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Many human diseases can be attributed to aberrant gene expression, regulation or both at the transcriptome or RNA level; genetic mutations; or abnormal epigenetic modifications at the genome or DNA level. Xing Li, Ph.D., mainly focuses on data mining and integration of large genomic data in family studies of cardiovascular diseases. Dr. Li's goal is to unveil the underlying molecular mechanisms for congenital heart defects, dilated cardiomyopathy and hypoplastic left heart syndrome (HLHS).
Sequencing analysis. Dr. Li performs high-throughput sequencing data mining on time course RNA sequencing, whole-genome shotgun sequencing and exome sequencing data in order to pinpoint aberrant gene expression, gene fusion, alternative isoforms and genetic alterations (for example, single nucleotide variants, copy-number variation or structural variation) in cardiovascular diseases.
Dr. Li helped develop the Mayo GenomeGPS pipeline for whole-genome sequencing analysis and implemented and maintains the RNA sequencing data analysis pipeline for Mayo's HLHS program. This RNA sequencing pipeline is able to perform short reads alignment, transcriptome assembly, differential analyses on genes, alternative splicing or isoforms, transcription start sites and coding sequencing in one run, which facilitates the data analysis and interpretation.
Dr. Li also developed another R package for 3-D visualization of principal component analysis results to dynamically reveal the structure of the sample profiles, especially for time course transcriptome data with large number of time points or groups.
Identifying the molecular mechanisms of cardiovascular diseases using high-throughput methods will be helpful for human disease diagnosis and may lead to better treatment for human heart diseases. In vitro disease modeling using patient-derived iPSCs will improve drug toxicity tests and advance the development of individualized medicine.
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