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Multi Objective Bacterial Foraging Optimization: A Survey
Published in Ranjeet Kumar Rout, Saiyed Umer, Sabha Sheikh, Amrit Lal Sangal, Artificial Intelligence Technologies for Computational Biology, 2023
R. Vasundhara Devi, S. Siva Sathya
In [44], a version of Bacterial Foraging Optimization was applied to align the biological sequences to produce non-dominated solutions. It employs multiple objectives namely, non-gap percentage, similarity maximization, gap penalty minimization and conserved blocks. In this work, two algorithms have been proposed: Hybrid Genetic Algorithm with ABC algorithm and Bacterial Foraging Optimization. Next algorithm proposed is MO-BFO (Multi-Objective Bacterial Foraging Optimization Algorithm) was compared with MSA methods MAFFT, Kalign, MUSCLE, Clustal Omega, Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). In this work, the combination of gap penalty, similarity, non-gap percentage and conserved blocks was used as multi objective to obtain a non-dominated optimal alignment. Getting an optimal alignment score by aligning the protein or DNA sequences is the major idea of multiple sequence alignment problem. The MOBFO algorithm uses BAliBASE3.0 benchmark datasets with 6255 protein sequences. This database also contains five reference sets.
NGS technologies for detection of SARS-CoV-2 strains and mutations
Published in Sanjeeva Srivastava, Multi-Pronged Omics Technologies to Understand COVID-19, 2022
Manisha Choudhury, Ayushi Verma, Ankit Halder, Arup Acharjee
Sequence alignment is the next process to identify the variants or cluster similar sequences to identify the virus’s different clades. In the analysis of the sequences of SARS-CoV-2, the reference genome is generally taken as Wuhan/WHO1/2019 (EPI_ISL_406798) (NC_045122), the first strain reported. Reads can be aligned to the reference genome using a tool like hisat2 (Banu et al. 2020). For the construction of the phylogenetic tree, generally, multiple sequence alignment (MSA) is performed using tools like MAFFT (https://mafft.cbrc.jp/alignment/software/), MUSCLE (www.ebi.ac.uk/Tools/msa/muscle/), T-Coffee (www.ebi.ac.uk/Tools/msa/tcoffee), and ClustalW2 (www.clustal.org/clustal2/). Nextstrain (https://nextstrain.org/), the software developed by Hadfield et al., provides a phylogenetic analysis pipeline. It makes use of Augur to assemble similar sequences. The MSA is performed using MAFFT to create an alignment file. The next step toward the identification of variants is the generation of the consensus sequence. The quality check should be performed on the binary alignment map (BAM) file generated to filter out the low-quality score and bcftools (http://samtools.github.io/bcftools/bcftools.html) can be employed to manipulate variant calls in the Variant Call Format (VCF).
Potential transmission pathways of clinically relevant fungi in indoor swimming pool facilities
Published in Yuli Ekowati, Protection of Public Health from Microbial and Chemical Hazards in Swimming Pool Environments, 2019
All sequences were aligned using online version multiple sequence alignment program, MAFFT version 7 (Katoh et al., 2017; Katoh and Standley, 2013). Phylogenetic analyses were conducted using MEGA version 7 (Kumar et al., 2016) with Maximum Likelihood method using 1,000 bootstrap replications.
A hybrid algorithm for identifying partially conserved regions in multiple sequence alignment
Published in International Journal of Computers and Applications, 2021
Gamage Kokila Kasuni Perera, Champi Thusangi Wannige
Multiple sequence alignment (MSA) algorithms infer the homologous regions within sequences and provide basis for many studies of deeper questions, such as the identification and quantification of conserved regions or functional motifs, profiling of genetic disease, phylogenetic analysis, and ancestral sequence profiling and prediction [1]. However, currently available sequence alignment algorithms [2–5] produce only a hypothesis of homology. Therefore alignments may contain error depending on the nature of the sequence data on which algorithms are applied to [1]. Sequence datasets which have partially conserved regions within sequence subsets are often not captured by the alignment algorithms [5]. As an example, the center star method produces poor results when the applied set of sequences has two or more conserved regions and partially conserved regions. As the alignment is the basis of many molecular biology researches, it is very important to reduce these errors in alignments. In this paper, we develop an algorithm to capture the hidden partially conserved regions when the center star method is applied.
Augmentation of metal-tolerant bacteria elevates growth and reduces metal toxicity in spinach
Published in Bioremediation Journal, 2021
K. M. Sarim, U. Sahu, M. S. Bhoyar, D. P. Singh, U. B. Singh, A. Sahu, A. Gupta, A. Mandal, J. K. Thakur, M. C. Manna
The nucleotide sequences of 16S rDNA gene were further compared with all the available sequences in database (NCBI), and based on similarity search, bacteria were identified up to species level. Sequences were aligned through CLUSTAL-W, a multiple sequence alignment program (Thompson, Higgins, and Gibson 1994) with set parameters, and the aligned sequence data were further converted to PHYLIP format. Slight modifications were done based on conserved domains, and with >50% gaps were removed, and aligned datasets were used to construct phylogenetic tree by neighbor joining (NJ) method (Saitou and Nei 1987) in MEGA 6 (Tamura et al. 2013). As described by Felsenstein (1985), a total 1000 bootstraps were performed for the multiple sequence alignments.