TITLE: Roadmap to the study of gene and protein phylogeny and evolution - a practical guide
AUTHORS: Florian G Jacques, Kristian Pietras, Paulina Bolivar, emma.hammarlund

[DESCRIPTION]
Developments in sequencing technologies and the sequencing of an ever-increasing number of genomes have revolutionised studies into biodiversity and organismal evolution. This accumulation of data has been paralleled by the creation of numerous public biological databases through which the scientific community can mine the sequences and annotations of genomes, transcriptomes, and proteomes of multiple species. However, to find the appropriate databases and bioinformatic tools for respective inquiries and aims can be challenging. Here, we present a compilation of DNA and protein databases, as well as bioinformatic tools for phylogenetic reconstruction and a wide range of studies on molecular evolution. We provide a protocol for information extraction from biological databases and simple phylogenetic reconstruction using probabilistic and distance methods, facilitating the study of biodiversity and evolution at the molecular level for the broad scientific community.

[STEPS]
SECTION: Introduction
1. We provide a step-by-step protocol for non-bioinformatic users to reconstruct the phylogeny and evolutionary history of genes or proteins. 

We present a compilation of DNA and protein databases, as well as bioinformatic tools for molecular phylogenetic reconstruction, and a wide range of studies on molecular evolution. We describe the protocol from sequence harvesting from databases to phylogenetic tree building and diverse evolutionary studies, and we illustrate our protocol with two test-case studies on the evolution of P53 and Cyclins/CDKs protein families.

 

SECTION: Sequence collection and comparison
2. Collecting sequence data and bibliography on genes and proteins

Evolutionary analyses on molecular data (genes, genomes, proteins, mRNA, transposable elements, ribosomal RNA or other parts of the genome), require retrieving sequences and other information from public databases. The sequences are generally stored in the Fasta format. Other information from specific databases, including structure, activity, biological function, tissue expression, sub-cellular localization and polymorphism can also prove relevant for evolutionary studies.

Several tools can be used to batch download a large number of sequences, such as the BioMart tool of Ensembl (https://www.ensembl.org/info/data/biomart/index.html), the Batch Entrez tool of NCBI (https://www.ncbi.nlm.nih.gov/sites/batchentrez), and the Retrieve/ID mapping tool of UniProt (https://www.uniprot.org/id-mapping). These tool can also be used to convert and retrieve the identifiers of different databases such as NCBI, GenBank, Ensembl, Uniprot, Pfam or the PDB. 

>Search for your gene or protein of interest (e.g. Human P53) in molecular databases (e.g. NCBI or Uniprot).

    >In Uniprot, HsTP53 is labelled as P04637 (https://www.uniprot.org/uniprot/P04637). click on "Sequence and Isoforms" to display the sequence.

    >In NCBI, select "Nucleotide" in the "Database" panel on the left, and type the name or GI number of the gene (https://www.ncbi.nlm.nih.gov/nuccore/?term=Homo+sapiens+P53). Click on FASTA to display the sequence.

>To create a Fasta file with the sequences, paste them in the Fasta format, including the headlines, in Notepad. Save the document using fasta as filename extention.

The Fasta format includes a headline starting with ">", and the nucleic acid or amino-acid sequence. For example, in the case of the human P53 protein:

>sp|P04637|P53_HUMAN Cellular tumor antigen p53 OS=Homo sapiens OX=9606 GN=TP53 PE=1 SV=4 MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGP DEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAK SVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHE RCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNS SCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELP PGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPG GSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD

Here follow non-exhaustive lists of nucleic acid databases and protein databases, with their main features.
 
       Database         Features         Link            BAR         Database of plant genes and proteins         http://bar.utoronto.ca/            Bgee         Gene expression patterns         https://bgee.org/            Ensembl         Genome browser of vertebrates, includes tools   for identification of homology         https://www.ensembl.org/index.html            Entrez         Gene sequences and structures         https://www.ncbi.nlm.nih.gov/Web/Search/entrezfs.html            FlyBase         Genome and proteome of the model insect D.   melanogaster         https://flybase.org/            GeneCards         Human   gene function, genomics, transcription factor binding sites and protein   products         https://www.genecards.org/            GenBank         Annotated DNA sequences         https://www.ncbi.nlm.nih.gov/genbank/            NCBI         Collection   of databases for molecular biology and medicine providing many bioinformatics   tools and services         https://www.ncbi.nlm.nih.gov/            PomBase         Genome and proteome of the model yeast S.   pombe         https://www.pombase.org/            TAIR         Genome and proteome of the model plant A.   thaliana         https://www.arabidopsis.org/            WormBase         Genome and proteome of the model nematode C.   elegans         https://wormbase.org//#012-34-5            Xenbase         Genome and proteome of the model amphibian X.   laevis         http://www.xenbase.org/entry/      
 
       Database         Features         Link            CATH         Classification of protein   domains based on their structure, functionality, and evolution         https://www.cathdb.info/                FSSP         Classification of protein   domains based on their structural similarity         https://archive.ph/20121222235655/http://srs.ebi.ac.uk/srsbin/cgi-bin/wgetz?-page+LibInfo+-id+5Ti2u1RffMj+-lib+FSSP                Gene Ontology         Unified annotation of   molecular function, biological processes, and cellular components of proteins         http://geneontology.org/            Human Protein Atlas         Information on human   protein and their link with diseases         https://www.proteinatlas.org/            InterPro         Classification of   proteins domains and functional sites         https://www.ebi.ac.uk/interpro/            KEGG         Protein function and   biological pathways         https://www.genome.jp/kegg/            PDB         3D structures of proteins         https://www.rcsb.org/            Pfam         Information about protein   families and domains         http://pfam.xfam.org/            PHAROS         Centralizes literature   for human proteins         https://pharos.nih.gov/            PRINTS         Protein fingerprints classification database         http://www.bioinf.man.ac.uk/dbbrowser/PRINTS/            PROSITE         Protein family database         https://prosite.expasy.org/            SCOP         Classification of protein   domains based on their structure, functionality and evolution         https://scop.mrc-lmb.cam.ac.uk/            SUPERFAMILY         Protein structure and   functions         https://supfam.org/            UniProt         General information on   proteins, including sequence, structure, classification, function,   subcellular localization and simple homology identification         https://www.uniprot.org/uniprot/      
 

SECTION: Sequence collection and comparison
2.1.  Protein domains classification (optional)

Studying protein classification can be useful for evolutionary studies. Protein domains are classified into different categories based on 3-dimensional structure, function, and evolutionary relationship. Identifying the main protein domains and retrieving their classification can provide valuable insight on its occurrence in living organisms and evolutionary origin. Several classification systems are published and listed in the table above.

>Use a classification system (e.g. Pfam or Interpro) to identify the main domains of a protein and study their occurrence in living organisms. Pfam presents their occurrence as a sunburst plot.

According to Pfam 35.0, HsTP53 contains four main protein domains: P53 TAD (transactivating domain), TAD2, P53 DNA binding domain, and P53 tetramer. P63 and p73 also contain the P53 DNA binding domain and the P53 tetramer domain. The P53 TAD and TAD2 domains are absent in P63 and P73, but both include a single SAM_2 domain instead.

P53 (PF00870 in Pfam) is the main domain of the p53 protein, covering the amino acids 99 to 289. Pfam contains 1765 P53-domain-contaning sequences from 382 species, all in choano-organisms (metazoans and choanoflagellates), including 5 sequences in choanoflagellates and 13 sequences in the genome of Homo sapiens (https://pfam.xfam.org/family/PF00870#tabview=tab7). 

P53 TAD and TAD2 are two transcription scaffold domains. Pfam includes 253 sequences containing the P53 TAD domain, in bilaterians only. The domain TAD2 is present in 81 sequences, from primates only. P53 tetramer serves for the oligomerization of the protein. Pfam includes 1392 sequences, in animals only, containing the domain p53 tetramer. The SAM 2 (sterile alpha motif) domain is a putative protein interaction domain. More than 20000 sequences containing this domain, in more than 1400 species, are present in Pfam.

>Retrieve the classification of the protein domains from SCOP (https://scop.mrc-lmb.cam.ac.uk/). 

The SCOP classification of the p53 DNA-binding domain is as follows (accessed September 06, 2021):
Class b: all beta-proteins. This class contains 178 folds.
Fold: b.2 Common fold of diphtheria toxin/transcription factors/cytochrome f. This fold contains 9 superfamilies.
Superfamily: b.2.5: p53-like transcription factors. This superfamily contains 8 families.
Family: b.2.5.2: p53 DNA-binding domain-like. 3 proteins belonging to this family are present in the database.
Protein p53 tumor suppressor. The p53 DNA-binding domains proteins of Homo sapiens and Mus musculus are present in SCOP.

>Retrieve the sunburst plot of the occurrence of the P53 domains in living organisms from Pfam. Click "sequences" on the right to display the plot.

The plot shows the distribution of the 1,765 sequences containing the P53 binding domain across 382 species. Every bar on the periphery represents one single species, containing one or several p53 paralogues in their genome


  
SECTION: Sequence collection and comparison
3. Identification of homologues

Studying the evolution of a gene or protein family requires the identification of homologues, i.e., genes or proteins with shared ancestry. Homologues include orthologues, that are present in different species and result from speciation events; and paralogues, that are present in the same genome and result from gene duplications. 

Bioinformatic tools can be used to identify gene or protein homology based on sequence similarity, in the genomes of any species (see the list below). Here is a list of tools that can be used to identify sequence homology.

       BLAST         Gene or protein homology   search from NCBI         https://blast.ncbi.nlm.nih.gov/Blast.cgi            BLAT         Sequence homology search   in animal genomes         https://genome.ucsc.edu/cgi-bin/hgBlat            Ensembl         Genome   browser of vertebrates, includes tools for identification of homology         https://m.ensembl.org/index.html            FASTA         Sequence search against   protein databases and sequence alignment          https://www.ebi.ac.uk/Tools/sss/fasta/            HMMER         Gene and protein homology   search         http://hmmer.org/            Pfam         Protein families and   domains, includes tools for identification of homology         http://pfam.xfam.org/            SSAHA         Gene sequence search and   alignment         https://www.sanger.ac.uk/tool/ssaha/            UniProt         General information   on proteins, including simple homology identification         https://www.uniprot.org/uniprot/      
 
In our example, we are studying the evolution of TP53 in animals. Vertebrates have three TP53 paralogues (TP53, TP63 and TP73). Our analysis is based on dos Santos et al, Plos One, 2016.

>Using BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi), paste the sequence of your gene of interest (in our example, human TP53) in the Fasta format to identify homologues in the genomes of a selection of other species covering the diversity of animals (e.g. all animals and choanoflagellates), for example.

In our example, we chose the cnidarian Hydra vulgaris, the fruit fly Drosophila melanogaster, three other insects (Bombus terrestris, Apis mellifera and Aedes aegyptus), the urochordate Ciona intestinalis, and the teleost fish Danio rerio, the coelacanth Latimeria chalumnae, the amphibian Xenopus tropicalis, the lizard Anolis carolinensis, the bird Gallus gallus, and the mammals Bos taurus and H. sapiens.

>Select the homologous sequences based on E.value and significant homology (>30% identity). For vertebrate species, select one sequence for every paralogue (p53, p63 and p73).

>Download all the sequences in the Fasta format in a Fasta file.

>(Optional): Calculate the identity matrix of the sequences using alignment tools (e.g., CLUSTALW 2.1).

   
According to the matrix, the 13 human p53 paralogues share 36% to 100% identity, and the two paralogues of Monosiga brevicollis share 21.4% identity. Human and Monosiga orthologues share 17% to 25% identity. Hence, all human paralogues are more similar to each other than to any of the Monosiga orthologues.


SECTION: Sequence collection and comparison
4. Multiple sequence alignment

Phylogenetic analysis requires identifying homologous bases or amino acid residues between the sequences. Homology is inferred by a sequence alignment. The sequences are put in every row one after the other to arrange every homologous base or amino acid in the same column. Alignment of the homologous residues necessitates adding gaps, indicated by the symbol “-” and corresponding to insertions or deletions (indels), into the sequences.

>Use an alignment tool (e.g. MAFFT, https://www.ebi.ac.uk/Tools/msa/mafft/) to align the sequences. Paste the alignment in the Fasta format and submit. Retrieve the alignment and save it in a new Fasta file.

Here is a list of tools for nucleic acid or amino acid sequence alignment. 
 
       Software         Features         Link            BAli-Phy         Multiple sequence   alignment of nucleotide and amino acid sequences and phylogenetic analysis   using a bayesian approach          http://www.bali-phy.org            CLUSTAL Omega*         Speed-oriented multiple   sequence alignment for nucleotide or amino acid data, suitable for large   datasets         https://www.ebi.ac.uk/Tools/msa/clustalo/            CLUSTALW*         Multiple sequence   alignment for nucleotide or amino acid data         https://www.genome.jp/tools-bin/clustalw            CONTRAlign (ProbCons)         Accuracy-oriented multiple   sequence alignment for amino acid data         http://contra.stanford.edu/contralign/            Kalign*         Multiple sequence   alignment for nucleotide or amino acid data, suitable for large datasets         https://www.ebi.ac.uk/Tools/msa/kalign/            MAFFT*         Accuracy-oriented multiple   sequence alignment for nucleotide or amino acid data         https://mafft.cbrc.jp/alignment/server/            MUSCLE*         Multiple sequence   alignment for nucleotide or amino acid data         https://www.ebi.ac.uk/Tools/msa/muscle/            PASTA         Speed-oriented multiple   sequence alignment for nucleotide or amino acid data, designed for very large   datasets         https ://bioinformaticshome.com/tools/msa/descriptions/PASTA.html            PRANK/   WebPRANK*         Speed-oriented multiple   sequence alignment for nucleotide or amino acid data, should be preferred for   close sequences and large datasets         http://wasabiapp.org/software/prank/ https://www.ebi.ac.uk/goldman-srv/webprank/            SATé         Software package for multiple   sequence alignments and phylogenetic inference         https ://phylo.bio.ku.edu/software/sate/sate.html            T-COFFEE*         Accuracy-oriented multiple   sequence alignment of nucleotide and amino acid sequences         http://tcoffee.crg.cat/            UPP         Speed-oriented   multiple sequence alignment of nucleotide and amino acid sequences, designed   for very large data sets          https://github.com/smirarab/sepp.          

SECTION: Sequence collection and comparison
5. Alignment trimming

It is recommended to check the alignment and, when necessary, to improve it manually or using alignment trimming tools. Trimming is the selection of phylogenetically informative sites in the alignment. Poorly aligned positions and highly variable regions are not phylogenetically informative, because these positions might not be homologous or subject to saturation. They should be excluded to maximize the phylogenetic signal of the alignment. 

>Use one of the tools below to compute the completeness of your alignment and exclude the poorly aligned regions (regions of the alignment with low scores).

>Alternatively, you can also directly download the sequences into the Guidance 2 server (http://guidance.tau.ac.il/) and proceed to the alignment using MAFFT. Open the color-coded MSA to identify poorly aligned and highly variable regions. You can delete them manually from the alignment or remove unreliable columns below a certain cutoff. 

>The new MSA, hereafter renamed sub-MSA, will be used for the phylogenetic analysis. Save the sub-MSA in the Fasta format.

Here is a selection of tools to quantify the completeness of alignments and selection of the phylogenetic informative regions of the alignment.
 
       Software         Features         Link            AliStat         Quantification of alignment   completeness for alignment refinement         https://github.com/thomaskf/AliStat            BMGE         Selection of informative regions on   multiple sequence alignments         https://gitlab.pasteur.fr/GIPhy/BMGE            GBlocks         Selection of informative regions on multiple   sequence alignments         http://molevol.cmima.csic.es/castresana/Gblocks_server.htmlhttp://molevol.cmima.csic.es/castresana/http://molevol.cmima.csic.es/castresana/Gblocks.htmlGblocks.html            Guidance 2*         Selection of informative regions on   multiple sequence alignments         http://guidance.tau.ac.il/            Noisy         Selection of informative regions on   multiple sequence alignments         http://www.bioinf.uni-leipzig.de/Software/noisy/            trimAl         Selection of informative regions on   multiple sequence alignments         http://trimal.cgenomics.org/      

SECTION: Sequence collection and comparison
6. Assessing phylogenetic assumptions (for more advanced users)

Phylogenetic models rely on simplifying assumptions stating for example that all sites in the alignment evolved under the same tree, that mutation rates have remained constant, and that substitutions are reversible. If the phylogenetic data violate these assumptions, the phylogeny and evolutionary analyses can be biased. Once the alignment is performed and the sites selected for phylogenetic inference, it is recommended to assess those phylogenetic assumptions when possible. Tests for some of these assumptions have been included in IQ-TREE. You can also use the package MOTMOT, written in the R language.
SECTION: Phylogenetic analysis
7. Phylogenetic inference
  
The evolutionary history of genes, proteins or species is generally presented as a phylogenetic tree, a graphical illustration of the evolutionary relationships between the sequences or taxa. Several methods for phylogenetic inference exist: Maximum Parsimony (MP), the distance-based methods and the probabilistic methods, nowadays the most widely used for molecular data.

>Choose one or several phylogenetic methods to reconstruct the evolutionary history of your gene, protein or species of interest. See the sub-steps below for the specificities of each method.

It can be interesting to combine several approaches and compare the results (e.g. Maximum Likelihood, Neighbor Joining and Bayesian Inference). However, confirming phylogenetic relationships with several methods does not necessarily mean that the tree is biologically correct. Several methods may give a same wrong result if they assume the same wrong assumptions and molecular evolution models.

Still, we propose to reconstruct the phylogeny of the TP53 family using a distance method: Neighbor Joining (NJ) with MEGA 11, and a probabilistic method: Maximum Likelihood (ML) using IQ-TREE 2.

Here is a list of tools for phylogenetic reconstruction using diverse methods.
 
     Software      Features      Link        APE      R-written package for molecular   phylogenetics      http://ape-package.ird.fr        BAli-Phy      Phylogenetic inference using a   bayesian approach       http://www.bali-phy.org        BayesTraits      Phylogenetic inference and other evolutionary   analyses using Bayesian inference      http://www.evolution.reading.ac.uk/BayesTraitsV4.0.0/BayesTraitsV4.0.0.html        CoGE      Comparative genomics analyses      https://genomevolution.org/coge/        DNAsp      Analysis of DNA polymorphism      http://www.ub.edu/dnasp/         FastMe      Phylogenetic inference using   distance methods      http ://www.atgc-montpellier.fr/fastme/        GARLI      Phylogenetic inference using ML      http ://evomics.org/resources/software/molecular-evolution-software/garli/        HYPHY*      Phylogenetic inference using ML and   distance methods      https ://www.hyphy.org/        IQ-TREE      Phylogenetic inference using ML,   including model selection and a very fast bootstrapping method      http ://www.iqtree.org/        MEGA      Sequence alignment, model selection,   phylogenetic analysis (distance methods, MP, ML)      https://www.megasoftware.net/        MrBayes      Phylogenetic inference using BI and diverse   evolutionary analyses including ancestral states reconstruction and   phylogenetic calibration      http://nbisweden.github.io/MrBayes/        PAML      phylogenetic inference using ML,   estimation of selection strength, ancestral states reconstruction and other evolutionary   analyses      http://abacus.gene.ucl.ac.uk/software/paml.html        PAUP      Phylogenetic inference using MP and ML      http://paup.phylosolutions.com/        PHYLIP      Phylogenetic inference using MP,   distance methods and ML      https ://evolution.genetics.washington.edu/phylip.html        PhyloBayes      Phylogenetic inference with protein   data using BI using a specific probabilistic model      http ://www.atgc-montpellier.fr/phylobayes/        PhyML      Phylogenetic inference using ML,   ancestral states reconstruction and various evolutionary analyses      http://atgc.lirmm.fr/phyml/        PyCogent      Phylogenetic inference and phylogeny   drawing, various evolutionary analyses including partition models and   ancestral states reconstruction      https ://github.com/pycogent/pycogent        RAxML      Phylogenetic inference using ML       https://cme.h-its.org/exelixis/web/software/raxml/        SeaView      Sequence alignment and phylogenetic inference   using MP, NJ and ML      http://doua.prabi.fr/software/seaview        SplitsTree      Phylogenetic inference, in   particular unrooted trees and phylogenetic networks      https ://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/algorithms-in-bioinformatics/software/splitstree/   

SECTION: Phylogenetic analysis
7.1. Selection of the molecular evolution model (for probabilistic methods and distance methods)

Prior to phylogenetic analysis, probabilistic methods and distance methods require selection of the model of molecular evolution that best describes the data. A model of molecular evolution is a combination of a substitution model and a model of rate heterogeneity between sites. Nucleotide or amino acid substitution models exist. They differ in the number of parameters considered, like substitution rates and base/aminoacid frequencies. 

The main nucleotide substitution models are, from the simplest to the most complex: JC69, K80, F81, HKY85, TN93 and GTR. The main amino acid substitution models include JTT, WAG, LG and Dayhoff. The most common models of rate heterogeneity between sites are the Gamma distribution (G) and the proportion of invariant nucleotide or amino acid sites (I). The FreeRate model (R), a more complex model of rate heterogeneity is included in ModelFinder, PhyML and IQ-TREE. The GHOST model, for alignments with variation in mutation rate, is also implemented in IQ-TREE.

The likelihood of the different models should be computed using appropriate program, such as ModelTest and jModelTest for nucleotide sequences, and ProtTest for amino acid sequences. ModelFinder, implemented in IQ-TREE, is designed for alignments of nucleotides, codons or amino acid data. PartitionFinder 2 can be used with nucleotide and amino acid data. Model test selectors are also included in programs such as MEGA and PhyML (SMS).

These tools calculate the Bayesian information criterion (BIC) and the Akaike information criterion (AIC) for every model of molecular evolution (combination of substitution model and rate-heterogeneity model). A model with lower AIC or BIC is considered more accurate. The model optimizing BIC or AIC (i.e., with the lowest score) should be selected. 

>Here, we are studying protein sequences. Use ProtTest 3.4.2 to calculate the log-likelihood of a panel of 56 amino acid substitution models, and select the most relevant one based on the BIC or AIC score.

>Alternatively, you can use the model selectors included in IQTREE or MEGA. For example, with the IQTREE web server (http://iqtree.cibiv.univie.ac.at/), open the "Model Selection" panel, download the sub-MSA, select “protein sequences”, choose a selection criterion (AIC or BIC) and proceed to the analysis. With MEGA, download the sub-MSA, and select “Find best DNA/protein models” in the "Model" panel.

>You will use this model to compute the phylogenetic tree of your protein and for further evolutionary analyses.

Here is a selection of tools that can be used for molecular evolution model selection.
 
       Software         Features         Link            ModelFinder         Fast model selection with   a model of rate heterogeneity between sites (nucleotide, amino acids or   codons)         Implemented in IQ-TREE            ModelTest / jModelTest         Nucleotide substitution   model selection         http://evomics.org/resources/software/molecular-evolution-software/modeltest/            PartitionFinder 2         Molecular evolution model   selection (nucleotide or amino acids)         http://www.robertlanfear.com/partitionfinder/            ProtTest         Aminoacid substitution   model selection         https://github.com/ddarriba/prottest3            SMS         Molecular evolution model   selection included in PhyML (nucleotide or aminoacid)         http://www.atgc-montpellier.fr/sms/      
 
SECTION: Phylogenetic analysis
7.2. Option 1: Maximum parsimony (MP)

Maximum parsimony is a classical and simple method, that calculates the minimum number of evolutionary steps, including nucleotide insertions, deletions or substitutions, between species.

However, this method ignores hidden mutations and does not consider branch lengths, potentially leading to long branch attraction, an incorrect clustering of unrelated taxa. Furthermore, it does not consider the possibility of hidden mutations, making it not relevant for distant taxa. While MP is still used for morphological data, it is rarely used for molecular data. 

PAUP, MEGA, SeaView and Phylip can be used for phylogenetic analysis using MP.

Phylogenetic analysis using MP with MEGA 11:
>To reconstruct the phylogenetic tree of p53 sequences using MP, import the sub-MSA in MEGA. Then, in the "Phylogeny panel", choose a phylogenetic analysis using parsimony. Select the bootstrap method with at least 1000 replicates and execute the analysis.
SECTION: Phylogenetic analysis
7.3. Option 2: Distance-based methods

Distance-based methods create a matrix of molecular distances based on the number of differences between the sequences, to reconstruct the phylogenetic tree. These methods ignore hidden mutations and are also subject to long branch attraction. Distance-based methods include the Unweighted Pair Group Method with Arithmetic mean (UPGMA), Neighbor Joining (NJ), and Minimum Evolution (ME). 

FastME, PAUP, MEGA, FastTree or Phylip can be used for distance-based methods.

Phylogenetic analysis using NJ with MEGA 11:
>To reconstruct the phylogenetic tree of p53 sequences using NJ, import the sub-MSA in MEGA. Then, in the "Phylogeny" panel, choose a phylogenetic analysis using NJ. Select the appropriate substitution model (e.g JTT+G) and the bootstrap method with at least 1000 replicates. Execute the analysis.
SECTION: Phylogenetic analysis
7.4. Option 3: Probabilistic methods (requires selection of the molecular evolution model, see below)

The strength of probabilistic methods is the use of specified models of molecular evolution. Probabilistic methods consider different mutation rates between sites to avoid mutation saturation. Nowadays, most studies of phylogenetic reconstruction use probabilistic methods.

They include Maximum Likelihood (ML, described below in the section Option 3-1) and Bayesian Inference (BI, described below in the section Option 3-2). ML calculates the probability of observing the data (in this case, the sequence alignment) under different explicit models of molecular evolution. ML aims to identify the best fit model by exploring multiple combinations of model parameters. Inversely, BI evaluates the probability of each model of molecular evolution given the data.
SECTION: Phylogenetic analysis
7.5. Option 3-1: Maximum Likelihood (ML)

Many programs for ML-based phylogenetic analysis exist. For beginners, we recommend SeaView or MEGA, which include several tools for sequence alignment, phylogenetic inference including probabilistic methods, and a tree editor. IQ-TREE, that includes ModelFinder and a very fast bootstrapping method (UFBOOT2), is reported to be both fast and accurate. IQ-TREE also includes a web version (http://iqtree.cibiv.univie.ac.at/). PhyML is accurate, easy of use and, like PAUP and MEGA, includes many common models of molecular evolution. PhyML also includes a web interface. RAxML and particularly FastTree are fast and well suited for large datasets (up to 1 million sequences with FastTree). They use only a specific model of rate heterogeneity (CAT), in addition to the Gamma law and the proportion of invariant sites. Like Garli, their choice of nucleotide evolution model is limited to GTR. PAUP is slower than other programs, and uses nucleotide data only.

>Choose a program relevant with the type and size of your dataset (see Table in Step 6). 

For ML-based phylogenetic analysis with IQ-TREE 2:
>Download the sub-MSA, select the appropriate sequence type (DNA or protein) and the appropriate model of molecular evolution (e.g JTT+G). In the panel “branch support analysis”, select the Ultrafast Bootstrap analysis with at least 1000 replicates. For single branch tests, you can also select the SH-aLRT test. Execute the analysis.
SECTION: Phylogenetic analysis
7.6. Option 3-2: Bayesian Inference (BI)

The most recent method for phylogenetic reconstruction uses Bayesian Inference (BI), which calculates the probability of the molecular evolution model given the data. The main programs used for BI-based phylogenetics are MrBayes and BEAST, that use the Markov Chain Monte Carlo (MCMC) algorithm. PhyloBayes is a Bayesian MCMC sampler for phylogenetic reconstruction with protein data using a specific probabilistic model, well adapted for large datasets and phylogenomics. Bali-Phy can also be used for phylogenetic analysis using BI.
SECTION: Phylogenetic analysis
8. Tree rooting

The root of a phylogenetic tree is the hypothetical last common ancestor of all the taxa present in the tree. Phylogenetic trees can be unrooted or rooted. Rooting a phylogenetic tree consist in identifying ancestral and derived states, to study the direction of the evolution of the sequences. 

Diverse rooting methods exist. The most common requires outgroups (taxa that do not belong to the studied group but are closely related) in the analysis. Typically, two outgroups are selected, one being more closely related to the ingroup than the other, allowing for a proper identification of the states of characters. Alternative methods include the Midpoint rooting, which places the root at the mid-point of the longest branches, and the molecular clock rooting, which assumes that the evolution speed is constant between the sequences.

>In our example, we include the P53 homologues from the choanoflagellate Monosiga brevicollis. Designate the P53 of M. brevicollis as the outgroup when drawing the phylogenetic tree with graphical programs.

>Alternatively, select "Midpoint rooting" when drawing the phylogenetic tree with graphical programs.

SECTION: Phylogenetic analysis
9. Tree drawing

Once the phylogenetic tree has been computed, it can be exported using e.g. Newick file format and visualized using a graphical software such as FigTree, ETE Toolkit or ITOL. MEGA and SeaView also include visualization tools. Using different sets of options, several types of phylogenetic trees can be drawn (rooted or not, cladogram or phylogram), and branch support values (bootstrap values or posterior probabilities) can be displayed.

With Mega 11:
>Click "file" > "export current tree (Newick)", select "Bootstrap" and "branch length", retrieve the phylogenetic tree in the Newick format and save it with nwk as filename extention.

With IQ-TREE 2:
>Paste the phylogenetic tree in the Newick format to Notepad, and save it using nwk as filename extension.

>Use a program (e.g. FigTree) in the list below and open the nwk files. You can also directly paste the phylogeny in the Newick format in the graphical tools.

>Many options of tree drawing are available. For example, you can display the bootstrap values, the posterior probabilities, or the SH-aLRT values, collapse clades below a certain bootstrap threshold (e.g. 50), and highlight or add color to the clades.
 
       Software         Features         Link            ETE Toolkit         Visualization and analysis of   phylogenetic trees         http://etetoolkit.org/            FigTree         Graphic software for phylogenetic   trees         http://tree.bio.ed.ac.uk/software/figtree/            ITOL         Visualization and annotation of   phylogenetic trees         https://itol.embl.de/            MEGA         Sequence alignment, model selection,   phylogenetic analysis (parsimony, distance methods), includes tree   visualization and annotation tools         https://www.megasoftware.net/            SeaView         Sequence alignment and phylogenetic   inference using maximum parsimony, NJ and ML, includes tree visualization and   annotation tools         http://doua.prabi.fr/software/seaview      
 
In our example, we used ITOL for the graphical representation of the phylogenetic tree of the P53 family. Both methods reveal four major clades containing respectively the p53 of insects and the p53, p63, and p73 of all vertebrates. The p53, p63, and p73 of vertebrates are more closely related to each other than to any other p53. Furthermore, the p63 and p73 of vertebrates are more closely related to each other than to vertebrate p53. This indicates that two duplication events in the p53 family preceded the origin of vertebrates. First, the p53 family and the p63/p73 cluster diverged. The second one caused the p63 and p73 families to diverge. The p53 of insects are clustered together. This indicates that insects diverged from the other bilaterians before these two duplications.

   

SECTION: Evolutionary analyses using phylogenetic trees
10. Reconstruct the evolution of the gene or protein

Sequence alignments and phylogenetic trees can be used to reconstruct diverse aspects of the evolutionary history of genes, proteins and species. In this last section, we provide a brief and non-exhaustive overview of evolutionary studies that can be performed using bioinformatic tools.
SECTION: Evolutionary analyses using phylogenetic trees
10.1. Time-calibration of phylogenetic trees

Phylogenetic calibration consists in estimating the age of speciation or duplication events (the nodes in the phylogenetic tree), using events with a known age, such as fossil and other geological data (that can only give minimal ages) as calibration points. Alternatively, mutation rates can be used to calculate the divergence time between two sequences.

Databases such as TimeTree compute the estimated divergence time between species. Mesquite also provides tools to calibrate phylogenetic trees in geological times using fossil data. Ohnologs can be used to estimate the divergence time between homologues resulting from whole genome duplications in vertebrates.

To calibrate the phylogenetic tree of P53 with Mega 11 and TimeTree:
>Download the alignment file in the Fasta format and the tree file in the Newick format in MEGA. In the Compute panel, select “Compute Timetree”.

>In the Specify outgroup section, define Monosiga brevicollis as the outgroup by moving it to the Outgroups panel. In the section Calibrate nodes, select “internal nodes constraints”. 

>In TimeTree (http://www.timetree.org/), enter the names of two species to retrieve their estimated divergence time. For example, Homo and Drosophila diverged between 630 and 830 million years ago, with 694 million years as median time. 

>In MEGA 11, click “add new calibration point” and select the node in the phylogenetic tree, or enter the names of the two taxa, and define the speciation age with a minimum, maximum or fixed time (for example, 694 million years between Homo and Drosophila). Use TimeTree to define several calibration points between different species in the tree before and after the duplication events.

>Click "Launch the analysis", and retrieve and save the calibrated tree. You can export it with the divergence times. Use the graphical programs to add color, highlight clades, etc...

  
 
     Software      Features      Link        BayesTraits      Evolutionary analyses using   Bayesian inference      http://www.evolution.rdg.ac.uk/BayesTraitsV3.0.5/BayesTraitsV3.0.5.html        BEAST      Phylogenetic calibration      http://www.beast.community        Mega      Sequence alignment, model   selection, phylogenetic analysis (parsimony, distance methods)      https://www.megasoftware.net/        Mesquite      Comparative analyses and   statistics      http://www.mesquiteproject.org/        MrBayes      Bayesian phylogenetic   inference, ancestral states reconstruction, phylogenetic calibration…      http://nbisweden.github.io/MrBayes/        Ohnologs      Database of vertebrate   ohnologues, resulting from whole genome duplications      http://ohnologs.curie.fr/        Timetree      Tree calibration      http://www.timetree.org/   
 
SECTION: Evolutionary analyses using phylogenetic trees
10.2. Reconstruction of ancestral states

Retracing the functional evolution of genes, proteins, or biological traits often requires the reconstitution of ancestral states. They can be inferred from a phylogenetic tree using MP, ML, or BI; and requires the aligned sequences and the model of molecular evolution that has been used for the phylogenetic analysis when using probabilistic and distance methods.

Reconstruction of ancestral states with Mega 11:
>Import the alignment file in the Fasta format, and the tree file in the Newick format in MEGA. In the Ancestors panel click “infer ancestral sequences” and select the method (MP or ML). In ML, select the appropriate substitution model. Launch the analysis to display the reconstructed ancestral state of each site of the sequence at every node.

 
    ABC1  Software    Features    Link  2  BayesTraits    Evolutionary analyses using   Bayesian inference    http://www.evolution.rdg.ac.uk/BayesTraitsV3.0.5/BayesTraitsV3.0.5.html  3  BEAST    Phylogenetic calibration    http://www.beast.community  4  Mega    Sequence alignment, model   selection, phylogenetic analysis (parsimony, distance methods)    https://www.megasoftware.net/  5  Mesquite    Comparative analyses and   statistics    http://www.mesquiteproject.org/  6  MrBayes    Bayesian phylogenetic   inference, ancestral states reconstruction, phylogenetic calibration…    http://nbisweden.github.io/MrBayes/  7  PAML    Maximum likelihood   phylogenetic inference, estimation of selection strength, ancestral states   reconstruction and other analyses    http://abacus.gene.ucl.ac.uk/software/paml.html  8  RASP    Ancestral states   reconstruction    http://mnh.scu.edu.cn/soft/blog/RASP/index.html  

SECTION: Evolutionary analyses using phylogenetic trees
10.3. Measure of selection strength

The type and strength of selection on protein coding genes may be of interest. It is calculated by evaluating the ratio of the number of non-synonymous substitutions (substitutions changing the protein sequence) per non-synonymous site (dN), and the number of synonymous substitutions (substitutions with no effect on the protein sequence due to the redundancy of the genetic code) per synonymous site (dS). If dN/dS > 1, then the non-synonymous substitutions are higher than expected and the gene is under positive selection. If dN/dS<1, the gene is under purifying selection and if dN/dS=1, the selection is neutral. It is recommended not to use the dN/dS ratio for closely related species. The ratio can be calculated using PAML, MEGA, Bio++ and HyPhy. 

Measure of selection strength with Mega 11:
>Import the alignment file in the Fasta format, and the tree file in the Newick format in MEGA. In the Selection panel click “infer ancestral sequences”.
SECTION: Evolutionary analyses using phylogenetic trees
10.4. Study of co-evolution

Co-evolution refers to the genetic and/or morphological changes between different species in interaction. It is widely used in evolutionary ecology and parasitology to study the evolution of hosts and parasites. Co-evolutionary events include co-speciation, host change, duplication and loss of interaction. The evolution of the parasite is partly driven by the evolution of the host, which is considered independent from the evolution of the parasite. The co-evolutionary history can be presented as a co-phylogeny with the two entities. 

Some programs co-evolution study, including Jane, CoRe-PA and TreeMap [170] (Table 8), are based on the hypothesis that the evolution of the parasite is driven by the evolution of the host. Others, such as Copycat [162], reconciliate the two phylogenies under the hypothesis that the situation is symmetric and evaluate the significance of co-evolution under a statistical framework. Co-evolution of genes or proteins can also be studied using these tools.

In our second test-case study, we used ML to reconstruct the evolution of cyclins and CDKs, two families of proteins involved in cell cycle control and closely interacting. We used Jane and TreeMap to reconstruct cophylogenies between the two families.

>With Jane and TreeMap, a single nexus file containing the phylogenies of cyclins and CDKs, and their associations is needed. Create a nexus file (starting with #NEXUS). This file should contain the two trees in Newick format, in the sections BEGIN HOST and BEGIN PARASITE, and the associations in the section BEGIN DISTRIBUTION. This section should mention every association between Cyclins and CDK following the pattern “Host: Parasite,”. All three sections should end with “ENDBLOCK;”. The names of the taxa in the three files should be identical. Cyclins interacting with several CDKs and vice versa should be repeated.

>Import this file to Jane and launch the analysis in the Solve Mode. The costs of coevolutionary events can be set. The stats mode can be used to compute the cost range of the solutions. With TreeMap, import the nexus file and launch the analysis in “Solve the tanglegram”. We optain a coevolutionary scenario that represents the best way to associate the two trees. You can test the significance of the reconstruction in “estimate significance” or perform a heuristic test.

In both figures, the clustering of cyclins and CDKs indicate an interaction (the cyclin can bind the CDK and activate it). Red spots indicate significant events of coevolution between the two families of proteins. Co-speciation (hollow red circle), duplications (solid red circle), duplications with host switch (yellow circle), loss of interaction (dashed lines), failures to diverge (jagged lines) are indicated on the figure.

 
     Software      Features      Link        Copycat      Software for studying   coevolution      http://www.cophylogenetics.com/        Core-PA      Software for studying   coevolution      http://pacosy.informatik.uni-leipzig.de/49-1-CoRe-PA.html        Jane      Software for studying   coevolution      https://www.cs.hmc.edu/~hadas/jane/        TreeMap      Software for studying co-evolution      https://sites.google.com/site/cophylogeny/treemap   
 

  

SECTION: Evolutionary analyses using phylogenetic trees
10.5. Genome evolution

Evolutionary events, such as mutations, insertions, deletions, gene or whole genome duplications, genome reorganization, and genetic exchanges can be identified using phylogenetic trees in complement with genomics tools and databases. Here is a list of databases tools to study diverse aspects of genome evolution, including genome browsers of diverse lineages and tools for comparative genomics and evolutionary genomics:

 
     Software      Features      Link        BAR      Database of plant genes and proteins      http://bar.utoronto.ca/        Bgee      Gene expression patterns      https://bgee.org/        CAFE      Gene   family evolution      https://github.com/hahnlab/CAFE5        CoGE      Comparative   genomics analyses      https://genomevolution.org/coge/        Ensembl      Genome browser of vertebrates, includes tools for   identification of homology      https://www.ensembl.org/index.html        Entrez      Gene sequences and structures      https://www.ncbi.nlm.nih.gov/Web/Search/entrezfs.html        FlyBase      Genome and proteins of the model insect D.   melanogaster      https://flybase.org/        GenBank      Annotated DNA sequences      https://www.ncbi.nlm.nih.gov/genbank/        HGT-Finder      Horizontal   gene transfer finding      http://cys.bios.niu.edu/HGTFinder/   HGTFinder.tar.gz        Ohnologs      Database   of vertebrate ohnologues, resulting from whole genome duplications      http://ohnologs.curie.fr/        PomBase      Genes and proteins of the model yeast S. pombe      https://www.pombase.org/        TAIR      Genome and proteins of the model plant A.   thaliana      https://www.arabidopsis.org/        WormBase      Genome and proteins of the model nematode C.   elegans      https://wormbase.org//#012-34-5        Xenbase      Genome and proteins of the model amphibian X.   laevis      http://www.xenbase.org/entry/   
 
SECTION: Evolutionary analyses using phylogenetic trees
10.6. Phylogenetic comparative analysis

Evolutionary biology often employs the so-called phylogenetic comparative methods to study the adaptive significance of biological traits. These methods aim at identifying biological characters, in terms of morphology, physiology, or ecology, that result from a shared ancestry. Comparative analyses can be done for quantitative or qualitative variables. Mesquite is a very appropriate tool for comparative analysis and to compute statistics on phylogenetic trees. BayesTraits can also be used.
SECTION: Evolutionary analyses using phylogenetic trees
10.7. Population genetics

Genetic diversity can also be explored at the population level by analyzing polymorphism between members of the same species. Bioinformatic tools are designed to study allele diversity within a population, including single nucleotide polymorphisms (SNPs), indels, microsatellites or transposable elements. Mathematical models have been developed to describe polymorphism. Several programs are suitable for population genetics studies.

 
       Software         Features         Link            Arlequin         Population genetics   analyses         http://cmpg.unibe.ch/software/arlequin35/            DNAsp         Analysis of DNA   polymorphism         http://www.ub.edu/dnasp/             Genepop         Population genetics   analyses         https://genepop.curtin.edu.au/            SNiplay         SNP detection and other   population genetics analyses         https://sniplay.southgreen.fr/cgi-bin/home.cgi      
 
SECTION: Evolutionary analyses using phylogenetic trees
10.8. Study of protein structure and function evolution

Studying the functional evolution of proteins can require structure alignments, that can be realized by appropriate programs such as PyMol, and the mean distance in ångström between homologous residues can be calculated. 

Protein structures are described by databases such as the Protein Data Bank (PDB). The PDB provides the 3-dimensional structures of proteins and their interacting ligands established by X-ray crystallography, electron microscopy, or NMR spectroscopy, which can be retrieved as pdb files. The PDB also displays a 3D visualization tool, programs for 3D analyses such as pairwise structure alignment and pairwise symmetry, and cross links to other protein databases. Annotation for protein families based on fingerprints, i.e., conserved 3-dimensional motifs specific for a protein family, are gathered in the database PRINTS. PRINTS includes a 3D visualization software and search tools for protein sequence homology and pairwise or multiple sequence alignments. 

I-TASSER, HHPred of the HH suite and Alpha fold can be used to predict the 3-dimensional structure of proteins from their amino-acid sequences. FoRSA is able to identify a protein fold from its amino acid sequence or a protein sequence in the proteome of a species from a crystal structure. 

Here is a list of tools that can be used for analyses on protein structures in an evolutionary framework:

     Software      Features      Link        PyMol      3D visualization of   molecules      http://www.mesquiteproject.org/        I-Tasser      Protein structure   prediction      https://zhanglab.dcmb.med.umich.edu/I-TASSER/        Forsa      Protein structure   prediction      http://www.bo-protscience.fr/forsa/        HHPred      Protein structure   prediction      https://toolkit.tuebingen.mpg.de/tools/hhpred