Genomics Inform.  2013 Dec;11(4):191-199. 10.5808/GI.2013.11.4.191.

A Primer for Disease Gene Prioritization Using Next-Generation Sequencing Data

• Wang S
• Xing J

• KMID: 2166502
• DOI: http://doi.org/10.5808/GI.2013.11.4.191

Abstract

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

Keyword

disease gene prioritization; high-throughput DNA sequencing; human genome; sequence alignment; variant discovery

MeSH Terms

Genome
Genome, Human
High-Throughput Nucleotide Sequencing
Humans
Sequence Alignment
Statistics as Topic