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Research Article

A workflow with R: Phylogenetic analyses and visualizations using mitochondrial cytochrome b gene sequences

Contributed equally to this work with: Emine Toparslan, Kemal Karabag, Ugur Bilge

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft

Current address: Department of Agricultural Biotechnology, Faculty of Agriculture, Akdeniz University, Antalya, Turkey

Affiliation Institute of Natural and Applied Sciences, Akdeniz University, Antalya, Turkey

Roles Conceptualization, Investigation, Resources, Validation, Visualization, Writing – review & editing

Affiliation Department of Agricultural Biotechnology, Faculty of Agriculture, Akdeniz University, Antalya, Turkey

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Biostatistics and Medical Informatics, Faculty of Medicine, Akdeniz University, Antalya, Turkey

• Emine Toparslan, 
• Kemal Karabag, 

• Published: December 15, 2020
• https://doi.org/10.1371/journal.pone.0243927
• Peer Review
• Reader Comments

Phylogenetic analyses can provide a wealth of information about the past demography of a population and the level of genetic diversity within and between species. By using special computer programs developed in recent years, large amounts of data have been produced in the molecular genetics area. To analyze these data, powerful new methods based on large computations have been applied in various software packages and programs. But these programs have their own specific input and output formats, and users need to create different input formats for almost every program. R is an open source software environment, and it supports open contribution and modification to its libraries. Furthermore, it is also possible to perform several analyses using a single input file format. In this article, by using the multiple sequences FASTA format file (.fas extension) we demonstrate and share a workflow of how to extract haplotypes and perform phylogenetic analyses and visualizations in R. As an example dataset, we used 120 Bombus terrestris dalmatinus mitochondrial cytochrome b gene (cyt b ) sequences (373 bp) collected from eight different beehives in Antalya. This article presents a short guide on how to perform phylogenetic analyses using R and RStudio.

Citation: Toparslan E, Karabag K, Bilge U (2020) A workflow with R: Phylogenetic analyses and visualizations using mitochondrial cytochrome b gene sequences. PLoS ONE 15(12): e0243927. https://doi.org/10.1371/journal.pone.0243927

Editor: Michael Scott Brewer, East Carolina University, UNITED STATES

Received: July 20, 2020; Accepted: November 30, 2020; Published: December 15, 2020

Copyright: © 2020 Toparslan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Phylogenetic relationships are mostly calculated using computer programs with several mathematical models. Although there are many software packages to estimate parameters, they don’t work together in a common workflow that can compute these parameters in one task [ 1 ]. To use these software packages, datasets need to be in different input file formats. Therefore, users have to prepare different input files for almost every program. These are; .fas, .mas, .meg, .arp, .gtx, .str, .nwk, .tree and .txt files with different formats. This way of working can cause increased workload and time loss [ 2 ]. At this point, there is a need for a platform where analyses can be performed in a single framework. Although R (programming language) is a software environment for statistical computing and graphics, it is increasingly used in bioinformatics and phylogenetic data analysis thanks to advanced packages and libraries [ 2 – 11 ].

R is an environment for linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering [ 10 ] and graphics. It is free and it enables static and dynamic program analyses [ 12 ]. On the other hand, RStudio is an open-source Integrated Development Environment (IDE) for the R programming language. RStudio is a software that combines various components of R, such as console, resource editing, graphics, history, help, in one workbench [ 13 ].

R and RStudio create an R Markdown document provided by the rmarkdown package, which can store all code snippets, analyses, results, and images in a document [ 14 ]. With knitr package, a new Markdown file is created and converted into different file formats such as PDF, HTML, Word etc. by using pandoc() function [ 15 ]. Additionally, with R Markdown, journal articles and multi-part books can be written, and websites and blogs can be generated [ 14 ]. Therefore, they provide a wide range of options and are quite practical.

One of the strongest biomarkers used to estimate phylogenetic relationships is also mitochondrial DNA. It is frequently used in phylogenetic research and it is possible to group individuals as haplotypes by defining variations in the mtDNA for every population. Moreover, a haplotype network based on nucleotide differences between haplotypes can be created [ 16 ].

We demonstrate how to perform phylogenetic analyses and graphics in a single workflow using R for mtDNA sequences. Additionally, we have shared all the R commands in these analyses for everyone to use. Furthermore, some of these commands can be used directly, and some are in modifiable form for users who have samples with different sequence lengths and numbers.

Packages, libraries and commands

As R is an open source software, a huge number of packages have been created to date [ 10 ]. R packages stored as a library in the R environment are a combination of functions, commands and sample data. We defined the packages and commands used in this study below. We shared the R code and mitochondrial cyt b sequences used in our article as S1 and S2 Appendices.

msa: Multiple alignment analysis package.

The msa package (version 1.18.0) is a unified R/Bioconductor interface and implements three multiple sequence alignment methods (ClustalW, ClustalOmega and MUSCLE). They do not need any other external software tools because they are all integrated in the package. Sequence types that this package can read for alignment are "B", "DNA", "RNA" or "AA" that is a single string specifying the type of sequences contained in the FASTA format file (.fa, .fas, or .fasta) or fastaq file. The readDNAStringSet() function and its family: readBStringSet() , readRNAStringSet() , readAAStringSet() load sequences from an input file (or multiple input files) into an XStringSet object [ 4 , 17 ]. Results are stored as objects provided by the Biostrings package. Therefore, multiple sequence alignment process is inherited from the Biostrings package [ 4 ].

The msaConvert() command enables the conversion of multiple sequence alignment objects to formats used in other analysis packages. It can convert to 6 different formats; "seqinr::alignment" , "bios2mds::align" , "ape::AAbin", "ape::DNAbin" , "phangorn::phyDat" , and "bio3d::fasta" [ 4 ].

bios2mds: From biological sequences to multidimensional scaling.

This package (version 1.2.3) realizes the analysis of biological sequences by metric Multidimensional Scaling (MDS). It has a variety of functions such as reading multiple sequence alignments, exporting aligned objects, calculating distance matrices, performing MDS analysis, and visualizing results [ 8 ].

adegenet: Exploratory analysis of genetic and genomic data.

adegenet package (version 2.1.3) is a toolset to explore genetic and genomic data. It generates class "genind" for hierarchical population structure, class "genpop" for alleles counts by populations, and class "genlight" for genome-wide SNP data [ 5 ].

The fasta2DNAbin() command reads FASTA format files and outputs a DNAbin object containing either the full alignments or only SNPs. At the same time, this command processes the massive datasets with its memory-efficiency [ 5 ].

ape: Analyses of phylogenetics and evolution.

The ape (version 5.3) is a package that can read, write, plot and manipulate phylogenetics data. Moreover, it has many functions such as comparing these data in a phylogenetic framework, character analysis of ancestors, reading, and writing nucleotide sequences [ 9 ]. The dist.dna() function calculates distances from DNA sequences by computing a matrix of pairwise distances from DNA sequences [ 9 ]. Also, the nj() function performs the neighbor-joining tree estimation of Saitou and Nei [ 18 ]. The boot.phylo() function performs the bootstrap analysis, and it can be used with the ggtree() command for visualization of the phylogenetic tree with bootstrap values. Additionally, it can extract data from Bioconductor and work together with adegenet and pegas packages [ 2 , 7 ].

ggtree: An R package for visualization of tree and annotation data.

The ggtree , R/Bioconducter package (version 2.0.4), is created for visualization of phylogenetic analysis such as annotation of the phylogenetic tree and other phylogenetic relationship structures. The ggtree() command is visualizing the phylogenetic tree as a tree object or as a phylo object by the as.phylo() command [ 11 ].

ggplot2: Create elegant data visualizations using the grammar of graphics.

The ggplot2 package (version 3.3.1) that is the extension of ggtree is a package that declaratively generates graphics based on Graphics Grammar [ 11 , 19 ]. It explains how to match variables with aesthetics and which graphical principles to use. It provides a better plotting of the graphics obtained with the ggtree package with a set of layers such as geom_tiplab() or geom_treescale() [ 11 , 20 ].

stats-package: The R stats package.

This package (version 3.6.3), which includes commands for statistical calculations and random number generation, provides methods for hierarchical cluster analysis based on a set of dissimilarities. The dist() command calculates the distances between the lines of a data matrix. It can use six different distance measures which are "euclidean" , "maximum" , "manhattan" , "canberra" , "binary" or "minkowski" . The heatmap() function it provides creates a heat map using the distance [ 10 ].

haplotypes: Manipulating DNA sequences and estimating unambiguous haplotype network with statistical parsimony.

The haplotypes package (version 1.1.2) reads and manipulates aligned DNA sequences, supports indel coding methods, shows base substitutions and indels, calculates absolute pairwise distances between DNA sequences. It provides or infers haplotypes by using identical DNA sequences or absolute pairwise character difference matrix. Furthermore, this package gives genealogical relationships among haplotypes using estimation of statistical parsimony and plots its networks [ 3 ].

pegas: Population and evolutionary genetics analysis system.

The pegas package (version 0.13) provides commands for reading, writing from different DNA sequences files including from VCF files. It generates plots, analyzing and manipulating allelic and haplotypic data. It requires packages ape and adegenet , making an integrated environment for population genetic data analysis. Additionally, it realizes the analysis of basic statistics, linkage disequilibrium, Fst and Amova, HWE, haplotype networks, minimum spanning tree and network, and median-joining networks [ 2 ].

Material and method

As an example, we used mitochondrial cyt b sequences (373 bp) dataset from 120 Bombus terrestris dalmatinus belonging to 8 different populations (Aksu = 15, Bayatbadem = 15, Demre = 15, Phaselis = 15, Geyikbayir = 15, Kumluca = 15, Termessos = 15, Firm = 15). Populations were grouped according to the regions from where they were collected; the Aksu, Demre and Kumluca populations belong to greenhouse regions, while the Bayatbadem, Phaselis, Geyikbayir and Termessos populations belong to nature areas and the commercial population was obtained from a firm which is located in Antalya. We want to show how to obtain multiple sequence alignments, haplotype networks, heat map and phylogenetic trees from a FASTA format input file using R (4.0.3. version) [ 10 ]. For all these analyses and graphics, we benefited from both R packages and short R commands.

Preparing the dataset

The file with the .fas extension obtained from the sequencing process was used as the input file. The sample names in the data were tagged with their population name and sample number. Names and numbers were separated by underscores or spaces, for example, “Kumluca_6” or “Bayatbadem 24”. This naming method allows extracting unique names as population names from sample names with the help of a short command. Thus, the name of the population in all the analyses do not need to be entered again.

Multiple sequence alignment and plotting aligned FASTA format file

The readDNAStringSet() command supported in Biostrings package (version 2.54.0) was used to read FASTA format file [ 17 ]. With msa() function implemented in msa package, all samples were aligned to the same length by ClustalW algorithm and stored as DNAStringSet object [ 4 , 17 ]. The as.DNAbin() function provided by ape package (version 5.3) was used to store multiple sequence alignments as a DNAbin object [ 7 ]. In this stage, the trim.Ends() function implemented in ips package (version 0.0.11) can be used for trimming the sequences [ 21 ]. The msaplot() command provided by ggtree package and ggplot2 package was used to demonstrate the aligned sequences with the phylogenetic tree [ 11 ]. Geometric layers ( geom_tiplab() , scale_color_continuous() , geom_tiplab() , geom_treescale() ) belonging to ggplot2 package were used fordetailing the tree [ 11 ]. To construct the phylogenetic tree, the dist.dna() function implemented in ape package was used [ 7 ]. The pairwise distance of the DNA sequences was computed with K80 model derived by Kimura [ 22 ]. The phylogenetic tree was estimated using the nj() function implemented in ape package [ 7 ]. The branch lengths of the tree have been colored to represent the genetic distance. As stated in the commands below, "lightskyblue1" was used for the longest branch of the tree and "coral4" was used for the shortest branch. Each of the nucleotides was represented by a different color. A, C, G, and T nucleotides have been colored with "rosybrown" , "sienna1" , "lightgoldenrod1" , and "lightskyblue1" , respectively, as stated in the commands below.

ggt <- ggtree (tree, cex = 0.8, aes (color = branch.length)) +

  scale_color_continuous (high = ‘lightskyblue1’,low = ‘coral4’) +

  geom_tiplab (align = TRUE, size = 2) +

  geom_treescale (y = -5, color = "coral4", fontsize = 4)

msaplot (ggt, nbin, offset = 0.009, width = 1, height = 0.5,

  color = c ( rep ("rosybrown", 1), rep ("sienna1", 1),

   rep ("lightgoldenrod1", 1), rep ("lightskyblue1", 1)))

Extraction of haplotypes

We wrote dynamic short R commands to find out information about haplotypes and sequence variations. Firstly, we converted the DNAbin object to the DNA matrix (120x373) using the as.matrix() command provided by R base package [ 10 ]. Secondly, by comparing the sequences, we extracted the haplotype number, haplotype frequency and variable regions. Thirdly, we identified unique haplotype sequences by ignoring common nucleotides between haplotypes and by printing variable regions.

The number of haplotypes per population was calculated using haplotypes package and short R commands [ 3 ]. Firstly, DNAbin object was converted to an object of class "DNA" using the as.dna() command which is provided by haplotypes package. Then haplotypes were extracted and grouped using the haplotype() and grouping() commands, respectively [ 3 ]. Finally, the population frequency matrix was extracted.

Haplotype distance matrix and heat map

Distance between the haplotypes was calculated by using dist.hamming() function from pegas package [ 2 ]. The Hamming distance method is a calculation of the pairwise distance matrix for the corresponding symbols between two strings of equal length [ 23 ]. Our data set consisted of haplotype sequences with 41 base pair long strings. We first separated each string into nucleotide arrays with strsplit() function, and formed a (20x41) haplotype sequences matrix, and then called dist.hamming() function for computing Hamming distance matrix.

For the construction of a heat map, we extracted the symmetric distance matrix (20x20) from the haplotype sequences matrix (20x41) using simple R commands. For this calculation, we compared the haplotype sequences in pairs, counting the nucleotide differences between them and writing them on a symmetric matrix. Then, we used this matrix for the visualization of heat map with the heatmap() command provided by stats package [ 10 ].

Haplotype network

The haplotype() and haploNet() functions implemented in pegas package were used for the construction of the haplotype network [ 2 ]. In this section, we wanted to show that data in R can be modified quickly and easily, creating multiple options for analysis. For this reason, we have shown three different haplotype graphs that were represented with different colors as hierarchical using the same data set. Thus, we have created options for those working both in individual datasets and those working with larger populations or groups. While the first haplotype network was represented by individuals, the second haplotype network was represented by populations and the third haplotype network was represented by groups. All haplotype networks were also plotted in different colors.

The haplotype network represented by individuals has been colored using rainbow colors defined as the default and the names and colors of the samples were described using fill argument in the legend() command, as below.

plot (net, size = attr (net, "freq"), scale.ratio = 2, cex = 0.6,

 labels = TRUE, pie = ind.hap, show.mutation = 1, font = 2,

 fast = TRUE)

legend (x = 57,y = 15, colnames (ind.hap), fill = rainbow ( ncol (ind.hap)),

  cex = 0.52, ncol = 6, x.intersp = 0.2, text.width = 11)

We chose special colors for the haplotype network represented by the populations. For the haplotype network, the desired colors were defined as a list in bg argument in the plot() command, as below.

bg <- c ( rep ("dodgerblue4", 15), rep ("olivedrab4",15),

   rep ("royalblue2", 15), rep ("red",15), rep ("olivedrab3",15),

   rep ("skyblue1", 15), rep ("olivedrab1", 15),

   rep ("darkseagreen1", 15))

plot (net, size = attr (net, "freq"), bg = bg, scale.ratio = 2, cex = 0.7,

 labels = TRUE, pie = ind.hap,show.mutation = 1, font = 2, fast = TRUE)

The names and colors of samples were described as a list in fill argument in the legend() command, as below.

hapcol <- c ("Aksu", "Demre", "Kumluca", "Firm", "Bayatbadem",

   "Geyikbayir", "Phaselis", "Termessos")

ubg < - c ( rep ("dodgerblue4",1), rep ("royalblue2",1),

   rep ("skyblue1",1),

   rep ("red",1), rep ("olivedrab4",1), rep ("olivedrab3",1),

   rep ("olivedrab1",1), rep ("darkseagreen1",1))

legend (x = -35,y = 45, hapcol, fill = ubg, cex = 0.8, ncol = 1, bty = "n",

 x.intersp = 0.2)

For the construction of the haplotype network represented by groups, each individual has been renamed with the name of the group to which it belongs. The sample names in the DNAbin object were replaced with the group names to which they belong with a few simple commands, and the haplotype network represented by the groups was plotted. The desired color set for the network diagram was defined in a list for the gbg argument in the plot() command, as below.

gbg <- c ( rep ("red"), rep ("blue"), rep ("green"))

plot (netg, size = attr (netg, "freq"), bg = gbg, scale.ratio = 2, cex = 0.7,

 labels = TRUE, pie = ind.hapg, show.mutation = 1, font = 2, fast = TRUE)

Colors of the groups were defined as a list in fill argument in the legend() command, as below.

legend (x = -35,y = 45, colnames (ind.hapg), fill = c ("red","blue","green"),

 cex = 0.8, ncol = 1, bty = "n", x.intersp = 0.2)

Phylogenetic trees

We demonstrated the circular phylogenetic tree by using ggtree , ggplot2 , ape , and stats packages [ 7 , 10 , 11 ]. To construct the phylogenetic tree, the dist.dna() and nj() commands were used supported by stats package. We have shown two circular phylogenetic trees. In the first tree, populations have been colored using the aes(color = Populations) command inherited from ggtree() and were drawn using ggplot2 package, as below.

emos <- ggtree (tree, layout = ‘circular’,

  branch.length = ‘branch.length’, lwd = 0.5) +

  xlim (-0.1, NA)

  groupOTU (emos, krp, ‘Populations’) +

  aes (color = Populations) +

  theme (legend.position = “right”) +

  geom_tiplab ( names (nbin), cex = 1.7, offset = 0.002) +

  guides (color = guide_legend (override.aes = list (size = 2.5))) +

  geom_treescale (x = -0.1,color = “coral4”, fontsize = 3, offset = 9)

In the second tree, the phylogenetic tree was colored according to branch lengths representing genetic distance. The aes(color = branch.length) command was used for coloring branches. Colors were defined using the scale_color_continuous() command. As stated in the commands below, "lightskyblue1" color was used for the longest branch and "coral4" color was used for the shortest branch.

ggtree (tree,layout = ‘circular’, branch.length = ‘branch.length’,

aes (color = branch.length), lwd = 0.5) +

xlim (-0.1, NA) +

geom_tiplab ( names (nbin), size = 1.7, offset = 0.002) +

scale_color_continuous (high = ‘lightskyblue1’,low = ‘coral4’) +

geom_treescale (x = -0.1, color = “coral4”, fontsize = 3, offset = 9)

On the other hand, we have constructed the phylogenetic relationship between haplotypes by using haplotype sequences. In this stage, treeio package (version 1.10.0) [ 24 ] was used with ggtree package. We calculated the genetic distance with the dist.hamming() function supported by pegas package [ 2 ]. The nj() function was used for neighbor-joining tree estimation. The confidence level between the branches was calculated using 100 bootstrap replicates by the boot.phylo() function implemented in the ape package [ 20 ]. The confidence interval was defined according to Kress et al. [ 25 ] criteria, as strong for 85% and above, moderate for 70–85%, weak for 50–70%, and poor for 50% and below. We colored node points using the scale_fill_manual() command inherited from the ggtree() command. As stated below, "black" , "red" , "pink1" , and "white" colors were selected according to the suggested four confidence intervals, respectively.

D <- dist.hamming (mat7) #pegas package

htre<- nj (D)

bp <- boot.phylo (htre, mat7, B = 100, function (x) nj ( dist.hamming (x)))

bp2 <- data.frame (node = 1: Nnode (htre) + Ntip (htre), bootstrap = bp)

htree <- full_join (htre, bp2, by = “node”)

boothap <- ggtree (htree, size = 1, branch.length = ‘branch.length’) +

  geom_tiplab (size = 4) +

  geom_nodepoint ( aes (fill = cut (bootstrap, c (0,50,70,85,100)),

  shape = 21, size = 4) +

  theme_tree (legend.position = c (0.85, 0.2)) +

  scale_fill_manual (values = c ("black","red","pink1","white",

   guide = ‘legend’,

   name = ‘Bootstrap Percentage (BP)’,

   breaks = c (‘(85,100]’, ‘(70,85]’,

    ‘(50,70]’, ‘(0,50]’),

   labels = expression (BP > = 85, 70 < = BP * “<85”,

     50 < = BP * “<70”, BP < 50))

The mitochondrial cty b with length 373 base pairs belonging to 120 Bombus terrestris dalmatinus was aligned with the ClustalW method using the msa package [ 4 ]. The aligned sequences were visualized by matching them with the phylogenetic tree by using the ggtree() and msaplot() commands [ 11 ]. In Fig 1 , the branch lengths of the tree have been colored to represent the genetic distance. Each of the nucleotides was represented by a different color. Thus, color changes on the plot have revealed nucleotide differences between the samples.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

The phylogenetic tree belonging to the mitochondrial cty b gene of 120 Bombus terrestris dalmatinus and nucleotide differences have been schematized. Colors both on the phylogenetic tree and on the branch scale represent genetic distance. mtDNA cyt b sequences have been colored by four different colors shown in the ‘seq’ column for each nucleotide. Color changes on the aligned sequences represent nucleotide differences.

https://doi.org/10.1371/journal.pone.0243927.g001

The distance scale has shown 0.6% genetic variation per nucleotide substitution. Each row of the multiple sequence alignments corresponding to the names at the end of the tree represents a complete sequence.

Haplotype sequences were obtained from the DNA matrix object by comparing each row with rows and each column with columns. As shown in Table 1 , when nucleotides were compared, the same nucleotides between haplotypes were expressed with dots while different nucleotides were directly written.

https://doi.org/10.1371/journal.pone.0243927.t001

The number of haplotypes per population was extracted using haplotypes package and short R commands. The most common haplotype was H11 that was submitted on NCBI (GenBank: MH221884.1), while the H16 and H14 haplotypes were observed in only one sample. While the Firm population has seven different haplotypes, only two haplotypes were detected in the Demre, Kumluca, and Termessos populations ( Table 2 ).

https://doi.org/10.1371/journal.pone.0243927.t002

The haplotype distance matrix was computed from the haplotype sequence matrix (20x41) using the dist.hamming() function. The distance matrix shows the difference in the number of nucleotides between the two haplotypes. In Table 3 , the smallest difference between the haplotype pairs was one nucleotide between the H9-H11, H10-H11, H12-H15 and H15-H16 haplotypes. The largest difference was 35 nucleotides between H5-H14 haplotypes.

https://doi.org/10.1371/journal.pone.0243927.t003

A heat map was constructed with phylogenetic trees from the haplotype sequences matrix using the heatmap() function [ 10 ] ( Fig 2 ). The haplotype distance matrix was extracted by using our own code from the haplotype sequence matrix (20x41). The matrix obtained was a symmetric version of Hamming distance matrix which is used to construct the heat map. The Heat Map is fully compatible with the haplotype distance matrix given in Table 3 .

Each branch of the phylogenetic tree represents the corresponding haplotype in the matrix. We defined the close relationships with "darkred" color and far relationships with "white" color.

https://doi.org/10.1371/journal.pone.0243927.g002

Haplotype networks were constructed using pegas package [ 2 ]. Circle sizes were provided to the plot() command using size = attr(x, "freq") argument; in this case based on the number of individuals each hapolotype has [ 10 ]. Each link between the haplotypes is a distance that showed the number of nucleotides between the two haplotypes. Every line on the links is a representation of one nucleotide. We demonstrated three haplotype networks represented by individuals ( Fig 3 ), populations ( Fig 4a ) and groups ( Fig 4b ).

Each slice in the circles represents an individual. Branch lengths between the circles have been represented by genetic distance between haplotypes. Every dash on the lines is a representation of one nucleotide. The rainbow() command was used for coloring the individuals. Haplotype network was demonstrated using the plot() command and names of individuals were added with the legend() command.

https://doi.org/10.1371/journal.pone.0243927.g003

(a) Populations have been colored in blue, red, and green representing the groups (greenhouse, firm and nature) they belong to. (b) Groups have been colored compatibly with the population haplotype network. Circle sizes were calculated based on the number of individuals they had. Each slice in the circles represents an individual. Every dash on the lines is a representation of one nucleotide. To visualize the two plots at the same time, the plot(new, TRUE) command was used after the first plot command and was continued with the second plot command.

https://doi.org/10.1371/journal.pone.0243927.g004

In Fig 3 , indivudals have been colored rainbow colors as default by the plot() command. In Fig 4a , the populations representing the greenhouse group have been colored in 3 different shades of blue, the Firm population belonging to the firm group was colored in red, and the populations of the nature group have been colored in 4 different shades of green. In Fig 4b , samples belonging to the greenhouse, firm, and nature groups have been colored blue, red and green, respectively. Therefore, each pie chart here represents individuals belonging to that group as a whole.

In Figs 3 and 4a , the largest circle in the center represents H11. Since the circles are schematized in the form of a pie chart according to the number of individuals, the name of the H11 haplotype does not appear in the small-sized images. But it looks quite clear in high resolution and large scale images or in the form of the larger pie chart ( Fig 4b ). 20 haplotypes with 71 links were determined. The closest haplotypes were H9-H10-H11; H12-H15, and H15-H16, while the most distant haplotypes were the H5-H14 haplotypes (Figs 3 and 4 ). Every dash on the lines is a representation of one nucleotide. The circles have been drawn according to their number of samples using " size = attr(net, "freq") " command. Therefore, the dashes (H11-H7; H11-H8, H11- H9, H11-H10) around the largest circle (H11) can’t be seen. If this command is removed and run again, all the mutation difference numbers can be seen as the dashes on the lines.

Phylogenetic trees were constructed using ggtree and ggplot2 packages. Tree estimation was calculated by the neighbor-joining method supported by ape package. While phylogenetic tree in Fig 5 was colored to represent populations, in Fig 6 it was colored to represent genetic distance. The biggest clade in both Figs 5 and 6 consisted of samples from Kumluca (12), Geyikbayir (6), Firm (4), Phaselis (7), Demre (13), Bayatbadem (6), and Aksu (7). Mostly, Aksu and Firm samples created more than one different phylogenetic clades.

The distance scale has shown 0.6% genetic variation per nucleotide substitution.

https://doi.org/10.1371/journal.pone.0243927.g005

https://doi.org/10.1371/journal.pone.0243927.g006

We demonstrated the phylogenetic relationship between haplotypes using the bootstrap method ( Fig 7 ). The distance was estimated by the Hamming distance method of nucleotide differences between the two sequences [ 2 ]. The confidence interval was defined as strong for 85% and above, moderate for 70–85%, weak for 50–70%, and poor for 50% and below [ 25 ]. The bootstrap values were specified by coloring according to these confidence intervals.

Colored internal nodes represent the bootstrap confidence level.

https://doi.org/10.1371/journal.pone.0243927.g007

There are many software packages available today that compute and visualize population genetic statistics and phylogenetics. Some of the most used software packages are MEGA, DnaSP, splitsTree, TASSEL and Arlequin [ 1 , 2 ]. With the tremendous advancements in technology in recent years, large amounts of molecular genetic data have been obtained. To process and analyze these data, there is a need for powerful computer software and hardware. Almost every software environment has its own specific input and output file format. While some analyses can be completed in one program, one software or in one workflow, the majority of them can only be completed in combined software packages. These multiple workflows create an imperative to convert inter-program input/output format changes, which is sometimes difficult and mostly time-consuming. Here we show phylogenetic relationships and statistical findings in a single workflow using R, on a sample mtDNA dataset. By sharing all the commands we use, we aim to present a ready-made format for researchers working in this field. Thus, by using only one FASTA format file as input, we are able to output multiple sequence alignments, haplotype sequences, heat map, haplotype networks, and phylogenetic trees. We have demonstrated the use and plotting of R in phylogenetic analyses, using both packages and R codes, taking advantage of R’s free language and free license. We tried to make some commands as modifiable as possible. One of these was the creation of a haplotype network [ 2 ]. We shared three haplotype networks representing individuals, populations, and groups. Some arguments such as coloring or plot scaling can be modified in accordance with other data sets. We demonstrated three different colored phylogenetic trees representing the populations, DNA distance and haplotypes bootstrap tree [ 11 , 20 ]. Likewise, by changing the commands we use for phylogenetic trees, arguments such as colors, tree type, and graphic scaling can be modified. We wanted to show that R can be used for researchers who study mtDNA and are interested in phylogenetics. While input-output files are frequently needed in phylogenetic analysis software, we showed that 8 different outputs can be obtained from a single .fas extension file and shared all the packages, libraries and commands we used in these analyses. At the same time, we recommend using RStudio for visualization studies, with features such as easier editing of the code with the source pane, easier manipulation of visuals with the plot pane, and to be a reminder for arguments or definitions. Consequently, the haplotypes sequences, heat map, haplotype networks, and phylogenetic trees gave results that are completely compatible with each other. We believe that R and RStudio will be increasingly used in phylogenetic analyses and visualization due to the fact that it is an open source and always up-to-date environment, free for all as well as open for researcher contributions such as this one.

Supporting information

S1 appendix. r codes..

It is for phylogenetic analyses by using FASTA format file.

https://doi.org/10.1371/journal.pone.0243927.s001

S2 Appendix. 120 mitochondrial cyt b sequences of B . terrestris dalmatinus .

It is an example dataset as FASTA format sequences file.

https://doi.org/10.1371/journal.pone.0243927.s002

Acknowledgments

The data used in this project were provided by the project numbered FYL-2016-1502, supported by the Scientific Research Projects Coordination Unit of Akdeniz University. We would like to thank Philippa Price for proofreading this article.

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Phylogenetic Analysis

• First Online: 25 September 2021

Cite this chapter

• Manoj Kumar Gupta 3 ,
• Gayatri Gouda 3 ,
• S. Sabarinathan 3 , 4 ,
• Ravindra Donde 3 ,
• N. Rajesh 5 ,
• Pallabi Pati 6 ,
• Sushil Kumar Rathore 7 ,
• Lambodar Behera 3 &
• Ramakrishna Vadde 5  

951 Accesses

In this chapter, the authors attempt to understand the underlying phylogeny principle and how researchers implement diverse methods to discover the appropriate phylogeny. Results obtained revealed that phylogenetic trees reflect evolutionary past as a canonical framework. Phylogenetic tree building step essentially comprises of five steps: (a) selecting molecular markers; (b) multiple sequence alignment; (c) determining the best evolutionary model; (d) determination of tree building method; and (e) assessment of tree reliability. Phylogenetic trees have various functional uses in different biological fields, such as conservation biology, epidemiology, forensics, cancer evolution, HIV transmission, gene expression prediction, protein structure prediction, and drug design. However, researchers face different challenges for generating a more accurate tree, like memory efficiency and implementation and optimization of the likelihood function. The authors believe, in the near future, the development of exciting new algorithms, which dramatically reduce the necessary amount of likeliness assessment, combined with enhanced knowledge of previously described high-performance machine problems in the group, is likely to detect more accurate phylogenetic tree that include 10,000–20,000 sequences. Additionally, it will also permit the tree inferences on medium-sized PC.

• Molecular markers
• Bootstrapping
• Character-based
• Distance-based
• Jackknifing

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Abbreviations

Bayesian inference

Chloroplast DNA

Non-synonymous

Genotyping-by-sequencing

Hypothetical taxonomic units

Internal transcribed spacer

Jukes and Cantor

Last common ancestor

Last universal common ancestor

Maximum-like

Multiple sequence alignment

Operational taxonomic units

Polymerase chain reaction

Ultra-conserved elements

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Gupta, M.K. et al. (2021). Phylogenetic Analysis. In: Gupta, M.K., Behera, L. (eds) Bioinformatics in Rice Research. Springer, Singapore. https://doi.org/10.1007/978-981-16-3993-7_9

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Phylogenetics Algorithms and Applications

Geetika munjal.

18 The NorthCap University, Gurugram, India

Madasu Hanmandlu

19 IIT Delhi, New Delhi, India

Sangeet Srivastava

Phylogenetics is a powerful approach in finding evolution of current day species. By studying phylogenetic trees, scientists gain a better understanding of how species have evolved while explaining the similarities and differences among species. The phylogenetic study can help in analysing the evolution and the similarities among diseases and viruses, and further help in prescribing their vaccines against them. This paper explores computational solutions for building phylogeny of species along with highlighting benefits of alignment-free methods of phylogenetics. The paper has also discussed the application of phylogenetic study in disease diagnosis and evolution.

Introduction

Phylogenetics can be considered as one of the best tools for understanding the spread of contagious disease, for example, transmission of the human immunodeficiency virus (HIV) and the origin and subsequent evolution of the severe acute respiratory syndrome (SARS) associated coronavirus (SCoV) [ 1 ]. Earlier, morphological traits were used for assessing similarities between species and building phylogenetic trees. Presently, phylogenetics relies on information extracted from genetic material such as deoxyribonucleic acid (DNA), ribonucleic acid (RNA) or protein sequences [ 2 ]. Methods used for phylogenetic inference have changed drastically during the past two decades: from alignment-based to alignment-free methods [ 3 ]. This paper has reviewed various methods under phylogenetic tree construction from character to distance methods and alignment-based to alignment-free methods. A brief review of phylogenetic tree applications is also given in cancer studies.

Literature Review

A phylogenetic tree can be unrooted or rooted, implying directions corresponding to evolutionary time, i.e. the species at the leaves of a tree relate to the current day species. The species can be expressed as DNA strings which are formed by combining four nucleotides A, T, C and G (A—adenine, T—thymine, C—cytosine and G—guanine). In literature, various string processing algorithms are reported which can quickly analyse these DNA and RNA sequences and build a phylogeny of sequences or species based on their similarity and dissimilarity. A high similarity among two sequences usually implies significant functional or structural likeliness, and these sequences are closely related in the phylogenetic tree. To get more precise information about the extent of similarity to some other sequence stored in a database, we must be able to compare sequences quickly with a set of sequences. For this, we need to perform the multiple sequence comparison. Dynamic programming concepts facilitate this comparison using alignment methods, but it involves more computation. Moreover, the iterative computational steps limit its utility for long length sequences [ 3 ]. Alignment-free methods overcome this limitation as they follow alternative metrics like word frequency or sequence entropy for finding similarity between sequences.

Methods of Phylogenetic Tree Construction

Phylogenetic tree generation consists of sequence alignment where the resulting tree reveals how alignment can influence the tree formation. Alignment-based methodologies are probably the most widely used tools in sequence analysis problems [ 4 ]. They consist of arranging two sequences: one on the top of another to highlight their common symbols and substrings. An alignment method is based on alignment parameters including insertion, deletions and gaps which play a pivotal role in the construction of the phylogenetic tree. A phylogenetic tree is formed as an outcome of sequence analysis performed on the DNA or RNA strings [ 5 ]. Sequence comparison reveals the patterns of shared history between species, helping in the prediction of ancestral states. The comparison of sequences also helps in understanding the biology of living organisms which is required to find similarity and relationship among species. For sequence comparison, we can follow alignment-based or alignment-free methods [ 3 , 6 , 7 ].

Sequence Alignment

Sequence alignment is a method to identify homologous sequences. It is categorized as pairwise alignment in which only two sequences are compared at a time whereas in multiple sequence alignment more than two sequences are compared. Alignment-based can be global or local [ 8 , 9 ]. These alignment-based algorithms can also be used with distance methods to express the similarity between two sequences, reflecting the number of changes in each sequence. Figure  1 gives a hierarchical view of various methods for phylogenetic tree building.

Hierarchical view of phylogenetic methods 2

Character-Based Methods

The character-based methods compare all sequences simultaneously considering one character/site at a time. These are maximum parsimony and maximum likelihood. These methods use probability and consider variation in a set of sequences [ 10 ]. Both approaches consider the tree with the best score tree, which requires the smallest number of changes to perform alignment. Maximum parsimony method suffers badly from the long-branch attraction and gives the least information about the branch lengths [ 10 ]. In such cases, if two external branches are separated by short internal branches, it leads to the incorrect tree. Some of the salient features of character-based methods are mentioned in Table  1 .

Table 1

Comparison of different phylogenetic tree construction methods

Distance-Based Methods

Distance-based methods use the dissimilarity (the distance) between the two sequences to construct trees. They are much less computationally intensive than the character based methods are mostly accurate as they take mutations into count. For tree generation, generally, hierarchical clustering is used in which dendrograms (clusters) are created. Table  1 briefly compares various phylogenetic tree construction methods.

Alignment-Based Versus Alignment-Free Sequence Comparison

Multiple alignments of related sequences may often yield the most helpful information on its phylogeny. However, it can produce incorrect results when applied to more divergent sequence rearrangements [ 3 ]. Some computationally intensive multiple alignment methods align sequences strictly based on the order in which they receive them. Multiple sequence alignment methods emphasize that more closely related sequences should be aligned first. In cases of sequences being less related to one another, however, sharing a common ancestor may be clustered separately [ 11 ]. This implies that they can be more accurately aligned, but may result in incorrect phylogeny. Alignment can provide an optimized tree if a recursive approach is followed; however, this will increase the complexity of the problem. If the differences among the lengths of sequences are very high, the alignment performance significantly impacts tree generation.

The use of dynamic programming in alignment makes computation more complicated, and iterative steps limit their utility for large datasets. Therefore, consistent efforts have been made in developing and improving multiple sequence alignment methods for supporting variable length sequences with high accuracy and also for aligning a larger number of sequences simultaneously. Because of the problems associated with alignment-based phylogeny the importance of alignment-free methods is apparent [ 3 ]. Hence, the alignment quality affects the relationship created in a phylogenetic tree based on the consideration discussed above.

Alignment-Free Methods for Sequence Analysis

Alignment-free methods proposed in recent years can be classified into various categories as shown in Fig.  1 . These include k-tuple based on the word frequencies, methods that represent the sequence without using the word frequencies, i.e. compression algorithms probabilistic methods and information theory-based method. In the k-tuple method, a genetic sequence is represented by a frequency vector of fixed length subsequence and the similarity or dissimilarity measures are found based on the frequency vector of subsequence. The probabilistic methods represent the sequences using the transition matrix of a Markov chain [ 12 ] of a pre-specified order, and comparison of two sequences is done by finding the distance between two transition matrices. Graphical representation comprising 2D or 3D or even 20D methods provides an easy way to view, sort and compare various sequences. Graphical representation further helps in recognizing major characteristics among similar biological sequences.

As discussed k-tuple method uses k-words to characterize the compositional features of a sequence numerically. A biological sequence is numerically converted into a vector or a matrix composed of the word frequency. The k-word frequency provides a fast arithmetic speed and can be applied to full sequences. The problem with k-tuple is a big value of k that poses a challenge in the computing time and space, and k-word methods underestimate or even ignore the importance of its location. The string-based distance measure uses substring matches with k mismatches.

Application of Phylogenetics in Cancer Studies

Cancer research is considered one of the most significant areas in the medical community. Mutations in genomic sequences are responsible for cancer development and increased aggressiveness in patients [ 13 , 14 ]. The combination of all such genes mutations, or progression pathways, across a population can be summarized in a phylogeny describing the different evolutionary pathways [ 9 ]. Application of the phylogenetic tree can be explored for finding similarities among breast cancer subtypes based on gene data [ 14 , 15 ]. Discovery of genes associated in cancer subtype help researchers to map different pathways to classify cancer subtypes according to their mutations. Methods of phylogenetic tree inference have proliferated in cancer genome studies such as breast cancer [ 13 ]. Phylogenetic can capture important mutational events among different cancer types; a network approach can also capture tumour similarities.

It has been observed from the literature that in cancer disease, the driver genes change the cancer progression, and it even affects the participation of other genes thus generating gene interaction network. Phylogenetic methods can solve the problem of class prediction by using a classification tree. Phylogenetic methods give us a deeper understanding of biological heterogeneity among cancer subtype.

The research focuses on the various methods of sequence analysis to generate phylogenetic trees. The limitations associated with sequence alignment methods lead to the development of alignment-free sequence analysis. However, most of the existing alignment-free methods are unable to build an accurate tree so more refinement is required in alignment-free methods. The phylogenetic study is not limited to species evolution, but disease evolution as well. Extending phylogenetic to disease diagnosis can give birth to new treatment options and understanding its progression.

Acknowledgements

The research is funded by Department of Science and Technology, Delhi, under the sanction number SR/WOS-A/ET-1015/2015.

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Geetika Munjal, Email: ude.aidniucn@akiteeg , Email: [email protected] .

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