Institute of Molecular Health Sciences, Department of Biology, Swiss Federal Institute of Technology ETH Hönggerberg, Zurich, Switzerland.
PLoS Comput Biol. 2018 Jan 16;14(1):e1005950. doi: 10.1371/journal.pcbi.1005950. eCollection 2018 Jan.
Haploid cells are increasingly used for screening of complex pathways in animal genomes. Hemizygous mutations introduced through viral insertional mutagenesis can be directly selected for phenotypic changes. Here we present HaSAPPy a tool for analysing sequencing datasets of screens using insertional mutations in large pools of haploid cells. Candidate gene prediction is implemented through identification of enrichment of insertional mutations after selection by simultaneously evaluating several parameters. We have developed HaSAPPy for analysis of genetic screens for silencing factors of X chromosome inactivation in haploid mouse embryonic stem cells. To benchmark the performance, we further analyse several datasets of genetic screens in human haploid cells for which candidates have been validated. Our results support the effective candidate prediction strategy of HaSAPPy. HaSAPPy is implemented in Python, licensed under the MIT license, and is available from https://github.com/gdiminin/HaSAPPy.
单倍体细胞越来越多地用于筛选动物基因组中的复杂途径。通过病毒插入诱变引入的半合突变可以直接选择表型变化。在这里，我们介绍了 HaSAPPy，这是一种用于分析通过大群单倍体细胞中的插入突变进行筛选的测序数据集的工具。通过同时评估几个参数来识别选择后插入突变的富集，从而实现候选基因预测。我们已经开发了 HaSAPPy，用于分析单倍体小鼠胚胎干细胞中 X 染色体失活沉默因子的遗传筛选。为了进行基准测试，我们进一步分析了几个人类单倍体细胞遗传筛选数据集，其中候选基因已经过验证。我们的结果支持 HaSAPPy 的有效候选预测策略。HaSAPPy 是用 Python 实现的，MIT 许可证授权，可从 https://github.com/gdiminin/HaSAPPy 获得。
