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ENJ algorithm can construct triple phylogenetic trees

ABSTRACT: Phylogenetic analysis is used to analyze the evolution of species according to the characteristics of biological sequences. The analytical results are generally represented by phylogenetic trees. NJ (neighbor joining) is a frequently used algorithm for constructing phylogenetic trees because of its few assumptions, fast operation, and high accuracy, and is based on the distance between taxa. It is known that NJ usually constructs different phylogenetic trees for the same dataset with differences in input order, which are known as “tied trees.” This article proposes an improved method of NJ, called ENJ (extended neighbor joining). The ENJ can join several (currently limited to three) nodes with the same minimum distance into a new node, rather than joining two nodes in one iteration, so it can construct triple phylogenetic trees. We have inferred the formulas for updating the distance values and calculating the branch lengths for the ENJ algorithm. We have tested the ENJ with simulated and real data. The experimental results show that, compared with other methods, the trees constructed by the ENJ have greater similarity to the initial trees, and the ENJ is much faster than the NJ algorithm. Moreover, we have constructed a phylogenetic tree for the novel coronavirus (COVID-19) and related coronaviruses by ENJ, which shows that COVID-19 and SARS-CoV are closer than other coronaviruses. Because it differs from the existing phylogenetic trees for those coronaviruses, we constructed a phylogenetic network for them. The network shows those species have had a reticulate evolution. Graphical Abstract Accurately inferring evolutionary relationships between species can help humans to understand the evolutionary history and the mechanism. ENJ is an efficient and effective algorithm for constructing phylogenetic trees, which can construct triple phylogenetic trees, if necessary. The trees constructed by ENJ can better describe the original information and reflect the real evolutionary relationship of species.

SUBMITTER: Hong Y

PROVIDER: S-EPMC7779534 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Project description:BackgroundIn the pursuit of a better understanding of biodiversity, evolutionary biologists rely on the study of phylogenetic relationships to illustrate the course of evolution. The relationships among natural organisms, depicted in the shape of phylogenetic trees, not only help to understand evolutionary history but also have a wide range of additional applications in science. One of the most challenging problems that arise when building phylogenetic trees is the presence of missing biological data. More specifically, the possibility of inferring wrong phylogenetic trees increases proportionally to the amount of missing values in the input data. Although there are methods proposed to deal with this issue, their applicability and accuracy is often restricted by different constraints.ResultsWe propose a framework, called PhyloMissForest, to impute missing entries in phylogenetic distance matrices and infer accurate evolutionary relationships. PhyloMissForest is built upon a random forest structure that infers the missing entries of the input data, based on the known parts of it. PhyloMissForest contributes with a robust and configurable framework that incorporates multiple search strategies and machine learning, complemented by phylogenetic techniques, to provide a more accurate inference of lost phylogenetic distances. We evaluate our framework by examining three real-world datasets, two DNA-based sequence alignments and one containing amino acid data, and two additional instances with simulated DNA data. Moreover, we follow a design of experiments methodology to define the hyperparameter values of our algorithm, which is a concise method, preferable in comparison to the well-known exhaustive parameters search. By varying the percentages of missing data from 5% to 60%, we generally outperform the state-of-the-art alternative imputation techniques in the tests conducted on real DNA data. In addition, significant improvements in execution time are observed for the amino acid instance. The results observed on simulated data also denote the attainment of improved imputations when dealing with large percentages of missing data.ConclusionsBy merging multiple search strategies, machine learning, and phylogenetic techniques, PhyloMissForest provides a highly customizable and robust framework for phylogenetic missing data imputation, with significant topological accuracy and effective speedups over the state of the art.

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ENJ algorithm can construct triple phylogenetic trees

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