2017/08/03 by Sara A. Shehab, Shehab, Sara, Sameh Shohdy +3
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #FOS: Biological sciences #Genomics and Phylogenetic Studies #Machine Learning in Bioinformatics #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.1708.01508
openalex publication_date 2017/08/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Analyzing the relation between a set of biological sequences can help to\nidentify and understand the evolutionary history of these sequences and the\nfunctional relations among them. Multiple Sequence Alignment (MSA) is the main\nobstacle to proper design and develop homology and evolutionary modeling\napplications since these kinds of applications require an effective MSA\ntechnique with high accuracy. This work proposes a novel Position-based\nMultiple Sequence Alignment (PoMSA) technique -- which depends on generating a\nposition matrix for a given set of biological sequences. This position matrix\ncan be used to reconstruct the given set of sequences in more aligned format.\nOn the contrary of existing techniques, PoMSA uses position matrix instead of\ndistance matrix to correctly adding gaps in sequences which improve the\nefficiency of the alignment operation. We have evaluated the proposed technique\nwith different datasets benchmarks such as BAliBASE, OXBench, and SMART. The\nexperiments show that PoMSA technique satisfies higher alignment score compared\nto existing state-of-art algorithms: Clustal-Omega, MAFTT, and MUSCLE.\n