Biological insights through omics data integration

Noor, Elad; Cherkaoui, Sarah; Sauer, Uwe

doi:10.1016/j.coisb.2019.03.007

Cited by 69 publications

(43 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a constraint-based modeling approach, netGS can be extended to integrate transcriptomics, proteomics and metabolomics data [34][35][36][37] and, thereby, impose additional constraints and explore their effect on prediction accuracies, as done in classical prediction of fresh weight 38 . The current state of plant metabolic modeling does not yet allow incorporation of information about catalytic rates, as plant-specific information about this is still lacking.…”

Section: Discussionmentioning

confidence: 99%

Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth

2020

View full text Add to dashboard Cite

The current trends of crop yield improvements are not expected to meet the projected rise in demand. Genomic selection uses molecular markers and machine learning to identify superior genotypes with improved traits, such as growth. Plant growth directly depends on rates of metabolic reactions which transform nutrients into the building blocks of biomass. Here, we predict growth of Arabidopsis thaliana accessions by employing genomic prediction of reaction rates estimated from accession-specific metabolic models. We demonstrate that, comparing to classical genomic selection on the available data sets for 67 accessions, our approach improves the prediction accuracy for growth within and across nitrogen environments by 32.6% and 51.4%, respectively, and from optimal nitrogen to low carbon environment by 50.4%. Therefore, integration of molecular markers into metabolic models offers an approach to predict traits directly related to metabolism, and its usefulness in breeding can be examined by gathering matching datasets in crops.

show abstract

Section: Discussionmentioning

confidence: 99%

Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth

2020

View full text Add to dashboard Cite

show abstract

“…To overcome the need for a known transcription regulation network, the TF activities could be inferred from the transcriptome data directly without using prior knowledge about the transcription regulation network. Previous studies showed for example that machine learning methods can infer TF activities in E. coli based on transcriptomics data 38 , and inference of regulatory metabolites with such methods was also suggested 39 . Future approaches could even consider determining TF activities and regulatory metabolites simultaneously.…”

Section: Discussionmentioning

confidence: 99%

Systematic identification of metabolites controlling gene expression in E. coli

et al. 2019

View full text Add to dashboard Cite

Metabolism controls gene expression through allosteric interactions between metabolites and transcription factors. These interactions are usually measured with in vitro assays, but there are no methods to identify them at a genome-scale in vivo. Here we show that dynamic transcriptome and metabolome data identify metabolites that control transcription factors in E. coli. By switching an E. coli culture between starvation and growth, we induce strong metabolite concentration changes and gene expression changes. Using Network Component Analysis we calculate the activities of 209 transcriptional regulators and correlate them with metabolites. This approach captures, for instance, the in vivo kinetics of CRP regulation by cyclic-AMP. By testing correlations between all pairs of transcription factors and metabolites, we predict putative effectors of 71 transcription factors, and validate five interactions in vitro. These results show that combining transcriptomics and metabolomics generates hypotheses about metabolism-transcription interactions that drive transitions between physiological states.

show abstract

“…As such, it seems hopeful that these barriers will soon be overcome. Similarly, the issue of multi-omics data integration and analysis is increasingly recognised as a barrier to progress, and promising tools are being developed to address this issue [82,83] More pressingly, much -omics work relies on having access to comprehensive databases, and fungal genomes are severely underrepresented. Meeting this need will require concerted effort and collaboration among researchers in all three sectors outlined here.…”

Section: Discussionmentioning

confidence: 99%

Cross-Disciplinary Genomics Approaches to Studying Emerging Fungal Infections

Ghosh

Brookes

Edwards

et al. 2020

Life

View full text Add to dashboard Cite

Emerging fungal pathogens pose a serious, global and growing threat to food supply systems, wild ecosystems, and human health. However, historic chronic underinvestment in their research has resulted in a limited understanding of their epidemiology relative to bacterial and viral pathogens. Therefore, the untargeted nature of genomics and, more widely, -omics approaches is particularly attractive in addressing the threats posed by and illuminating the biology of these pathogens. Typically, research into plant, human and wildlife mycoses have been largely separated, with limited dialogue between disciplines. However, many serious mycoses facing the world today have common traits irrespective of host species, such as plastic genomes; wide host ranges; large population sizes and an ability to persist outside the host. These commonalities mean that -omics approaches that have been productively applied in one sphere and may also provide important insights in others, where these approaches may have historically been underutilised. In this review, we consider the advances made with genomics approaches in the fields of plant pathology, human medicine and wildlife health and the progress made in linking genomes to other -omics datatypes and sets; we identify the current barriers to linking -omics approaches and how these are being underutilised in each field; and we consider how and which -omics methodologies it is most crucial to build capacity for in the near future.

show abstract

Biological insights through omics data integration

Cited by 69 publications

References 52 publications

Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth

Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth

Systematic identification of metabolites controlling gene expression in E. coli

Cross-Disciplinary Genomics Approaches to Studying Emerging Fungal Infections

Contact Info

Product

Resources

About