Chenlab: Difference between revisions
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We are bioinformatics laboratory interested in the following area: | We are bioinformatics laboratory interested in the following area: | ||
* '''Developing statistical and computational methodology to make discovery in high throughput DNA sequencing data'''. Bioinformatics tools for new sequencing technologies, statistical solutions for combining different types of sequencing data for integrative analysis, innovative methodologies to make novel observation or answer new biological questions. | * '''Developing statistical and computational methodology to make discovery in high throughput DNA sequencing data'''. Bioinformatics tools for new sequencing technologies, statistical solutions for combining different types of sequencing data for integrative analysis, innovative methodologies to make novel observation or answer new biological questions. | ||
* '''Understanding gene regulation network that determines cell | * '''Understanding gene regulation network that determines cell identity and tissue specificity in development'''. The identity of a cell is determined by its unique gene expression program. Many factors can regulate or influence transcription, e.g., genomic elements, transcription factors, histone modifications, DNA methylations, and noncoding RNA. Our goal is to investigate not only how each factor works, but also how different factors interact to form a gene regulation network. | ||
* '''Discovering driver genes of cancer, genetic disease, or other important phenotypes'''. With Over 10 years research experiences in epigenomics, genomics, and bioinformatics, we are highly interested in developing machine learning algorithms to combine epigenetic and genetic data for disease gene discovery. | * '''Discovering driver genes of cancer, genetic disease, or other important phenotypes'''. With Over 10 years research experiences in epigenomics, genomics, and bioinformatics, we are highly interested in developing machine learning algorithms to combine epigenetic and genetic data for disease gene discovery. | ||
Revision as of 14:38, 5 November 2015
Chen Laboratory
We are bioinformatics laboratory interested in the following area:
- Developing statistical and computational methodology to make discovery in high throughput DNA sequencing data. Bioinformatics tools for new sequencing technologies, statistical solutions for combining different types of sequencing data for integrative analysis, innovative methodologies to make novel observation or answer new biological questions.
- Understanding gene regulation network that determines cell identity and tissue specificity in development. The identity of a cell is determined by its unique gene expression program. Many factors can regulate or influence transcription, e.g., genomic elements, transcription factors, histone modifications, DNA methylations, and noncoding RNA. Our goal is to investigate not only how each factor works, but also how different factors interact to form a gene regulation network.
- Discovering driver genes of cancer, genetic disease, or other important phenotypes. With Over 10 years research experiences in epigenomics, genomics, and bioinformatics, we are highly interested in developing machine learning algorithms to combine epigenetic and genetic data for disease gene discovery.
Recent news:
October, 2015
- Dr. Dongyu Zhao has joined us as postdoctoral Fellow, welcome onboard!
September, 2015
- Our Broad H3K4me3 research work is highlighted by the Cancer Discovery journal (IF 20.26)
- Dr. Alin Tomonaga has joined us as postdoctoral Fellow, welcome onboard!
August, 2015
- Kaifu's data-revisiting paper is accepted to Nature Genetics, we find that conserved broad H3K4me3 uniquely marks tumor suppressor in normal cell types.
- Kaifu's new manuscript is under the 3rd round review at Nature Genetics now.
- Kaifu's MeCP2 mCH-binding paper (with Zoghbi lab) has been accepted to PNAS.
