Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development. Author Jian Zhou, Ignacio Schor, Victoria Yao, Chandra Theesfeld, Raquel Marco-Ferreres, Alicja Tadych, Eileen Furlong, Olga Troyanskaya Publication Year 2019 Type Journal Article 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 spatio-temporal predictions for all genes. We developed structured in silico nano-dissection, a computational approach that predicts gene expression in >200 tissue-developmental stages. The algorithm integrates expression signals from a compendium of 6,378 genome-wide expression and chromatin profiling experiments in a cell lineage-aware fashion. We systematically evaluated our performance via cross-validation and experimentally confirmed 22 new predictions for four different embryonic tissues. The model also predicts complex, multi-tissue expression and developmental regulation with high accuracy. We further show the potential of applying these genome-wide predictions to extract tissue specificity signals from non-tissue-dissected experiments, and to prioritize tissues and stages for disease modeling. This resource, together with the exploratory tools are freely available at our webserver http://find.princeton.edu, which provides a valuable tool for a range of applications, from predicting spatio-temporal expression patterns to recognizing tissue signatures from differential gene expression profiles. Keywords Animals, Drosophila, Gene Expression Profiling, Computational Biology, Forecasting, Algorithms, Computer Simulation, Embryonic Development, Gene Expression Regulation, Developmental, Transcriptome, Genome-Wide Association Study, Machine Learning, Spatio-Temporal Analysis, Genes, Developmental Journal PLoS Genet Volume 15 Issue 9 Pages e1008382 Date Published 2019 Sep ISSN Number 1553-7404 DOI 10.1371/journal.pgen.1008382 Alternate Journal PLoS Genet PMCID PMC6779412 PMID 31553718 PubMedPubMed CentralGoogle ScholarBibTeXEndNote X3 XML