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BURST enables mathematically optimal short-read alignment for big data

View ORCID ProfileGabriel Al-Ghalith, View ORCID ProfileDan Knights
doi: https://doi.org/10.1101/2020.09.08.287128
Gabriel Al-Ghalith
1Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN 55455, USA
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  • ORCID record for Gabriel Al-Ghalith
Dan Knights
1Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN 55455, USA
2Biotechnology Institute, University of Minnesota, Minneapolis, MN 55455, USA
3Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
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Article Information

doi 
https://doi.org/10.1101/2020.09.08.287128
History 
  • September 8, 2020.
Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

Author Information

  1. Gabriel Al-Ghalith1 and
  2. Dan Knights1,2,3,*
  1. 1Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN 55455, USA
  2. 2Biotechnology Institute, University of Minnesota, Minneapolis, MN 55455, USA
  3. 3Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
  1. ↵*Corresponding author; email: dknights{at}umn.edu
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Posted September 08, 2020.
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BURST enables mathematically optimal short-read alignment for big data
Gabriel Al-Ghalith, Dan Knights
bioRxiv 2020.09.08.287128; doi: https://doi.org/10.1101/2020.09.08.287128
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BURST enables mathematically optimal short-read alignment for big data
Gabriel Al-Ghalith, Dan Knights
bioRxiv 2020.09.08.287128; doi: https://doi.org/10.1101/2020.09.08.287128

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