Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA 02129, USA.
BMC Bioinformatics. 2010 Nov 2;11:543. doi: 10.1186/1471-2105-11-543.
Precise and efficient methods for gene targeting are critical for detailed functional analysis of genomes and regulatory networks and for potentially improving the efficacy and safety of gene therapies. Oligomerized Pool ENgineering (OPEN) is a recently developed method for engineering C2H2 zinc finger proteins (ZFPs) designed to bind specific DNA sequences with high affinity and specificity in vivo. Because generation of ZFPs using OPEN requires considerable effort, a computational method for identifying the sites in any given gene that are most likely to be successfully targeted by this method is desirable.
Analysis of the base composition of experimentally validated ZFP target sites identified important constraints on the DNA sequence space that can be effectively targeted using OPEN. Using alternate encodings to represent ZFP target sites, we implemented Naïve Bayes and Support Vector Machine classifiers capable of distinguishing "active" targets, i.e., ZFP binding sites that can be targeted with a high rate of success, from those that are "inactive" or poor targets for ZFPs generated using current OPEN technologies. When evaluated using leave-one-out cross-validation on a dataset of 135 experimentally validated ZFP target sites, the best Naïve Bayes classifier, designated ZiFOpT, achieved overall accuracy of 87% and specificity+ of 90%, with an ROC AUC of 0.89. When challenged with a completely independent test set of 140 newly validated ZFP target sites, ZiFOpT performance was comparable in terms of overall accuracy (88%) and specificity+ (92%), but with reduced ROC AUC (0.77). Users can rank potentially active ZFP target sites using a confidence score derived from the posterior probability returned by ZiFOpT.
ZiFOpT, a machine learning classifier trained to identify DNA sequences amenable for targeting by OPEN-generated zinc finger arrays, can guide users to target sites that are most likely to function successfully in vivo, substantially reducing the experimental effort required. ZiFOpT is freely available and incorporated in the Zinc Finger Targeter web server (http://bindr.gdcb.iastate.edu/ZiFiT).
精确和高效的基因靶向方法对于基因组和调控网络的详细功能分析以及潜在地提高基因治疗的疗效和安全性至关重要。寡聚池工程（OPEN）是一种最近开发的方法，用于工程化 C2H2 锌指蛋白（ZFPs），旨在在体内以高亲和力和特异性结合特定的 DNA 序列。由于使用 OPEN 生成 ZFPs 需要相当大的努力，因此需要一种计算方法来识别任何给定基因中最有可能被该方法成功靶向的位点。
对实验验证的 ZFP 靶位点的碱基组成进行分析，确定了对可以使用 OPEN 有效靶向的 DNA 序列空间的重要限制。我们使用替代编码来表示 ZFP 靶位点，实现了能够区分“活性”靶位点（即可以高成功率靶向的 ZFP 结合位点）和“非活性”或 ZFP 较差的靶位点的朴素贝叶斯和支持向量机分类器，使用当前的 OPEN 技术生成。当在 135 个实验验证的 ZFP 靶位点数据集上使用留一交叉验证进行评估时，最佳的朴素贝叶斯分类器，指定为 ZiFOpT，实现了 87%的整体准确性和 90%的特异性+，ROC AUC 为 0.89。当在 140 个新验证的 ZFP 靶位点的完全独立测试集上受到挑战时，ZiFOpT 的性能在整体准确性（88%）和特异性+（92%）方面相当，但 ROC AUC 降低（0.77）。用户可以使用 ZiFOpT 返回的后验概率得出的置信得分对潜在的活性 ZFP 靶位点进行排序。
ZiFOpT 是一种经过训练以识别可用于 OPEN 生成锌指阵列靶向的 DNA 序列的机器学习分类器，可以指导用户靶向最有可能在体内成功发挥功能的靶位点，大大减少了所需的实验工作量。ZiFOpT 是免费的，并包含在锌指靶向器网络服务器（http://bindr.gdcb.iastate.edu/ZiFiT）中。
