This work was supported by the National Natural Science Foundation of China (31371596), the Bio-technology Research Center, China Three Gorges University (2016KBC04), and the Natural Science Foundation of Jiangsu Province, China (BK20151424).

1. Sequence Alignment
<ol>
	<li>Collect amino acid sequences of eukaryotic SWEET and prokaryotic SemiSWEET in separate documents and list them in FASTA format. Download sequences from the National Center for Biotechnology Information (NCBI), European Molecular Biology Laboratory (EMBL), and the DNA Data Bank of Japan (DDBJ) databases by similarity search with the Basic Local Alignment Search Tool (BLAST) tool.
	<ol>
		<li>In the example files, collect 228 putative SWEET protein sequences possessing two MtN3/saliva domains (7 transmembrane helices) of eukaryotes and 57 SemiSWEET protein sequences possessing a single MtN3/saliva domain (3 transmembrane helices) of prokaryotes13.</li>
		<li>To simplify the process, select 35 candidate SWEET proteins from unicellular eukaryotic organisms among the 228 putative SWEETs for phylogenetic tree construction. These sequences are attached so that the reader may practice on a real data set.</li>
	</ol>
	</li>
	<li>Align the 35 SWEET sequences by inputting them into Clustal Omega (http://www.ebi.ac.uk/Tools/msa/clustalo/).
	<ol>
		<li>Copy and paste the protein sequences in FASTA format into the input box or upload a sequence file in FASTA format. Specify that they are&#160;amino acid sequence by clicking the icon under the pull-down menu in the &#39;STEP 1&#39; section.</li>
		<li>Specify output format and other parameters in the &#39;STEP 2&#39; section, if necessary. For this study, set output format as &#34;clustal w/o number&#34;, and leave the other parameters on default settings. In most cases, the default parameters work well without any specification.</li>
	</ol>
	</li>
	<li>Submit and run the alignment in the &#39;STEP 3&#39; section. It may take anywhere from several seconds to minutes until the alignment is finished. In the &#34;Result Summary&#34; panel, right-click the link under the &#34;Alignment in CLUSTAL format&#34; and save the aligned sequences as &#34;35.clustal&#34; (Figure 1).</li>
	<li>Open the alignment result file in BioEdit.
	<ol>
		<li>On the main panel of BioEdit, click &#34;Sequence&#34; and select &#34;Edit Mood&#34;in the first pull-down menu, then click &#34;Edit Residues&#34; in the sub-menu (Figure 2).</li>
		<li>Select the protruding sequences on the left side of the alignment with the cursor (the selected sequence will be shown in black) and click the &#34;Delete&#34; icon under the &#34;Edit&#34; menu to remove the selected sequences (Figure 3).</li>
		<li>Select and delete the protruding sequences on the right side of the first MtN3/saliva domain, and save the trimmed first MtN3/saliva domain sequences as 35-I.fas (Figure 4). Likewise, delete the left and right side protruding sequences of the second MtN3/saliva domain and save it as 35-II.fas. The first and the second MtN3/saliva domain sequences can be predicted with RHYTHM (http://proteinformatics.charite.de/rhythm/inndex.php?site=helix) or TMHMM (http://www.cbs.dtu.dk/services/TMHMM/) in advance.</li>
	</ol>
	</li>
	<li>Open the file 35-I.fas with MEGA, and click &#34;align&#34; when prompted. Under the &#34;Edit&#34; menu, click &#34;Select All&#34;, then click &#34;Select Sequence(s)&#34;; the names and sequences of the taxa will be selected in black (Figure 5).
	<ol>
		<li>Choose &#34;Copy&#34; from the &#34;Edit&#34; menu to copy the sequences onto the clipboard, and then paste the copied sequences into a doc file.</li>
		<li>In the doc file, replace all &#34;#&#34; with &#34;&#62;&#34;, and then delete any unrelated characters to convert them to FASTA format. Add &#34;-I&#34; at the end of each taxon name to mark them as the first MtN3/saliva domain sequences. Process the second MtN3/saliva domain sequence following the same method and add &#34;-II&#34; after each taxon name.</li>
	</ol>
	</li>
	<li>Combine the first and second MtN3/saliva domain sequences in FASTA format in a doc file.
	<ol>
		<li>Load the combined sequences into Clustal Omega again and align the sequences as described above. Save the result as &#34;35 realigned.clustal&#34;.</li>
		<li>Open the &#34;35 realigned.clustal&#34; file in BioEdit, delete the uneven (protruding) amino acid residues at either end of the aligned sequences, and then save the sequences as &#34;35 realigned.fas&#34;. Click &#34;Yes&#34; when warned that some non-standard characters cannot be saved.</li>
	</ol>
	</li>
</ol>
2. Computation of the Phylogenetic Tree
<ol>
	<li>Open &#34;35 realigned.fas&#34; in MEGA.
	<ol>
		<li>Click the &#34;Data&#34; menu and choose &#34;Export Alignment&#34;, and save the alignment in PAUP format (nexus) as &#34;35.nex&#34; for later use in MrBayes (Figure 6).</li>
		<li>Meanwhile, click the &#34;Models&#34; icon on the main panel of MEGA, choose &#34;Find Best DNA/Protein Models (ML)&#34;, and click &#34;OK&#34; on the pop-up window. Click &#34;Compute&#34; to begin the model searching process (Figure 7). A new progress panel will open; this process lasts several minutes to several days, depending on the complexity of the loaded sequences and the computer&#39;s performance. 
		NOTE: A table showing the results will open after the model searching process is finished ( Figure 8). The smallest BIC score will be listed first, followed by a series of different models with gradually increasing BIC scores. The first model &#34;LG+G+F&#34; with the smallest BIC score is the recommended model for ML tree based on the &#34;35 realigned.fas&#34; file.</li>
	</ol>
	</li>
	<li>Click the &#34;Phylogeny&#34; icon on the main panel of MEGA, click &#34;Construct/Test the Maximum Likelihood Tree&#34;, and then click &#34;Yes&#34; on the pop-up panel. A new window will open showing different parameters that need to be specified (Figure 9).
	<ol>
		<li>First, set the bootstrap value in the test of the phylogeny box; 500 or 1,000 is adequate in most cases. Under the substitution model, choose &#34;amino acid&#34; as the substitution type. The purpose of choosing a substitution model is to estimate the true difference between sequences based on their present states3.</li>
		<li>Select &#34;LG with Freqs. (+F) model&#34; (LG+F) in the model/method box. In the rates and pattern box, select &#34;Gamma Distributed&#34; (G) to describe rate variations across sites, i.e., giving more weight to changes at slowly evolving sites3. In the data subset box, select &#34;Complete deletion&#34; to remove all of the columns containing hyphens.</li>
		<li>Keep all other parameters in their default states (Figure 9). After specification of these parameters, click the &#34;Compute&#34; icon to start the calculation.</li>
	</ol>
	</li>
</ol>
3. Presentation of the Phylogenetic Tree
NOTE: A phylogenetic ML tree will be presented when the computation using MEGA is finished (Figure 10).
<ol>
	<li>Under the pull-down menu of the &#34;File&#34; icon on the tree panel, choose &#34;Save Current Session&#34; to save the result (.mas is the default file type). In the present study, the result was saved as &#34;35.mas&#34;. On the tree panel, many parameters including length of clade, tree style, tree topology, font of the taxon name, size, and color, are displayed and can be set to different options.</li>
	<li>Save the final tree file by clicking the image icon, and save the figure in different formats or copy the image as the source for photo-editing.</li>
</ol>
4. Analysis of the Relationship of SWEETs and SemiSWEETs Using Sequence Alignment
NOTE: This step may not be needed in ordinary sequence analysis.
<ol>
	<li>Align the 228 eukaryotic SWEETs and 57 prokaryotic SemiSWEETs in Clustal Omega as described above. The alignment results can be shown in Jalview, which is integrated in Clustal Omega, and copied to save in a photo editor (Figure 11). 
	NOTE: In the example alignment, some SemiSWEETs from &#945;-Proteobacteria are aligned with the first MtN3/saliva domain of the SWEET sequences, whereas SemiSWEETs from Methanobacteria (archaea) are aligned with the second MtN3/saliva domain of the SWEET sequences.</li>
</ol>
5. Phylogenetic Tree Construction with MrBayes
<ol>
	<li>For Bayesian Inferences with MrBayes, open the MrBayes executable file and a DOS interface will come up in a new window. The first step is to read the nexus data &#64257;le. Input &#34;execute 35.nex&#34; after the prompt (remember to save the 35.nex file in the same directory of the MrBayes executable file, or point out the pathway of the file before uploading it). A &#34;successful read matrix&#34; message will be shown following the last of the listed taxa (Figure 12). The 35.nex file has already been prepared and saved in MEGA (see 2.1 above).</li>
	<li>Set the evolutionary model.
	<ol>
		<li>After the prompt, type &#34;prset aamodelpr = fixed(lg); lset rates = g&#34;. The &#34;lg&#34; and &#34;g&#34; correspond to the &#34;LG&#34; and &#34;G&#34; model which is set in MEGA. After successfully setting the model, type &#34;mcmc nchains = 4 ngen = 5,000,000&#34; after the prompt. Use of the &#34;nchains=4&#34; entry signifies a total number of one cold chain and three hot chains for Metropolis coupling. &#34;ngen = 5,000,000&#34; means to run 5,000,000 generations of Metropolis coupling for convergence of the cold and hot chains. In this study, average standard deviation of split frequencies below 0.01 was regarded as convergence of the hot and cold chains.</li>
		<li>Note that the ngen number cannot be predicted accurately at the beginning of the process, and usually needs to be adjusted based on the change in the average standard deviation of split frequencies. In addition, the ngen number for convergence may be different each time when running the program based on the same data.</li>
	</ol>
	</li>
	<li>Run the analysis: This step lasts from several minutes to several days, depending on the complexity of input data and the performance of the computer. After completing the preset computation, a prompt will ask &#34;Continue with analysis (yes/no)?&#34; If &#34;no&#34; is typed after the prompt, the computing will stop (Figure 13), otherwise it will continue to compute after the number of further generations is input. When the computation is finished (with an average standard deviation of split frequencies &#60;0.01 or 0.05), stop the computation by typing &#34;no&#34; after the inquiry prompt. 
	NOTE: 0.01 is a strict criterion, 0.05 is moderate and usually adequate.</li>
	<li>Summarize the samples: Type &#34;sump&#34; after the prompt to summarize samples of model parameters (Figure 14). Then type &#34;sumt relburnin=yes burninfrac=0.25&#34; after the prompt to summarize tree samples. Detailed information about phylogenetic tree construction will be displayed as in&#160;Figure 15, followed by two tree figures that will appear in ASC II code on the screen, one showing clade credibility and the other showing branch lengths. At the same time, a tree file with the name of &#34;35.nex.con&#34; will be saved automatically.</li>
	<li>For a better presentation of the phylogenetic tree, open the &#34;35.nex.con&#34; tree file with the FigTree tool (http://tree.bio.ed.ac.uk/software/figtree/), select a style or size to display the result (Figure 16), or even edit it in a photo editor to make it more reader-friendly.</li>
</ol>

<ol><li>Nei, M., Kumar, S. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aNei%2C+M+%22Molecular+Evolution+and+Phylogenetics%22">Molecular Evolution and Phylogenetics</a>. , Oxford University Press. Oxford. (2000).</li>
<li>Foth, B. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Phylogenetic+analysis+to+uncover+organellar+origins+of+nuclear-encoded+genes%5BTitle%5D)+AND+%22Methods+Mol+Biol%22%5BJournal%5D)">Phylogenetic analysis to uncover organellar origins of nuclear-encoded genes.</a> Methods Mol Biol. 390, 467-488 (2007).</li>
<li>Baldauf, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Phylogeny+for+the+faint+of+heart%3A+a+tutorial%5BTitle%5D)+AND+%22Trends+Genet%22%5BJournal%5D)">Phylogeny for the faint of heart: a tutorial.</a> Trends Genet. 19, 345-351 (2003).</li>
<li>Feng, D. F., Doolittle, R. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Progressive+sequence+alignment+as+a+prerequisite+to+correct+phylogenetic+trees%5BTitle%5D)+AND+%22J+Mol+Evol%22%5BJournal%5D)">Progressive sequence alignment as a prerequisite to correct phylogenetic trees.</a> J Mol Evol. 25, 351-360 (1987).</li>
<li>Persson, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Bioinformatics+in+protein+analysis%5BTitle%5D)+AND+%22EXS%22%5BJournal%5D)">Bioinformatics in protein analysis.</a> EXS. 88, 215-231 (2000).</li>
<li>Sievers, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fast%2C+scalable+generation+of+high-quality+protein+multiple+sequence+alignments+using+Clustal+Omega%5BTitle%5D)+AND+%22Mol+Syst+Biol%22%5BJournal%5D)">Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega.</a> Mol Syst Biol. 7, 539(2011).</li>
<li>Sievers, F., Higgins, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Clustal+omega%5BTitle%5D)+AND+%22Curr+Protoc+Bioinformatics%22%5BJournal%5D)">Clustal omega.</a> Curr Protoc Bioinformatics. 48, 1-16 (2014).</li>
<li>Yang, Z., Rannala, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Molecular+phylogenetics%3A+principles+and+practice%5BTitle%5D)+AND+%22Nat+Rev+Genet%22%5BJournal%5D)">Molecular phylogenetics: principles and practice.</a> Nat Rev Genet. 13, 303-314 (2012).</li>
<li>Hall, B. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Comparison+of+the+accuracies+of+several+phylogenetic+methods+using+protein+and+DNA+sequences%5BTitle%5D)+AND+%22Mol+Biol+Evol%22%5BJournal%5D)">Comparison of the accuracies of several phylogenetic methods using protein and DNA sequences.</a> Mol Biol Evol. 22, 792-802 (2005).</li>
<li>Tamura, K., Stecher, G., Peterson, D., Filipski, A., Kumar, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((MEGA6%3A+Molecular+Evolutionary+Genetics+Analysis+version+6.0%5BTitle%5D)+AND+%22Mol+Biol+Evol%22%5BJournal%5D)">MEGA6: Molecular Evolutionary Genetics Analysis version 6.0.</a> Mol Biol Evol. 30, 2725-2729 (2013).</li>
<li>Ronquist, F., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/pubmed/22357727">MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space.</a> Syst Biol. 61, 539-542 (2012).</li>
<li>Chen, L. Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Sugar+transporters+for+intercellular+exchange+and+nutrition+of+pathogens%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Sugar transporters for intercellular exchange and nutrition of pathogens.</a> Nature. 468, 527-532 (2010).</li>
<li>Xuan, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Functional+role+of+oligomerization+for+bacterial+and+plant+SWEET+sugar+transporter+family%5BTitle%5D)+AND+%22Proc+Natl+Acad+Sci+USA%22%5BJournal%5D)">Functional role of oligomerization for bacterial and plant SWEET sugar transporter family.</a> Proc Natl Acad Sci USA. 110, 3685-3694 (2013).</li>
<li>Hu, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Phylogenetic+evidence+for+a+fusion+of+archaeal+and+bacterial+SemiSWEETs+to+form+eukaryotic+SWEETs+and+identification+of+SWEET+hexose+transporters+in+the+amphibian+chytrid+pathogen+Batrachochytrium+dendrobatidis%5BTitle%5D)+AND+%22FASEB+J%22%5BJournal%5D)">Phylogenetic evidence for a fusion of archaeal and bacterial SemiSWEETs to form eukaryotic SWEETs and identification of SWEET hexose transporters in the amphibian chytrid pathogen Batrachochytrium dendrobatidis.</a> FASEB J. 30, 3644-3654 (2016).</li>
<li>Holder, M. T., Zwickl, D. J., Dessimoz, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Evaluating+the+robustness+of+phylogenetic+methods+to+among-site+variability+in+substitution+processes%5BTitle%5D)+AND+%22Philos+Trans+R+Soc+Lond+B+Biol+Sci%22%5BJournal%5D)">Evaluating the robustness of phylogenetic methods to among-site variability in substitution processes.</a> Philos Trans R Soc Lond B Biol Sci. 363, 4013-4021 (2008).</li>
<li>Alfaro, M. E., Holder, M. T. <a target="_blank" href="http://www.annualreviews.org/doi/abs/10.1146/annurev.ecolsys.37.091305.110021?journalCode=ecolsys">The Posterior and the Prior in Bayesian Phylogenetics.</a> Annu Rev Ecol Evol Syst. 37, 19-42 (2006).</li>
<li>Suchard, M., Rambaut, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Many-core+algorithms+for+statistical+phylogenetics%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">Many-core algorithms for statistical phylogenetics.</a> Bioinformatics. 25, 1370-1376 (2009).</li>
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</ol>

The authors have nothing to disclose.

It is becoming increasingly popular in biological research to make a phylogenetic tree based on nucleotide or amino acid&#160;sequences8. Generally, there are three critical stages of the practice including sequence alignment, evaluation of the aligned sequences with the proper method or algorithm, and visualization of the computational result as a phylogenetic tree. In the presented study, three rounds of sequence alignment were conducted: first, the SWEET protein sequences, including the first a...

DNA or RNA sequences carry genetic information for underlying phenotypes that can be analyzed through physiological and biochemical methods or observed through morphological and fossil evidence. In a sense, genetic information is more reliable than evaluating external phenotypes because the former is the basis for the latter. In evolutionary study, fossil evidence is very direct and convincing. However, many organisms, such as microorganisms, have little chance to form a fossil during long geologic ages. Therefore, molecular information such as nucleotide sequences and amino acid sequences from related extant organisms are of value for exploring evolutionary relationships1. In the present study, a simple introduction of basic phylogenetic knowledge and an easy-to-learn protocol was provided for newcomers who need to construct a phylogenetic tree on their own.
Both DNA (nucleotide) and protein (amino acid) sequences can be used to infer phylogenetic relationships between homologous genes, organelles, or even organisms2. DNA sequences are more likely to be affected by changes during evolution. In contrast, amino acid sequences are much more stable given that synonymous mutations in nucleotide sequences do not cause mutations in amino acid sequences. As a result, DNA sequences are useful for comparison of homologous genes from closely related organisms, whereas amino acid sequences are appropriate for homologous genes from distantly related organisms3.
A phylogenetic analysis begins with the alignment of amino acid or nucleotide sequences4 retrieved from an annotated genome sequencing database5&#160;listed in FASTA format, i.e., putative or expressed protein sequences, RNA sequences, or DNA sequences. It is worth noting that it is critical to collect high-quality sequences for the analysis, and only homologous sequences can be used to analyze phylogenetic relationships. Many different platforms such as Clustal W, Clustal X, Muscle, T-coffee, MAFFT, can be used for sequence alignment. The most widely used is Clustal Omega6,7 (http://www.ebi.ac.uk/Tools/msa/clustalo/), which can be used online or can be downloaded free of charge. The alignment tool has many parameters that the user can adjust before starting the alignment, but the default parameters work well in most cases. After the process is complete, the aligned sequences should be saved in the correct format for the next step. They should then be edited or trimmed using an editing software, such as BioEdit, because phylogenetic tree construction by MEGA requires the sequences to be of equal length (including both amino acid abbreviations and hyphens. In the aligned sequence, any position without an amino acid or nucleotide is represented by a hyphen &#34;-&#34;). Generally, all of the protruding amino acids or nucleotides at either end of the alignment should be removed. In addition, columns containing poorly aligned sequences in the alignment can be deleted because they convey little valuable information, and can sometimes give confusing or false information3. The columns containing one or more hyphens can be deleted at this time or in the later tree construction stage. Alternatively, they can be used for phylogenetic computation. When the sequence alignment and trimming is finished, the aligned sequences should be saved in FASTA format, or the desired format, for later use.
Many software platforms provide tree construction functions using different methods or algorithms. In general, the methods can be classified as either distance matrix methods or discrete data methods. Distance matrix methods are simple and fast to compute, while discrete data methods are complicated and time-consuming. For very closely related taxa with a high degree of sharing of amino acid or nucleotide sequence identity, a distance matrix method (Neighbor Joining: NJ; Unweighted Pair Group Method with Arithmetic mean: UPGMA) is appropriate; for distantly related taxa, a discrete data method (Maximum Likelihood: ML; Maximum Parsimony: MP; Bayesian Inference) is optimal3,8. In this study, the ML methods in MEGA (6.0.6) and Bayesian Inference (MrBayes 3.2) were applied to construct phylogenetic trees9. Ideally, when the proper model and parameters are used, the results derived from different methods may be consistent, and they are thus more reliable and convincing.
For a ML phylogenetic tree constructed using MEGA10, the aligned sequence file in FASTA format must be uploaded into the program. The first step then is to select the optimal substitution model for the uploaded data. All available substitution models are compared based on the uploaded sequences, and their final scores will be shown in a results table. Select the model with the smallest Bayesian Information Criterion (BIC) score (listed first in the table), set ML parameters according to the recommended model, and start the computation. The computation time varies from several minutes to several days, depending on the complexity of the loaded data (length of the sequences and number of taxa) and the performance of the computer on which the programs are run. When the computation is finished, a phylogenetic tree will be shown in a new window. Save the file as &#34;FileName.mat&#34;. After setting parameters to specify the appearance of the tree, save once more. Using this method, MEGA can generate publication grade phylogenetic tree figures.
For tree construction with MrBayes11, the first step is to transform the aligned sequence, which is usually listed in FASTA format, into nexus format (.nex as the file type). Transforming FASTA files into nexus format can be processed in MEGA. Next, the aligned sequence in nexus format can be uploaded into MrBayes. When the file is successfully uploaded, specify detailed parameters for the tree computation. These parameters include details such as amino acid substitution model, variation rates, chain number for Markov chain Monte Carlo (MCMC) coupling, ngen number, average standard deviation of split frequencies, and so on. After these parameters have been specified, start the computation. In the end, two tree figures in ASC II code, one showing clade credibility and the other showing branch lengths, will be displayed on the screen.
The tree result will be saved automatically as &#34;FileName.nex.con&#34;. This tree file can be opened and edited by FigTree, and the figure displayed in FigTree can be modified further to make it more suitable for publication.
In this study, 228 SWEET proteins, including 35 SWEETs from unicellular eukaryotes and 57 SemiSWEETs from prokaryotes, were analyzed as an example. Both the SWEETs and SemiSWEETs were characterized as glucose, fructose, or sucrose transporters across membranes12,13. Phylogenetic analysis suggests that the two MtN3/saliva domains containing SWEETs might be derived from an evolutionary fusion of a bacterial SemiSWEET and of an archaeon14.

<table><tbody><tr><td>Adobe Illustration</td><td></td><td></td><td>a graphical tool developed by Adobe Systems Software Ireland Ltd. Copyright &amp;copy; 2017</td></tr><tr><td>BioEdit</td><td></td><td></td><td>a biological sequence alignment editor written for Windows 95/98/NT/2000/XP/7. Copyright &amp;copy; Tom Hall</td></tr><tr><td>Clustal Omega</td><td></td><td></td><td>a package for making multiple sequence alignments of amino acid or nucleotide sequences.&amp;nbsp; http://www.clustal.org/</td></tr><tr><td>CorelDRAW</td><td></td><td></td><td>a graphic design software. Copyright &amp;copy; 2017 Corel Corporation</td></tr><tr><td>FigTree</td><td></td><td></td><td>a graphical viewer of phylogenetic trees designed by the University of Edinburgh</td></tr><tr><td>MEGA</td><td></td><td></td><td>MolecularEvolutionary Genetics Analysis version6.0 http://www.megasoftware.net/home</td></tr><tr><td>MrBayes</td><td></td><td></td><td>an Bayesian phylogenetic inference tool</td></tr><tr><td>NVIDIA</td><td></td><td></td><td>a company designs graphics processing units (GPUs) for the gaming and professional markets. Corporation Copyright &amp;copy; 2017</td></tr><tr><td>PAUP</td><td></td><td></td><td>Phylogenetic Analysis Using Parsimony. David Swofford&#039;s program implements the maximum likelihood method under a number of nucleotide models.</td></tr><tr><td>Photoshop</td><td></td><td></td><td>a raster graphics editor developed and published by Adobe Systems Software Ireland Ltd. Copyright &amp;copy; 2017</td></tr><tr><td>RHYTHM</td><td></td><td></td><td>a knowledge based prediction of hekix contacts. Charit&amp;eacute; Berlin &amp;ndash; Protein Formatics Group - Copyright 2007-2009</td></tr><tr><td>TMHMM</td><td></td><td></td><td>a tool for prediction of transmembrane helices in proteins. http://www.cbs.dtu.dk/services/TMHMM/</td></tr><tr><td>Compter</td><td></td><td></td><td>4 GB memory, Core 2 or above CPU. Windows 7, Windows 10</td></tr></tbody></table>

using phylogenetic analysis to investigate eukaryotic gene origin

Phylogenetic analysis uses nucleotide or amino acid sequences or other parameters, such as domain sequences and three-dimensional structure, to construct a tree to show the evolutionary relationship among different taxa (classification units) at the molecular level. Phylogenetic analysis can also be used to investigate domain relationships within an individual taxon, particularly for organisms that have undergone substantial change in morphology and physiology, but for which researchers lack fossil evidence due to the organisms&#39; long evolutionary history or scarcity of fossilization.
In this text, a detailed protocol is described for using the phylogenetic method, including amino acid sequence alignment using Clustal Omega, and subsequent phylogenetic tree construction using both Maximum Likelihood (ML) of Molecular Evolutionary Genetics Analysis (MEGA) and Bayesian Inference via MrBayes. To investigate the origin of eukaryotic Sugars Will Eventually be Exported Transporters (SWEET) genes, 228 SWEETs including 35 SWEET proteins from unicellular eukaryotes and 57 SemiSWEET proteins from prokaryotes were analyzed. Interestingly, SemiSWEETs were found in prokaryotes, but SWEETs were found in eukaryotes. Two phylogenetic trees constructed using theoretically distinct methods have consistently&#160;suggested that the first eukaryotic SWEET gene might stem from the fusion of a bacterial SemiSWEET gene and an archaeal SemiSWEET gene.&#160;It is worth noting that one should be cautious to draw a conclusion based only on phylogenetic analysis, although it is useful to explain the underlying relationship between different taxa, which is difficult or even impossible to discern through experimental means.

Phylogenetic trees show that all of the first MtN3/saliva domains of the 35 SWEET sequences clustered as one clade and the second MtN3/saliva domains of the SWEET sequences clustered as another clade. In addition, alignment results of the SWEETs and SemiSWEETs show that some SemiSWEETs from &#945;-Proteobacteria aligned with the first MtN3/saliva domain of the SWEET sequences, whereas SemiSWEETs from Methanobacteria (archaea) aligned with the second MtN3/saliva domain of the SWEET sequenc...

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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

A method of constructing a phylogenetic tree based on sequence homology of SWEETs from eukaryotes and SemiSWEETs from prokaryotes is described. Phylogenetic analysis is a useful tool for explaining the evolutionary relatedness between homologous proteins or genes from different organism groups.

Phylogenetic analysis uses nucleotide or amino acid sequences or other parameters, such as domain sequences and three-dimensional structure, to construct ...

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JoVE Journal

JoVE Core

Molecular Biology

Immunology and Infection

Unit 2

Genomes and Evolution

SCIENTISTS IN ACTION

15.9K Views.  China Three Gorges University. A method of constructing a phylogenetic tree based on sequence homology of SWEETs from eukaryotes and SemiSWEETs from prokaryotes is described. Phylogenetic analysis is a useful tool for explaining the evolutionary relatedness between homologous proteins or genes from different organism groups.

Video: Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried out by Public Health laboratories and regulatory agencies. Large volumes of publicly available WGS data can be used to study pathogenic populations at a large scale. Recently, a freely available computational platform called ProkEvo was published to enable reproducible, automated, and scalable hierarchical-based population genomic analyses using bacterial WGS data. This implementation of ProkEvo demonstrated the importance of combining standard genotypic mapping of populations with mining of accessory genomic content for ecological inference. In particular, the work highlighted here used ProkEvo-derived outputs for population-scaled hierarchical analyses using the R programming language. The main objective was to provide a practical guide for microbiologists, ecologists, and epidemiologists by showing how to: i) use a phylogeny-guided mapping of hierarchical genotypes; ii) assess frequency distributions of genotypes as a proxy for ecological fitness; iii) determine kinship relationships and genetic diversity using specific genotypic classifications; and iv) map lineage differentiating accessory loci. To enhance reproducibility and portability, R markdown files were used to demonstrate the entire analytical approach. The example dataset contained genomic data from 2,365 isolates of the zoonotic foodborne pathogen Salmonella Newport. Phylogeny-anchored mapping of hierarchical genotypes (Serovar -&#62; BAPS1 -&#62; ST -&#62; cgMLST) revealed the population genetic structure, highlighting sequence types (STs) as the keystone differentiating genotype. Across the three most dominant lineages, ST5 and ST118 shared a common ancestor more recently than with the highly clonal ST45 phylotype. ST-based differences were further highlighted by the distribution of accessory antimicrobial resistance (AMR) loci. Lastly, a phylogeny-anchored visualization was used to combine hierarchical genotypes and AMR content to reveal the kinship structure and lineage-specific genomic signatures. Combined, this analytical approach provides some guidelines for conducting heuristic bacterial population genomic analyses using pan-genomic information.

This analytical computational platform provides practical guidance for microbiologists, ecologists, and epidemiologists interested in bacterial population genomics. Specifically, the work presented here demonstrated how to perform: i) phylogeny-guided mapping of hierarchical genotypes; ii) frequency-based analysis of genotypes; iii) kinship and clonality analyses; iv) identification of lineage differentiating accessory loci.

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried ...

Genetics

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

The cerebral cortex is composed of numerous cell types exhibiting various morphological, physiological, and molecular features. This diversity hampers easy identification and characterization of these cell types, prerequisites to study their specific functions. This article describes the multiplex single cell reverse transcription polymerase chain reaction (RT-PCR) protocol, which allows, after patch-clamp recording in slices, to detect simultaneously the expression of tens of genes in a single cell. This simple method can be implemented with morphological characterization and is widely applicable to determine the phenotypic traits of various cell types and their particular cellular environment, such as in the vicinity of blood vessels. The principle of this protocol is to record a cell with the patch-clamp technique, to harvest and reverse transcribe its cytoplasmic content, and to detect qualitatively the expression of a predefined set of genes by multiplex PCR. It requires a careful design of PCR primers and intracellular patch-clamp solution compatible with RT-PCR. To ensure a selective and reliable transcript detection, this technique also requires appropriate controls from cytoplasm harvesting to amplification steps. Although precautions discussed here must be strictly followed, virtually any electrophysiological laboratory can use the multiplex single cell RT-PCR technique.

This protocol describes the critical steps and precautions required to perform single cell multiplex reverse transcription polymerase chain reaction after patch-clamp. This technique is a simple and effective method to analyze the expression profile of a predetermined set of genes from a single cell characterized by patch-clamp recordings.

The cerebral cortex is composed of numerous cell types exhibiting various morphological, physiological, and molecular features. This diversity hampers ...

Neuroscience

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the prediction of family functions based on several features, such as the presence of sequence motifs or of particular sites for post-translational modification and the expression profile of family members in different conditions. This work describes a detailed protocol for gene family characterization. Here, the procedure is applied to the characterization of the Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligase family in grapevine. The methods include the genome-wide identification of family members, the characterization of gene localization, structure, and duplication, the analysis of conserved protein motifs, the prediction of protein localization and phosphorylation sites as well as gene expression profiling across the family in different datasets. Such procedure, which could be extended to further analyses depending on experimental purposes, could be applied to any gene family in any plant species for which genomic data are available, and it provides valuable information to identify interesting candidates for functional studies, giving insights into the molecular mechanisms of plant adaptation to their environment.

This article describes the procedure for the identification and characterization of a gene family in grapevine applied to the family of Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligases.

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the ...

Biology

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus on one or more gene families that are significant to the organism and study. In this protocol, we outline bioinformatic steps to generate a phylogeny and to quantify the expression of genes of interest. Phylogenetic trees can give insight into how genes are evolving within and between species as well as reveal orthology. These results can be enhanced using RNA-seq data to compare the expression of these genes in different individuals or tissues. Studies of molecular evolution and expression can reveal modes of evolution and conservation of gene function between species. The characterization of a gene family can serve as a springboard for future studies and can highlight an important gene family in a new genome or transcriptome paper.

The purpose of this protocol is to investigate the evolution and expression of candidate genes using RNA sequencing data.

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus ...

An Integrated Approach for Microprotein Identification and Sequence Analysis

Next-generation sequencing (NGS) has propelled the field of genomics forward and produced whole genome sequences for numerous animal species and model organisms. However, despite this wealth of sequence information, comprehensive gene annotation efforts have proven challenging, especially for small proteins. Notably, conventional protein annotation methods were designed to intentionally exclude putative proteins encoded by short open reading frames (sORFs) less than 300 nucleotides in length to filter out the exponentially higher number of spurious noncoding sORFs throughout the genome. As a result, hundreds of functional small proteins called microproteins (&#60;100 amino acids in length) have been incorrectly classified as noncoding RNAs or overlooked entirely.
Here we provide a detailed protocol to leverage free, publicly available bioinformatic tools to query genomic regions for microprotein-coding potential based on evolutionary conservation. Specifically, we provide step-by-step instructions on how to examine sequence conservation and coding potential using Phylogenetic Codon Substitution Frequencies (PhyloCSF) on the user-friendly University of California Santa Cruz (UCSC) Genome Browser. Additionally, we detail steps to efficiently generate multiple species alignments of identified microprotein sequences to visualize amino acid sequence conservation and recommend resources to analyze microprotein characteristics, including predicted domain structures. These powerful tools can be used to help identify putative microprotein-coding sequences in noncanonical genomic regions or to rule out the presence of a conserved coding sequence with translational potential in a noncoding transcript of interest.

The protocol described here provides detailed instructions on how to analyze genomic regions of interest for microprotein-coding potential using PhyloCSF on the user-friendly UCSC Genome Browser. Additionally, several tools and resources are recommended to further investigate sequence characteristics of identified microproteins to gain insight into their putative functions.

Next-generation sequencing (NGS) has propelled the field of genomics forward and produced whole genome sequences for numerous animal species and model ...

Evolutionary Relationships

Humans have been attempting to properly classify living things since Aristotle made the first attempt during the 4th century BC. Aristotle&#8217;s system was improved upon during the Renaissance and then, subsequently, by Carolus Linnaeus in the mid 1700&#8217;s. These more formal classification and organization systems grouped species by their physical similarity to one another. For example, all vertebrates have a backbone, but invertebrates do not. Traits like the backbone are called synapomorphies, which are traits that are shared by a group of organisms, presumably because they were derived from a common ancestor. As we will explore, this method has been shown to have limitations and has more recently been amended to include genetic analysis. Still, scientists construct trees called dendrograms to create a visual representation of how species are related to one another and share common ancestors. These dendrograms can aid in our understanding of the evolutionary processes that drive these relationships. Genetic comparisons have added an important tool guiding the analysis of evolutionary relationships.
<h4>Types of Dendrogram</h4>
A type of dendrogram, called a cladogram, depicts the hypothetical genealogical relationships between species with the tips (or leaves) of the tree representing a species and the branches showing how species are related to each other. A slightly more complicated type of tree, called a phylogram, differs from a cladogram in that the branches leading to the species are of different lengths. The length of a branch in this type of tree represents the degree of change between species: the longer the branch, the more time since the species have diverged from a common ancestor. In both types of tree, the common ancestor of a group of species is indicated by a node, which is the point where a series of branches meet. Species that are more closely related to each other (most recently shared a common ancestor) will be located closest to the node. The two species that share a node are called a sister group1.
<h4>Understanding Evolutionary Relationships using Genetic Data</h4>
Historically, cladograms were constructed by comparing the morphology (physical structure) of organisms. This method is still practiced but the techniques have been modernized to include comparison of DNA (deoxyribonucleic acid) sequences between species. Using DNA for building trees has several advantages over relying solely on morphology, including being able to calculate an estimate of how long ago different species shared a common ancestor1. However, using DNA is not always feasible, especially when trees include extinct organisms. DNA is best found in soft tissues, which are not preserved during the fossilization process, and therefore it is uncommon for a DNA sample of an extinct species to be available.
DNA is passed on from parents to their offspring in hereditary units called genes. The nucleotide (A, G, C, and T) sequence of genes found in different species are frequently quite similar, likely due to their having come from a common ancestor. This fact allows researchers to align sequences from different species with one another to build the trees described above. Species with more similarity between their nucleotide sequences will be placed next to each other in a tree, and species with less sequence similarity will be placed further apart from each other.
Bioinformatics are the tools used by biologists to analyze large datasets using a combination of computer science, mathematical modeling, and statistics. One such tool is called BLAST (Basic Local Alignment Search Tool), which can be used to quickly search the entire genome of any species that is available in the NCBI (National Center for Biotechnology Information) database2. The NCBI database combines several different databases that hold different types of DNA sequence information. The process of a BLAST search includes complex computer algorithms, but basically, BLAST aligns sequences of each nucleotide base from a submitted DNA sequence (known as the query sequence) with sequences in the data base that most closely match it. The DNA sequences that are found will be listed in order of similarity to the sequence in question, and will therefore be from species closely related to the species containing the query gene. This comparison may or may not depict the actual evolutionary relationship between species because genes evolve at different rates. Additionally, genomes sometimes contain more than one instance of a similar sequence.
Comparison of DNA sequences of genes is valuable beyond consideration of evolutionary relationships. Frequently, genes are identified in model organisms, such as the fruit fly, Drosophila melanogaster, or the mouse3. Integral to studying a gene, the function of its product is commonly identified and analyzed. If a researcher is interested in studying that function in a different organism (humans for example), BLAST or other bioinformatic tools can be used to find candidate genes based on their similarities to the genes of known function from model organisms.
Human genes can also be used as the starting point to find homologs in model organisms. In fact, human disease research depends heavily on this. Once a human gene of interest is identified, mice can be genetically manipulated to have the homologous gene disrupted, or &#8220;knocked out,&#8221; creating a model of the human disease that can be studied in order to understand and treat the disease. There are many of these mouse strains currently available. For example, there is a mouse model for human Cystic Fibrosis (CF) called the Cftr knockout mouse and another modeling atherosclerosis, called the Apoe knockout3. 
 
<h4>References:</h4>
<ol class="references">
	<li>Annenberg Learner. Online Textbook: Unit 3 Evolution and Phylogenetics. Rediscovering Biology. [Online] 2017. [Cited: August 23, 2018.] https://www.learner.org/courses/biology/textbook/compev/compev_3.html.</li>
	<li>National Center for Biotechnology Information, U.S. National Library of Medicine. BLAST: Basic Local Alignment Search Tool. [Online] https://blast.ncbi.nlm.nih.gov/Blast.cgi.</li>
	<li>National Human Genome Research Institute. Background on Mouse as a Model Organism. National Human Genome Research Institute. [Online] December 2002. [Cited: August 23, 2018.] <a href="https://www.genome.gov/10005834/background-on-mouse-as-a-model-organism/" target="__blank">https://www.genome.gov/10005834/background-on-mouse-as-a-model-organism/</a>.</li>
</ol>

Evolutionary Relationships: Using BLAST to Test Evolutionary Hypotheses - Concept

Humans have been attempting to properly classify living things since Aristotle made the first attempt during the 4th century BC. Aristotle&#8217;s system ...

JoVE Lab Manual

Evolution

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse organisms.
Genome comparison can reveal three levels of evolutionary relationships. The first level provides deep insight into sequences and protein domains that are conserved across diverse groups of organisms, such as humans and fishes. The second level increases the resolution further to identify the unique DNA elements present in the closely related species, such as humans and chimpanzees. The third level with even higher data resolution distinguishes the genetic differences within a species, such as different variants and subtypes of an organism. This high-level resolution may identify mutations particular to individual microbial strains or clusters of infected cases, helping to track the disease outbreaks.
<h4>DNA sequencing tools</h4>
Several methods can be used to obtain the DNA sequence data required to deduce evolutionary relationships. Among them, whole-genome sequencing or WGS is a widely used technique. It provides high-resolution data extremely helpful to analyze mutations and conserved sequences among several organisms. It can also identify the cause of genetic disorders by comparing the DNA sequence of affected individuals to those of other unaffected subjects.
<h4>Data analysis tools</h4>
The data obtained by WGS or similar sequencing methods is analyzed by appropriate software tools to deduce evolutionary relationships. Molecular Evolutionary Genetics Analysis (MEGA) is one of the most widely used software tools. The programs present in the MEGA, such as assembly sequence alignment, building evolutionary trees, estimating genetic distances, and computation of evolutionary time trees, allow the users to curate and interpret the raw data obtained from sequencing techniques.

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is ...

KEY TERMS AND CONCEPTS

Genomic DNA in Eukaryotes

Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.

<h4>The Human Genome Measured in Meters</h4>

Most cells in the human body contain about 6 billion base pairs of DNA packaged into 23 pairs of chromosomes. It is hard to imagine exactly how much DNA these numbers represent, and therefore it is difficult to grasp how densely packed DNA must be to fit into a cell. We can gain some insight by expressing the genome in terms of length. If we were to arrange the DNA of a single diploid cell into a straight line, it would be about two meters long!

Note that humans do not have unusually large genomes. Many fish, amphibians, and flowering plants have much larger genomes than humans. For example, the haploid genome of the Japanese flowering plant Paris japonica contains about 50 times more DNA than the human haploid genome. These figures emphasize the astonishing work that histones and other chromatin remodeling proteins must do to package DNA.

Compaction of GenomIc DNA in Eukaryotes

Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the ...

Cellular Processes

Cell Cycle and Division

Eukaryotic Evolution

The endosymbiont theory is the most widely accepted theory of eukaryotic evolution; however, its progression is still somewhat debated. According to the nucleus-first hypothesis, the ancestral prokaryote first evolved a membrane to enclose DNA and form the nucleus. Conversely, the mitochondria-first hypothesis suggests that the nucleus was formed after endosymbiosis of mitochondria.
Contrary to the endosymbiont theory, the eukaryote-first hypothesis proposes that the simpler prokaryotic and archaeal life forms evolved from the already existing complex eukaryotic cells.
Evidence for Endosymbiont Theory 
Evidence for the endosymbiont theory comes from the many similarities that the eukaryotic organelles, like mitochondria and chloroplast, share with prokaryotic cells. Like the prokaryotic cells, mitochondria and chloroplasts contain circular, double-stranded genomes devoid of histones. These organelles also divide by the process of fission, similar to bacterial cell division, rather than by mitosis. Of the two membranes that surround them, the inner membranes resemble the bacterial plasma membrane in lipid and protein composition. Finally, the structure of genes, genetic code, and translational machinery (such as ribosomes) in these organelles are more similar to the prokaryotic machinery than eukaryotic.
LECA - Last Eukaryotic Common Ancestor
All eukaryotes are believed to have evolved from the Last Eukaryotic Common Ancestor or LECA about 1100 to 2300 million years ago. It is thought that LECA had already acquired endosymbionts like mitochondria and chloroplast by this stage. The new organelles made these ancestral cells more efficient and powerful, which allowed them to evolve novel characteristics such as multicellularity and cellular specialization. Their large cell size and internal compartmentalization also allowed their genomes to increase in size during the course of evolution. However, only a small portion of this genome encoded proteins; the major portion was non-coding DNA mostly involved in regulatory processes. The ability to turn genes on and off when required was key to cell specialization and responding to environmental changes. Phylogenetic and comparative genomic studies show five to six eukaryotic supergroups that evolved from LECA and later diverged into the plethora of life forms we see today.

The endosymbiont theory is the most widely accepted theory of eukaryotic evolution; however, its progression is still somewhat debated. According to the ...

Cell Biology

Unit 1

Cells, Genomes, and Evolution

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code for a protein might experience high selection pressure, because any changes in their sequence are likely to result in a protein which is less capable of performing its function optimally. However, occasionally a mutation in one of these regions will result in a beneficial outcome that contributes to the overall fitness of the organism, and such mutations often persist and may even become fixed in populations. When comparing the frequency of these mutation events to the relatively regular changes seen in non-coding sequences, this is exceedingly rare, and so in general coding regions are considered as evolving slowly.
It is also true that there is a measurable amount of variation in the levels of sequence conservation within coding sequences, and this is seen across all organisms. For instance, take the example of a receptor protein. Such proteins typically have different regions that may perform functions such as ligand binding, or intracellular signaling, or membrane integration. In this case, a mutation in the region that is involved in ligand binding may produce a protein that is less efficient at binding the ligand. Therefore, selection pressure would likely be high on the particular nucleotides coding for this part of the protein. However, in the section of the protein which spans the membrane, there may be less effect seen if an amino acid substitution occurs, and therefore lower levels of selection pressure. Under these conditions, we might see that two regions of the same protein-coding gene might have different rates of evolution.
Sequencing Genes or Genomic Regions to Build Phylogenies
This variation in the speed of genome evolution over different regions can be studied to answer questions about evolutionary relationships. Genes and gene regions can be selected and sequenced over groups of individuals to answer questions as narrow as &#8220;are these populations potentially different species?&#8221; or as broad as &#8220;how do these phyla place into the tree of life?&#8221;. For the former, selecting a gene that has a relatively lightly conserved region would help to identify population-level differences. Conversely, to answer questions over groups as diverse as phyla, a highly conserved gene region may provide enough homology to produce a phylogeny of such groups. Commonly used regions for molecular phylogenetic analyses such as these include ribosomal rRNA genes (such as 16s rRNA, 18s rRNA, or 28s rRNA), or genomic regions known as ITS (Internal Transcribed Spacers, I or II) which sit between the ribosomal rRNA subunit genes.

Gene Evolution and Sequencing Genomic Regions to Build Phylogenies

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain ...