A Positive Detecting Code and Its Decoding Algorithm for DNA Library Screening
A Positive Detecting Code and Its Decoding Algorithm for DNA Library Screening
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DOI：
10.1109/tcbb.2007.70266
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发表时间：
2009-10
期刊：
影响因子：
--
通讯作者：
Hiroaki Uehara;Masakazu Jimbo
中科院分区：
文献类型：
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作者：
Hiroaki Uehara;Masakazu Jimbo
基因功能的研究需要高质量的DNA文库。然而，大量的测试和筛选是必要的编译这样的库。我们描述了一种算法，用于从库筛选的汇集实验中提取尽可能多的信息。克隆的集合称为池，池实验是用于检测所有阳性克隆的组测试。根据合并实验的结果估计每个克隆的阳性概率。对阳性机会高的克隆进行确证性检测。本文提出了一种新的阳性克隆检测算法，称为贝叶斯网络池结果解码器（BNPD）。BNPD的性能进行了比较，通过仿真，与马尔可夫链池结果解码器（MCPD）的Knill等人在1996年提出的。结合组合设计和d-析取矩阵讨论了适用于该算法的池化设计的组合性质。我们还展示了利用包装设计或BIB设计的BNPD算法的优势。
The study of gene functions requires high-quality DNA libraries. However, a large number of tests and screenings are necessary for compiling such libraries. We describe an algorithm for extracting as much information as possible from pooling experiments for library screening. Collections of clones are called pools, and a pooling experiment is a group test for detecting all positive clones. The probability of positiveness for each clone is estimated according to the outcomes of the pooling experiments. Clones with high chance of positiveness are subjected to confirmatory testing. In this paper, we introduce a new positive clone detecting algorithm, called the Bayesian network pool result decoder (BNPD). The performance of BNPD is compared, by simulation, with that of the Markov chain pool result decoder (MCPD) proposed by Knill et al. in 1996. Moreover, the combinatorial properties of pooling designs suitable for the proposed algorithm are discussed in conjunction with combinatorial designs and d-disjunct matrices. We also show the advantage of utilizing packing designs or BIB designs for the BNPD algorithm.