Traceback Memory Reduction for Three-Sequence Alignment Algorithm with Affine Gap Models

2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)(2022)

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Abstract
In many hardware aligners, on-chip traceback is not supported because it requires large memory usage. The issue becomes even worse for three-sequence alignment, which is an algorithm to improve the accuracy of multiple sequence alignment. In this paper, we propose a design to reduce the usage of traceback memory for three-sequence alignment with affine gap penalty models. Using the pre-computed results from the forward dynamic programming stage, we are able to encode traceback directions with fewer bits. Our algorithm could save 37.5% memory usage when compared to direct implementations. The proposed bit-reduction method can be further combined with existing region-reduction traceback methods to lower required memory sizes.
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Key words
affine gap models,affine gap penalty models,bit-reduction method,forward dynamic programming stage,hardware aligners,multiple sequence alignment,on-chip traceback,region-reduction traceback methods,required memory sizes,three-sequence alignment algorithm,traceback directions,traceback memory reduction
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