Genome-wide cis-decoding for expression designing in tomato using cistrome data and explainable deep learning

In the evolutionary paths of plants, variations of the cis-regulatory elements (CREs) resulting in expression diversification have played a central role in driving the establishment of lineage-specific traits. We used cistrome datasets and explainable convolutional neural network (CNN) frameworks to predict genome-wide expression patterns in tomato fruits from the DNA sequences in gene regulatory regions. This cis-decoding framework will not only contribute to understanding the regulatory networks derived from CREs and transcription factor interactions, but also provide a flexible way of designing alleles with optimized expression. This project includes six cistrome datasets of tomato transcription factors potentially involving fruit ripening processes.