Intra-speciesModelsAndLeave-one-outCross-validation.md

Intra-species models and leave-one-out cross-validation

Cross-species validation instead of an in-species split for the validation and training data was deemed closer to the real-world use case of predicting ATAC- and ChIP-seq data for an entire species. However, two models were trained using an intra-species training and validation split. These models, IS_10 and IS_20, used 10% and 20% of each species dataset as the validation set respectively. The input files were split using Predmoter's intra_species_train_val_split.py script in “side_scripts.” This method ensured that each sequence ID from the original fasta file was fully assigned to either training or validation set. Since the focus of this study is on cross-species prediction, all 25 plant species were used in leave-one-out cross-validation (LOOCV) to evaluate the best model setup on different species. All these setups were trained on ATAC- and ChIP-seq datasets simultaneously (Table 4). When performing LOOCV the model performance was evaluated on all datasets available in the left-out species.

All models excluded gap subsequences, subsequences of 21 384 bp only containing Ns, and flagged subsequences. For more details on exact model parameters see Supplementary Section S1.3 and Supplementary Table S4.