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Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

A primer for disease gene prioritization using next-generation sequencing data.

Shuoguo Wang¹, Jinchuan Xing¹

¹Department of Genetics, The State University of New Jersey, Piscataway, NJ 08854, USA. ; Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.

Genomics & Informatics

|January 28, 2014

Summary

This summary is machine-generated.

Next-generation sequencing (NGS) generates vast data. This guide simplifies NGS data analysis for disease gene discovery, detailing processing, mapping, variant identification, and prioritization using computational tools.

Keywords:

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

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Area of Science:

Genomics and Bioinformatics
Computational Biology
Molecular Biology

Background:

High-throughput next-generation sequencing (NGS) generates massive amounts of raw sequence data.
Analyzing NGS data for disease gene identification involves complex computational steps.
Researchers often face challenges in processing, mapping, and variant analysis.

Purpose of the Study:

To provide a clear overview of the NGS data analysis pipeline for disease gene identification.
To describe common principles and computational tools used in NGS data analysis.
To assist researchers, especially those new to the field, in navigating NGS data analysis.

Main Methods:

Outline of a standard NGS data analysis workflow.
Description of key computational algorithms and software.
Focus on sequence data processing, genome mapping, variant discovery, and prioritization.

Main Results:

The development of computational tools has significantly enhanced the ability to extract valuable information from NGS data.
A structured analysis pipeline facilitates the translation of raw sequence data into insights for disease gene identification.
Commonly used principles and tools are presented to demystify the NGS analysis process.

Conclusions:

NGS technology is a powerful tool for disease gene identification.
Understanding the analysis pipeline and available computational tools is crucial for effective utilization of NGS data.
This guide aims to simplify the complex process of NGS data analysis for researchers.