Table S1.xlsxPlantC2U: Convolutional neural network based deep learning of cross-species sequence landscapes predicts plastid C-to-U RNA editing in plants

Table S1. The detailed information of plastid RNA editing sites collected in REDIdb 3.0.
Table S2. The summary of the chloroplast C-to-U RNA editing sites dataset.
Table S3. The performance of PlantC2U in independent test based on the different flanking sequence and the ratio of positive and negative instances from primary negative samples.
Table S4. The performance of PlantC2U in independent test based on the different flanking sequence and the ratio of positive and negative instances from sequence clusters.
Table S5. Identification of plastid C-to-U editing sites across eight tissues in K. obovata.
Table S6. Annotation and functional prediction of plastid C-to-U editing sites on the protein-coding genes in K. obovata.
Table S7. Identification of plastid C-to-U editing sites in K. obovata leaf tissues under chilling events.
Table S8. The functional enrichment analysis of four DEGs (psbA, psbM, rps12 and ndhA)
Table S9. The detailed information of 50 RNA-seq datasets, covering 4 tissues from 24 mangrove species.
Table S10. Identification of plastid C-to-U editing sites in other 24 mangrove species.