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Clover: tree structure-based efficient DNA clustering for DNA-based data storage.

Guanjin Qu1, Zihui Yan1, Huaming Wu1

  • 1Center for Applied Mathematics, Tianjin University, Tianjin, 300072, China.

Briefings in Bioinformatics
|August 17, 2022
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Summary
This summary is machine-generated.

Clover efficiently clusters DNA data for storage, overcoming errors with a novel tree structure. This method achieves high accuracy and linear time complexity, even for billions of DNA sequences.

Keywords:
DNA clusteringDNA storageLevenshtein distanceTree structure

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Area of Science:

  • Computer Science
  • Bioinformatics
  • Data Storage

Background:

  • DNA data storage offers high capacity and longevity but faces challenges from sequence errors.
  • Existing DNA clustering algorithms are ill-suited for DNA storage due to domain mismatch and computational demands.

Purpose of the Study:

  • To develop an efficient DNA clustering method for DNA data storage.
  • To address the limitations of existing algorithms in terms of speed, memory, and accuracy for large-scale DNA data.

Main Methods:

  • Introduced Clover, a novel DNA clustering method utilizing a tree structure for efficient retrieval.
  • Avoided computationally intensive Levenshtein distance calculations.
  • Provided theoretical proofs for linear computational and low space complexity.

Main Results:

  • Clover clustered 10 million DNA sequences into 50,000 classes in 10 seconds with over 99% accuracy.
  • Successfully clustered 10 billion DNA data points on a home computer, maintaining linear time complexity.

Conclusions:

  • Clover provides an efficient, accurate, and scalable solution for DNA sequence clustering in DNA data storage.
  • The method's performance demonstrates its potential for handling massive DNA datasets.