Multi-omics data analysis, also known as integrative, trans- or pan- omics is a trending and very challenging topic which has shown great advances in the last decade. However, there are still many specific aspects to address and improve, such as standardization of the methods, a limited number of samples in most studies, and limitations on the interpretation of results, among many others. On one side, there is still a need to improve the evaluation of novel tools for multi-omics analysis. Additionally, methods are needed that are well-adapted and tested for longitudinal multi-omics datasets. In the context of personalized medicine, multi-omics is playing a leading role in the identification of potential novel biomarkers. In the context of single-cell data analysis, the analysis of several omics has made huge progress and it holds the promise to help to move further our understanding of fundamental biology and health. And to reach this promise, methods and tools hold a key to make the most of the acquired data.
The aim of this Research Topic is to showcase novel techniques and tools in the evolving field of multi-omics data analysis, in particular those focused on metabolomics data and metabolic profiling. Additionally, the presentation of new resources for multi-omics data analysis, such as test datasets, evaluation matrices, data standards, visualization tools, apps, packages, and others would also be a great contribution to the growing research community.
This Research Topic welcomes Original Research, Reviews, and Reports focusing on the, but not limited to, the following subjects:
• Studies encompassing advances in multi-omics techniques including Metabolomics, Proteomics, Lipidomics, Genomics, Epigenomics, Transcriptomics and Metagenomics
• Machine/deep learning tools for analysis.
• Multi-factor analysis
• Bayesian statistics
• Network-based models
• Single-cell data analysis
• Data standards
• Tools / packages / workflows
Dr. Ornella Cominetti is an employee of Societé des Produits Nestlé SA. All other editors declare no conflicts of interest.
Multi-omics data analysis, also known as integrative, trans- or pan- omics is a trending and very challenging topic which has shown great advances in the last decade. However, there are still many specific aspects to address and improve, such as standardization of the methods, a limited number of samples in most studies, and limitations on the interpretation of results, among many others. On one side, there is still a need to improve the evaluation of novel tools for multi-omics analysis. Additionally, methods are needed that are well-adapted and tested for longitudinal multi-omics datasets. In the context of personalized medicine, multi-omics is playing a leading role in the identification of potential novel biomarkers. In the context of single-cell data analysis, the analysis of several omics has made huge progress and it holds the promise to help to move further our understanding of fundamental biology and health. And to reach this promise, methods and tools hold a key to make the most of the acquired data.
The aim of this Research Topic is to showcase novel techniques and tools in the evolving field of multi-omics data analysis, in particular those focused on metabolomics data and metabolic profiling. Additionally, the presentation of new resources for multi-omics data analysis, such as test datasets, evaluation matrices, data standards, visualization tools, apps, packages, and others would also be a great contribution to the growing research community.
This Research Topic welcomes Original Research, Reviews, and Reports focusing on the, but not limited to, the following subjects:
• Studies encompassing advances in multi-omics techniques including Metabolomics, Proteomics, Lipidomics, Genomics, Epigenomics, Transcriptomics and Metagenomics
• Machine/deep learning tools for analysis.
• Multi-factor analysis
• Bayesian statistics
• Network-based models
• Single-cell data analysis
• Data standards
• Tools / packages / workflows
Dr. Ornella Cominetti is an employee of Societé des Produits Nestlé SA. All other editors declare no conflicts of interest.
