Comparison of parametric and nonparametric methods to map oligogenes by linkage

A sample of 95 sib pairs affected with insulin-dependent diabetes and typed with their normal parents for 28 markers on chromosome 6 has been analyzed by several methods. When appropriate parameters are efficiently estimated, a parametric model is equivalent to the β model, which is superior to nonparametric alternatives both in single point tests (as found previously) and in multipoint tests. Theory is given for meta-analysis combined with allelic association, and problems that may be associated with errors of map location and/or marker typing are identified. Reducing by multipoint analysis the number of association tests in a dense map can give a 3-fold reduction in the critical lod, and therefore in the cost of positional cloning.

Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Rescue, propagation, and partial purification of a helper virus-dependent adenovirus vector.

Adenoviral vectors are widely used as highly efficient gene transfer vehicles in a variety of biological research strategies including human gene therapy. One of the limitations of the currently available adenoviral vector system is the presence of the majority of the viral genome in the vector, resulting in leaky expression of viral genes particularly at high multiplicity of infection and limited cloning capacity of exogenous sequences. As a first step to overcome this problem, we attempted to rescue a defective human adenovirus serotype 5 DNA, which had an essential region of the viral genome (L1, L2, VAI + II, pTP) deleted and replaced with an indicator gene. In the presence of wild-type adenovirus as a helper, this DNA was packaged and propagated as transducing viral particles. After several rounds of amplification, the titer of the recombinant virus reached at least 4 x 10(6) transducing particles per ml. The recombinant virus could be partially purified from the helper virus by CsCl equilibrium density-gradient centrifugation. The structure of the recombinant virus around the marker gene remained intact after serial propagation, while the pBR sequence inserted in the E1 region was deleted from the recombinant virus. Our results suggest that it should be possible to develop a helper-dependent adenoviral vector, which does not encode any viral proteins, as an alternative to the currently available adenoviral vector systems.