Assessing the performance of next-generation sequencing techniques in accurate genome/epigenome/transcriptome profiling and understanding the clinical and functional role of epigenome heterogeneity in the cancer evolution.
Our research focus
The primary focus of the Li Lab is on assessing the performance of next-generation sequencing techniques in accurate genome/epigenome/transcriptome profiling and understanding the clinical and functional role of epigenome heterogeneity in the cancer evolution. The Li laboratory utilizes computational and sequencing methodologies to identify and characterize the essential epigenetic lesions that guide cancer cells to evolve and escape from therapy. Our research interest is to understand the inner workings of cancer cells – the genetic and epigenetic heterogeneity that drive cancer initiation and progression. Specifically, the research involves (1) determining the drivers of epigenetic heterogeneity; (2) evaluating the functional impact of the cross-talk among epigenetic modifications on transcriptome; (3) assessing epigenetic heterogeneity/subpopulations in treatment resistance.
Dr. Li has developed a series of computational methods and software for the epigenome sequencing data analysis, to comprehensively detect the significant DNA methylation aberration and epigenetic heterogeneity during disease progression. Dr. Li's work has helped to establish the first principles and metrics for examining changes in RNA splicing and expression profiling and set standards at the FDA for clinical-grade RNA-sequencing. Dr. Li further applied these approaches to study the epigenetic heterogeneity and dynamics using acute myeloid leukemia (AML) as a model. Dr. Li found that epigenetic allele burden was linked to inferior clinical outcome, and epigenetic dynamics was related to hypervariable transcriptional regulation and was divergent from the genetic burden.
The Li Lab has the current open positions.
Postdoctoral Associate - Computational Cancer Biology
About the position:
Dr. Sheng Li’s lab at JAX-GM is seeking a computational biology postdoctoral associate who is interested in studying cancer epigenomics using large-scale genomics datasets. The Li Lab focuses on combining computational approaches and high throughput datasets to uncover novel mechanisms that contribute to drug resistance and cancer evolution. For additional information, visit the Li Lab online.
We are looking for applicants who are excited to work in the collegial, collaborative, interdisciplinary, and diverse research environment offered by The Jackson Laboratory.
Develop computational, bioinformatics, and statistical approaches to address the challenges in cancer genome, epigenome, chromatin, and transcriptome data integration
Analyze bulk and single-cell sequencing data sets from clinical studies to deconvolute cell populations and associated cellular phenotypes
Work to identify molecular patterns and predictive markers for diagnosis and treatment
PhD in Computational Biology, Biostatistics, Bioinformatics, or a related field with knowledge of statistics and an interest in working in an interdisciplinary computational biology and cancer research environment.
Strong computational background, including proficiency in at least one programming language (Perl, Python, C++, etc.) and one data analysis software program (R, MATLAB, etc.). Linux experience is required.
Research experience in next generation sequencing data analysis, especially in epigenomics, and utilization of publicly available bioinformatics resources.
Excellent communication skills and fully fluent in both spoken and written English.
A passion for solving cutting edge research problems of computational biology.
The following optional qualifications are considered advantageous:
Knowledge of (and/or work experience with) machine learning/deep learning methods.
Experience with large-scale data analysis using software pipelines in a HPC environment.
Experience in software development.
Rotation projects are available for enrolled students at UConn Health. Please email email@example.com.