Interpretation of pooling experiments using the Markov chain Monte Carlo method
Interpretation of pooling experiments using the Markov chain Monte Carlo method
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DOI：
10.1089/cmb.1996.3.395
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发表时间：
1996-09-01
影响因子：
1.7
通讯作者：
Torney, DC
中科院分区：
文献类型：
--
作者：
Knill, E;Schliep, A;Torney, DC
本文描述了一种有效的方法，用于从文库筛选的汇集实验中提取尽可能多的信息，收集克隆集合，并用探针筛选汇集物以确定这些克隆中的任何一个是否对探针呈阳性。汇集物筛选的结果被解释或解码，以推断哪些克隆是阳性的候选者，这些候选阳性者经受确认测试，由于错误的存在，解码池筛选结果变得复杂，这通常导致阳性克隆推断中的模糊性。然而，在许多应用中，存在用于阳性和错误的先验分布的合理模型，并且贝叶斯推断是用于对候选阳性进行排序的优选方法。由于贝叶斯公式的组合复杂性，我们使用马尔可夫链蒙特卡罗方法实现了解码算法，该算法用于使用47个库筛选具有1298个克隆的文库。我们用确认筛选的结果证实了阳性的后验概率，我们还模拟了33个10倍覆盖文库的筛选，000克隆使用253池，使用我们的算法，有效的条件下，组合解码技术是轻率的，允许使用更少的池，也介绍了所需的鲁棒性。
This paper describes an effective method for extracting as much information as possible from pooling experiments for library screening, Pools are collections of clones, and screening a pool with a probe determines whether any of these clones are positive for the probe, The results of the pool screenings are interpreted, or decoded, to infer which clones are candidates to be positive, These candidate positives are subjected to confirmatory testing, Decoding the pool screening results is complicated by the presence of errors, which typically lead to ambiguities in the inference of positive clones, However, in many applications there are reasonable models for the prior distributions for positives and for errors, and Bayes inference is the preferred method for ranking candidate positives, Because of the combinatoric complexity of the Bayes formulation, we implemented a decoding algorithm using a Markov chain Monte Carlo method, The algorithm was used in screening a library with 1298 clones using 47 pools, We corroborated the posterior probabilities for positives with results from confirmatory screening, We also simulated the screening of a 10-fold coverage library of 33,000 clones using 253 pools, The use of our algorithm, effective under conditions where combinatorial decoding techniques are imprudent, allows the use of fewer pools and also introduces needed robustness.