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High-probability grants
According to our matching algorithm, Xiaoming Liu is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2017 — 2020 |
Liu, Xiaoming |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Accurately Inferring Demographic Histories of Human Populations Using Large Whole Genome Sequence Data @ University of Texas Hlth Sci Ctr Houston
PROJECT SUMMARY Inferring the demographic history of a population is an important task in population genetics. Although several methods are available for this task, how to take advantage of large sample size of whole genome sequence data and provide accurate estimation of demographic history remains an open question. We propose several approaches to overcome the shortcomings of existing methods and specifically improve their accuracy and scalability for large sample size and whole genome sequence data. The resulting methods will be applied to the whole genome sequences of the genotype-rich human populations such as the TOPMED European American cohorts (~30,000 individuals) and Icelander whole genome sequence data (2,636 individuals), and provide good estimation of the demographic histories. Finally, a software package will be developed to incorporate the new methods and assist other researchers to easily apply the method to their own data.
|
0.946 |
2020 — 2021 |
Liu, Xiaoming |
R03Activity Code Description: To provide research support specifically limited in time and amount for studies in categorical program areas. Small grants provide flexibility for initiating studies which are generally for preliminary short-term projects and are non-renewable. |
Extend and Improve the Functional Annotation Tools Dbnsfp and Wgsa @ University of South Florida
PROJECT SUMMARY Whole exome sequencing (WES) and whole genome sequencing (WGS) have increasingly been used to identify variants, genes and regulatory regions that are associated with human diseases. As a result, we are witnessing a tsunami of DNA sequence data from both healthy human subjects and those with Mendelian or complex diseases. Identifying variants that are causal of a disease or associated with disease risks from a huge amount of DNA variants identified in sequencing is like looking for a needle in a hay stack. To accomplish this daunting task, investigators have relied on functional annotation to filter or prioritize variants based on our current knowledge or prediction models. We previously developed the dbNSFP database with deleteriousness prediction scores of all possible missense mutations in humans, as well as the WGS annotator (WGSA) software to facilitate functional annotation for both coding and non-coding variants which current contains > 1.5 Tb (compressed) resource data. These software tools are widely used by worldwide investigators. In the proposed study, to extend and harden our functional annotation tools and resources for handling the rapidly-increasing amount of WES and WGS data. Specifically, we will extend and improve the functional annotation resources of dbNSFP and WGSA, and improve the speed, user-interface and dissemination approach of dbNSFP and WGSA. Successful completion of these aims will accelerate the progress to study newly discovered variants for their involvement in human disease in the era of big data and precision medicine. This contribution will also benefit the human genomics and human biomedical sciences in general because DNA sequence analyses have become the essential approach in those areas and DNA variant functional annotation will certainly help us to understand and interpret the functions of the variants.
|
0.946 |