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A Content-Addressable DNA Database with Learned Sequence Encodings

  • Kendall Stewart
  • Yuan-Jyue Chen
  • David Ward
  • Xiaomeng Liu
  • Georg Seelig
  • Karin Strauss
  • Luis Ceze
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11145)

Abstract

We present strand and codeword design schemes for a DNA database capable of approximate similarity search over a multidimensional dataset of content-rich media. Our strand designs address cross-talk in associative DNA databases, and we demonstrate a novel method for learning DNA sequence encodings from data, applying it to a dataset of tens of thousands of images. We test our design in the wetlab using one hundred target images and ten query images, and show that our database is capable of performing similarity-based enrichment: on average, visually similar images account for 30% of the sequencing reads for each query, despite making up only 10% of the database.

Notes

Acknowledgments

We would like to thank the anonymous reviewers for their input, which were very helpful to improve the manuscript. We also thank the Molecular Information Systems Lab and Seelig Lab members for their input, especially Max Willsey, who helped frame an early version. We thank Dr. Anne Fischer for suggesting a better way to present some of the data. This work was supported in part by Microsoft, and a grant from DARPA under the Molecular Informatics Program.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Kendall Stewart
    • 1
  • Yuan-Jyue Chen
    • 2
  • David Ward
    • 1
  • Xiaomeng Liu
    • 1
  • Georg Seelig
    • 1
  • Karin Strauss
    • 1
    • 2
  • Luis Ceze
    • 1
  1. 1.Paul G. Allen School of Computer Science & EngineeringUniversity of WashingtonSeattleUSA
  2. 2.Microsoft ResearchRedmondUSA

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