David Botstein

BACKGROUND: Large gene expression studies, such as those conducted using DNA arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called 'gene shaving'. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be 'unsupervised', that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful groupings. RESULTS: We illustrate the use of the gene shaving method to analyze gene expression measurements made on samples from patients with diffuse large B-cell lymphoma. The method identifies a small cluster of genes whose expression is highly predictive of survival. CONCLUSIONS: The gene shaving method is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worth further investigation.

We adapted a fusion polymerase chain reaction (PCR) strategy to synthesize gene disruption alleles of any sequenced yeast gene of interest. The first step of the construction is to amplify sequences flanking the reading frame we want to disrupt and to amplify the selectable marker sequence. Then we fuse the upstream fragment to the marker sequence by fusion PCR, isolate this product and fuse it to the downstream sequence in a second fusion PCR reaction. The final PCR product can then be transformed directly into yeast. This method is rapid, relatively inexpensive, offers the freedom to choose from among a variety of selectable markers and allows one to construct precise disruptions of any sequenced open reading frame in Saccharomyces cerevisiae.