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Fang, H., Wu, Y., Narzisi, G., O’Rawe, J.A., Barrón, L.T.J., Rosenbaum, J., Ronemus, M., Iossifov, I., Schatz, M.C. and Lyon, G.J. (2014) Reducing INDEL Calling Errors in Whole Genome and Exome Sequencing Data. Genome Medicine, 6, 89-89.
https://doi.org/10.1186/s13073-014-0089-z

has been cited by the following article:

• TITLE: Ultra-Fast Next Generation Human Genome Sequencing Data Processing Using DRAGENTM Bio-IT Processor for Precision Medicine

  AUTHORS: Amit Goyal, Hyuk Jung Kwon, Kichan Lee, Reena Garg, Seon Young Yun, Yoon Hee Kim, Sunghoon Lee, Min Seob Lee

  KEYWORDS: NGS Data Analysis, BWA-GATK, DRAGEN Bio-IT Processor, Genomics, INDEL, Mapping

  JOURNAL NAME: Open Journal of Genetics, Vol.7 No.1, March 9, 2017

  ABSTRACT: Slow speed of the Next-Generation sequencing data analysis, compared to the latest high throughput sequencers such as HiSeq X system, using the current industry standard genome analysis pipeline, has been the major factor of data backlog which limits the real-time use of genomic data for precision medicine. This study demonstrates the DRAGEN Bio-IT Processor as a potential candidate to remove the “Big Data Bottleneck”. DRAGENTM accomplished the variant calling, for ~40× coverage WGS data in as low as ~30 minutes using a single command, achieving the over 50-fold data analysis speed while maintaining the similar or better variant calling accuracy than the standard GATK Best Practices workflow. This systematic comparison provides the faster and efficient NGS data analysis alternative to NGS-based healthcare industries and research institutes to meet the requirement for precision medicine based healthcare.