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

Yukuto Sato, Kaname Kojima, Naoki Nariai, Yumi Yamaguchi-Kabata, Yosuke Kawai, Mamoru Takahashi, Takahiro Mimori, Masao Nagasaki

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Background: Next-generation sequencers (NGSs) have become one of the main tools for current biology. To obtain useful insights from the NGS data, it is essential to control low-quality portions of the data affected by technical errors such as air bubbles in sequencing fluidics. Results: We develop a software SUGAR (subtile-based GUI-assisted refiner) which can handle ultra-high-throughput data with user-friendly graphical user interface (GUI) and interactive analysis capability. The SUGAR generates high-resolution quality heatmaps of the flowcell, enabling users to find possible signals of technical errors during the sequencing. The sequencing data generated from the error-affected regions of a flowcell can be selectively removed by automated analysis or GUI-assisted operations implemented in the SUGAR. The automated data-cleaning function based on sequence read quality (Phred) scores was applied to a public whole human genome sequencing data and we proved the overall mapping quality was improved.Conclusion: The detailed data evaluation and cleaning enabled by SUGAR would reduce technical problems in sequence read mapping, improving subsequent variant analysis that require high-quality sequence data and mapping results. Therefore, the software will be especially useful to control the quality of variant calls to the low population cells, e.g., cancers, in a sample with technical errors of sequencing procedures.

Original languageEnglish
Article number664
JournalBMC Genomics
Issue number1
Publication statusPublished - 2014 Aug 8


  • Automated analysis
  • Data cleaning
  • Illumina HiSeq
  • MiSeq
  • NGS


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