Determining divergence times with a protein clock: Update and reevaluation

A recent study of the divergence times of the major groups of organisms as gauged by amino acid sequence comparison has been expanded and the data have been reanalyzed with a distance measure that corrects for both constraints on amino acid interchange and variation in substitution rate at different sites. Beyond that, the availability of complete genome sequences for several eubacteria and an archaebacterium has had a great impact on the interpretation of certain aspects of the data. Thus, the majority of the archaebacterial sequences are not consistent with currently accepted views of the Tree of Life which cluster the archaebacteria with eukaryotes. Instead, they are either outliers or mixed in with eubacterial orthologs. The simplest resolution of the problem is to postulate that many of these sequences were carried into eukaryotes by early eubacterial endosymbionts about 2 billion years ago, only very shortly after or even coincident with the divergence of eukaryotes and archaebacteria. The strong resemblances of these same enzymes among the major eubacterial groups suggest that the cyanobacteria and Gram-positive and Gram-negative eubacteria also diverged at about this same time, whereas the much greater differences between archaebacterial and eubacterial sequences indicate these two groups may have diverged between 3 and 4 billion years ago.

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.