DOI: 10.6084/m9.figshare.22802885.v4
title: <strong>PlantC2U:</strong> <strong>Convolutional neural network based deep learning of cross-species sequence landscapes predicts plastid C-to-U RNA editing in plants </strong>

description: Table S1. The detailed information of plastid RNA editing sites collected in REDIdb 3.0.Table S2. The summary of the plastid 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. Prediction of C-to-U RNA editing sites based on the same testing data using CNN, RF, SVM, and PREPACT tools.Table S6. The C-to-U RNA editing sites identified by RNA-seq datasets were further predicted by PREPACT and PlantC2U tools.Table S7. The C-to-U RNA editing sites identified by RNA-seq and WGS data were further predicted by PlantC2U tool.Table S8. Identification of plastid C-to-U editing sites across eight tissues in <i>K. obovata</i>.Table S9. Annotation and functional prediction of plastid C-to-U editing sites on the protein-coding genes in <i>K. obovata</i>.Table S10. Identification of plastid C-to-U editing sites in <i>K. obovata</i> leaf tissues under chilling events.Table S11. The functional enrichment analysis of four DEGs (<i>psbA</i>, <i>psbM</i>, <i>rps12 </i>and <i>ndhA</i>)Table S12. The primers used for PCR assays.Table S13. The detailed information of 50 RNA-seq datasets, covering 4 tissues from 24 mangrove species.Table S14. Identification of plastid C-to-U editing sites in other 24 mangrove species.Figure S1. The evaluation of model performance under different combinations of negative and positive samples.Figure S2. Sequences clustering and nucleotide context flanking the C-to-U editing sites.Figure S3. <i>In silico</i> mutagenesis analysis and SNV identified from 24 <i>K. obovata</i> RNA-seq datasets.Figure S4. Quality control of the cleaned reads and the RNA editing level of plastid genes.Figure S5. SNV identified in 50 mangrove transcriptomes.Figure S6. The experimental verification of 20 C-to-U RNA editing events in <i>K. obovata</i>. Red boxes indicate the RNA editing sites in the cDNA.Figure 1. A schematic diagram of prediction of plastid C-to-U RNA editing sites. The 100-nt flanking sequences surrounding the target cytidines are extracted and converted into a 5×100 binary matrix as the input for PlantC2U. The CNN architecture includes 2 one-dimensional convolution layers (Conv1D), a max pooling layer, and a flatten layer. The nonlinear softmax activation function is used to calculate the prediction probability of C-to-U RNA editing. Figure 2. The evaluation of model performance under different combinations of negative and positive samples. (A) The model performance based on the Matthew's Correlation Coefficient (MCC) using RNS1 and RNS2 as negative samples, respectively. RNS1 represents random negative sequences from primary negative samples. RNS2 represents random negative sequences from different sequences clusters. The Ratio is positive-negative samples ratio. (B) The receiver operating characteristic (ROC) curves show the better performance of convolutional neural network (CNN) than the random forest (RF), support vector machine (SVM) models and PREPACT tool. (C) Evaluation of model performance based on the area under the precision-recall curves (AUC-PRC). Figure 3. Representative map of the plastid genome of mangrove plant <i>K. obovata</i>, including 83 genes, 8 rRNAs, 37 tRNAs, and 35% GC content. IRA and IRB indicate inverted repeat regions. LSC is large single copy. SSC is small single copy. Figure 4. Identification of plastid RNA editing sites in K. obovata. (A) Overall C-to-U RNA editing level in different <i>K. obovata</i> tissues. Values indicate mean ±SD of three biological replicates. The significant difference of RNA editing level were represented by different lowercases through one-way ANOVA (P &lt; 0.05). (B) SNV identified from K. obovata leaf transcriptomes. The bar plots show the percentage of each SNV type (e.g., C&gt;T, CT) identified in each K. obovata transcriptome. (C) The box plots show the overall C-to-U RNA editing level in cold tolerant (CT) and non-cold tolerant (NCT) transcriptomes. (D) The box plots of the expression level of four genes (psbA, psbM, rps12 and ndhA) in CT and NCT transcriptomes. * represent 0.01 &lt; P ≤ 0.05, ** represent 0.001 &lt; P ≤ 0.01. (E) The functional enrichment analysis of four genes (psbA, psbM, rps12 and ndhA) using STRING website tool. (F) The C-to-U RNA editing level of C371 and C991 sites from ndhA in CT and NCT transcriptomes. Figure 5. A workflow for identification and annotation of plastid RNA editing sites. (A) A pipeline includes quality control of raw sequencing data, clean read mapping, RNA editing sites calling, and C-to-U RNA editing sites annotation. (B) Number of C-to-U RNA editing sites identified from different tissues of 24 mangrove species. Figure 6. Interface of the online predictor, PlantC2U, on plants plastid C-to-U RNA editing sites.