Review of Clustering Methods: Toward Phylogenetic Tree Constructions

Sharma, Arun; Jaloree, Shailesh; Thakur, Ramjeevan Singh

doi:10.1007/978-981-10-8198-9_50

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Most studies have looked into the aspects of real-world SARS-CoV-2 evolution and strain diversification through phylogenetic tree [3][4][5][6][7]. Phylogenetic tree is a graph that shows the evolutionary relationships among various biological entities based on their genetic closeness [8,9]. The distances from one entity to the other entities indicate the degree of relationships.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised clustering analysis of SARS-Cov-2 population structure reveals six major subtypes at early stage across the world

Li¹,

Liu

Luo³

2020

Preprint

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The SARS-CoV-2 virus has been spreading rapidly and across the globe since first being reported in December 2019. To understand the evolutionary trajectory of the coronavirus, phylogenetic analysis is needed to study the population structure of SARS-CoV-2. As sequencing data worldwide is accruing rapidly, grouping them into clusters helps to organize the landscape of population structures. To effectively group these data, computational methodologies are needed to provide more productive and robust solutions for clustering. In this study, using the single nucleotide polymorphisms of the viral sequences as input features, we utilized three clustering algorithms, namely K-means, hierarchical clustering and balanced iterative reducing and clustering using hierarchies to partition the viral sequences into six major clusters. Comparison of the three clustering results reveals that the three methods produced highly consistent results, but K-means performed best and produced the smallest intra-cluster pairwise genetic distances among the three methods. The partition of the viral sequences revealed that the six clusters differed in their geographical distributions. Using comprehensive approaches to compare the diversity and selective pressure across the clusters, we discovered a high genetic diversity between the clusters. Based on characteristics of the mutation profiles in each cluster along with their geographical distributions and evolutionary histories, we identified the extent of molecular divergence within and between the clusters. The identification of the mutations that are strongly associated with clusters have potential implications for diagnosis and pathogenesis of COVID-19. In addition, the clustering method will enable further study of variant population structures in specific regions of these fast-growing viruses.

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Section: Introductionmentioning

confidence: 99%

Unsupervised clustering analysis of SARS-Cov-2 population structure reveals six major subtypes at early stage across the world

Li¹,

Liu

Luo³

2020

Preprint

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“…There are three ways of reserving conflicts: reversal (return to the original state), convergence (unrelated taxa evolved the same characteristic completely independently) and parallelism (different taxa may have similar mechanisms that develop a character in a certain way) [19,20]. The tree with a lowest tree score or length, as determined by the number of changes accumulated along the branches, is called the most parsimonious tree, and it best represents the evolutionary pattern [20,21].…”

Section: Maximum Parsimonymentioning

confidence: 99%

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

Saif¹,

Nadeem²,

Iftekhar³

et al. 2020

Preprint

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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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“…For that reason, studies have implemented phylogenetic trees to investigate the evolutionary trajectory of SARS-CoV-2 and its strain diversification [3][4][5]. A phylogenetic tree is a graph showing the dissimilarities and distances between various biological entities based on their pairwise distance metrics [6,7]. Nevertheless, given the continuous accumulation of entities, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of entities through the phylogenetic tree.…”

Section: Introductionmentioning

confidence: 99%

Using an Unsupervised Clustering Model to Detect the Early Spread of SARS-CoV-2 Worldwide

Liu

Zeng

et al. 2022

Genes

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Deciphering the population structure of SARS-CoV-2 is critical to inform public health management and reduce the risk of future dissemination. With the continuous accruing of SARS-CoV-2 genomes worldwide, discovering an effective way to group these genomes is critical for organizing the landscape of the population structure of the virus. Taking advantage of recently published state-of-the-art machine learning algorithms, we used an unsupervised deep learning clustering algorithm to group a total of 16,873 SARS-CoV-2 genomes. Using single nucleotide polymorphisms as input features, we identified six major subtypes of SARS-CoV-2. The proportions of the clusters across the continents revealed distinct geographical distributions. Comprehensive analysis indicated that both genetic factors and human migration factors shaped the specific geographical distribution of the population structure. This study provides a different approach using clustering methods to study the population structure of a never-seen-before and fast-growing species such as SARS-CoV-2. Moreover, clustering techniques can be used for further studies of local population structures of the proliferating virus.

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Review of Clustering Methods: Toward Phylogenetic Tree Constructions

Cited by 6 publications

References 9 publications

Unsupervised clustering analysis of SARS-Cov-2 population structure reveals six major subtypes at early stage across the world

Unsupervised clustering analysis of SARS-Cov-2 population structure reveals six major subtypes at early stage across the world

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

Using an Unsupervised Clustering Model to Detect the Early Spread of SARS-CoV-2 Worldwide

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