High-throughput next-generation sequencing (NGS) technology produces a tremendous amount of raw sequence data. Thechallenges for researchers are to process the raw data, to map the sequences to genome, to discover variants that aredifferent from the reference genome, and to prioritize/rank the variants for the question of interest. The recent developmentof many computational algorithms and programs has vastly improved the ability to translate sequence data into valuableinformation for disease gene identification. However, the NGS data analysis is complex and could be overwhelming forresearchers who are not familiar with the process. Here, we outline the analysis pipeline and describe some of the mostcommonly used principles and tools for analyzing NGS data for disease gene identification.