Development of a Multi-State Decoding Framework

Period of Performance: 02/03/2005 - 01/31/2006

$481K

Phase 2 SBIR

Recipient Firm

Illumina, Inc.
5200 Illumina Way
San Diego, CA 92121
Principal Investigator

Research Topics

Abstract

DESCRIPTION (provided by applicant): This proposal aims to increase the information content and decrease the cost of the Illumina random array platform. Random arrays are assembled by attaching DMA probes to silica beads and loading the beads onto an etched substrate. The bead identities are determined through a sequential hybridization based decode process. Once the arrays are decoded, they can be used in applications such as SNP genotyping and gene expression profiling. The cost of decoding is proportional to the number of sequential hybridizations. The number of probes that can be decoded on the array is a function of the number of hybridizations and the number of distinguishable labels (decode states) used in the process. An increase in the number of probes requires either an increase in the number of stages hybridizations or an increase in the number of states. Prior to the completion of Phase I, arrays containing 1,500 probes were decoded with 8 hybridizations and 3 states. The completion of Phase I has demonstrated the feasibility of 7-state decoding. The increase in states dramatically increases decoding capabilities, allowing the decoding of 24,000 probes in 6 stages. The first goal of Phase II is to implement the 7-state decode system in manufacturing. The next goal is to improve and extend the decode system through the addition of intermediate intensity states and the reduction of process variability. The ability to efficiently decode tens of thousand of probes presents an opportunity to increase the density of the random arrays. Arrays with a range of feature sizes will be manufactured and the optimal density will be determined. The efficient decoding combined with the higher density of features will lead to microarrays with high information content, low cost per data point, and low sample consumption.