Summary Mesendodermal specification is one of the earliest events in embryogenesis, where cells first acquire distinct identities. Cell differentiation is a highly regulated process that involves the function of numerous transcription factors (TFs) and signaling molecules, which can be described with gene regulatory networks (GRNs). Cell differentiation GRNs are difficult to build because existing mechanistic methods are low-throughput, and high-throughput methods tend to be non-mechanistic. Additionally, integrating highly dimensional data comprised of more than two data types is challenging. Here, we use linked self-organizing maps to combine ChIP-seq/ATAC-seq with temporal, spatial and perturbation RNA-seq data from Xenopus tropicalis mesendoderm development to build a high resolution genome scale mechanistic GRN. We recovered both known and previously unsuspected TF-DNA/TF-TF interactions and validated through reporter assays. Our analysis provides new insights into transcriptional regulation of early cell fate decisions and provides a general approach to building GRNs using highly-dimensional multi-omic data sets. Highlights Built a generally applicable pipeline to creating GRNs using highly-dimensional multi-omic data sets Predicted new TF-DNA/TF-TF interactions during mesendoderm development Generate the first genome scale GRN for vertebrate mesendoderm and expanded the core mesendodermal developmental network with high fidelity Developed a resource to visualize hundreds of RNA-seq and ChIP-seq data using 2D SOM metaclusters.

Uncovering the mesendoderm gene regulatory network through multi-omic data integration