Advancements in genetic engineering最新文献

Genetically engineered mouse models are indispensable for understanding the biological function of genes, understanding the genetic basis of human diseases and for preclinical testing of novel therapies. Generation of such mouse models has been possible because of our ability to manipulate the mouse genome. Recombineering is a highly efficient recombination-based method of genetic engineering that has revolutionized our ability to generate mouse models. Since recombineering technology is not dependent on the availability of restriction enzyme recognition sites, it allows us to modify the genome with great precision. It requires homology arms as short as 40 bases for recombination, which makes it relatively easy to generate targeting constructs to insert, change or delete either a single nucleotide or a DNA fragment several kb in size; insert selectable markers, reporter genes or add epitope tags to any gene of interest. In this review, we focus on the development of recombineering technology and its application in the generation of transgenic and knockout or knock-in mouse models. High throughput generation of gene targeting vectors, used to construct knockout alleles in mouse embryonic stem cells, is now feasible because of this technology. The challenge now is to use the "designer" mice to develop novel therapies to prevent, cure or effectively manage some the most debilitating human diseases.

It is usually observed that among genes there exist strong statistical interactions associated with diseases of public health importance. Gene interactions can potentially contribute to the improvement of disease classification accuracy. Especially when gene expression differs across different classes are not great enough, it is more important to take use of gene interactions for disease classification analyses. However, most gene selection algorithms in classification analyses merely focus on genes whose expression levels show differences across classes, and ignore the discriminatory information from gene interactions. In this study, we develop a two-stage algorithm that can take gene interaction into account during a gene selection procedure. Its biggest advantage is that it can take advantage of discriminatory information from gene interactions as well as gene expression differences, by using "Bayes error" as a gene selection criterion. Using simulated and real microarray data sets, we demonstrate the ability of gene interactions for classification accuracy improvement, and present that the proposed algorithm can yield small informative sets of genes while leading to highly accurate classification results. Thus our study may give a novel sight for future gene selection algorithms of human diseases discrimination.

It is realized that a combined analysis of different types of genomic measurements tends to give more reliable classification results. However, how to efficiently combine data with different resolutions is challenging. We propose a novel compressed sensing based approach for the combined analysis of gene expression and copy number variants data for the purpose of subtyping six types of Gliomas. Experimental results show that the proposed combined approach can substantially improve the classification accuracy compared to that of using either of individual data type. The proposed approach can be applicable to many other types of genomic data.