Omics-based studies have enabled and greatly accelerated progress of systems biology. Each layer of omics data provides a comprehensive characterization of a particular type of biomolecules (RNA, proteins, metabolites, etc.) in biological systems, being involved in different yet inter-related biological processes. Data at any individual omics level cannot, however, answer how these different multi-layered biological processes interact, leading to complex phenotypes. With the purpose of unraveling these interactions and complex phenotypes, many recent studies have employed multi-omics strategy in order to obtain a broader view of the interactions that connect genotypes to phenotypes of interest.
Omics data integration can provide a more reliable and holistic picture of the biochemistry and dynamics of biological systems, as compared to data from any omics layer alone. Multi-omics is also a powerful tool for biomarker discovery and prioritization, which is a topic of high interest in clinical research. Therefore, the description of biological processes, within the systems biology context, is possible through statistical and computational tools employed in multi-omics. Many recent studies on multi-omics involve the integration of genomics and transcriptomics data, although there is a growing interest on the integration of proteomics and metabolomics at the downstream of omics cascades to provide biological information closer to the phenotype. In clinical studies, data integration from different omics sciences is a promising tool for the early detection of illnesses, as well as the efficacy evaluation of different treatments.
The aim of the present Research Topic is to cover state-of-the-art strategies and recent advances in the multi-omics research field, as well as clinical applications. A variety of article types are welcome: Original Research, Review, Hypothesis and Theory, Methods, Mini-Review, Perspective and Systematic Review. Themes to be addressed in this Research Topic may include, but are not limited to:
• Novel approaches in biostatistics for single- and multi-omics
• Computational biology tools for omics data integration
• Strategies for biological interpretation/pathway analysis of multi-omics data
• Applications of multi-omics in clinical studies
Omics-based studies have enabled and greatly accelerated progress of systems biology. Each layer of omics data provides a comprehensive characterization of a particular type of biomolecules (RNA, proteins, metabolites, etc.) in biological systems, being involved in different yet inter-related biological processes. Data at any individual omics level cannot, however, answer how these different multi-layered biological processes interact, leading to complex phenotypes. With the purpose of unraveling these interactions and complex phenotypes, many recent studies have employed multi-omics strategy in order to obtain a broader view of the interactions that connect genotypes to phenotypes of interest.
Omics data integration can provide a more reliable and holistic picture of the biochemistry and dynamics of biological systems, as compared to data from any omics layer alone. Multi-omics is also a powerful tool for biomarker discovery and prioritization, which is a topic of high interest in clinical research. Therefore, the description of biological processes, within the systems biology context, is possible through statistical and computational tools employed in multi-omics. Many recent studies on multi-omics involve the integration of genomics and transcriptomics data, although there is a growing interest on the integration of proteomics and metabolomics at the downstream of omics cascades to provide biological information closer to the phenotype. In clinical studies, data integration from different omics sciences is a promising tool for the early detection of illnesses, as well as the efficacy evaluation of different treatments.
The aim of the present Research Topic is to cover state-of-the-art strategies and recent advances in the multi-omics research field, as well as clinical applications. A variety of article types are welcome: Original Research, Review, Hypothesis and Theory, Methods, Mini-Review, Perspective and Systematic Review. Themes to be addressed in this Research Topic may include, but are not limited to:
• Novel approaches in biostatistics for single- and multi-omics
• Computational biology tools for omics data integration
• Strategies for biological interpretation/pathway analysis of multi-omics data
• Applications of multi-omics in clinical studies
