Interpreting how an individual’s genome informs health care decision-making or causes disease remains one of the most compelling promises of the post-genomic age. With the increasing use of genomic sequencing, the number of uninterpreted human genetic variations is rapidly increasing. Most genomic variants are too rare to have been studied and lack evidence for mechanisms. Clinically interpreting these variants is challenging and mostly results in uncertain conclusions. Novel methods capable of mechanistically modelling genomic variation at a population scale are needed. 3D Genomics is a new discipline that combines conventional genomics with basic and translational sciences, biochemistry, biophysics, network biology, and more, to meet this need. 3D Genomics focuses on genome organization, protein structure, and functional genomics using mathematics, statistics, molecular modelling, machine learning, and artificial intelligence to generate information that enhances conventional genomics and improves predictive models.
Nearly all computational tools developed to interpret genomic variants rely on genome linear representations. However, proteins are 3D, time-dynamic molecules with functional and regulatory properties that are not currently predictable from sequences. Similarly, genomic variation altering gene regulation can in turn modulate gene expression and has a complicated biological effect. To unravel this level of complexity, systems biology approaches have modelled cellular response to genomic variation. To further advance this field, 3D Genomics moves beyond the linear study of genomic variation and integrates 3D and 4D models of genetic variation on proteins and other diverse biomolecular structures with existing disciplines. Such cross-disciplinary approach will improve our understanding of how tissues and body systems integrate each layer of information, resulting in changes to physiology and pathological dysregulation. The goal of this Research Topic is to explore methods and approaches to inform the field of 3D Genomics for mechanistic and systems-level interpretation of genetic variation.
This Research Topic collects publications focusing on emerging transformational approaches that combine conventional bioinformatics with computational biophysics and biochemistry. The aim will be not only better classifying variants but also bringing mechanistic information to genomic variant interpretation, currently absent from clinical workflows.
Research focused on modelling the human genome at all levels of resolution (e.g., atomic, molecular, domain, pathway, nuclear, cellular, tissue, organ, organism, community) are within scope. Considering recent advances in both experimental and computational approaches, this Research Topic focuses on complementary methods of molecular modelling and systems biology serving as driving forces to continue shaping next-generation genomics interpretation.
Specific included – but not limited to – themes are:
• Methods and applications for leveraging:
o Protein structure to interpret genetic variations
o Nuclear structure or chromatin remodeling to interpret genetic variations
o Integration across multiple biologic scales and data types
• Approaches for knowledge aggregation and management to understand gene function
• Multi-tissue effects of genetic variation
• Functional genomics at scale
• Predictive genomics modeling
Interpreting how an individual’s genome informs health care decision-making or causes disease remains one of the most compelling promises of the post-genomic age. With the increasing use of genomic sequencing, the number of uninterpreted human genetic variations is rapidly increasing. Most genomic variants are too rare to have been studied and lack evidence for mechanisms. Clinically interpreting these variants is challenging and mostly results in uncertain conclusions. Novel methods capable of mechanistically modelling genomic variation at a population scale are needed. 3D Genomics is a new discipline that combines conventional genomics with basic and translational sciences, biochemistry, biophysics, network biology, and more, to meet this need. 3D Genomics focuses on genome organization, protein structure, and functional genomics using mathematics, statistics, molecular modelling, machine learning, and artificial intelligence to generate information that enhances conventional genomics and improves predictive models.
Nearly all computational tools developed to interpret genomic variants rely on genome linear representations. However, proteins are 3D, time-dynamic molecules with functional and regulatory properties that are not currently predictable from sequences. Similarly, genomic variation altering gene regulation can in turn modulate gene expression and has a complicated biological effect. To unravel this level of complexity, systems biology approaches have modelled cellular response to genomic variation. To further advance this field, 3D Genomics moves beyond the linear study of genomic variation and integrates 3D and 4D models of genetic variation on proteins and other diverse biomolecular structures with existing disciplines. Such cross-disciplinary approach will improve our understanding of how tissues and body systems integrate each layer of information, resulting in changes to physiology and pathological dysregulation. The goal of this Research Topic is to explore methods and approaches to inform the field of 3D Genomics for mechanistic and systems-level interpretation of genetic variation.
This Research Topic collects publications focusing on emerging transformational approaches that combine conventional bioinformatics with computational biophysics and biochemistry. The aim will be not only better classifying variants but also bringing mechanistic information to genomic variant interpretation, currently absent from clinical workflows.
Research focused on modelling the human genome at all levels of resolution (e.g., atomic, molecular, domain, pathway, nuclear, cellular, tissue, organ, organism, community) are within scope. Considering recent advances in both experimental and computational approaches, this Research Topic focuses on complementary methods of molecular modelling and systems biology serving as driving forces to continue shaping next-generation genomics interpretation.
Specific included – but not limited to – themes are:
• Methods and applications for leveraging:
o Protein structure to interpret genetic variations
o Nuclear structure or chromatin remodeling to interpret genetic variations
o Integration across multiple biologic scales and data types
• Approaches for knowledge aggregation and management to understand gene function
• Multi-tissue effects of genetic variation
• Functional genomics at scale
• Predictive genomics modeling
