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Scalable, Flexible, and Cost-Effective Seedling Grafting
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A performant bridge between fixed-size and variable-size seeding.

Arne Kutzner1, Pok-Son Kim2, Markus Schmidt3

  • 1Department of Information Systems, College of Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea.

BMC Bioinformatics
|July 25, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel merge-extend strategy for computing maximal exact matches (MEMs) from fixed-size seeds, improving efficiency for sequence alignment. The new method outperforms existing techniques, especially for long and high-quality PacBio reads.

Keywords:
FMD-indexHigh-throughput sequence alignmentMEMMinimizerSMEM

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Seeding is a critical initial step in high-throughput sequence alignment.
  • Common seeding strategies include fixed-size (k-mers, minimizers) and variable-size (MEMs, SMEMs, maximal spanning seeds).
  • Bridging these strategies is key to combining speed and uniqueness.

Purpose of the Study:

  • To develop an efficient strategy for computing maximal exact matches (MEMs) from fixed-size seeds.
  • To introduce techniques for extracting spaced maximal exact matches (SMEMs) and maximal spanning seeds from MEMs.
  • To analyze and compare different seeding strategies and their algorithmic relationships.

Main Methods:

  • A novel merge-extend strategy for computing MEMs from fixed-size seeds.
  • Techniques for extracting SMEMs and maximal spanning seeds from computed MEMs.
  • Comprehensive benchmarking of seeding techniques, including seed occurrence filters.

Main Results:

  • The merge-extend strategy efficiently computes MEMs, avoiding duplicate creation and filtering.
  • The proposed filters for SMEMs and maximal spanning seeds outperform FMD-index based methods.
  • Benchmarking demonstrates the applicability, strengths, and computational demands of various seeding techniques.

Conclusions:

  • The merge-extend strategy for MEM computation shows superior performance over extend-purge methods for PacBio reads, especially with larger and higher-quality data.
  • The developed filters for SMEMs and maximal spanning seeds are more effective than FMD-index extension techniques.
  • All benchmarking code is publicly available on GitHub.