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

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.

Filogenetik Kutu Penghisap Darah (Haematopinus sp.) pada Beberapa Jenis Sapi berdasarkan Gen 18S rRNA

The purpose of this study was to determine the phylogenetic relationship of Haematopinus sp. which became parasites in several types of cattle, namely Simmental, Limousin, PO (Peranakan Ongole), and FH (Friesian Holstein) from Jember Regency and Simmental and PO cattle from Karanganyar Regency. The method used was to isolate DNA from 6 samples of Haematopinus sp. and amplified using 18S rRNA universal primers with 18S (5'-TCATTACGAGCTCTGCAAT-3) reverse primers and 18S (5'-TTCAAAGTAAACGTGTCGGC-3) sequential PCR sequencing. Sequencing results were analyzed using MEGA 6 software for phylogenetic tree construction using the Neighbor-Joining and Maximum Parsimony Methods. The results showed that the sample Haematopinus sp. originating from Simmental, Limousin, and PO cattle from Jember, and Simmental and PO from Karanganyar were included in one cluster by Haematopinus quadripertusus. Haematopinus sp. sample from Jember FH cattle had a considerable genetic distance from Haematopinus quadripertusus which was possible because the sequence that could be analyzed was only 236 nt.

Phylogenetic Tree Construction Using K-Mer Forest- Based Distance Calculation

Phylogenetics is one of the dominant data engineering research disciplines based on biological information. More particularly here, we consider raw DNA sequences and do comparative analysis in order to come up with important conclusions. When representing evolutionary relationships among different organisms in a concise manner, the phylogenetic tree helps significantly. When constructing phylogenetic trees, the elementary step is to calculate the genetic distance among species. Alignment-based sequencing and alignment-free sequencing are the two main distance computation methods that are used to find genetic relatedness of different species. In this paper we propose a novel alignment-free, pairwise, distance calculation method based on k-mers and a state of art machine learning-based phylogenetic tree construction mechanism. With the proposed approach we can convert longer DNA sequences into compendious k-mer forests which gear up the efficiency of comparison. Later we construct the phylogenetic tree based on calculated distances with the help of an algorithm build upon k-medoid clustering, which guaranteed significant efficiency and accuracy compared to traditional phylogenetic tree construction methods.

Immunobioinformatics analysis and phylogenetic tree construction of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Indonesia: spike glycoprotein gene

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has spread worldwide and as a result, the World Health Organization (WHO) declared it a pandemic. At present, there are no approved vaccines against SARS-CoV-2. Therefore, the aim of this study was to predict epitope-based vaccines using bioinformatics approaches and phylogenetic tree construction of SARS-CoV-2 against the backdrop of the COVID-19 pandemic. In this study, we employed 27 isolates of SARS-CoV-2 spike glycoprotein genes retrieved from GenBank® (National Center for Biotechnology Information, USA) and the GISAID EpiCoV™ Database (Germany). We analyzed the candidate epitopes using the Immune Epitope Database and Analysis Resource. Furthermore, we performed a protective antigen prediction with VaxiJen 2.0. Data for B-cell epitope prediction, protective antigen prediction, and the underlying phylogenetic tree of SARS-CoV-2 were obtained in this research. Therefore, these data could be used to design an epitope-based vaccine against SARS-CoV-2. However, the advanced study is recommended for confirmation (in vitro and in vivo).