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Next-generation Sequencing

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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.
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Updated: Apr 26, 2026

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

SUGAR: graphical user interface-based data refiner for high-throughput DNA sequencing.

Yukuto Sato, Kaname Kojima, Naoki Nariai

¹Department of Integrative Genomics, Tohoku Medial Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku Sendai, Miyagi, 980-8573, Japan. nagasaki@megabank.tohoku.ac.jp.

|August 9, 2014

Summary

This summary is machine-generated.

We developed SUGAR, a user-friendly software to refine next-generation sequencing data by identifying and removing technical errors. This improves data quality for more accurate biological insights and variant analysis.

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Area of Science:

Genomics
Bioinformatics

Background:

Next-generation sequencing (NGS) is crucial in modern biology.
Technical errors, like air bubbles, can compromise NGS data quality.
Accurate analysis requires stringent quality control of sequencing data.

Purpose of the Study:

To develop a software tool for refining NGS data.
To enable users to identify and remove technically compromised sequencing data.
To improve the quality of downstream genomic analyses.

Main Methods:

Developed SUGAR (subtile-based GUI-assisted refiner) software.
Implemented a user-friendly graphical user interface (GUI) for interactive analysis.
Utilized high-resolution flowcell quality heatmaps for error detection.
Enabled selective removal of error-affected sequencing data via automated or GUI-assisted operations.

Main Results:

SUGAR handles ultra-high-throughput data effectively.
High-resolution heatmaps aid in identifying technical sequencing errors.
Automated data cleaning using Phred scores improved overall mapping quality on public whole human genome data.

Conclusions:

SUGAR facilitates detailed data evaluation and cleaning, reducing technical issues in sequence read mapping.
Improved mapping results enhance subsequent variant analysis, especially for low-frequency variants.
The software is valuable for quality control in variant calling for samples with sequencing errors.