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81.
SplitsTree: analyzing and visualizing evolutionary data   总被引:15,自引:0,他引:15  
MOTIVATION: Real evolutionary data often contain a number of different and sometimes conflicting phylogenetic signals, and thus do not always clearly support a unique tree. To address this problem, Bandelt and Dress (Adv. Math., 92, 47-05, 1992) developed the method of split decomposition. For ideal data, this method gives rise to a tree, whereas less ideal data are represented by a tree-like network that may indicate evidence for different and conflicting phylogenies. RESULTS: SplitsTree is an interactive program, for analyzing and visualizing evolutionary data, that implements this approach. It also supports a number of distances transformations, the computation of parsimony splits, spectral analysis and bootstrapping.   相似文献   
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Humanized mouse models are important tools in many areas of biological drug development including, within oncology research, the development of antagonistic antibodies that have the potential to block tumor growth by controlling vascularization and are key to the generation of in vivo proof-of-concept efficacy data. However, due to cross reactivity between human antibodies and mouse target such studies regularly require mouse models expressing only the human version of the target molecule. Such humanized knock-in/knock-out, KIKO, models are dependent upon the generation of homozygous mice expressing only the human molecule, compensating for loss of the mouse form. However, KIKO strategies can fail to generate homozygous mice, even though the human form is expressed and the endogenous mouse locus is correctly targeted. A typical strategy for generating KIKO mice is by ATG fusion where the human cDNA is inserted downstream of the endogenous mouse promoter elements. However, when adopting this strategy it is possible that the mouse promoter fails to express the human form in a manner compensating for loss of the mouse form or alternatively the human protein is incompatible in the context of the mouse pathway being investigated. So to understand more around the biology of KIKO models, and to overcome our failure with a number of ATG fusion strategies, we developed a range of humanized models focused on Delta-like 4 (Dll4), a target where we initially failed to generate a humanized model. By adopting a broader biologic strategy, we successfully generated a humanized DLL4 KIKO which led to a greater understanding of critical biological aspects for consideration when developing humanized models.  相似文献   
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