diseaseGPS: auxiliary diagnostic system for genetic disorders based on genotype and phenotype

Author:

Huang Daoyi12ORCID,Jiang Jianping123,Zhao Tingting34,Wu Shengnan3,Li Pin3,Lyu Yongfen3,Feng Jincai3,Wei Mingyue3,Zhu Zhixing34,Gu Jianlei12,Ren Yongyong12ORCID,Yu Guangjun345,Lu Hui123ORCID

Affiliation:

1. 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

2. SJTU-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University , Shanghai, China

3. Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University , Shanghai, China

4. Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine , Shanghai, China

5. School of Medicine, The Chinese University of Hong Kong , Shenzhen, Guangdong, China

Abstract

Abstract Summary 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. Availability and implementation diseaseGPS is available to freely accessed at https://diseasegps.sjtu.edu.cn with source code at https://github.com/BioHuangDY/diseaseGPS.

Funder

National Key R&D Program of China

Neil Shen’s SJTU Medical Research Fund

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference18 articles.

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