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Block Aligner: an adaptive SIMD-accelerated aligner for sequences and position-specific scoring matrices.

Daniel Liu¹, Martin Steinegger²

¹University of California Los Angeles, Los Angeles, CA, United States.

Bioinformatics (Oxford, England)

|August 3, 2023

Summary

This summary is machine-generated.

Block Aligner accelerates sequence alignment using a novel dynamic programming approach. This method significantly speeds up nucleotide and protein sequence alignment, even with large gaps.

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Area of Science:

Bioinformatics
Computational Biology
Algorithm Development

Background:

Sequence alignment is crucial in bioinformatics.
Existing Smith-Waterman-Gotoh dynamic programming (DP) algorithms use SIMD for speed.
Current methods struggle with complex scoring matrices and large gaps.

Purpose of the Study:

To develop a new SIMD-accelerated algorithm for efficient sequence alignment.
To address limitations of existing algorithms in handling complex scoring and large gaps.

Main Methods:

Introduced Block Aligner, a novel SIMD-accelerated algorithm.
Employs a new paradigm using adaptively computed blocks in the DP matrix.
Supports nucleotide and protein sequence alignment against sequences or position-specific scoring matrices.

Main Results:

Block Aligner achieves 5-10x speedup over previous methods.
Maintains an error rate below 3% on protein and long read datasets.
Effectively handles large gaps and low sequence identities.

Conclusions:

Block Aligner offers a significant advancement in sequence alignment speed and efficiency.
The algorithm is robust for various datasets, including those with challenging features.
Implemented for global, local, and X-drop alignments, available as a Rust library with C bindings.