用于文库筛选的高效合并设计
Efficient pooling designs for library screening
摘要：
我们描述了筛选克隆文库的有效方法，基于我们称之为“随机$k$ -集设计”的合并方案。在这些设计中，出现任何克隆的池同样可能是从$v$池中选择的任何可能的$k$。可以选择$k$和$v$的值以优化所需的性质。随机$k$ -集设计与其他池化方案相比具有很大的优势：它们高效、灵活、易于指定、需要较少的池，并且具有纠错和检错能力。此外，筛选通常只需一次通过，从而便于自动化。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。我们提出了预期的{ \em解析阳性}克隆数-根据合并试验确定为阳性的克隆-作为合并设计效率的标准。我们确定相对于该标准最优的$k$的值，作为$v$、$n$和$c$的函数。我们还描述了称为$k$-集填充设计的优良$k$-集设计。作为一个例子，我们讨论了一个2.5倍覆盖，人类染色体16 YAC库的$n=1,298$克隆的机器人实现的设计。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。
英文摘要：
We describe efficient methods for screening clone libraries, based on pooling schemes which 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, 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 {\em resolved positive} clones---clones which are definitely positive based upon the pool assays---as a criterion for the efficiency of a pooling design. We determine the value of $k$ which is optimal, with respect to this criterion, as a function of $v$, $n$ and $c$. We also describe superior $k$-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 $n=1,298$ clones. We also estimate the probability each clone is positive, given the pool-assay data and a model for experimental errors.
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