Ya Cui
Research Fellow, University of California, Irvine
6 active projects
Explore the potential mechanisms of noncoding variants in human diseases test08
Scientific Questions Being Studied
Noncoding regulatory elements, such as enhancers, act as regulators of gene expression. The Encyclopedia of DNA Elements (ENCODE) project and other studies have identified millions of regulatory elements in various tissues. The roles of noncoding regulatory elements in many human diseases have acquired considerable attention, including in cancer, cardiovascular disease and psychiatric disorders. GWAS studies in understanding the genetic basis of these diseases has also identified many noncoding regulatory variants responsible for genetic risk, yet the mechanisms behind these risk variants remain poorly understood. Recent integrating multidimensional genomic data, such as expression, methylation, histone modification, chromatin accessibility and three-dimensional organization data, have enhanced interpretation of noncoding risk variants in human diseases. However, systematic analysis multidimensional genomic data make things more complex and has become a big challenge in the field.
Project Purpose(s)
- Ancestry
Scientific Approaches
In this project, we will develop novel and powerful computational methods to systematically discover the potential biomarker and drug target in human diseases, such as cardiovascular disease, arteriosclerosis, lung diseases, asthma, T2D, and many other metabolism diseases based on multidimensional genomic data. We first will identify molecular differences between healthy individuals and patients based on these sequencing data. The All of Us project provides a great resource of genomic data in well-phenotype, disease-relevant populations. We will identify differences between healthy individuals and patients based on All of Us sequencing data. And then using newly developed computational methods to systematically integrate multidimensional genomic data from other cohorts.
Anticipated Findings
In this study, we will develop novel computational methods to systematically discover these diseases causing changes in noncoding elements, which may be used as drug target in the future. Computational tools will be shared with all researchers. Findings from this project will be disseminated widely and shared with the scientific community by presenting results at national scientific meetings and publishing in peer-reviewed journals.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled Tier
Explore the potential mechanisms of noncoding variants in human diseases testrun
Scientific Questions Being Studied
Noncoding regulatory elements, such as enhancers, act as regulators of gene expression. The Encyclopedia of DNA Elements (ENCODE) project and other studies have identified millions of regulatory elements in various tissues. The roles of noncoding regulatory elements in many human diseases have acquired considerable attention, including in cancer, cardiovascular disease and psychiatric disorders. GWAS studies in understanding the genetic basis of these diseases has also identified many noncoding regulatory variants responsible for genetic risk, yet the mechanisms behind these risk variants remain poorly understood. Recent integrating multidimensional genomic data, such as expression, methylation, histone modification, chromatin accessibility and three-dimensional organization data, have enhanced interpretation of noncoding risk variants in human diseases. However, systematic analysis multidimensional genomic data make things more complex and has become a big challenge in the field.
Project Purpose(s)
- Ancestry
Scientific Approaches
In this project, we will develop novel and powerful computational methods to systematically discover the potential biomarker and drug target in human diseases, such as cardiovascular disease, arteriosclerosis, lung diseases, asthma, T2D, and many other metabolism diseases based on multidimensional genomic data. We first will identify molecular differences between healthy individuals and patients based on these sequencing data. The All of Us project provides a great resource of genomic data in well-phenotype, disease-relevant populations. We will identify differences between healthy individuals and patients based on All of Us sequencing data. And then using newly developed computational methods to systematically integrate multidimensional genomic data from other cohorts.
Anticipated Findings
In this study, we will develop novel computational methods to systematically discover these diseases causing changes in noncoding elements, which may be used as drug target in the future. Computational tools will be shared with all researchers. Findings from this project will be disseminated widely and shared with the scientific community by presenting results at national scientific meetings and publishing in peer-reviewed journals.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierExplore the potential mechanisms of noncoding variants in human diseases test98
Scientific Questions Being Studied
Noncoding regulatory elements, such as enhancers, act as regulators of gene expression. The Encyclopedia of DNA Elements (ENCODE) project and other studies have identified millions of regulatory elements in various tissues. The roles of noncoding regulatory elements in many human diseases have acquired considerable attention, including in cancer, cardiovascular disease and psychiatric disorders. GWAS studies in understanding the genetic basis of these diseases has also identified many noncoding regulatory variants responsible for genetic risk, yet the mechanisms behind these risk variants remain poorly understood. Recent integrating multidimensional genomic data, such as expression, methylation, histone modification, chromatin accessibility and three-dimensional organization data, have enhanced interpretation of noncoding risk variants in human diseases. However, systematic analysis multidimensional genomic data make things more complex and has become a big challenge in the field.
Project Purpose(s)
- Ancestry
Scientific Approaches
In this project, we will develop novel and powerful computational methods to systematically discover the potential biomarker and drug target in human diseases, such as cardiovascular disease, arteriosclerosis, lung diseases, asthma, T2D, and many other metabolism diseases based on multidimensional genomic data. We first will identify molecular differences between healthy individuals and patients based on these sequencing data. The All of Us project provides a great resource of genomic data in well-phenotype, disease-relevant populations. We will identify differences between healthy individuals and patients based on All of Us sequencing data. And then using newly developed computational methods to systematically integrate multidimensional genomic data from other cohorts.
Anticipated Findings
In this study, we will develop novel computational methods to systematically discover these diseases causing changes in noncoding elements, which may be used as drug target in the future. Computational tools will be shared with all researchers. Findings from this project will be disseminated widely and shared with the scientific community by presenting results at national scientific meetings and publishing in peer-reviewed journals.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierExplore the potential mechanisms of noncoding variants in human diseases
Scientific Questions Being Studied
Noncoding regulatory elements, such as enhancers, act as regulators of gene expression. The Encyclopedia of DNA Elements (ENCODE) project and other studies have identified millions of regulatory elements in various tissues. The roles of noncoding regulatory elements in many human diseases have acquired considerable attention, including in cancer, cardiovascular disease and psychiatric disorders. GWAS studies in understanding the genetic basis of these diseases has also identified many noncoding regulatory variants responsible for genetic risk, yet the mechanisms behind these risk variants remain poorly understood. Recent integrating multidimensional genomic data, such as expression, methylation, histone modification, chromatin accessibility and three-dimensional organization data, have enhanced interpretation of noncoding risk variants in human diseases. However, systematic analysis multidimensional genomic data make things more complex and has become a big challenge in the field.
Project Purpose(s)
- Methods Development
- Ancestry
Scientific Approaches
In this project, we will develop novel and powerful computational methods to systematically discover the potential biomarker and drug target in human diseases, such as cardiovascular disease, arteriosclerosis, lung diseases, asthma, T2D, and many other metabolism diseases based on multidimensional genomic data. We first will identify molecular differences between healthy individuals and patients based on these sequencing data. The All of Us project provides a great resource of genomic data in well-phenotype, disease-relevant populations. We will identify differences between healthy individuals and patients based on All of Us sequencing data. And then using newly developed computational methods to systematically integrate multidimensional genomic data from other cohorts.
Anticipated Findings
In this study, we will develop novel computational methods to systematically discover these diseases causing changes in noncoding elements, which may be used as drug target in the future. Computational tools will be shared with all researchers. Findings from this project will be disseminated widely and shared with the scientific community by presenting results at national scientific meetings and publishing in peer-reviewed journals.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierExplore the potential mechanisms of noncoding variants in human diseases test
Scientific Questions Being Studied
Noncoding regulatory elements, such as enhancers, act as regulators of gene expression. The Encyclopedia of DNA Elements (ENCODE) project and other studies have identified millions of regulatory elements in various tissues. The roles of noncoding regulatory elements in many human diseases have acquired considerable attention, including in cancer, cardiovascular disease and psychiatric disorders. GWAS studies in understanding the genetic basis of these diseases has also identified many noncoding regulatory variants responsible for genetic risk, yet the mechanisms behind these risk variants remain poorly understood. Recent integrating multidimensional genomic data, such as expression, methylation, histone modification, chromatin accessibility and three-dimensional organization data, have enhanced interpretation of noncoding risk variants in human diseases. However, systematic analysis multidimensional genomic data make things more complex and has become a big challenge in the field.
Project Purpose(s)
- Ancestry
Scientific Approaches
In this project, we will develop novel and powerful computational methods to systematically discover the potential biomarker and drug target in human diseases, such as cardiovascular disease, arteriosclerosis, lung diseases, asthma, T2D, and many other metabolism diseases based on multidimensional genomic data. We first will identify molecular differences between healthy individuals and patients based on these sequencing data. The All of Us project provides a great resource of genomic data in well-phenotype, disease-relevant populations. We will identify differences between healthy individuals and patients based on All of Us sequencing data. And then using newly developed computational methods to systematically integrate multidimensional genomic data from other cohorts.
Anticipated Findings
In this study, we will develop novel computational methods to systematically discover these diseases causing changes in noncoding elements, which may be used as drug target in the future. Computational tools will be shared with all researchers. Findings from this project will be disseminated widely and shared with the scientific community by presenting results at national scientific meetings and publishing in peer-reviewed journals.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierExplore the potential mechanisms of noncoding variants in human diseases new
Scientific Questions Being Studied
Noncoding regulatory elements, such as enhancers, act as regulators of gene expression. The Encyclopedia of DNA Elements (ENCODE) project and other studies have identified millions of regulatory elements in various tissues. The roles of noncoding regulatory elements in many human diseases have acquired considerable attention, including in cancer, cardiovascular disease and psychiatric disorders. GWAS studies in understanding the genetic basis of these diseases has also identified many noncoding regulatory variants responsible for genetic risk, yet the mechanisms behind these risk variants remain poorly understood. Recent integrating multidimensional genomic data, such as expression, methylation, histone modification, chromatin accessibility and three-dimensional organization data, have enhanced interpretation of noncoding risk variants in human diseases. However, systematic analysis multidimensional genomic data make things more complex and has become a big challenge in the field.
Project Purpose(s)
- Ancestry
Scientific Approaches
n this project, we will develop novel and powerful computational methods to systematically discover the potential biomarker and drug target in human diseases, such as cardiovascular disease, arteriosclerosis, lung diseases, asthma, T2D, and many other metabolism diseases based on multidimensional genomic data. We first will identify molecular differences between healthy individuals and patients based on these sequencing data. The All of Us project provides a great resource of genomic data in well-phenotype, disease-relevant populations. We will identify differences between healthy individuals and patients based on All of Us sequencing data. And then using newly developed computational methods to systematically integrate multidimensional genomic data from other cohorts.
Anticipated Findings
In this study, we will develop novel computational methods to systematically discover these diseases causing changes in noncoding elements, which may be used as drug target in the future. Computational tools will be shared with all researchers. Findings from this project will be disseminated widely and shared with the scientific community by presenting results at national scientific meetings and publishing in peer-reviewed journals.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierRequest a Review of this Research Project
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