Accurate genome-wide predictions of spatiotemporal gene expression during embryonic development

Jian Zhou, Ignacio E. Schor, Victoria Yao, Chandra L. Theesfeld, Raquel Marco-Ferreres, Alicja Tadych, Eileen E.M. Furlong, Olga G. Troyanskaya

Research output: Contribution to journalArticle


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 spatiotemporal 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, 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.

Original languageEnglish (US)
Article numbere1008382
JournalPLoS genetics
Issue number9
StatePublished - 2019

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics
  • Genetics(clinical)
  • Cancer Research

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  • Cite this

    Zhou, J., Schor, I. E., Yao, V., Theesfeld, C. L., Marco-Ferreres, R., Tadych, A., Furlong, E. E. M., & Troyanskaya, O. G. (2019). Accurate genome-wide predictions of spatiotemporal gene expression during embryonic development. PLoS genetics, 15(9), [e1008382].