S, Wang Y, Wang Z, Cao Y, Liu Q, Tan S, Dai Z, Zhou W. iPReditor-CMG: Improving a predictive RNA editor for crop mitochondrial genomes using genomic sequence features and an optimal support vector machine. Phytochemistry. 2022 Aug;200:113222. doi: 10.1016/j.phytochem.2022.113222. Epub 2022 May 11. PMID: 35561852.
Commenter:
The commenter has declared there is no conflict of interests.