About this Research Topic
Over the past decades, sequencing technologies, especially next-generation sequencing, fundamentally changed our view of cellular signaling events and their dysregulation-related diseases. These sequencing technologies have been widely used in clinical diagnosis for genetic disorders, infectious diseases, and complicated diseases such as cancer. With the advent of high-throughput techniques, other omics approaches, such as proteomics, metabolomics, lipidomics and spatial transcriptomics, also provide much useful data reflecting the features of diseases from different pespectives. However, the utilization of these omics approaches in signaling and disease research have lagged behind when compared to the sequencing techniques. Therefore, developing new methods as well as new algorithms for data integration and interpretation will not only advance our understanding of signaling events for disease initiation and progression, but also help unveil new leads for the prevention, diagnosis and treatment of diseases.
Advances in high-throughput techniques enable studying signaling pathways and their correlation with diseases at multiple levels, such as genome, epigenome, proteome, and metabolome. While these “multi-omics” data has revolutionized the fields of medicine and biology by creating avenues for integrated system-level approaches, revealed many mechanisms driving diseases, and suggested novel targeted therapies and precision medicine for individual patients, key problems still remain for multi-omics research. For data generation, how to generate large quantity of high-quality data in a timely and cost-effective manner is the most challenging part. This special issue will focus on new experimental and computational approaches in proteomics, including proteogenomics, interactomics and PTMomics, and metabolomics, to generate high-quality, spatial- and tempol-precised data for signaling studies. For data integration, most so-called “multi-omics” studies simply overlapped the results generated by different omics approaches. Integrated approaches combine individual omics data comprehensively, in a sequential or simultaneous manner, can help better elucidate the complex signaling events involved in development and diseases. Finally, for functional studies, one would expect to using omics-driven methods in in-depth function studies, to help our understanding of signaling pathways in development and diseases.
In this Research Topic, we aim to collect research articles, reviews and opinions focusing on the following topics:
• Proteomics, including proteogenomics, interactomics and PTMomics, in signaling and disease research
• Metabolomics and lipidomics in signaling and disease research
• Developing new methods and algorithms to generate high-quality multi-omics data
• Multi-omics data integration and interpretation
• Omics-directed in-depth function studies
Please note: studies consisting solely of bioinformatic investigation of publicly available genomic/transcriptomic/proteomic data do not fall within the scope of the journal unless they are expanded and provide significant biological or mechanistic insight into the process being studied and will not be accepted as part of this Research Topic.
Advances in high-throughput techniques enable studying signaling pathways and their correlation with diseases at multiple levels, such as genome, epigenome, proteome, and metabolome. While these “multi-omics” data has revolutionized the fields of medicine and biology by creating avenues for integrated system-level approaches, revealed many mechanisms driving diseases, and suggested novel targeted therapies and precision medicine for individual patients, key problems still remain for multi-omics research. For data generation, how to generate large quantity of high-quality data in a timely and cost-effective manner is the most challenging part. This special issue will focus on new experimental and computational approaches in proteomics, including proteogenomics, interactomics and PTMomics, and metabolomics, to generate high-quality, spatial- and tempol-precised data for signaling studies. For data integration, most so-called “multi-omics” studies simply overlapped the results generated by different omics approaches. Integrated approaches combine individual omics data comprehensively, in a sequential or simultaneous manner, can help better elucidate the complex signaling events involved in development and diseases. Finally, for functional studies, one would expect to using omics-driven methods in in-depth function studies, to help our understanding of signaling pathways in development and diseases.
In this Research Topic, we aim to collect research articles, reviews and opinions focusing on the following topics:
• Proteomics, including proteogenomics, interactomics and PTMomics, in signaling and disease research
• Metabolomics and lipidomics in signaling and disease research
• Developing new methods and algorithms to generate high-quality multi-omics data
• Multi-omics data integration and interpretation
• Omics-directed in-depth function studies
Please note: studies consisting solely of bioinformatic investigation of publicly available genomic/transcriptomic/proteomic data do not fall within the scope of the journal unless they are expanded and provide significant biological or mechanistic insight into the process being studied and will not be accepted as part of this Research Topic.
Keywords: Multi-omics, Signaling pathways, Data integration, Cancer, Metabolic Diseases
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
