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


Autoři: Jian Zhou aff001;  Ignacio E. Schor aff004;  Victoria Yao aff001;  Chandra L. Theesfeld aff001;  Raquel Marco-Ferreres aff004;  Alicja Tadych aff001;  Eileen E. M. Furlong aff004;  Olga G. Troyanskaya aff001
Působiště autorů: Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America aff001;  Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, New Jersey, United States of America aff002;  Center for Computational Biology, Flatiron Institute, New York, New York, United States of America aff003;  Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany aff004;  Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America aff005
Vyšlo v časopise: Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development. PLoS Genet 15(9): e32767. doi:10.1371/journal.pgen.1008382
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008382

Souhrn

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.

Klíčová slova:

Drosophila melanogaster – Embryos – Gene expression – Gene prediction – Machine learning algorithms – Transcriptome analysis – Pharyngeal muscles


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Štítky
Genetika Reprodukční medicína

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PLOS Genetics


2019 Číslo 9

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