Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli
By Minseung Kim, N. Rai, V. Zorraquino, Ilias Tagkopoulos. 2016
134 citations recorded by the source corpus.
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. Multi-omics data integration is a great challenge. Here, the authors compile a database of E. coliproteomics, transcriptomics, metabolomics and fluxomics data to train models of recurrent neural network and constrained regression, enabling prediction of bacterial responses to perturbations.
Subfields: Molecular Biology
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In Molecular Biology
- John C. Marioni
- Oliver Stegle
- R. Argelaguet
- Wolfgang Huber
- Florian Buettner
- Fabian J Theis
- Rahul Satija
- Sarah A. Teichmann
Citations in the corpus
- Machine Learning and Integrative Analysis of Biomedical Big Data (2019, methodology): “In multi-omics studies, imputation based on k-nearest neighbors for profiles and genes expression [76], autocorrelation with cubic interpolation for spectral analysis of time series molecular data [115], fully conditional specification (FCS) for metabolite concentrations [88], etc.”
- Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets (2018, background): “…applied across biological domains, including cancer biology (Gerstung et al , 2015; Iorio et al , 2016; Mertins et al , 2016; Cancer Genome Atlas Research Network, 2017), regulatory genomics (Chen et al , 2016), micro-biology (Kim et al , 2016) or host-pathogen biology (Soderholm et al , 2016).”