Autors: Georgieva, H. G., Vetova, S. M., Gancheva, V. S., Lazarova, M. K.
Title: An Algorithm for Local Alignment of DNA and Protein Sequences
Keywords: Algorithm for Local Alignment, Amino Acids, Codons, DNA Sequence, Local Alignment, Match Percent, Protein Sequence

Abstract: The following paper presents a novel and effective algorithm for local alignment of DNA and protein sequences. It is designed to align unknown sequences with the sequences contained in the database. The algorithm has the ability to align sequences of different length. As a result of computations, it displays the final sequences, the match percent, the number of matching symbols, the number of sequence length, organism name, gene name, protein name. To test the effectiveness of the algorithm, a comparative analysis with BLAST is accomplished. The experimental results show the effectiveness of the proposed algorithm.

References

  1. Suchindra, S., Nagaraj, P.: Local pairwise sequence alignment algorithms – survey. Int. J. Curr. Eng. Technol. (2023)
  2. Polyanovsky, V., Roytberg, M., Tumanyan, V.: Comparative analysis of the quality of a global algorithm and a local algorithm for alignment of two sequences. Algorithms Mol. Biol. (2011).https://doi.org/10.1186/1748-7188-6-25
  3. Bani Baker, Q., Al-Hussien, R.A., Al-Ayyoub, M.: Accelerating multiple sequence alignments using parallel computing. Computation 12, 32 (2024). https://doi.org/10.3390/computation1 2020032
  4. Alqahtani, A., Almutairy, M.: Evaluating the performance of multiple sequence alignment programs with application to genotyping SARS-CoV-2 in the Saudi population. Computation 11, 212 (2023). https://doi.org/10.3390/computation11110212
  5. Ibrahim, M.K., Yusof, U.K., Eisa, T.A.E., Nasser, M.: Bioinspired algorithms for multiple sequence alignment: a systematic review and roadmap. Appl. Sci. 14, 2433 (2024). https://doi.org/10.3390/app14062433
  6. Gancheva, V., Stoev, H.: Optimization and performance analysis of CAT method for DNA sequence similarity searching and alignment. MDPI, Genes 15, 341 (2024). https://doi.org/10.3390/genes15030341
  7. Wang, G., Dunbrack, R.: Scoring profile-to-profile sequence alignments. Protein Sci. 13, 1612–1626 (2004)
  8. Chao, J., Tang, F., Xu, L.: Developments in algorithms for sequence alignment: a review. Biomolecules 12, 546 (2022). https://doi.org/10.3390/biom12040546
  9. Melo, A., Walter, M., Melo, R., Santana, M., Batista, R.: Local DNA sequence alignment in a cluster of workstations: algorithms and tools. J. Braz. Comput. Soc. (2004). https://doi.org/10.1590/S0104-65002004000300006
  10. Sun, X., Wu, C.-C., Liu, Y.-F.: The design and implementation of an improved lightweight BLASTP on CUDA GPU. Symmetry 13, 2385 (2021). https://doi.org/10.3390/sym13122385
  11. Petrovskiy, D.V., et al.: SAFoldNet: a novel tool for discovering and aligning three-dimensional protein structures based on a neural network. Int. J. Mol. Sci. 24, 14439 (2023). https://doi.org/10.3390/ijms241914439
  12. Mahmood, K., Webb, G., Song, J., Whisstock, J., Konagurthu, A.: Efficient large-scale protein sequence comparison and gene matching to identify orthologs and co-orthologs. Nucleic Acids Res. 40(6), e44 (2012). https://doi.org/10.1093/nar/gkr1261
  13. Pearson, W., Lipman, D.: Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. U.S.A. 85, 2444–2448 (1988)
  14. Ho, C.-T., Huang, Y.-W., Chen, T.-R., Lo, C.-H., Lo, W.-C.: Discovering the ultimate limits of protein secondary structure prediction. Biomolecules 11, 1627 (2021). https://doi.org/10. 3390/biom11111627
  15. Edgar, R.: Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19), 2460–2461 (2010). https://doi.org/10.1093/bioinformatics/btq461
  16. Chen, Y., Ye, W., Zhang, Y., Xu, T.: High speed BLASTN: an accelerated MegaBLAST search tool. 762–7768 Nucleic Acids Res. 43(16). Published online 6 August 2015, https://doi.org/10.1093/nar/gkv784
  17. Nasrin A., Amin, N., Sima, E., Reza, G.: PSALR: parallel sequence alignment for long sequence read with hash model. Research Square (2024). https://doi.org/10.21203/rs.3.rs-3923668/v1
  18. Muhamad, F., Ahmad, R., Asi, S., Murad, M.: Performance analysis of needleman-wunsch algorithm (global) and smith-waterman algorithm (Local) In: Reducing Search Space And Time For Dna Sequence Alignment. IOP Conference Series: Journal of Physics: Conf. Series vol. 1019, p. 012085 (2018). https://doi.org/10.1088/1742-6596/1019/1/012085
  19. Suchindra, S., Nagaraj, P.: New sequence alignment algorithm using ai rules and dynamic seeds. Biosci. Eng. Int. J. (BIOEJ), 10(½) (2023)

Issue

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14849 LNBI, pp. 73-86, 2024, , https://doi.org/10.1007/978-3-031-64636-2_6

Вид: монография/части от монография, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science