Learned KATMAP models

Models of splicing regulation learned from splicing factor (SF) perturbation RNA-seq experiments using KATMAP (Knockdown Activity and Target Models from Additive regression Predictions). 00_LearnedModels: Contains *.katmap files from the cassette exon models that can be used to score exons best_model_per_SF: These are the SF models we are most confident in. If a significant model was learned from more than one knockdown experiment, we select the model with the strongest evidence for splicing activity. models_for_both_cells: The significant SF models used in the manuscript. If we obtained a model from more than one cell type, both are included 01_ModelOutputs: Inference results for the significant models used in the analyses, as well as marginal models with significant activity maps but which did not significantly outperform a reduced model. Each subdirectory contains the models, posterior samples, summaries, target predictions, and visualizations. The *.katmap files in 00_LearnedModels are more lightweight representations of the cassette exon models, which can be used with the KATMAP python library 00_CassetteExons: Models with significant activity maps learned from cassette exons 01_A3SS: Models with significant activity maps learned from alternative A3SS events 02_A5SS: Models with significant activity maps learned from alternative A5SS events 02_TargetPredictions: Tables of target predictions from the cassette exon models