Skolnick M M, Neel J V
Adv Hum Genet. 1986;15:55-160. doi: 10.1007/978-1-4615-8356-1_2.
An algorithm dedicated to the detection of presumed mutational events involving the polypeptides displayed with two-dimensional polyacrylamide gel electrophoresis has been described. Because of the large number of gels necessary in most studies of mutation, the algorithm has been designed to minimize operator intervention in its execution. The basic principle involves a comparison of the graph structures of the gels of a father, mother, and one or more children, searching for protein spots in the child not found in either parent. These so-called "orphan" spots are considered a probable manifestation of mutation only after other possible causes of such an isolated event have been excluded as rigorously as possible. At present, the analysis of gels prepared from a platelet or erythrocyte lysate yields about 2% "false-positive" findings, i.e., results in the incorrect designation of a unique spot in a child. These errors can be disposed of by technician intervention. In an experiment designed to simulate the occurrence of mutational events, the algorithm operated with 70% accuracy. Most of the "errors" ("false negatives") occurred when the position of the simulated mutant polypeptide coincided in whole or part with that of a preexisting polypeptide, resulting in a class of mutation not detectable by the eye either. With correction for this fact, the accuracy was 84%. Possible improvements in the algorithm which would substantially increase accuracy have been discussed at some length, as have some ideas as to how to manage the large body of data resulting from the operation of the algorithm. A murine experiment designed to validate the approach has been outlined.
本文描述了一种专门用于检测二维聚丙烯酰胺凝胶电泳显示的多肽中假定突变事件的算法。由于在大多数突变研究中需要大量的凝胶,该算法旨在尽量减少操作人员在执行过程中的干预。其基本原理是比较父亲、母亲和一个或多个孩子的凝胶图谱结构,寻找在父母双方凝胶中均未发现的孩子凝胶中的蛋白质斑点。只有在尽可能严格地排除了这种孤立事件的其他可能原因之后,这些所谓的“孤立”斑点才被认为可能是突变的表现。目前,对血小板或红细胞裂解物制备的凝胶进行分析时,会产生约2%的“假阳性”结果,即错误地将孩子凝胶中的一个独特斑点认定为突变。这些错误可以通过技术人员的干预来处理。在一个模拟突变事件发生的实验中,该算法的准确率为70%。大多数“错误”(“假阴性”)发生在模拟突变多肽的位置全部或部分与预先存在的多肽位置重合时,导致出现一类肉眼也无法检测到的突变。考虑到这一事实进行校正后,准确率为84%。文中详细讨论了可能大幅提高算法准确率的改进方法,以及如何处理算法运行产生的大量数据的一些想法。还概述了一个旨在验证该方法的小鼠实验。