An index structure for pattern similarity searching in DNA microarray data

DNA microarray technology is about to bring an explosion of gene expression data that may dwarf even the human sequencing projects. Researchers are motivated to identify genes whose expression levels rise and fall coherently under a set of experimental perturbations, that is, they exhibit fluctuation of a similar shape when conditions change. In this paper, we show that queries based on pattern correlations against large-scale microarray databases can be supported by the weighted-sequence model, an index structure designed for sequence matching. A weighted-sequence is a two-dimensional structure where each element in the sequence is associated with a weight. We transform the DNA microarray data, as well as pattern-based queries, into weighted-sequences, and use subsequence matching algorithms to retrieve from the database all genes that match the query pattern. We demonstrate, using both synthetic and real-world data sets, that our method is effective and efficient.