Name: A Practical Guide to Phylogenetics for Nonexperts (Video) | JoVE Journal
Uploaded: 2014-02-05T17:00:00.000+00:00
Duration: 12 min
Description: Learn phylogenetic tree reconstruction from DNA or protein sequences using BLAST, alignment, and model selection. This computational pipeline guides researchers through reliable evolutionary analysis.

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5 minutes

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/pubmed?term=((Weights+for+data+related+by+a+tree%5BTitle%5D)+AND+%22J.+Mol.+Biol%22%5BJournal%5D)">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/pubmed?term=((A+new+look+at+the+statistical+model+identification%5BTitle%5D)+AND+%22IEEE+Trans.+Automat.+Contr%22%5BJournal%5D)">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/pubmed?term=((Estimating+the+dimension+of+a+model%5BTitle%5D)+AND+%22Ann.+Stat%22%5BJournal%5D)">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/pubmed?term=((A+simple%2C+fast%2C+and+accurate+algorithm+to+estimate+large+phylogenies+by+maximum+likelihood%5BTitle%5D)+AND+%22Syst.+Biol%22%5BJournal%5D)">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/pubmed?term=((MRBAYES%3A+Bayesian+inference+of+phylogenetic+trees%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">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/pubmed?term=((CLUSTAL+W%3A+Improving+the+sensitivity+of+progressive+multiple+sequence+alignment+through+sequence+weighting%2C+position-specific+gap+penalties+and+weight+matrix+choice%5BTitle%5D)+AND+%22Nucleic+Acids+Res%22%5BJournal%5D)">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/pubmed?term=((Kalign--an+accurate+and+fast+multiple+sequence+alignment+algorithm%5BTitle%5D)+AND+%22BMC+Bioinformatics%22%5BJournal%5D)">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/pubmed?term=((MAFFT+version+5%3A+Improvement+in+accuracy+of+multiple+sequence+alignment%5BTitle%5D)+AND+%22Nucleic+Acids+Res%22%5BJournal%5D)">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/pubmed?term=((MAFFT%3A+A+novel+method+for+rapid+multiple+sequence+alignment+based+on+fast+fourier+transform%5BTitle%5D)+AND+%22Nucleic+Acids+Res%22%5BJournal%5D)">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/pubmed?term=((MUSCLE%3A+Multiple+sequence+alignment+with+high+accuracy+and+high+throughput%5BTitle%5D)+AND+%22Nucleic+Acids+Res%22%5BJournal%5D)">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/pubmed?term=((T-coffee%3A+A+novel+method+for+fast+and+accurate+multiple+sequence+alignment%5BTitle%5D)+AND+%22J.+Mol.+Biol%22%5BJournal%5D)">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/pubmed?term=((ProbCons%3A+Probabilistic+consistency-based+multiple+sequence+alignment%5BTitle%5D)+AND+%22Genome+Res%22%5BJournal%5D)">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/pubmed?term=((ProtTest+3%3A+Fast+selection+of+best-fit+models+of+protein+evolution%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">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/pubmed?term=((TreeView%3A+An+application+to+display+phylogenetic+trees+on+personal+computers%5BTitle%5D)+AND+%22Comput.+Appl.+Biosci%22%5BJournal%5D)">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/pubmed?term=((jModelTest+2%3A+More+models%2C+new+heuristics+and+parallel+computing%5BTitle%5D)+AND+%22Nat.+Methods%22%5BJournal%5D)">jModelTest 2: More models, new heuristics and parallel computing.</a> Nat. Methods. 9 (8), 772 (2012).</li>
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</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 phylog...

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

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 “&gt;” sign (Figure 2). The description must follow immediately after the “&gt;” sign, the sequence (i.e. nucleotides or amino acids) follow the description on the next line. Wh...

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 ...

The overall goal of this article is to reconstruct a reliable phylogenetic tree from DNA or protein sequences. This is accomplished by first identifying similar sequences using blast programs at NCBI. The second step is to align similar sequences.Next, the best fit model of evolution is determined from the alignment. The final step is to infer the phylogenetic relationship from the aligned sequences. Ultimately, the step-by-step pipeline is used to show how users can go from sequence data to reliable phylogenies.This method can help answer key questions in diverse fields by inferring identity and function to novel sequences. To use the online version of the basic local alignment search tool or blast navigate to the National Center for Biotechnology Information or NNC B'S Blast Web Server. Click on the appropriate blast program.Input a FASTA formatted text sequence such as the one shown here into the query box. Click the appropriate blast program for use in the search, and then click blast. The output is in HTML format by default and displays the most similar sequences to the input text sequence.The next section covers using a local blast executable on Windows Mac. Users can skip to the following section called Blast Local executables for Macs x. To run the blast command line program on a Windows machine, download the appropriate Windows executable from the NCBI blast website.After installing the Blast program, configure the PC environment variable as follows, click the PCs start button and right click computer. Then click properties. In the new window, select advanced system settings, and in the advanced tab of the new pop-up, click the environment variables button.Then under the user variables for user section, click the new button. In the new popup, add the variable name path, and the variable value shown here. Next, download a pre-formatted blast database, which are updated daily from the NCBI website or a genome for a particular organism.Then open an MS DOS prompt by clicking start and typing CMD in the search bar and change to the NCBI blast folder. Create the database using the Make blast DB command shown here. Create a query protein sequence called test by inserting a FASTA formatted protein text sequence into the DB folder.Then to identify the most similar sequences to the test protein, interrogate the database via a blast P query command. The following section repeats this information for Mac users. Windows users can skip to section five generating multiple sequence alignments.To run the blast command line program on a Mac, download the appropriate MAC executable by remotely accessing the N-C-B-I-F-T-P site. To do this open finder and search for terminal in the terminal window, type the FTP address for the N-C-B-I-F-T-P site. Type anonymous for name and password, and then type CD blast slash executables slash latest.List the executables by typing LS and download the latest version that matches your system requirements by typing the following. Now, decompress the downloaded files. Now 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, download a pre-formatted blast database or a genome from the NCBI website. Search the genomes directory by typing CD genomes. Then download the genome or sequence of interest as follows, and then type quit to exit the FTP site.Next, make the database by typing the Make Blast DB instruction. Insert a FASTA formatted query sequence into the bin folder and interrogate the database with the blast P query command to find the most similar sequence to the test sequence data among the commonly used multiple sequence alignment or MSA programs is tea coffee. After inputting fasta formatted sequence data into the query box at the tea coffee site, the output indicates similar residues by the color coding.Another commonly used MSA program is the clusteral MSA, which can be downloaded as a command line version, CLUSTERAL W, or a graphical version CLUSTERAL X for various operating systems. Next, load the data to the clusteral program as fast a formatted sequence text by selecting the file tab. Then clicking the load sequences button.Now switch to the align tab and click the do complete alignment button to align the sequences for a best fit model of evolution. Download the protest program. Once protest is downloaded, double click on protest.Once protest is launched, click on select file in the alignment box to load the sequence data. Then click start to run the program. After completing the run, the program indicates the best model based on the criteria for inferring sequences.After downloading and launching Phi ML, load the input sequence as a file lip formatted sequence by typing the file name and PY.Then launch the program by typing y. After downloading a Bayesian inference program from the Mr Bays website, start the program by clicking on the executable file. Then read Nexus formatted sequence data into the program by typing execute file name dot NEX.Next, set the evolutionary model and select the number of generations to run. After running the analysis with the mc mc command, summarize the trees using the sum T command to view a phylogenetic tree. Download the tree view program.As a final note, there are constant releases of new software aimed at providing better alignments, similarity predictions, or phylogenetic trees. While the overview in this video covered popular programs, the viewer is encouraged to explore additional options. The blast algorithm performs local alignments, which searches for short stretches of sequence similarity.After the algorithm has looked up all possible stretches from the query sequence and maximally extended these sequences, it then assembles alignments. For each query sequence pair, 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 five 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.A multiple sequence alignment or MSA is a sequence alignment of three or more primary sequences composed of amino acids, DNA or RNA. The output from the MSA tea coffee seen here, color codes similar residues. A sample alignment of six protein sequences aligned using cluster X is shown here for amino acid alignments.The program protest is used to determine the selection of best fit models of amino acid replacements. Within the data, the program lists the models as they are being analyzed and displays the best fit after completion of the program, Phi ML estimates maximum likelihood phylogenies from alignments of nucleotide or amino acid sequences. It incorporates a large number of substitution models coupled to various options to search tree topology space.Mr.Bayes utilizes Bayesian CMC inference across a number of evolutionary models to reconstruct phylogenetic relationships. Once the program is running, progress can be viewed in specific intervals as shown here. Once a phylogenetic tree is generated, the topology needs to be visualized.In this figure, the tree view window displays a sample tree of proteins from fly.Base. Tree view includes a tree editor that allows the user to move branches and reroute trees. While attempting this procedure, it's important to remember to read thoroughly the user guides for each program.This protocol provides a practical starting point to introduce the reader as to how these programs work. However, I encourage the reader to play around with and become familiar with the many settings associated with each program.

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

Research

JoVE Journal

Biology

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 ITS2 Database

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.

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

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.

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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

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

The protocol presented here aims to guide the users on the computer-based processing of cytochrome c oxidase subunit I (COI) gene sequences generated from beetles, such that the species clustering hypothesis called molecular operational taxonomic units (MOTUs) can be generated from DNA.

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

The ladder rung walking task is a new test to assess skilled walking and measure both forelimb and hindlimb placing, stepping, and inter-limb co-ordination.

The Ladder Rung Walking Task: A Scoring System and its Practical Application.

Genetic Inducible Fate Mapping (GIFM) marks and tracks cells with fine spatial and temporal control in vivo and elucidates how cells from a specific genetic lineage contribute to developing and adult tissues.  Demonstrated here are the techniques required to fate map E12.5 mouse embryos for epifluorescent and explant analysis.

A Practical Approach to Genetic Inducible Fate Mapping: A Visual Guide to Mark and Track Cells In Vivo

We are describing a new method of isolating genomic DNA from whole blood collected for plasma/serology.  After plasma collection, the compacted blood is usually discarded. Our novel method represents a significant improvement over existing methods and makes DNA and plasma available from a single collection, without requesting additional blood.

A Practical and Novel Method to Extract Genomic DNA from Blood Collection Kits for Plasma Protein Preservation

The fate of an individual embryonic cell can be influenced by inherited molecules and/or by signals from neighboring cells. Utilizing fate maps of the cleavage stage Xenopus embryo, single blastomeres can be identified for culture in isolation to assess the contributions of inherited molecules versus cell-cell interactions.

Blastomere Explants to Test for Cell Fate Commitment During Embryonic Development

V3 workflow is a western blot procedure using stain-free gels. The stain-free technology allows researchers to visualize protein separation quality, to verify the transfer efficiency, and most importantly, to validate the change in the protein of interest using total protein quantification as a reliable loading control.

V3 Stain-free Workflow for a Practical, Convenient, and Reliable Total Protein Loading Control in Western Blotting

Learn phylogenetic tree reconstruction from DNA or protein sequences using BLAST, alignment, and model selection. This computational pipeline guides researchers through reliable evolutionary analysis.

A Practical Guide to Phylogenetics for Nonexperts

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