DNA阵列杂交实验中选择对照克隆的数学方法
Algorithmic approaches to selecting control clones in DNA array hybridization experiments
摘要：
研究了DNA阵列杂交实验中控制克隆的选择问题。问题出现在OFRG方法分析微生物群落。OFRG方法使用从一系列杂交实验中创建的二元指纹对rRNA基因克隆进行分类，其中每个实验包括将阵列克隆的集合与单个寡核苷酸探针杂交。该实验产生模拟信号，每个克隆一个，然后需要将其分类，即转换为代表杂交和非杂交事件的二进制值1和0。除了样本rRNA基因克隆外，阵列还包含校准杂交信号分类程序所需的许多对照克隆。必须小心选择这些对照克隆以优化分类过程。我们将其表述为一个称为平衡覆盖的组合优化问题。我们证明了这个问题是NP-困难的，我们展示了一些结果的硬度近似。我们提出了近似算法的基础上随机舍入，我们表明，以很高的概率，我们的算法近似最优解。实验结果证实，该算法找到高质量的控制克隆。这些算法已经实现，并作为名为CloneTools的软件包的一部分公开提供。
英文摘要：
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 combinatorial optimization problem called Balanced Covering. We prove that the problem is NP-hard, and we show some results on hardness of approximation. We propose approximation algorithms based on randomized rounding and we show that, with high probability, our algorithms approximate well the optimum solution. The experimental results confirm that the algorithms find high quality control clones. The algorithms have been implemented and are publicly available as part of the software package called CloneTools.
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