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

ABSTRACT: In the evolutionary history of plants, variation in cis-regulatory elements (CREs) resulting in diversification of gene expression has played a central role in driving the evolution of lineage-specific traits. However, it is difficult to predict expression behaviors from CRE patterns to properly harness them, mainly because the biological processes are complex. In this study, we used cistrome datasets and explainable convolutional neural network (CNN) frameworks to predict genome-wide expression patterns in tomato (Solanum lycopersicum) fruit from the DNA sequences in gene regulatory regions. By fixing the effects of trans-acting factors using single cell-type spatiotemporal transcriptome data for the response variables, we developed a prediction model for crucial expression patterns in th

SUBMITTER: Akagi T 

PROVIDER: S-EPMC9134063 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature