scholarly journals Implementing Hierarchical Clustering Method for Multiple Sequence Alignment and Phylogenetic Tree Construction

2013 ◽  
Vol 3 (1) ◽  
pp. 1-12
Author(s):  
Harmandeep Singh ◽  
Er. Rajbir Singh ◽  
Navjot Kaur
Keyword(s):  
Phylogenetic Tree ◽  
Hierarchical Clustering ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Clustering Method ◽  
Multiple Sequence ◽  
Phylogenetic Tree Construction ◽  
Tree Construction

scholarly journals Multiple Sequence Alignment Reveals Diversity among Eight African Bush Mango (Irvingia gabonensis Aubry-Lecomte ex O’Rorke) Cultivars

2021 ◽  
pp. 91-96
Author(s):  
U. G. Adebo ◽  
J. O. Matthew
Keyword(s):  
Sequence Analysis ◽  
Phylogenetic Tree ◽  
Genetic Resources ◽  
Data Base ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Multiple Sequence ◽  
Bush Mango ◽  
Irvingia Gabonensis ◽  
Cluster 2

Multiple sequence analysis is one of the most widely used model in estimating similarity among genotypes. In a bid to access useful information for the utilization of bush mango genetic resources, nucleotide sequences of eight bush mango (Irvingia gabonensis) cultivars were sourced for and retrieved form NCBI data base, and evaluated for diversity and similarity using computational biology approach. The highest alignment score (26.18), depicting the highest similarity, was between two pairs of sequence combinations; BM07:BM58 and BM12:BM69 respectively, while the least score (19.43) was between BM01: BM13. The phylogenetic tree broadly divided the cultivars into four distinct groups; BM07, BM58 (cluster one), BM01 (cluster 2), BM15, BM13 and BM35 (cluster 3), and BM12, BM69 (cluster 4), while the sequences obtained from the analysis revealed only few fully conserved regions, with the single nucleotides A, and T, which were consistent throughout the evolution. Results obtained from this study indicate that the bush mango cultivars are divergent and can be useful genetic resources for bush mango improvement through breeding.

scholarly journals Mathematical Understanding of Sequence Alignment and Phylogenetic Algorithms: A Comprehensive Review of Methods

2020 ◽  
Author(s):  
Rashid Saif ◽  
Sadia Nadeem ◽  
Ali Iftekhar ◽  
Alishba Khaliq ◽  
Saeeda Zia
Keyword(s):  
Phylogenetic Tree ◽  
Sequence Alignment ◽  
Phylogenetic Trees ◽  
Evolutionary Relationship ◽  
Biological Sequences ◽  
Pairwise Sequence Alignment ◽  
Phylogenetic Tree Construction ◽  
Local Sequence Alignment ◽  
Tree Construction ◽  
Local Sequence

Abstract Context: Pairwise sequence alignment is one of the ways to arrange two biological sequences to identify regions of resemblance that may suggest the functional, structural, and/or evolutionary relationship (proteins or nucleic acids) between the sequences. There are two strategies in pairwise sequence alignment: Local sequence Alignment (Smith-waterman algorithm) and Global sequence Alignment (Needleman-Wunsch algorithm). In local sequence alignment, two sequences that may or may not be related are aligned to find regions of local similarities in large sequences whereas in global sequence alignment, two sequences same in length are aligned to identify conserved regions. Similarities and divergence between biological sequences identified by sequence alignment also have to be rationalized and visualized in the sense of phylogenetic trees. The phylogenetic tree construction methods are divided into distance-based and character-based methods. Evidence Acquisition: In this article, different algorithms of sequence alignment and phylogenetic tree construction were studied with examples and compared to establish the best among them to look into background of these methods for the better understanding of computational phylogenetics.Conclusions: Pairwise sequence alignment is a very important part of bioinformatics to compare biological sequences to find similarities among them. The alignment data is visualized through phylogenetic tree diagram that shows evolutionary history among organisms. Phylogenetic tree is constructed through various methods some are easier but does not provide accurate evolutionary data whereas others provide accurate evolutionary distance among organism but are computationally exhaustive.

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