Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli.

Minseung Kim,Violeta Zorraquino,Ilias Tagkopoulos,Navneet Rai

doi:10.1038/ncomms13090

Abstract

A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information. We then use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. The genetic and environmental ontology reconstructed from the omics data is substantially different and complementary to the genetic and chemical ontologies. The integration of different layers confers an incremental increase in the prediction performance, as does the information about the known gene regulatory and protein-protein interactions. The predictive performance of the model ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines. This work provides an integrative framework of omics-driven predictive modelling that is broadly applicable to guide biological discovery.

Highlights

A significant obstacle in training predictive cell models is the lack of integrated data sources
We present the Multi-Omics Model and Analytics (MOMA) platform, an integrated model that learns from the Ecomics and other available network data to predict genome-wide expression and growth, which shows higher performance than several baselines and two recent metabolic-expression models
The MOMA platform is an integrated model that learns from the Ecomics and other available network data to predict genome-wide expression and growth (Fig. 3 and Supplementary Fig. 3)

Summary

Introduction

A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information We use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. We present Ecomics, a normalized, well-annotated, multi-omics database for E. coli, developed to provide high-quality data and associated meta-data for performing predictive analysis and training datadriven algorithms. This compendium houses 4389 normalized expression profiles across 649 different conditions. We present the Multi-Omics Model and Analytics (MOMA) platform, an integrated model that learns from the Ecomics and other available network data to predict genome-wide expression and growth, which shows higher performance than several baselines and two recent metabolic-expression models

Methods

Results

Conclusion