Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6, 9450-9460 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i6.4024 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: scicomphd2210@uodiyala.edu.iq Constructing phylogenetic tree for viral hemorrhagic fever (VHF) by RAXML Meaad Hussein Al-Abadi1*, Bashar Talib Al-Nuaimi2 1,2Department of Computer Science, College of Science, University of Diyala, Iraq; scicomphd2210@uodiyala.edu.iq (M.H.A.A.). Abstract: Various viruses can produce severe and frequently fatal infections known as viral hemorrhagic fevers (VHF). Comprehending the evolutionary connections among these viruses is essential for developing vaccines, treating illnesses, and conducting epidemiological surveillance. The evolutionary history and genetic relatedness of viruses that cause viral heart failure are better understood by phylogenetic research. Thus, this work aimed to undertake multiple sequence alignment and then use the Maximum Likelihood Estimate approach to produce a phylogenetic tree architecture of VHF. This study utilized 27 isolates of VHF (Ebola & Marburg) retrieved from the GenBank database (National Center for Biotechnology Information) for this investigation. The recent findings demonstrated that the Maximum Likelihood Estimate approach yielded a phylogenetic tree that was highly accurate and reliable for establishing the evolutionary relationships between VHF. Keywords: Maximum likelihood, Phylogenetic analysis, Phylogenetic tree, Viral hemorrhagic fever viruses. 1. Introduction Viral hemorrhagic fever continues to pose a concern to global public health. VHF persists today; according to the Iraqi Ministry of Health, the "hemorrhagic fever" has killed around 50 Iraqis and caused 273 injuries since the beginning of 2024 [1][2]. It is distinguished by fever, tiredness, and a propensity to bleed, frequently resulting in severe sickness and even death. The viruses that cause VHF are categorized into six families: (Arenaviridae, Hantaviridae, Nairoviridae, Phenuiviridae, Flaviviridae, and Filoviridae)[3]. The viruses that cause VHF are Lassa, Dengue, Marburg, and Ebola. Marburg virus illness is a highly infectious that causes hemorrhagic fever [4]. It belongs to the same viral family as the Ebola virus [5]. Understanding the evolutionary relationships between these viruses is necessary to establish their origins, transmission methods, and potential for outbreaks [6]. Genetic sequence data, phylogenetic analysis, and a solid molecular biology approach may rebuild evolutionary relationships [7]. We advocate utilizing VHF in phylogenetic tree reconstruction to estimate and demonstrate the evolutionary relationships between those genomes [8]. The evolutionary analysis of viruses is a valuable tool in epidemiology. Phylogenetic trees may represent different viruses' evolutionary history and relatedness by comparing their genomic sequences [9]. By looking at genomic sequences, scientists may identify evolutionary relationships between viral isolates or strains and evaluate how the virus has changed over time by looking for mutations, genetic recombination events, and the emergence of new viral varieties[10][11]. It can reveal the mechanics of the virus's spread, such as how it spreads among different populations. Its inference is based on a maximum likelihood estimate due to its precision and efficiency. The maximum likelihood approach is one of the most used statistical estimating techniques. Applying the maximum probability strategy is more challenging and requires a deeper comprehension of the evolutionary models that these approaches are based on [12]. The maximum likelihood approach is limited to a small number of sequences due to its greater complexity, necessitating many computational steps that rise quickly with 9451 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9450-9460, 2024 DOI: 10.55214/25768484.v8i6.4024 © 2024 by the authors; licensee Learning Gate the number of sequences [13]. A supercomputer can be used to carry them out to assess numerous sequences at once [14]. To predict the best trees, illustrate the evolutionary relationships between the sequences, and create a well-supported phylogenetic tree using the maximum likelihood method, this paper thus attempts to investigate the possibility of finding relationships among the nucleotide sequences of VHF using the genes shared by these species. For example, scientists can generate vaccines or antivirals that target multiple viral variants by identifying conserved regions of a virus' genome that need to be genetic gun sights linked with any virulence mean Temecula properties [15]. The confidence (corresponding to the support for individual clades in a consensus tree) may be inferred bootstrap values are placed on nodes [16]. 2. Material and Methods Description of Figure 1: A complete diagram detailing the many steps conducted (like a flow chart) shows how the maximum likelihood methods were used to build a phylogenetic VHF tree. Figure 1. General steps in phylogenetic tree construction. 2.1. Dataset Selection To build a phylogenetic tree for VHF viruses using ML, we collected the genetic sequences of representative viruses (27) complete genomes, (10) Ebola, and (17) Marburg from public databases (NCBI) [17]. Some sequences covering the genomic regions of relevance for phylogenetic analysis were selected to ensure comprehensive coverage of the variation among the viral families that cause VHF [18]. Data about an organism's DNA or genetic makeup is known as genomic information. Listed Accession numbers, definitions, genome size in base pairs (bp), publication titles, host information, and place of origin are a few examples of the data that can be tabled in Table 1. 9452 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9450-9460, 2024 DOI: 10.55214/25768484.v8i6.4024 © 2024 by the authors; licensee Learning Gate 2.1.1. Choose Out-group Select a suitable out-group to establish the phylogenetic tree's root. Although it should be geographically far away to serve as an evolutionary benchmark, the out-group should share phylogenetically with the VHF viruses [19]. Table 1. Information about some complete genomes (Ebola and Marburg) [19]. Accession Organism name Genome size(bp) Host Geolocation Collection Data Species KY785985 Ebola virus 18871 Macaca fascicularis Gabon 2001 Orthoebolavirus zairense DQ217792 Marburg virus 19112 human| vertebrates Kenya, 11-JAN- 2024 Orthomarburgvirus marb KU182901 Ebola virus 18959 Homo sapiens Zaire 1995-05- 04 Orthoebolavirus zairense KT345616 Ebola virus 18794 Homo sapiens Sierra Leone 2015-02- 19 Orthoebolavirus zairense KU143834 Ebola virus 18959 Homo sapiens Sierra Leone 2014 Orthoebolavirus zairense MK731986 Ebola virus 18912 Homo sapiens Democratic Republic of the Congo 2018-08- 16 Orthoebolavirus zairense KR063670 Sudan ebolavirus 18875 Homo sapiens Uganda 2000-10- 01 Orthoebolavirus sudanense KY471123 Zaire ebolavirus 18871 Macaca fascicularis Gabon 2015 Orthoebolavirus zairense MH121169 Sudan ebolavirus 18831 Homo sapiens Sudan: Yambio 2004 Orthoebolavirus sudanense 2.2. Sequence Alignment Perform multiple sequence alignments, including gap and ambiguity removal of the sequences for all VHF viruses, along with those selected from out-group viruses. Use MUSCLE (Multiple Sequence Comparison by Log-Expectation) on the combined set of sequences [20]. Like any analysis, phylogenetic analyses require all sequences to be in the proper reading frame. Multiple tools Genomics, or multiple sequence alignment, is an essential domain bioinformatics tool used in proteomics, evolutionary biology, and genomics, among other domains, that helps to perform numerous types of analyses. [21] The most relevant shape of the tree in biology is a phylogenetic tree a diagram that illustrates the evolutionary relations between organisms or genes, for example, and they are made by aligning sequences, so if you cannot create an MSA, then there is no phylogeny. It is an approach to identify genetic variants such as insertions and deletions (idles) or single nucleotide polymorphisms (SNPs) by comparing sequences from different individuals and populations. Using multiple sequence alignment (MSA) helps us quickly identify similarities and differences in related sequences, which is essential for grasping how genes and proteins function and how they relate evolutionarily [22]. Figure 2 presents the MSA for complete Ebola genomes, while Figure 3 presents the MSA for Marburg genomes. 9453 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9450-9460, 2024 DOI: 10.55214/25768484.v8i6.4024 © 2024 by the authors; licensee Learning Gate Figure 2. Show alignment for complete genome ebola with the outgroup (DQ217792.2). Figure 3. Show alignment for complete genomes Marburg with the outgroup (NC_001608.3). 2.3. Find Synteny Synteny analysis is a powerful method that offers insights into genomes' structure, function, and evolution, making it an essential part of genomics research. After aligning sequences, researchers can compare the genomic contexts of different species [23]. Typically, it involves looking for conserved gene arrangements, identifying syntenic blocks, and exploring chromosomal rearrangements or gene duplications [24]. While synteny analysis and MSA are valuable methods in genomics research, their applications and stages of study are usually distinct. While synteny analysis evaluates how genes or genomic areas are 9454 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9450-9460, 2024 DOI: 10.55214/25768484.v8i6.4024 © 2024 by the authors; licensee Learning Gate organized across species, MSA is used to compare sequences. Figures (4) and (5) show synteny for Ebola and Marburg. Figure 4. Shows synteny for Ebola, where the dark blue color of the small circle shows similar genomes with average values, the light blue color shows good values, and the red color shows high values, meaning the similarity between the genomes is excellent. 9455 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9450-9460, 2024 DOI: 10.55214/25768484.v8i6.4024 © 2024 by the authors; licensee Learning Gate Figure 5. Show synteny for Marburg, where the dark blue color of the small circle shows similar genomes with average values, the light blue color shows good values, and the red color shows high values, meaning the similarity between the genomes is excellent. 9456 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9450-9460, 2024 DOI: 10.55214/25768484.v8i6.4024 © 2024 by the authors; licensee Learning Gate 2.3. Model Selection This step entails deciding which evolutionary model best matches the available data. Using this model, a hypothesis regarding the organism's or gene family's evolutionary history will be produced. Choosing evolutionary tree models is a crucial stage in building phylogenetic trees for VHF. Using the proper evolutionary tree model appropriately depicts the evolutionary relationships between the various EBOLAs and Marburg. The alignment of the various VHF nucleotide or amino acid sequences was the basis for this model's selection. The features of the sequence data, such as the quantity, location, and rate of evolution of sequence differences, should be taken into consideration while selecting the evolutionary tree model. A range of accessible software tools can be used to generate the GTR-GAMMA model, which is the proper evolutionary tree model after it has been selected. The evolutionary links between the various species may then be determined using the resulting phylogenetic tree, which can reveal crucial information about the virus's evolution and distribution [25]. The Maximum Likelihood Estimate (MLE) computes the likelihood of the observed data given the model and represents the evolutionary relationships among the species using a structure resembling a tree. Based on the tree topology, the model that maximizes the likelihood of the observed data is chosen. Using this method, phylogenetic trees between Marburg and Ebola have been created. Reconstructing the evolutionary history of any group of organisms, including Ebola and Marburg, is a powerful application of MLE [26]. 2.4. Phylogenetic Tree The aligned sequences were then utilized to create phylogenetic trees using ML [27]. Given the input sequencing data, it employs a maximum likelihood method to find the most likely evolutionary tree. In phylogenetic analysis, an out-group is a reference sequence or taxon distinct from the group of interest yet phylogenetically connected to it. The phylogenetic tree aims to determine the direction of evolutionary change within the in-group or group of interest. The out-group sequences usually occupy a basal position in the evolutionary tree concerning the VHF viruses, making the interactions directional. Frequently employ related filoviruses like the Marburg virus as an out-group for phylogenetic trees of the Ebola virus. The Marburg virus, which belongs to the Filoviridae family, shares phylogenetic similarities with Ebola viruses. Knowledge of the evolutionary relationships within the Ebola virus genus can be gained by using the Marburg virus as an out-group, which also aids in rooting the tree. The species with similar genetic sequences are grouped to form the tree, with the most nearly. Figures (6) and (7) show the phylogenetic tree for Ebola and Marburg. 9457 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9450-9460, 2024 DOI: 10.55214/25768484.v8i6.4024 © 2024 by the authors; licensee Learning Gate Figure 6. The phylogenetic tree of (10) Ebola by the Likelihood approach with 100 bootstraps Figure 7. The phylogenetic tree of (17) Marburg by the maximum likelihood approach with 100 bootstraps. The correctness of the data used to construct a phylogenetic tree determines how well the tree is evaluated. A few examples of the variables that affect data correctness are the caliber of the data sources, the dependability of the analysis procedures, and the precision of the inference methods. A higher quality of data will result in a more accurate phylogenetic tree. One way to assess the correctness of the branch lengths is to see how closely the tree matches the species' natural evolutionary history. By 9458 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9450-9460, 2024 DOI: 10.55214/25768484.v8i6.4024 © 2024 by the authors; licensee Learning Gate bootstrapping the quality of the data supporting the tree, one can determine how certain the conclusions derived from it are. 2.5. Phylogenetic Tree Analysis The proposed method extracts single nucleotide polymorphisms (SNPs) in the last stage, and genomes mutate. These are the steps that make up a phylogenetic tree study. SNPs aid in predicting the proper medication or immunization for a specific illness, the risk of infection and transmission, and environmental factors influencing the sickness. SNPs can help monitor family members' traits. Most substitution variations are associated with disease. Table 2. Information about SNPs and Mutation SNPs for Ebola Mutations for Ebola SNPs for Marburg Mutations for Marburg C -> C: 4569 SNPs A -> -: 6897 SNPs A -> G: 8266 SNPs G -> -: 4258 SNPs C -> -: 4365 SNPs C -> G: 2788 SNPs G -> A: 7164 SNPs G -> T: 3398 SNPs T -> A: 6765 SNPs T -> G: 4210 SNPs T -> -: 7284 SNPs -> T: 5694 SNPs -> G: 4074 SNPs -> A: 6448 SNPs A -> C: 5790 SNPs T -> C: 8083 SNPs A -> T: 5999 SNPs C -> A: 4959 SNPs C -> T: 6495 SNPs G -> C: 2980 SNPs total for ebola: 16285 -> A: 1452 C -> G: 4385 - -> G: 642 A -> G: 12177 G -> A: 12705 A -> C: 11638 C -> A: 15093 A -> T: 15852 T -> G: 11654 G -> T: 8092 T -> -: 1080 T -> A: 11752 T -> C: 11492 C -> T: 11027 G -> C: 7379 A -> -: 1334 G -> -: 682 C > -: 788 - -> T: 1007 -> C: 783 total for ebola: 141014 A -> G: 12488 SNPs A > -: 27 SNPs A -> T: 2644 SNPs T -> G: 1568 SNPs T -> C: 13947 SNPs C -> T: 13440 SNPs G -> A: 11701 SNPs C -> A: 1908 SNPs C -> G: 685 SNPs G -> T: 1378 SNPs T -> A: 2559 SNPs A -> C: 2000 SNPs G -> C: 766 SNPs -> A: 2 SNPs C -> -: 2 SNPs -> G: 26 SNPs T -> -: 16 SNPs -> T: 6 SNPs total for Marburg:5835 A -> G: 20411 - -> G: 2 G -> A: 21469 A -> C: 19138 C -> A: 24984 A -> T: 29777 T -> G: 20702 G -> T: 15059 T -> A: 22834 T -> C: 20515 C -> T: 19227 G -> C: 10652 C -> G: 6081 G -> -: 17 -> A: 53 - -> T: 1 A -> -: 28 T -> -: 12 C -> -: 13 total for Marburg: 230975 9459 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 9450-9460, 2024 DOI: 10.55214/25768484.v8i6.4024 © 2024 by the authors; licensee Learning Gate Figure 8. Explain SNPs and Mutation for EBOLA and MARBURGE. 3. Results and Conclusion The results of this research paper cover the study's results and the interpretation of these outcomes. We used an MSA program called Muscle because it is the best way to align the complete genome, use a function (find_synteny) to select a suitable complete genome from NCBI, and use the maximum likelihood estimate (MLE) approach to generate phylogenetic trees based on whole genome sequences. The first tree (Figure 6) was for Ebola, and the second (Figure 7) was for Marburg. Given the accuracy of the tree created, tracing the evolution of VHF with the phylogenetic tree and MLE approach is a successful and valuable strategy. Copyright: © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] Srivastav, Y., Mansoori, M. F., & Pandey, V. K. 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