We thank members of the O&#8217;Halloran lab for comments on the manuscript. We thank The George Washington University Department of Biological Sciences and Columbian College of Arts and Sciences for Funding to D. O&#8217;Halloran.

1. Basic Local Alignment Search Tool (BLAST): Online Interface
<ol>
	<li>Click on this link to visit the BLAST1 web server at the National Center for Biotechnology Information (NCBI). - <a href="http://blast.ncbi.nlm.nih.gov/Blast.cgi">http://blast.ncbi.nlm.nih.gov/Blast.cgi</a>&#160; (Figure 1).</li>
	<li>Input a FASTA formatted text sequence (see Figure 2 for example) into the query box.</li>
	<li>Click the appropriate BLAST program and relevant database or individual species of interest to use in the search and then click &#8220;BLAST&#8221;. 
	Note: FASTA formatted sequence begins with a description line indicated by a &#8220;&#62;&#8221; sign. The description must follow immediately after the &#8220;&#62;&#8221; sign, the sequence (i.e. nucleotides or amino acids) follow the description on the next line. The output from the BLAST search is viewed as HTML, plain text, XML, or hit tables (Text or csv) with the default set to HTML (Figure 3).</li>
</ol>
2. Basic Local Alignment Search Tool (BLAST): Local Executables
<ol>
	<li>Download the latest BLAST command-line BLAST executables from this link: 
	<a href="ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/">ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/</a> -</li>
	<li>em&#62;For PC users: double-click the latest blast win32.exe file and accept the license agreement and click install. 
	Note: The default installation directory is C:\ncbi-blast-2.2.27+.</li>
	<li>Configure the PC environment variable as follows:
	<ol>
		<li>Click the PC &#8220;start&#8221; button, and then right click &#8220;computer&#8221;,</li>
		<li>Click &#8220;Properties&#8221; and in the pop-up click on the &#8220;advanced&#8221; tab</li>
		<li>Click the &#8220;Environment Variables button&#8221; and in the new pop-up click the &#8220;new&#8221; button under the &#8220;User variables for user&#8221; section</li>
		<li>In the pop-up add the variable name &#8220;Path&#8221; and variable value &#8220;C:\ncbi-blast-2.2.27+\bin. 
		Note: the bin directory contains the executable (i.e. blastp,&#160;etc.).</li>
	</ol>
	</li>
	<li>em&#62;For Mac users: Open the Terminal application (to do this just open &#8220;Finder&#8221; and search &#8220;Terminal&#8221; and this will display the &#8220;terminal&#8221; icon). Into the terminal window type: 
	&#62;ftp <a href="ftp://ftp.ncbi.nih.gov">ftp.ncbi.nih.gov</a> 
	Note: can also type the URL used above in the example for PC</li>
	<li>To access the NCBI ftp site type &#8220;anonymous&#8221; for Name and Password, and then type: 
	&#62;cd blast/executables/LATEST</li>
	<li>List the executables by typing: 
	&#62;ls</li>
	<li>Get the latest version by typing the following (or whatever the latest version currently is): 
	&#62;get ncbi-blast-2.2.7-macosx.tar.gz</li>
	<li>Exit the NCBI ftp server site by typing &#8220;exit&#8221;.</li>
	<li>Decompress the downloaded files by typing: 
	&#62;tar -xzf ncbi-blast-2.2.7-macosx.tar.gz</li>
	<li>Add the location of the binaries for the blast executable to your path so that the shell can search through this directory when looking for commands by typing: 
	&#62;PATH=$PATH:new_folder_location</li>
	<li>Check if this added the location to your path by typing: 
	&#62;echo $PATH</li>
	<li>Download a preformatted BLAST databases (which are updated daily) by clicking here: 
	<a href="ftp://ftp.ncbi.nlm.nih.gov/blast/db/">ftp://ftp.ncbi.nlm.nih.gov/blast/db/</a></li>
	<li>Place the database into the &#8220;db&#8221; folder.</li>
	<li>em&#62;On a PC: open a MS-DOS prompt (to do this click &#8220;start&#8221; and type &#8220;cmd&#8221; in the search bar) and change the directory to the ncbi-blast folder by typing: 
	C:\Users&#62;cd ..\ [moves up one folder] 
	C:\&#62;cd ncbi-blast-2.2.27+ 
	This will change the directory to: 
	C:\ncbi-blast-2.2.27+&#62;</li>
	<li>Create the database using the following &#8220;makedb&#8221; command: 
	&#62;makedb &#8211;in db/briggsae.fasta &#8211;dbtype prot &#8211;out db/briggsae 
	Note: In the example below (Figure 4) the database is named &#8220;briggsae&#8221; and is comprised of one linkage group from the organism Caenorhabditis briggsae.</li>
	<li>Create a query protein sequence called &#8220;test&#8221; by inserting a FASTA formatted protein text sequence into the &#8220;db&#8221; folder.</li>
	<li>Interrogate the database via a blastp search by typing the following command: 
	&#62;blastp &#8211;query db/test.txt &#8211;db db/briggsae &#8211;out text.txt</li>
	<li>em&#62;On a Mac: download a database for local Blast searches by accessing the NCBI ftp website as per the instructions above (step 2.4) and then type: 
	&#62;lcd ../databases/</li>
	<li>Download the genome or sequence of interest by typing: 
	&#62;get NC_[Accession #].fna 
	Note: &#8220;.fna&#8221; refers to the FASTA formatted nucleotide sequence and &#8220;.faa&#8221; refers to the FASTA formatted amino acid sequences.</li>
	<li>Type &#8220;quit&#8221; to exit the ftp site.</li>
	<li>Make the database by typing: 
	&#62;makeblastdb -in db/mouse.faa -out mouse -dbtype prot</li>
	<li>Insert a FAST formatted query sequence into the &#8220;bin&#8221; folder and interrogate the database with the following command: 
	&#62; blastp -query &#8220;your query.fasta&#8221; -db &#8221;your database&#8221; -out results.txt</li>
</ol>
3. Generating Multiple Sequence Alignments
<ol>
	<li>Click on these links to access commonly used Multiple Sequence Alignment (MSA) programs: 
	ClustalW6 <a href="http://www.clustal.org/">http://www.clustal.org/</a> 
	Kalign7 <a href="http://msa.sbc.su.se/cgi-bin/msa.cgi">http://msa.sbc.su.se/cgi-bin/msa.cgi</a> 
	MAFFT8,9 <a href="http://mafft.cbrc.jp/alignment/software/">http://mafft.cbrc.jp/alignment/software/</a> 
	MUSCLE10 <a href="http://www.drive5.com/muscle/">http://www.drive5.com/muscle/</a> 
	T-Coffee11 <a href="http://www.tcoffee.org/Projects/tcoffee/">http://www.tcoffee.org/Projects/tcoffee/</a> 
	PROBCONS12 <a href="http://toolkit.tuebingen.mpg.de/probcons">http://toolkit.tuebingen.mpg.de/probcons</a></li>
	<li>Click on this link - <a href="http://tcoffee.crg.cat/apps/tcoffee/do:regular">http://tcoffee.crg.cat/apps/tcoffee/do:regular</a> - and input FASTA formatted sequence data into the query box 
	Note: A sample output from T-Coffee can be seen in Figure 5, similar residues are color coded.</li>
	<li>Download the Clustal MSA as a command line version (ClustalW) or a graphical version (ClustalX) by clicking this link: <a href="http://www.clustal.org/clustal2/">http://www.clustal.org/clustal2/</a> - then click on the appropriate executable (i.e. win, Linux, Mac OS X).</li>
	<li>Upload data as FASTA formatted sequence text and align (Figure 6).</li>
</ol>
4. Determining Best-fit Models of Evolution
<ol>
	<li>Click here to download the ProtTest13 program: 
	<a href="http://darwin.uvigo.es/our-software/">http://darwin.uvigo.es/our-software/</a></li>
	<li>Once ProtTest is downloaded, double-click on the ProtTest.jar file</li>
	<li>Once ProtTest is launched, click on &#8220;select file&#8221; and load the sequence data (Figure 7).</li>
	<li>Then click &#8220;start&#8221; and the program will begin (Figure 8). 
	Note: After completion of the run (Figure 8), the program will indicate the best model based on criteria e.g. &#8220;Best model according to AIC: WAG+I+G&#8221;</li>
</ol>
5. Inferring Sequence Based Phylogenies by Maximum Likelihood or Bayesian Inference
<ol>
	<li>Downloaded PhyML4 here: 
	<a href="https://code.google.com/p/phyml/">https://code.google.com/p/phyml/</a></li>
	<li>Launch the executable by double clicking the appropriate application (i.e. phyml Windows, phyml Linux,&#160;etc.) and the interface window will pop up (Figure 9).</li>
	<li>Load the input sequence as a PHYLIP formatted sequence by typing: 
	&#62;&#8221;file name&#8221;.phy 
	Note: To convert between sequence formats, use the &#8220;Readseq&#8221; web program available at - <a href="http://iubio.bio.indiana.edu/cgi-bin/readseq.cgi">http://iubio.bio.indiana.edu/cgi-bin/readseq.cgi</a>.</li>
	<li>Launch the program by typing &#8220;Y&#8221;.</li>
	<li>Download MrBayes5 here: 
	<a href="http://mrbayes.sourceforge.net/download.php">http://mrbayes.sourceforge.net/download.php</a></li>
	<li>To start the program click on the executable file and read NEXUS formatted sequence data into the program by typing: 
	&#62;execute &#8220;file name&#8221;.nex</li>
	<li>Set the evolutionary model.</li>
	<li>Select the number of generations to run by typing: 
	&#62;mcmcp ngen = 1000000 [this sets the number of generations to 1000000] 
	&#62;sump burnin =10000 [this sets the burnin to 10000]</li>
	<li>Save the branch lengths in the results file by typing: 
	&#62;mcmcp savebrlens = yes&#160;</li>
	<li>Run the analysis by typing: 
	&#62;mcmc</li>
	<li>Summarize the trees using the &#8220;sumt&#8221; command.</li>
</ol>
6. Visualizing Phylogenies
<ol>
	<li>View a list of tree viewer programs here: 
	<a href="http://www.treedyn.org/overview/editors.html">http://www.treedyn.org/overview/editors.html</a></li>
	<li>Download the TreeView14 program here: 
	<a href="http://taxonomy.zoology.gla.ac.uk/rod/treeview.html">http://taxonomy.zoology.gla.ac.uk/rod/treeview.html</a></li>
</ol>

<ol>
	<li>Altschul, S. F., Carroll, R. J., Lipman, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Weights+for+data+related+by+a+tree.">Weights for data related by a tree.</a> J. Mol. Biol. 207 (4), 647-653 (1989).</li><li>Akaike, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+new+look+at+the+statistical+model+identification.">A new look at the statistical model identification.</a> IEEE Trans. Automat. Contr. 19 (6), 706-723 (1974).</li><li>Schwarz, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimating+the+dimension+of+a+model.">Estimating the dimension of a model.</a> Ann. Stat. 6 (2), 461-464 (1978).</li><li>Guindon, S., Gascuel, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+simple,+fast,+and+accurate+algorithm+to+estimate+large+phylogenies+by+maximum+likelihood.">A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood.</a> Syst. Biol. 52 (5), 696-704 (2003).</li><li>Huelsenbeck, J. P., Ronquist, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MRBAYES:+Bayesian+inference+of+phylogenetic+trees.">MRBAYES: Bayesian inference of phylogenetic trees.</a> Bioinformatics. 17 (8), 754-755 (2001).</li><li>Thompson, J. D., Higgins, D. G., Gibson, T. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CLUSTAL+W:+Improving+the+sensitivity+of+progressive+multiple+sequence+alignment+through+sequence+weighting,+position-specific+gap+penalties+and+weight+matrix+choice.">CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.</a> Nucleic Acids Res. 22 (22), 4673-4680 (1994).</li><li>Lassmann, T., Sonnhammer, E. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kalign--an+accurate+and+fast+multiple+sequence+alignment+algorithm.">Kalign--an accurate and fast multiple sequence alignment algorithm.</a> BMC Bioinformatics. 6, 298 (2005).</li><li>Katoh, K., Kuma, K., Toh, H., Miyata, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MAFFT+version+5:+Improvement+in+accuracy+of+multiple+sequence+alignment.">MAFFT version 5: Improvement in accuracy of multiple sequence alignment.</a> Nucleic Acids Res. 33 (2), 511-518 (2005).</li><li>Katoh, K., Misawa, K., Kuma, K., Miyata, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MAFFT:+A+novel+method+for+rapid+multiple+sequence+alignment+based+on+fast+fourier+transform.">MAFFT: A novel method for rapid multiple sequence alignment based on fast fourier transform.</a> Nucleic Acids Res. 30 (14), 3059-3066 (2002).</li><li>Edgar, R. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MUSCLE:+Multiple+sequence+alignment+with+high+accuracy+and+high+throughput.">MUSCLE: Multiple sequence alignment with high accuracy and high throughput.</a> Nucleic Acids Res. 32 (5), 1792-1797 (2004).</li><li>Notredame, C., Higgins, D. G., Heringa, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=T-coffee:+A+novel+method+for+fast+and+accurate+multiple+sequence+alignment.">T-coffee: A novel method for fast and accurate multiple sequence alignment.</a> J. Mol. Biol. 302 (1), 205-217 (2000).</li><li>Do, C. B., Mahabhashyam, M. S., Brudno, M., Batzoglou, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ProbCons:+Probabilistic+consistency-based+multiple+sequence+alignment.">ProbCons: Probabilistic consistency-based multiple sequence alignment.</a> Genome Res. 15 (2), 330-340 (2005).</li><li>Darriba, D., Taboada, G. L., Doallo, R., Posada, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ProtTest+3:+Fast+selection+of+best-fit+models+of+protein+evolution.">ProtTest 3: Fast selection of best-fit models of protein evolution.</a> Bioinformatics. 27 (8), 1164-1165 (2011).</li><li>Page, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TreeView:+An+application+to+display+phylogenetic+trees+on+personal+computers.">TreeView: An application to display phylogenetic trees on personal computers.</a> Comput. Appl. Biosci. 12 (4), 357-358 (1996).</li><li>Darriba, D., Taboada, G. L., Doallo, R., Posada, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=jModelTest+2:+More+models,+new+heuristics+and+parallel+computing.">jModelTest 2: More models, new heuristics and parallel computing.</a> Nat. Methods. 9 (8), 772 (2012).</li><li>Chevenet, F., Brun, C., Banuls, A. L., Jacq, B., Christen, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TreeDyn:+Towards+dynamic+graphics+and+annotations+for+analyses+of+trees.">TreeDyn: Towards dynamic graphics and annotations for analyses of trees.</a> BMC Bioinformatics. 7, 439 (2006).</li></ol>

We have nothing to disclose.&#160;

Our hope for this article is that it will serve as a starting point to guide researchers or students that are new to phylogenetics. Genome sequencing projects have become less expensive over the last few years and as a consequence the user demand for this technology is increasing, and now the production of large sequence datasets is commonplace in small labs. These datasets often provide researchers with sets of genes that require a phylogenetic framework to begin to understand their function. Furthermore, because phylogenetics is finding a home in an ever increasing number of research labs, we also intend for this article to serve as an educational device for students interested broadly in biological research. By providing user information on the &#8220;why&#8221;, &#8220;how&#8221;, and &#8220;where&#8221; for commonly used tree-building tools, we provide a framework for the reader to begin to familiarize themselves with these applications and how they work. However, we advise the reader to play around with all the settings within each tool in an attempt to understand how the various parameters can influence their sequence data, and to ensure compatibility between platform and software in each case. The analysis outlined above was computed using a Dell Optiplex 990 with Intel core i7 processor and a MacBook laptop with an Intel Core 2 Duo processor, however, the speed of analysis and also the specific binaries (e.g. 32 bit or 64 bit) will depend on the user&#8217;s platform.
A challenge when compiling a user guide like this one for phylogenetics, is that the field of phylogenetics, and bioinformatics as a whole, is a rapidly expanding area of research that constantly releases new software aimed at providing better alignments, similarity predictions, or phylogenetic trees. To mitigate this problem, we tried to focus on programs that have been around for a number of years and are still popular on account of how well they work. That said, we want to point out that there are many other tools available to tackle the problems we have outlined in this article, and so encourage the reader to exploit this and incorporate multiple applications into their analyses.&#160;&#160;&#160;

In order to understand how two (or more) species evolved, it is first necessary to obtain sequence or morphological data from each sample; these data represent quantities that we can use to measure their relationship through evolutionary space. Just like when measuring linear distance, having more data available (e.g. miles, inches, microns) will equate to a more accurate measurement. Ergo, the accuracy with which a researcher can deduce evolutionary distance is heavily influenced by the volume of informative data available to measure relationships. Furthermore, because different samples evolve at different rates and by different mechanisms, the method that we use to measure the relationship between two taxa also directly influences the accuracy of evolutionary measurements.&#160;Therefore, because evolutionary relationships are not directly observed but instead are extrapolated from sequence or morphological data, the problem of inferring evolutionary relationships becomes one of statistics. Phylogenetics is the branch of biology concerned with applying statistical models to patterns of evolution in order to optimally reconstruct the evolutionary history between taxa. This reconstruction between taxa is referred to as the taxa&#8217;s phylogeny.
To help bridge the gap in expertise between molecular biologists and evolutionary biologists we describe here a step by step pipeline for inferring phylogenies from a set of sequences. Firstly, we detail the steps involved in database interrogation using the Basic Local Alignment Search Tool (BLAST1) algorithm through the web based interface and also by using local executables; this is often the first step in obtaining a list of similar sequences to an unidentified query, although some researchers may also be interested in gathering data for a single group via web interfaces such as Phylota (http://www.phylota.net/). BLAST is an algorithm for comparing primary amino acid or nucleotide sequence data against a database of sequences to search for &#8220;hits&#8221; that resemble the query sequence. The BLAST program was designed by Stephen Altschul et al. at the National Institutes of Health (NIH)1. The BLAST server consists of a number of different programs, and here is a list of some of the most common BLAST programs:
i) Nucleotide-nucleotide BLAST (blastn): This program requires a DNA sequence input and returns the most similar DNA sequences from the DNA database that the user specifies (e.g. for a specific organism).
ii) Protein-protein BLAST (blastp): Here the user inputs a protein sequence and the program returns the most similar protein sequences from the protein database that the user specifies.
iii) Position-Specific Iterative BLAST (PSI-BLAST) (blastpgp): The user input is a protein sequence which returns a set of closely related proteins, and from this dataset a conserved profile is generated. Next a new query is generated using only these conserved &#8220;motifs&#8221; which is used to interrogate a protein database and this returns a larger group of proteins from which a new set of conserved &#8220;motifs&#8221; are extracted and then used to interrogate a protein database until an even larger set of proteins are retuned and another profile is generated and the process repeated. By including related proteins into the query in each step this program allows the user to identify sequences that are more divergent.
iv) Nucleotide 6-frame translation-protein (blastx): Here the user provides a nucleotide sequence input which is converted into the six-frame conceptual translation products (i.e. both strands) against a protein sequence database.
v) Nucleotide 6-frame translation-nucleotide 6-frame translation (tblastx): This program takes a DNA nucleotide sequence input and translates the input into all six-frame conceptual translation products which it compares against the six-frame translations of a nucleotide sequence database.
vi) Protein-nucleotide 6-frame translation (tblastn): This program uses a protein sequence input to compare against all six reading frames of a nucleotide sequence database.
Next, we describe commonly used programs for generating a Multiple Sequence Alignment (MSA) from a sequence dataset, and this is followed by a user guide to programs that determine the best-fit models of evolution for a sequence dataset. Phylogenetic reconstruction is a statistical problem, and because of this, phylogenetic methods need to incorporate a statistical framework. This statistical framework becomes an evolutionary model that incorporates sequence change within the dataset. This evolutionary model is comprised of a set of assumptions about the process of nucleotide or amino-acid substitutions, and the best-fit model for a particular dataset can be selected through statistical testing. The fit to the data of different models can be compared via likelihood ratio tests (LRTs) or information criteria to select the best-fit model within a set of possible ones. Two common information criteria are the Akaike information criterion (AIC)2 and the Bayesian information criterion (BIC)3. Once an optimal alignment is generated, there are many different methods to create a phylogeny from the aligned data. There are numerous methods of inferring evolutionary relationships; broadly, they can be divided into two categories: distance-based methods and sequence-based methods. Distance-based methods compute pairwise distances from sequences, and then use these distances to obtain the tree. Sequence-based methods use the sequence alignment directly, and usually search the tree space using an optimality criterion. We outline two sequence-based methods for reconstructing phylogenetic relationships: these are PhyML4 which implements the maximum likelihood framework, and MrBayes5 which uses Bayesian Markov Chain Monte Carlo inference. Likelihood and Bayesian methods provide a statistical framework for phylogenetic reconstruction. By providing user information on commonly used tree-building tools, we introduce the reader to the necessary data required to infer phylogenetic relationships.

<table><tbody><tr><td>BLAST webpage&amp;nbsp;</td><td></td><td></td><td>&lt;a href=&quot;http://blast.ncbi.nlm.nih.gov/Blast.cgi&quot;&gt;http://blast.ncbi.nlm.nih.gov/Blast.cgi&lt;/a&gt;</td></tr><tr><td>BLAST executables&amp;nbsp;</td><td></td><td></td><td>&lt;a href=&quot;ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/&quot;&gt;ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/&lt;/a&gt;</td></tr><tr><td>Preformatted BLAST databases</td><td></td><td></td><td>&lt;a href=&quot;ftp://ftp.ncbi.nlm.nih.gov/blast/db/&quot;&gt;ftp://ftp.ncbi.nlm.nih.gov/blast/db/&lt;/a&gt;</td></tr><tr><td>Clustal</td><td></td><td></td><td>&lt;a href=&quot;http://www.clustal.org/&quot;&gt;http://www.clustal.org/&lt;/a&gt;</td></tr><tr><td>Kalign</td><td></td><td></td><td>&lt;a href=&quot;http://msa.sbc.su.se/cgi-bin/msa.cgi&quot;&gt;http://msa.sbc.su.se/cgi-bin/msa.cgi&lt;/a&gt;</td></tr><tr><td>MAFFT</td><td></td><td></td><td>&lt;a href=&quot;http://mafft.cbrc.jp/alignment/software/&quot;&gt;http://mafft.cbrc.jp/alignment/software/&lt;/a&gt;</td></tr><tr><td>MUSCLE</td><td></td><td></td><td>&lt;a href=&quot;http://www.drive5.com/muscle/&quot;&gt;http://www.drive5.com/muscle/&lt;/a&gt;</td></tr><tr><td>T-Coffee</td><td></td><td></td><td>&lt;a href=&quot;http://www.tcoffee.org/Projects/tcoffee/&quot;&gt;http://www.tcoffee.org/Projects/tcoffee/&lt;/a&gt;</td></tr><tr><td>PROBCONS</td><td></td><td></td><td>&lt;a href=&quot;http://toolkit.tuebingen.mpg.de/probcons&quot;&gt;http://toolkit.tuebingen.mpg.de/probcons&amp;nbsp;&lt;/a&gt;</td></tr><tr><td>Se-Al&amp;nbsp;</td><td></td><td></td><td>&lt;a href=&quot;http://tree.bio.ed.ac.uk/software/seal/&quot;&gt;http://tree.bio.ed.ac.uk/software/seal/&lt;/a&gt;</td></tr><tr><td>BSEdit&amp;nbsp;</td><td></td><td></td><td>&lt;a href=&quot;http://www.bsedit.org/&quot;&gt;http://www.bsedit.org/&lt;/a&gt;</td></tr><tr><td>JalView</td><td></td><td></td><td>&lt;a href=&quot;http://www.jalview.org/&quot;&gt;http://www.jalview.org/&lt;/a&gt;</td></tr><tr><td>SeaView</td><td></td><td></td><td>&lt;a href=&quot;http://pbil.univ-lyon1.fr/software/seaview.html&quot;&gt;http://pbil.univ-lyon1.fr/software/seaview.html&lt;/a&gt;</td></tr><tr><td>ProtTest&amp;nbsp;</td><td></td><td></td><td>&lt;a href=&quot;https://code.google.com/p/prottest3/&quot;&gt;https://code.google.com/p/prottest3/&lt;/a&gt;</td></tr><tr><td>Java Runtime&amp;nbsp;</td><td></td><td></td><td>&lt;a href=&quot;http://www.java.com/en/download/chrome.jsp&quot;&gt;http://www.java.com/en/download/chrome.jsp&lt;/a&gt;</td></tr><tr><td>Readseq</td><td></td><td></td><td>&lt;a href=&quot;http://iubio.bio.indiana.edu/cgi-bin/readseq.cgi&quot;&gt;http://iubio.bio.indiana.edu/cgi-bin/readseq.cgi&lt;/a&gt;</td></tr><tr><td>jModelTest</td><td></td><td></td><td>&lt;a href=&quot;https://code.google.com/p/jmodeltest2/&quot;&gt;https://code.google.com/p/jmodeltest2/&lt;/a&gt;</td></tr><tr><td>PhyML</td><td></td><td></td><td>&lt;a href=&quot;https://code.google.com/p/phyml/&quot;&gt;https://code.google.com/p/phyml/&lt;/a&gt;</td></tr><tr><td>MrBayes</td><td></td><td></td><td>&lt;a href=&quot;http://mrbayes.sourceforge.net/download.php&quot;&gt;http://mrbayes.sourceforge.net/download.php&lt;/a&gt;</td></tr><tr><td>TreeView</td><td></td><td></td><td>&lt;a href=&quot;http://taxonomy.zoology.gla.ac.uk/rod/treeview.html&quot;&gt;http://taxonomy.zoology.gla.ac.uk/rod/treeview.html&lt;/a&gt;</td></tr><tr><td>TreeDyn</td><td></td><td></td><td>&lt;a href=&quot;http://www.treedyn.org/&quot;&gt;http://www.treedyn.org/&lt;/a&gt;</td></tr></tbody></table>

a practical guide to phylogenetics for nonexperts

Many researchers, across incredibly diverse foci, are applying phylogenetics to their research question(s). However, many researchers are new to this topic and so it presents inherent problems. Here we compile a practical introduction to phylogenetics for nonexperts.&#160;We outline in a step-by-step manner, a pipeline for generating reliable phylogenies from gene sequence datasets. We begin with a user-guide for similarity search tools via online interfaces as well as local executables. Next, we explore programs for generating multiple sequence alignments followed by protocols for using software to determine best-fit models of evolution. We then outline protocols for reconstructing phylogenetic relationships via maximum likelihood and Bayesian criteria&#160;and finally describe tools for visualizing phylogenetic trees. While this is not by any means an exhaustive description of phylogenetic approaches, it does provide the reader with practical starting information on key software applications commonly utilized by phylogeneticists. The vision for this article would be that it could serve as a practical training tool for researchers embarking on phylogenetic studies&#160;and also serve as an educational resource that could be incorporated into a classroom or teaching-lab.

Finding similarities to a query allows researchers to ascribe a potential identity to new sequences and also infer relationships between sequences. The file input type for BLAST1 is FASTA formatted text sequence or GenBank accession number. FASTA formatted sequence begins with a description line indicated by a &#8220;&#62;&#8221; sign (Figure 2). The description must follow immediately after the &#8220;&#62;&#8221; sign, the sequence (i.e. nucleotides or amino acids) follow the description on the next line. When saving and editing sequence files, it is best to use a text editor such as &#8220;Notepad&#8221; on PC&#160;or TextWrangler (<a href="http://www.barebones.com/products/textwrangler/">http://www.barebones.com/products/textwrangler/</a>) for Mac. The BLAST algorithm performs &#34;local&#34; alignments, which searches for short stretches of sequence similarity. After the algorithm has looked up all possible &#34;stretches&#34; from the query sequence and maximally extended these sequences, it then assembles alignments for each query sequence pair. It is then important to understand how good these matches are, and so BLAST applies statistics to each hit which comprise an expect value (E) and a bit score. The E value gives an indication of the statistical significance for a match. The lower the E-value, the more significant the hit, for example a sequence alignment with an E-value of 0.05 means that the likelihood of this match occurring by chance alone is 5 in 100. The bit score uses a specific scoring matrix to provide an indication of how good the alignment is. The higher the bit score, the better the alignment. Similar to the online version of BLAST, there are a number of parameters that can be set via commands using the local BLAST executable. A comprehensive resource describing these commands can be found here - <a href="http://www.ncbi.nlm.nih.gov/books/NBK1762/">http://www.ncbi.nlm.nih.gov/books/NBK1762/</a>. The output of the local search is a text file just like the output from the online BLAST interface (Figure 4).
A Multiple Sequence Alignment (MSA) is a sequence alignment of three or more primary sequences composed of amino acids, DNA, or RNA. ClustalW6 released in 1994, is one of the most popular MSA tools for biologists. A user friendly online interface that provides one-stop access to several popular MSA tools can be found at the EMBL-EBI server here - <a href="http://www.ebi.ac.uk/Tools/msa">http://www.ebi.ac.uk/Tools/msa</a>. The input for each program can be FASTA formatted sequence data (see Figure 2) although many different formats are also accepted, and numerous mirror sites for each can be found online. Numerous parameters like gap penalties and output formats can be easily chosen. A sample output from the MSA T-Coffee can be seen in Figure 5, where similar residues are color coded. In some cases, the MSA tool can also be downloaded and executed locally. Clustal can be downloaded as a command line version (ClustalW) or a graphical version (ClustalX) from this website - <a href="http://www.clustal.org/clustal2/">http://www.clustal.org/clustal2/</a>. To download, just click on the appropriate executable (i.e. win, Linux, Mac OS X). For Windows the program executable will download and a pop-up menu will require the user to click &#8220;Run&#8221;, and then installation will begin. The program is very intuitive, sequences can be loaded from a text file containing sequences formatted as NBRF/PIR, FASTA, EMBL/Swiss-Prot, Clustal, GCC/MSF, GCG9 RSF, and GDE. Sequences are aligned by clicking &#8220;do complete alignment&#8221; from the &#8220;alignment&#8221; menu. A sample alignment of six protein sequences aligned using ClustalX can be seen in Figure 6. Various parameters such as font size and color can be easily modified, and editing of sequences is done by clicking on the &#8220;Edit&#8221; menu. Manually re&#64257;ned alignments are often superior to fully automated methods and because of this, MSA tool development is a very active area of research. Some common alignment editors can be found at the following links: Se-Al - <a href="http://tree.bio.ed.ac.uk/software/seal/">http://tree.bio.ed.ac.uk/software/seal/</a>; BSEdit - <a href="http://www.bsedit.org/">http://www.bsedit.org/</a>; JalView - <a href="http://www.jalview.org/">http://www.jalview.org/</a>; SeaView - <a href="http://pbil.univ-lyon1.fr/software/seaview.html">http://pbil.univ-lyon1.fr/software/seaview.html</a>.
For amino-acid alignments the program ProtTest13 is used to determine the selection of best-fit models of amino acid replacements within the data. ProtTest makes this selection by finding the model from the list of candidate models with the smallest Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) score, or Decision Theory Criterion (DT). The latest version of ProtTest (version&#160;3.2) includes 15 different rate matrices that result in 120 different models. The user must have Java Runtime on their system to run ProtTest. Java Runtime is freely available here - <a href="http://www.java.com/en/download/chrome.jsp">http://www.java.com/en/download/chrome.jsp</a>. Sequences are inputted as PHYLIP or NEXUS format. To convert between sequence formats, use the &#8220;Readseq&#8221; web program available at - <a href="http://iubio.bio.indiana.edu/cgi-bin/readseq.cgi">http://iubio.bio.indiana.edu/cgi-bin/readseq.cgi</a>. Click on &#8220;select file&#8221; and load the sequence data. Then click &#8220;start&#8221; and the program will begin. To modify the number of models selected, you can click the &#8220;models&#8221; button. Once the program begins it will display a progress bar at the bottom and list the models as they are being analyzed (Figure 8). The developer&#8217;s manual provides a comprehensive user guide to ProtTest (can be found in the &#8220;manual&#8221; folder when the program is downloaded) and also provides descriptions of the models and parameters. More background information can be found here - <a href="https://code.google.com/p/prottest3/wiki/Background">https://code.google.com/p/prottest3/wiki/Background</a>. There is also an online web interface for ProtTest which functions just like the downloaded version except that it can only handle a limited number of sequences. This web interface can be accessed by clicking here - <a href="http://darwin.uvigo.es/software/prottest2_server.html">http://darwin.uvigo.es/software/prottest2_server.html</a>. For nucleotide datasets the program jModelTest15 is used to examine the statistical selection of best-fit models of nucleotide substitutions by implementing the AIC, BIC, and DT criteria outlined above and also hierarchical and dynamical likelihood ration tests (hLRT and dLRT). jModelTest is optimized for Mac OS X. For the input, multiple formats are permitted. A clear step-by-step guide is available by the developers here - <a href="http://computing.bio.cam.ac.uk/local/doc/jmodeltest.pdf">http://computing.bio.cam.ac.uk/local/doc/jmodeltest.pdf</a>
PhyML is a program that estimates maximum likelihood phylogenies from alignments of nucleotide or amino acid sequences. PhyML will incorporate a large number of substitution models coupled to various options to search tree topology space (Figure 10). The program will save results into two text files. The first file will contain the ML tree in Newick format which can easily be viewed using a Tree viewer (see protocol&#160;6), and the other file will contain the statistics (filename, model, Log-likelihood scores,&#160;etc.) of the analysis. All parameters are very easily set by following the Menu items. More detailed descriptions of each Menu option are explained in the PhyML manual available on the PhyML download page - <a href="https://code.google.com/p/phyml/downloads/list">https://code.google.com/p/phyml/downloads/list</a>. MrBayes5 is a program that utilizes Bayesian MCMC inference across a number of evolutionary models to reconstruct phylogenetic relationships. The program behaves the same on all platforms and once downloaded the installer will install the executable. To start the program, simply click on the executable. There are numerous models that can be set and details of each model and their commands can be found here - <a href="http://mrbayes.sourceforge.net/wiki/index.php/Tutorial">http://mrbayes.sourceforge.net/wiki/index.php/Tutorial</a>. Another help option is to type &#8220;help lset&#8221; &#8211; this will provide details on Model setting. For example &#8220;Prset aamodelpr=mixed&#8221; will permit mixed modeling or &#8220;prset aamodelpr=fixed(wag)&#8221; will set the amino acid model to the WAG model. An outgroup can be easily set by specifying the Taxon number &#8220;outgroup 30&#8221;; the program automatically lists the sequences/Taxa by number. If an outgroup is not specified the tree will be unrooted. Once the program is running (Figure 11) the progress can be viewed in specific intervals which can be set using the &#8220;printfreq=X&#8221; command. More details on when to stop the analysis (i.e. how many generations to run for) can be found in the user&#8217;s manual. Clade values on a cladogram are provided in the results alongside a phylogram which is also provided in Newick format that can easily be viewed using a tree viewer (see protocol 6).
Once a phylogenetic tree is generated, the topology needs to be visualized. There are many online tools and downloadable applications used to visualize tree topologies. A partial list of popular programs can be viewed here - <a href="http://en.wikipedia.org/wiki/List_of_phylogenetic_tree_visualization_software">http://en.wikipedia.org/wiki/List_of_phylogenetic_tree_visualization_software</a>, and a more comprehensive list can be found here - <a href="http://www.treedyn.org/overview/editors.html">http://www.treedyn.org/overview/editors.html</a>. TreeView14 and TreeDyn16 are two popular choices. Both are very user friendly and easy to become familiar with the various options. TreeView runs on Mac and Windows, using almost identical interfaces. The input can be one of several formats including NEXUS, PHYLIP, Hennig86, MEGA, and ClustalW/X. TreeView (Figure 12) also includes a tree editor that allows the user to move branches, reroot trees, and rearrange the appearance of the tree.
<img alt="Figure 1" fo:content-width="5in" fo:src="/files/ftp_upload/50975/50975fig1highres.jpg" src="/files/ftp_upload/50975/50975fig1.jpg" /> 
Figure 1.&#160;NCBI BLAST web-page. The BLAST web server contains a suite of BLAST programs and is hosted by the National Center for Biotechnology Information (NCBI). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50975/50975fig1highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 2" fo:content-width="5in" fo:src="/files/ftp_upload/50975/50975fig2highres.jpg" src="/files/ftp_upload/50975/50975fig2.jpg" /> 
Figure 2.&#160;FASTA formatted sequence. FASTA format begins with a description line indicated by a &#8220;&#62;&#8221;. The description must follow immediately after the &#8220;&#62;&#8221; sign, the sequence (i.e. nucleotides or amino acids) follow the description on the next line. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50975/50975fig2highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 3" fo:content-width="4in" fo:src="/files/ftp_upload/50975/50975fig3highres.jpg" src="/files/ftp_upload/50975/50975fig3.jpg" /> 
Figure 3.&#160;HTML output from a BLAST search. The output from the BLAST search illustrates the areas of identity within the query sequence, and also provides bit-scores, expect values and pairwise alignments with each match. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50975/50975fig3highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 4" fo:content-width="5in" fo:src="/files/ftp_upload/50975/50975fig4highres.jpg" src="/files/ftp_upload/50975/50975fig4.jpg" /> 
Figure 4.&#160;A sample output from a local BLAST executable search. The output of this search is a text file just like the output from the online BLAST interface, that include the expect value and bit score, as well as match description. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50975/50975fig4highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 5" fo:content-width="5in" fo:src="/files/ftp_upload/50975/50975fig5highres.jpg" src="/files/ftp_upload/50975/50975fig5.jpg" /> 
Figure 5.&#160;Output of a MSA using T-Coffee. The output highlights similar sites and weights the match by color. Gaps are inserted as &#8220;-&#8220; signs and the residue or nucleotide position is preserved for each taxon. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50975/50975fig5highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 6" fo:content-width="5in" fo:src="/files/ftp_upload/50975/50975fig6highres.jpg" src="/files/ftp_upload/50975/50975fig6.jpg" /> 
Figure 6.&#160;A sample alignment using ClustalX. Similar matches are color coded and gaps are inserted as a &#8220;-&#8220; sign. The menu bar is seen in the top-left. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50975/50975fig6highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 7" fo:content-width="5in" fo:src="/files/ftp_upload/50975/50975fig7highres.jpg" src="/files/ftp_upload/50975/50975fig7.jpg" /> 
Figure 7.&#160;The ProtTest program interface. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50975/50975fig7highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 8" fo:content-width="5in" fo:src="/files/ftp_upload/50975/50975fig8highres.jpg" src="/files/ftp_upload/50975/50975fig8.jpg" /> 
Figure 8.&#160;The ProtTest console. ProtTest console while running an analysis. The progress bar indicates how many models have been completed, and the main window displays the log likelihood score for each model. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50975/50975fig8highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 9" fo:content-width="5in" fo:src="/files/ftp_upload/50975/50975fig9highres.jpg" src="/files/ftp_upload/50975/50975fig9.jpg" /> 
Figure 9.&#160;The PhyML interface. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50975/50975fig9highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 10" fo:content-width="5in" fo:src="/files/ftp_upload/50975/50975fig10highres.jpg" src="/files/ftp_upload/50975/50975fig10.jpg" /> 
Figure 10.&#160;The PhyML interface menu. Once sequences are loaded into PhyML the first menu appears, which can be navigated by typing the letter or symbol in the square bracket. Submenus can be reached by typing the &#8220;+&#8221; sign. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50975/50975fig10highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 11" fo:content-width="5in" fo:src="/files/ftp_upload/50975/50975fig11highres.jpg" src="/files/ftp_upload/50975/50975fig11.jpg" /> 
Figure 11.&#160;MrBayes Interface. When MrBayes is launched the progress can be viewed in specific intervals set using the &#8220;printfreq=X&#8221; command. Although the program cannot be stopped during a run, after the specified number of generations are computed the user will be asked if they want to run more generations. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50975/50975fig11highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 12" fo:content-width="5in" fo:src="/files/ftp_upload/50975/50975fig12highres.jpg" src="/files/ftp_upload/50975/50975fig12.jpg" /> 
Figure 12.&#160;The TreeView interface. In this figure the TreeView window displays a sample tree of proteins from Flybase (http://flybase.org/). Files are imported by clicking the &#8220;open&#8221; option, and selecting an appropriate file type (e.g. Newick format). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/50975/50975fig12highres.jpg" target="_blank">Click here to view larger image.</a>

Watch this Scientific Journal Video about A Practical Guide to Phylogenetics for Nonexperts at JoVE.com

A Practical Guide to Phylogenetics for Nonexperts

Here we describe a step-by-step pipeline for generating reliable phylogenies from nucleotide or amino acid sequence datasets. This guide aims to serve researchers or students new to phylogenetic analysis.&#160;

Many researchers, across incredibly diverse foci, are applying phylogenetics to their research question(s). However, many researchers are new to this ...

a-practical-guide-to-phylogenetics-for-nonexperts

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Biology

35.2K Views.  The George Washington University. Here we describe a step-by-step pipeline for generating reliable phylogenies from nucleotide or amino acid sequence datasets. This guide aims to serve researchers or students new to phylogenetic analysis. 

Video: A Practical Guide to Phylogenetics for Nonexperts

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Related proteins that have been studied in different labs using varying organisms may lack a uniform system of nomenclature and classification, making it difficult to discuss the group as a whole and to place new sequences into the appropriate context. Developing a reference that prioritizes important sequence features related to structure and/or activity can be used in addition to established names to add some coherency to a diverse group of proteins. This paper uses the cysteine-stabilized alpha-helix (CS-&#945;&#946;) superfamily as an example to show how a reference generated in&#160;spreadsheet software can clarify relationships between existing proteins in the superfamily, as well as facilitate the addition of new sequences. It also shows how the reference can help to refine sequence alignments generated in commonly used software, which impacts the validity of phylogenetic analyses. The use of a reference will likely be most helpful for protein groups that include highly divergent sequences from a broad spectrum of taxa, with features that are not adequately captured by molecular analyses.

The goal of this protocol is to develop a reference for divergent proteins in a group that lacks coherent criteria for nomenclature and classification. This reference will facilitate analyses and discussion of the group as a whole and can be used in addition to established names.

Related proteins that have been studied in different labs using varying organisms may lack a uniform system of nomenclature and classification, making it ...

Behavior

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

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.

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 ...

Immunology and Infection

The ITS2 Database

The internal transcribed spacer 2 (ITS2) has been used as a phylogenetic marker for more than two decades. As ITS2 research mainly focused on the very variable ITS2 sequence, it confined this marker to low-level phylogenetics only. However, the combination of the ITS2 sequence and its highly conserved secondary structure improves the phylogenetic resolution1 and allows phylogenetic inference at multiple taxonomic ranks, including species delimitation2-8.
The ITS2 Database9 presents an exhaustive dataset of internal transcribed spacer 2 sequences from NCBI GenBank11 accurately reannotated10. Following an annotation by profile Hidden Markov Models (HMMs), the secondary structure of each sequence is predicted. First, it is tested whether a minimum energy based fold12 (direct fold) results in a correct, four helix conformation. If this is not the case, the structure is predicted by homology modeling13. In homology modeling, an already known secondary structure is transferred to another ITS2 sequence, whose secondary structure was not able to fold correctly in a direct fold.
The ITS2 Database is not only a database for storage and retrieval of ITS2 sequence-structures. It also provides several tools to process your own ITS2 sequences, including annotation, structural prediction, motif detection and BLAST14 search on the combined sequence-structure information. Moreover, it integrates trimmed versions of 4SALE15,16 and ProfDistS17 for multiple sequence-structure alignment calculation and Neighbor Joining18 tree reconstruction. Together they form a coherent analysis pipeline from an initial set of sequences to a phylogeny based on sequence and secondary structure.
In a nutshell, this workbench simplifies first phylogenetic analyses to only a few mouse-clicks, while additionally providing tools and data for comprehensive large-scale analyses.

The ITS2 Database is a workbench for phylogenetic inference simultaneously considering sequence and secondary structure of the internal transcribed spacer 2. This includes data collection with accurate annotation, structure prediction, multiple sequence-structure alignment and fast tree calculation. In a nutshell, this workbench simplifies first phylogenetic analyses to a few clicks.

The internal transcribed spacer 2 (ITS2) has been used as a phylogenetic marker for more than two decades.  As ITS2 research mainly focused on the very ...

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 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.

Relacje ewolucyjne poprzez porównania genomów

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

Education

JoVE Core

Molecular Biology

Unit 2

Genomes and Evolution

KEY TERMS AND CONCEPTS

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.

The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the phylogenetic tree. The exact meaning must be shown clearly on a legend accompanying the phylogenetic tree. If such a legend is not present, the branch length is arbitrary, and the reader should not infer any information.

Trees may or may not have a root. The tree is unrooted if the most recent common ancestor of all organisms of interest is unknown. In this case, the depiction of the phylogenetic relationships resembles a snowflake, not a tree. The scientist can root the tree by including an outgroup into the analysis. An outgroup is an organism that is not closely related to any of the organisms that the scientist wishes to arrange on the tree.

Evolutionary Relationships and Phylogenetic Trees

Drzewa filogenetyczne

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches ...

Evolution

Evolutionary History

Evolutionary Relationships

<ol class="note-items"><li data-note-item="1" data-parent-collapse="">Forming an Evolutionary Hypothesis
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="1-1"><div class="lab-step">NOTE: A cladogram is an important tool for forming an evolutionary hypothesis. A cladogram is a tree-shaped chart used to depict the hypothetical genealogical relationships between species. The tips or leaves of the chart represent specific species and the branches of the tree are different lengths. The different lengths represent the degree of change between each of the species. The common ancestor of all of the species that a specific line branches from is located at a node, where the branches intersect. Species that share a node are called a sister group.
</div></li><li data-sub-note-item="1-2"><div class="lab-step">To begin, look at the provided blank cladogram and make a hypothesis about where the assigned animals should be placed on the tree. If a blank cladogram has not been provided please <a target="_blank" href="/files/ftp_upload/10562/Blank_Cladogram.png">download one here</a>.
</div></li><li data-sub-note-item="1-3"><div class="lab-step">Next, observe the image of the fossilized animal.
</div></li><li data-sub-note-item="1-4"><div class="lab-step">Using the morphology, or physical characteristics, of the fossil, make a hypothesis about where on your cladogram it should be placed. Add a new line or mark to indicate your proposed position for the fossil.
</div></li></ul></li><li data-note-item="2" data-parent-collapse="">Comparing Gene Sequences Using BLAST
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="2-1"><div class="lab-step">NOTE: Historically, cladograms have been constructed through the comparison of morphology. Now, genetic sequences can also be compared between species of interest to construct cladograms. DNA sequences are not usually preserved in fossils, so for this lab you will use the BLAST database to compare the DNA sequences of living species that are closely related to the fossil to thousands of other living species.
</div></li><li data-sub-note-item="2-2"><div class="lab-step">Before beginning this lab, your instructor will have downloaded the three preserved gene sequences to a folder on your desktop.
</div></li><li data-sub-note-item="2-3"><div class="lab-step">Navigate to the BLAST homepage, which the instructor should have left open in your browser, and click on Saved Strategies from the menu at the top of the page.
</div></li><li data-sub-note-item="2-4"><div class="lab-step">Click Open on the Evolutionary_Relationships_Gene1.txt file.
</div></li><li data-sub-note-item="2-5"><div class="lab-step">Then, click View to go to the Standard Nucleotide BLAST search page. You should be able to see the DNA sequence in the Query Sequence box at the top-right of the screen.
</div></li><li data-sub-note-item="2-6"><div class="lab-step">Scroll to the bottom of the screen and click the BLAST button. Note: It will take a few moments to process and analyze the data. 
</div></li><li data-sub-note-item="2-7"><div class="lab-step">When the data has been processed, a graphic summary will appear. NOTE: Your query sequence is represented by the blue line at the top of the box. Each of the lines below the blue line represents another sequence in the BLAST database that matches your query. The length and location of these bars represent where and how much of the sequence match up to the query. The color of the bar represents its score, or how identical the sequence is to the query. Below the graphic summary is the Sequences producing significant alignments list, which contains descriptions of the DNA sequences retrieved from the database that most closely align with the query sentence. These are listed in descending order, from the most similar at the top to the least similar at the bottom. On the right, there are several statistics on how closely related each database sequence is to the experimental sequence. The higher the Max, Total, Query Coverage, and Identity scores are, the more similar the query and retrieved sequences are to each other. Similarly, the lower the E value number, the less likely the sequences match was found by random chance and more significantly the alignment is accurate. An E value of 0 indicates a highly significant match.
</div></li><li data-sub-note-item="2-8"><div class="lab-step">Click on the name of the description of the most similar alignment listed. This will take you further down the page to the exact DNA sequence alignment between the retrieved sequence and the query sequence.
</div></li><li data-sub-note-item="2-9"><div class="lab-step">Click on the Sequence ID number. This will open a new tab with more specific information on the retrieved gene sequence.
</div></li><li data-sub-note-item="2-10"><div class="lab-step">Identify and record the scientific and common names of the organism, which should be listed next to Source.
</div></li><li data-sub-note-item="2-11"><div class="lab-step">Identify and record the protein that the gene codes for, which should be listed next to Definition.
</div></li><li data-sub-note-item="2-12"><div class="lab-step">Close the tab and hit Back to return to the Sequences producing significant alignments list.
</div></li><li data-sub-note-item="2-13"><div class="lab-step">Then, repeat steps 7 &#8211; 9 for the next most similar sequences.
</div></li><li data-sub-note-item="2-14"><div class="lab-step">After collecting several species names, return to the Sequences producing significant alignments list and click Select: All to check all of the boxes next to the names of each listed alignment sequence.
</div></li><li data-sub-note-item="2-15"><div class="lab-step">Click on Distance of tree results to create a phylogram based on the similarity of all of the BLAST result gene sequences to the query gene sequence. Note: Your query fossil relative sequence will be highlighted in yellow.
</div></li><li data-sub-note-item="2-16"><div class="lab-step">Record your sequence&#8217;s location in relation to the other taxon groups shown in the tree.
</div></li><li data-sub-note-item="2-17"><div class="lab-step">Finally, return to the Saved Strategies tab and upload the next saved gene sequence.
</div></li><li data-sub-note-item="2-18"><div class="lab-step">Query the remaining two fossil relative DNA sequences as previously demonstrated (steps 1 &#8211; 16).
</div></li></ul></li><li data-note-item="3" data-parent-collapse="">Data Analysis
<button class="expand">Expand</button><ul class="lab-items"><li data-sub-note-item="3-1"><div class="lab-step">Based on your gene sequence results, come up with a second hypothesis about where your fossil specimen fit in the original cladogram.
</div></li><li data-sub-note-item="3-2"><div class="lab-step">Compare this placement with your first hypothesis.
</div></li><li data-sub-note-item="3-3"><div class="lab-step">Compare your tree with your classmates&#8217;. If any of your classmates put the fossil on a different branch, ask them how they came to that decision.
</div></li></ul></li></ol>

Evolutionary Relationships: Using BLAST to Test Evolutionary Hypotheses - Procedure

Związki ewolucyjne

Forming an Evolutionary Hypothesis
ExpandNOTE: A cladogram is an important tool for forming an evolutionary hypothesis. A cladogram is a tree-shaped chart ...

JoVE Lab Manual

Lab Manual Sub Articles

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

Ewolucja genów - szybka czy wolna?

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 ...