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Ibrahim OAS, Hamed BA, El-Hafeez TA. A new fast technique for pattern matching in biological sequences. THE JOURNAL OF SUPERCOMPUTING 2023; 79:367-388. [DOI: 10.1007/s11227-022-04673-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/20/2022] [Indexed: 09/02/2023]
Abstract
AbstractAt numerous phases of the computational process, pattern matching is essential. It enables users to search for specific DNA subsequences or DNA sequences in a database. In addition, some of these rapidly expanding biological databases are updated on a regular basis. Pattern searches can be improved by using high-speed pattern matching algorithms. Researchers are striving to improve solutions in numerous areas of computational bioinformatics as biological data grows exponentially. Faster algorithms with a low error rate are needed in real-world applications. As a result, this study offers two pattern matching algorithms that were created to help speed up DNA sequence pattern searches. The strategies recommended improve performance by utilizing word-level processing rather than character-level processing, which has been used in previous research studies. In terms of time cost, the proposed algorithms (EFLPM and EPAPM) increased performance by leveraging word-level processing with large pattern size. The experimental results show that the proposed methods are faster than other algorithms for short and long patterns. As a result, the EFLPM algorithm is 54% faster than the FLPM method, while the EPAPM algorithm is 39% faster than the PAPM method.
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Reinert K, Dadi TH, Ehrhardt M, Hauswedell H, Mehringer S, Rahn R, Kim J, Pockrandt C, Winkler J, Siragusa E, Urgese G, Weese D. The SeqAn C++ template library for efficient sequence analysis: A resource for programmers. J Biotechnol 2017; 261:157-168. [PMID: 28888961 DOI: 10.1016/j.jbiotec.2017.07.017] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2017] [Revised: 07/17/2017] [Accepted: 07/19/2017] [Indexed: 11/27/2022]
Abstract
BACKGROUND The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome (Venter et al., 2001) would not have been possible without advanced assembly algorithms and the development of practical BWT based read mappers have been instrumental for NGS analysis. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there was a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use. We previously addressed this by introducing the SeqAn library of efficient data types and algorithms in 2008 (Döring et al., 2008). RESULTS The SeqAn library has matured considerably since its first publication 9 years ago. In this article we review its status as an established resource for programmers in the field of sequence analysis and its contributions to many analysis tools. CONCLUSIONS We anticipate that SeqAn will continue to be a valuable resource, especially since it started to actively support various hardware acceleration techniques in a systematic manner.
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Affiliation(s)
- Knut Reinert
- Algorithmic Bioinformatics, Institute for Bioinformatics, FU Berlin, Takustrasse 9, 14195 Berlin, Germany.
| | - Temesgen Hailemariam Dadi
- Algorithmic Bioinformatics, Institute for Bioinformatics, FU Berlin, Takustrasse 9, 14195 Berlin, Germany
| | - Marcel Ehrhardt
- Algorithmic Bioinformatics, Institute for Bioinformatics, FU Berlin, Takustrasse 9, 14195 Berlin, Germany
| | - Hannes Hauswedell
- Algorithmic Bioinformatics, Institute for Bioinformatics, FU Berlin, Takustrasse 9, 14195 Berlin, Germany
| | - Svenja Mehringer
- Algorithmic Bioinformatics, Institute for Bioinformatics, FU Berlin, Takustrasse 9, 14195 Berlin, Germany
| | - René Rahn
- Algorithmic Bioinformatics, Institute for Bioinformatics, FU Berlin, Takustrasse 9, 14195 Berlin, Germany
| | - Jongkyu Kim
- Efficient Algorithms for -Omics Data, Max Planck Institute for Molecular Genetics, Ihnestrasse 62-73, 14195 Berlin, Germany
| | - Christopher Pockrandt
- Efficient Algorithms for -Omics Data, Max Planck Institute for Molecular Genetics, Ihnestrasse 62-73, 14195 Berlin, Germany
| | - Jörg Winkler
- Efficient Algorithms for -Omics Data, Max Planck Institute for Molecular Genetics, Ihnestrasse 62-73, 14195 Berlin, Germany
| | | | - Gianvito Urgese
- Department of Control and Computer Engineering, Politecnico di Torino, Italy
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