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Vargas: heuristic-free alignment for assessing linear and graph read aligners.

Charlotte A Darby1, Ravi Gaddipati2, Michael C Schatz1,3,4

  • 1Department of Computer Science.

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|April 23, 2020
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Summary
This summary is machine-generated.

Vargas is a new heuristic-free algorithm that finds optimal read alignments in genomics. It improves the accuracy of existing aligners by providing "gold standard" alignments for parameter optimization.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Read alignment is crucial for modern genomics.
  • Heuristic aligners may miss optimal read alignments.
  • Current accuracy validation uses potentially inaccurate simulated reads.

Purpose of the Study:

  • To develop a heuristic-free algorithm for optimal read alignment.
  • To enable accurate alignment of sequencing reads to linear or graph genomes.
  • To provide a "gold standard" for evaluating and improving existing alignment tools.

Main Methods:

  • Implemented a heuristic-free algorithm (Vargas) in C++.
  • Supported semiglobal and local alignment modes with affine gap and quality-scaled mismatch penalties.
  • Utilized multi-core parallelization and SIMD instructions for computational efficiency.

Main Results:

  • Vargas guarantees finding the highest-scoring alignment for real sequencing reads.
  • Achieved a maximum speed of 456 billion cell updates per second.
  • Demonstrated improved heuristic alignment accuracy by optimizing parameters using Vargas alignments.

Conclusions:

  • Vargas provides a computationally practical method for achieving optimal read alignments.
  • The "gold standard" alignments from Vargas can enhance the accuracy of widely used aligners like Bowtie 2, BWA, and vg.
  • This approach offers a more reliable way to assess and improve read alignment accuracy in genomics.