State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
SJTU-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
Bioinformatics. 2023 Sep 2;39(9). doi: 10.1093/bioinformatics/btad517.
The next-generation sequencing brought opportunities for the diagnosis of genetic disorders due to its high-throughput capabilities. However, the majority of existing methods were limited to only sequencing candidate variants, and the process of linking these variants to a diagnosis of genetic disorders still required medical professionals to consult databases. Therefore, we introduce diseaseGPS, an integrated platform for the diagnosis of genetic disorders that combines both phenotype and genotype data for analysis. It offers not only a user-friendly GUI web application for those without a programming background but also scripts that can be executed in batch mode for bioinformatics professionals. The genetic and phenotypic data are integrated using the ACMG-Bayes method and a novel phenotypic similarity method, to prioritize the results of genetic disorders. diseaseGPS was evaluated on 6085 cases from Deciphering Developmental Disorders project and 187 cases from Shanghai Children's hospital. The results demonstrated that diseaseGPS performed better than other commonly used methods.
diseaseGPS is available to freely accessed at https://diseasegps.sjtu.edu.cn with source code at https://github.com/BioHuangDY/diseaseGPS.
下一代测序技术具有高通量的特点，为遗传疾病的诊断带来了新的机会。然而，现有的大多数方法仅限于对候选变异进行测序，而将这些变异与遗传疾病的诊断联系起来的过程仍然需要医学专业人员查阅数据库。因此，我们引入了 diseaseGPS，这是一个用于遗传疾病诊断的集成平台，它结合了表型和基因型数据进行分析。它不仅为没有编程背景的用户提供了用户友好的 GUI 网络应用程序，还为生物信息学专业人员提供了可批量执行的脚本。遗传和表型数据使用 ACMG-Bayes 方法和一种新颖的表型相似性方法进行整合，以优先考虑遗传疾病的结果。我们在 Deciphering Developmental Disorders 项目的 6085 个病例和上海儿童医院的 187 个病例上评估了 diseaseGPS。结果表明，diseaseGPS 的性能优于其他常用方法。
diseaseGPS 可在 https://diseasegps.sjtu.edu.cn 上免费访问，源代码可在 https://github.com/BioHuangDY/diseaseGPS 上获取。
