Medhat A. Tawfeek^1,2,*, Saad Alanazi¹, A. A. Abd El-Aziz^3,4

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6091-6106, 2022, DOI:10.32604/cmc.2022.028391

Abstract The alignment operation between many protein sequences or DNA sequences related to the scientific bioinformatics application is very complex. There is a trade-off in the objectives in the existing techniques of Multiple Sequence Alignment (MSA). The techniques that concern with speed ignore accuracy, whereas techniques that concern with accuracy ignore speed. The term alignment means to get the similarity in different sequences with high accuracy. The more growing number of sequences leads to a very complex and complicated problem. Because of the emergence; rapid development; and dependence on gene sequencing, sequence alignment has become important in every biological relationship analysis… More >

Mary Aksa¹, Junaid Rashid^2,*, Muhammad Wasif Nisar¹, Toqeer Mahmood³, Hyuk-Yoon Kwon⁴, Amir Hussain⁵

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3931-3946, 2021, DOI:10.32604/cmc.2021.016081

Abstract Advancements in next-generation sequencer (NGS) platforms have improved NGS sequence data production and reduced the cost involved, which has resulted in the production of a large amount of genome data. The downstream analysis of multiple associated sequences has become a bottleneck for the growing genomic data due to storage and space utilization issues in the domain of bioinformatics. The traditional string-matching algorithms are efficient for small sized data sequences and cannot process large amounts of data for downstream analysis. This study proposes a novel bit-parallelism algorithm called BitmapAligner to overcome the issues faced due to a large number of sequences… More >

Miguel-Angel Sicilia^1,*, Elena García-Barriocanal¹, Marçal Mora-Cantallops¹, Salvador Sánchez-Alonso¹, Lino González²

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1661-1672, 2021, DOI:10.32604/cmc.2021.015874

Abstract Existing studies have challenged the current definition of named bacterial species, especially in the case of highly recombinogenic bacteria. This has led to considering the use of computational procedures to examine potential bacterial clusters that are not identified by species naming. This paper describes the use of sequence data obtained from MLST databases as input for a k-means algorithm extended to deal with housekeeping gene sequences as a metric of similarity for the clustering process. An implementation of the k-means algorithm has been developed based on an existing source code implementation, and it has been evaluated against MLST data. Results… More >