Improved Tag Set Design and Multiplexing Algorithms for Universal Arrays

TL;DR

This paper presents new algorithms for designing universal array tag sets and optimizing multiplexing in genomic assays, significantly reducing the number of arrays needed through integrated optimization techniques.

Contribution

It extends previous tag set design models to include hybridization constraints and introduces methods to improve multiplexing rates by combining primer selection with tag assignment.

Findings

Up to 50% reduction in required arrays with integrated optimization.

Extended tag set design to include hybridization constraints.

Proposed a greedy algorithm for tag selection.

Abstract

In this paper we address two optimization problems arising in the design of genomic assays based on universal tag arrays. First, we address the universal array tag set design problem. For this problem, we extend previous formulations to incorporate antitag-to-antitag hybridization constraints in addition to constraints on antitag-to-tag hybridization specificity, establish a constructive upper bound on the maximum number of tags satisfying the extended constraints, and propose a simple greedy tag selection algorithm. Second, we give methods for improving the multiplexing rate in large-scale genomic assays by combining primer selection with tag assignment. Experimental results on simulated data show that this integrated optimization leads to reductions of up to 50% in the number of required arrays.