EFFICIENT POOLING DESIGNS FOR LIBRARY SCREENING

EFFICIENT POOLING DESIGNS FOR LIBRARY SCREENING
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DOI:
10.1016/0888-7543(95)80078-z
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发表时间:
1995-03-01
期刊:
影响因子:
4.4
通讯作者:
TORNEY, DC
TORNEY, DC
中科院分区:
生物学3区
文献类型:
--
作者:
BRUNO, WJ;KNILL, E;TORNEY, DC

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我们描述了筛选克隆库的有效方法,基于我们称之为“随机k集设计”的合并方案。在这些设计中,出现任何克隆的池都有可能是从v个池中选择k的任何可能性。可以选择k和v的值以优化期望的性质。随机k-集设计比其他池化方案有很大的优势:它们高效,灵活,易于指定,需要较少的池,并具有纠错和检错能力。此外,筛选通常只需一次通过即可完成,因此便于自动化。对于设计比较,我们假设“阳性"克隆数服从二项分布,参数n为克隆数,c为覆盖率。我们提出的预期数量的解决阳性克隆克隆,肯定是积极的基础上池检测作为一个标准的合并设计的效率。我们确定k的最优值,关于这个标准,作为v,It和c的函数。我们还描述了称为k-集包装设计的上级h-集设计。作为一个例子,我们讨论了一个机器人实现的设计为2.5倍的覆盖率,人类染色体16 YAC库n = 1298克隆。我们还估计了每个克隆是阳性的概率,给出了合并试验数据和实验误差模型。(C)出版社:Academic Press
We describe efficient methods for screening clone libraries, based on pooling schemes that we call ''random k-sets designs.'' In these designs, the pools in which any clone occurs are equally likely to be any possible selection of k from the v pools. The values of k and v can be chosen to optimize desirable properties. Random k-sets designs have substantial advantages over alternative pooling schemes: they are efficient, flexible, and easy to specify, require fewer pools, and have error-correcting and error-detecting capabilities. In addition, screening can often be achieved in only one pass, thus facilitating automation. For design comparison, we assume a binomial distribution for the number of ''positive'' clones, with parameters n, the number of clones, and c, the coverage. We propose the expected number of resolved positive clones-clones that are definitely positive based upon the pool assays-as a criterion for the efficiency of a pooling design. We determine the value of k that is optimal, with respect to this criterion, as a function of v, It, and c. We also describe superior h-sets designs called k-sets packing designs. As an illustration, we discuss a robotically implemented design for a 2.5-fold-coverage, human chromosome 16 YAC library of n = 1298 clones. We also estimate the probability that each clone is positive, given the pool-assay data and a model for experimental errors. (C) 1995 Academic Press, Inc.