The core of the Solexa technology is based on short-read, high-quality sequences. “The system has the potential for dramatically improving the cost and efficiency of a variety of experiments,” says Ostadan. “We are developing the system for a number of key applications, including whole-genome sequencing, expression profiling, and small RNA discovery.”
In time, the technology could be used for other applications, such as whole-genome methylation analysis, chromatin immunoprecipitation assays, and other assays that require enzymatic cleavage and subsequent sequencing of tags. Moreover, researchers can use the same instrument to carry out all of these applications. “That’s very appealing, especially for core facilities that cater to a diverse set of customers and applications,” Ostadan adds.
The Solexa Genome Analysis System can generate sequence-based genome-wide expression profiles of transcripts derived from any gene of any species without prior knowledge of that species’ transcriptome. This makes the technology ideal for analyzing organisms with poorly annotated genomes or for discovering transcripts in well-annotated genomes.
In addition, few discovery tools exist today for biotechnology and pharmaceutical researchers who study the role of small RNA in the control of regulation. The new system fills that void for expression profiling of small RNA, Ostadan notes.
The system will complement association studies and genotyping experiments that rely on panels of SNPs, for example, the HapMap SNP set, predicts Linda Rubinstein, CFO. Although HapMap represents a useful starting point for understanding sequence variation, a more comprehensive understanding of sequence variation is needed. “The future lies in capturing all the variation rather than sampling parts of it,” she says.
Although several companies have created products that offer panels of these predefined SNPs, based on the HapMap Project, Solexa’s Genome Analysis System can help researchers interrogate all the bases in a human genome. For instance, thousands of samples from diabetes patients could be screened to find SNPs that are linked to the disease.
However, “typically the SNP itself is not the causative mutation,” Rubinstein cautions, and a more intensive sequencing effort will be required to investigate any candidate SNPs that emerge. “Our technology is perfect for that complementary approach,” Rubinstein says.