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Berkeley upc shared array block1/26/2024 It can effectively be used as a drop-in replacement for CPU libraries. ADEPT provides an added advantage of built in capability of scaling across multiple GPUs with minimal effort from the developer. Our analysis shows that ADEPT out-performs similar CPU approaches and either closely matches or out-performs domain specific existing GPU approaches. ADEPT derives its performance from architecture specific optimizations and is performant regardless of the type of sequence. In this paper we present ADEPT, a novel domain independent sequence alignment strategy for GPU architectures and demonstrate it by implementing a GPU-accelerated complete Smith-Waterman algorithm for the use case of pairwise sequence alignments. As a solution, heuristic based strategies were presented in the form of BLAST and Gapped BLAST which speed up the process considerably with the trade-off being an approximate solution. Its quadratic time complexity makes performing large number of alignments or aligning long sequences time consuming. Because of its exhaustive nature, the Smith-Waterman algorithm has a worst-case time and space complexity of O( n m) where n and m represent the lengths of two sequences to be aligned. Gotoh modifications enable the algorithm to account for gap openings and extensions. Smith-Waterman is a sequence alignment algorithm that scores all possible local alignments between two sequences using a dynamic programming method and outputs the optimal alignment. It has been used to determine conserved regions in proteins and genes, which has applications in evolutionary biology and functional genomics. Local sequence alignment has been used in de novo sequence assembly to determine how different regions of a genome are connected and for determining overlapping regions of long reads. Aligning two sequences determines a degree of similarity which may yield homology of the proteins or genes and their functional information. ![]() Sequence alignment lies at the core of most bioinformatics applications. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. ![]() Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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