Algorithmic approaches to selecting control clones in DNA array hybridization experiments
Qi Fu, Elizabeth Bent, James Borneman, Marek Chrobak, Neal E. Young

TL;DR
This paper addresses selecting control clones in DNA array hybridization experiments, formulating it as a NP-hard optimization problem, and proposes approximation algorithms with proven effectiveness and practical implementation.
Contribution
It introduces the Balanced Covering problem for control clone selection, proves its NP-hardness, and develops approximation algorithms with empirical validation.
Findings
Algorithms achieve high-quality control clone selection
Proposed methods are effective and computationally feasible
Software implementation available as CloneTools
Abstract
We study the problem of selecting control clones in DNA array hybridization experiments. The problem arises in the OFRG method for analyzing microbial communities. The OFRG method performs classification of rRNA gene clones using binary fingerprints created from a series of hybridization experiments, where each experiment consists of hybridizing a collection of arrayed clones with a single oligonucleotide probe. This experiment produces analog signals, one for each clone, which then need to be classified, that is, converted into binary values 1 and 0 that represent hybridization and non-hybridization events. In addition to the sample rRNA gene clones, the array contains a number of control clones needed to calibrate the classification procedure of the hybridization signals. These control clones must be selected with care to optimize the classification process. We formulate this as a…
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