Integration of multiple types of omic data is enabling the understanding of the combined influence of complex biological processes at the cellular level. The focus on application of multi-omics to the same biological question is rapidly increasing due to the advent of sophisticated and robust instrumentation such as next-generation sequencing and mass-spectrometry along with new computational approaches to integrate, analyze and visualize the combined data. The availability of multi-omics data from large-scale systematic projects such as The Cancer Genome Atlas (TCGA) and the Clinical Proteomics Tumor Analysis Consortium (CPTAC) are helping to drive the development of tools specific for multiple data integration, visualization and integrated network modeling.
Application of multi-omics approaches are not only restricted to cancer. There are multi-omics integrative studies for understanding host-pathogen interactions and infectious diseases like Zika virus, host-signaling regulation by the gut microbiota and pluripotency regulatory network in stem cells. The integrative approaches of metabolomics and transcriptomics are also well explored in plant systems. The cross-talk between multi-omics layers including transcriptomics, proteomics and metabolomics in several biological systems help in understanding the complexity of biological networks.
There are several challenges in integrating multi-omics data sets for studying the interactome. Still, there is a need for establishing data processing standards and data normalization procedures across different omics layers. Many of the interactions among heterogeneous networks (e.g. Protein-DNA, DNA-metabolite, miRNA-mRNA) are inferred from correlation of their expression data sets. High-throughput technologies used for identifying protein-protein interactions (PPIs) like Yeast-two Hybrid (Y2H) and Affinity Purification-Mass Spectrometry (AP-MS) can provide different profile of PPIs. There is still a considerable gap in generating PPIs in all human cells lines, tissues and disease state.
At this exciting time in the development of multi-omics approaches, we invite investigators to submit original research articles and reviews under this Research Topic that will contribute to furthering the systems approach to studying the interactome. We are particularly interested in articles describing the generation of multi-omics datasets and integrative analysis tools applied in understanding the complex biological networks of a system.
Potential sub-topics include, but are not limited to:
- New approaches and computational tools for integrating multi-omics datasets
- Integrated Omics applications in identifying biomarkers and drug targets in cancer, respiratory and cardiac diseases
- Integrated Omics for studying physiology of plant systems
- Integrated Omics for developing stem cell pluripotency regulatory networks
- Integrated Omics for studying host-pathogen interactions and gut-microbiota networks
Integration of multiple types of omic data is enabling the understanding of the combined influence of complex biological processes at the cellular level. The focus on application of multi-omics to the same biological question is rapidly increasing due to the advent of sophisticated and robust instrumentation such as next-generation sequencing and mass-spectrometry along with new computational approaches to integrate, analyze and visualize the combined data. The availability of multi-omics data from large-scale systematic projects such as The Cancer Genome Atlas (TCGA) and the Clinical Proteomics Tumor Analysis Consortium (CPTAC) are helping to drive the development of tools specific for multiple data integration, visualization and integrated network modeling.
Application of multi-omics approaches are not only restricted to cancer. There are multi-omics integrative studies for understanding host-pathogen interactions and infectious diseases like Zika virus, host-signaling regulation by the gut microbiota and pluripotency regulatory network in stem cells. The integrative approaches of metabolomics and transcriptomics are also well explored in plant systems. The cross-talk between multi-omics layers including transcriptomics, proteomics and metabolomics in several biological systems help in understanding the complexity of biological networks.
There are several challenges in integrating multi-omics data sets for studying the interactome. Still, there is a need for establishing data processing standards and data normalization procedures across different omics layers. Many of the interactions among heterogeneous networks (e.g. Protein-DNA, DNA-metabolite, miRNA-mRNA) are inferred from correlation of their expression data sets. High-throughput technologies used for identifying protein-protein interactions (PPIs) like Yeast-two Hybrid (Y2H) and Affinity Purification-Mass Spectrometry (AP-MS) can provide different profile of PPIs. There is still a considerable gap in generating PPIs in all human cells lines, tissues and disease state.
At this exciting time in the development of multi-omics approaches, we invite investigators to submit original research articles and reviews under this Research Topic that will contribute to furthering the systems approach to studying the interactome. We are particularly interested in articles describing the generation of multi-omics datasets and integrative analysis tools applied in understanding the complex biological networks of a system.
Potential sub-topics include, but are not limited to:
- New approaches and computational tools for integrating multi-omics datasets
- Integrated Omics applications in identifying biomarkers and drug targets in cancer, respiratory and cardiac diseases
- Integrated Omics for studying physiology of plant systems
- Integrated Omics for developing stem cell pluripotency regulatory networks
- Integrated Omics for studying host-pathogen interactions and gut-microbiota networks
