Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development.

ABSTRACT: Comprehensive information on the timing and location of gene expression is fundamental to our understanding of embryonic development and tissue formation. While high-throughput in situ hybridization projects provide invaluable information about developmental gene expression patterns for model organisms like Drosophila, the output of these experiments is primarily qualitative, and a high proportion of protein coding genes and most non-coding genes lack any annotation. Accurate data-centric predictions of spatio-temporal gene expression will therefore complement current in situ hybridization efforts. Here, we applied a machine learning approach by training models on all public gene expression and chromatin data, even from whole-organism experiments, to provide genome-wide, quantitative spati

SUBMITTER: Zhou J 

PROVIDER: S-EPMC6779412 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature