Data for paper: Transfer learning for cross-context prediction of protein expression from 5'UTR sequence

This depsit contains data for the paper entitled: "Transfer learning for cross-context prediction of protein expression from 5'UTR sequence". The rebeca.zip file contains a snapshot of the rebeca package which can be used to train, fine tune and test the CONV-LSTM model used in this study. The datasets.zip file contains the compiled sequence to expression datasets from across all Flow-seq expressions considered in this study. The analysis.zip file contains all data files and jupyter notebooks necessary to reproduce our analysis. Each Flow-seq study has a dedicated folder (e.g., `fepB') with two sub-folders: 1. The `data\_split' folder, which contains the steps necessary to split the Flow-seq data for our ML experiments (a `readme.txt' file describes the input and output files and a jupyter notebook is available to reproduce the data split); 2. The `data\_analysis' folder, which contains a jupyter notebook and the necessary input files to reproduce the analysis of our experiments.