It is common knowledge that non-coding variants are important sources of human diseases and diversity, yet identifying them and understanding their impacts towards disease risk, which facilitates the promise of precision medicine and the development of novel drugs, remain open issues. Unlike protein-coding variants, of which the functional signals can be directly denoted by the change of gene expression, the functional impact of non-coding variants is hard to predict. Non-coding variants have been shown to relate to disease in different ways, including modifications to 3D genome structures, changes to DNA methylation, alterations to transcription factor-DNA binding, etc. These regulatory effects and pathogenicity of non-coding variants can be studied by genome-wide mapping of chromatin interaction, computational prediction with machine learning (deep learning) models, or advanced genome editing technologies. Furthermore, non-coding variants can be identified as useful disease biomarkers in clinical studies and many other tools and methods have been developed to predict contributions of non-coding variants to specific diseases (e.g. autism spectrum disorder, breast and ovarian cancer, and Mendelian diseases) in patients or even their risks in healthy individuals, with the aim to facilitate disease prevention and improve personalized medicine.
This Research Topic is focused on the prediction of the potential impact of non-coding variants with the goal to provide a better understanding of human diseases and, ultimately, achieve precision medicine. Original Research, Methods (both computational and experimental), Database, and Review articles are all welcome. The papers may cover, but not restricted to, the following topics:
1. Annotation of pathogenic non-coding variants and the role of non-coding sequence variants in cancer;
2. Study of impacts of non-coding variants on DNA methylation;
3. Analysis of the effects of non-coding variations on gene regulatory network;
4. Applications of non-coding variants as biomarkers in clinical applications;
5. Genome editing technologies for dissecting potential functional consequences of non-coding variants;
6. Deep learning methods that aim at providing insights into the non-coding sequence features;
7. Making use of data from high-throughput screening experiments (CRISPR-ko/i/a, siRNA, MPRA, etc.) to model variant effects.
It is common knowledge that non-coding variants are important sources of human diseases and diversity, yet identifying them and understanding their impacts towards disease risk, which facilitates the promise of precision medicine and the development of novel drugs, remain open issues. Unlike protein-coding variants, of which the functional signals can be directly denoted by the change of gene expression, the functional impact of non-coding variants is hard to predict. Non-coding variants have been shown to relate to disease in different ways, including modifications to 3D genome structures, changes to DNA methylation, alterations to transcription factor-DNA binding, etc. These regulatory effects and pathogenicity of non-coding variants can be studied by genome-wide mapping of chromatin interaction, computational prediction with machine learning (deep learning) models, or advanced genome editing technologies. Furthermore, non-coding variants can be identified as useful disease biomarkers in clinical studies and many other tools and methods have been developed to predict contributions of non-coding variants to specific diseases (e.g. autism spectrum disorder, breast and ovarian cancer, and Mendelian diseases) in patients or even their risks in healthy individuals, with the aim to facilitate disease prevention and improve personalized medicine.
This Research Topic is focused on the prediction of the potential impact of non-coding variants with the goal to provide a better understanding of human diseases and, ultimately, achieve precision medicine. Original Research, Methods (both computational and experimental), Database, and Review articles are all welcome. The papers may cover, but not restricted to, the following topics:
1. Annotation of pathogenic non-coding variants and the role of non-coding sequence variants in cancer;
2. Study of impacts of non-coding variants on DNA methylation;
3. Analysis of the effects of non-coding variations on gene regulatory network;
4. Applications of non-coding variants as biomarkers in clinical applications;
5. Genome editing technologies for dissecting potential functional consequences of non-coding variants;
6. Deep learning methods that aim at providing insights into the non-coding sequence features;
7. Making use of data from high-throughput screening experiments (CRISPR-ko/i/a, siRNA, MPRA, etc.) to model variant effects.
