It really depends on the type of analysis that you want to do... Can you be a little more specific?

Are you comparing DNA or protein sequences? Do you want maximum likelihood, bayesian, neighbour-joining? Etc...

MEGA is pretty versatile. For my analyses, I use ClustalW2 for DNA or M-COFFEE for protein alignments, cure the alignment with GBlocks, search for the best model using jmodeltest (DNA) or prottest (protein), and then I do maximum likelihood analyses using PhyML.

Useful links:

http://www.ebi.ac.uk/Tools/msa/clustalw2/

http://www.phylogeny.fr/version2_cgi/index.cgi

http://www.atgc-montpellier.fr/phyml/

http://evolution.genetics.washington.edu/phylip/software.html

Then you can use MEGA and Figtree to edit the trees.

I will suggest you use the CLUSTALW software from EBI server. www.ebi.ac.uk/clustalw2/.

I wanna suggest to use MEGA4, Phylip

RaxML at http://www.phylo.org/portal2/login!input.action

Phylogenetic Analysis method is depend on the need..like how many sequences do u have..what do u want to explain with phylogenetic tree , what is the length of sequence, what is the knowledge of computer like if u know linux or not .the easy for learning is MEGA....

I suggest Phylip and MEGA4

So, we now have a tool. My next question is which method to reconstruct the phylogeny would you recommend?

MEGA5

It really depends on the type of analysis that you want to do... Can you be a little more specific?

Are you comparing DNA or protein sequences? Do you want maximum likelihood, bayesian, neighbour-joining? Etc...

MEGA is pretty versatile. For my analyses, I use ClustalW2 for DNA or M-COFFEE for protein alignments, cure the alignment with GBlocks, search for the best model using jmodeltest (DNA) or prottest (protein), and then I do maximum likelihood analyses using PhyML.

Useful links:

http://www.ebi.ac.uk/Tools/msa/clustalw2/

http://www.phylogeny.fr/version2_cgi/index.cgi

http://www.atgc-montpellier.fr/phyml/

http://evolution.genetics.washington.edu/phylip/software.html

Then you can use MEGA and Figtree to edit the trees.

It really depends on the objective of your research, but BEAST "http://beast.bio.ed.ac.uk/Main_Page" could be one of the best toll when dealing with different genes. Not easy, but quite complete.

Presuming you have sets of homologous sequences, phylogenetic analyses may include alignment, phylogeny inference, estimating evolutionary rates and testing for positive selection.

There are a number of good tools for each of these tasks.

For alignment, we wrote a review summarizing different alignment tools and the concepts they are based on:

http://www.pagepress.org/journals/index.php/eb/article/view/eb.2010.e7

For phylogeny inference, ML packages PhyML, RAxML can be recommended. If you opt for Bayesian inference, go for MrBayes. If you have protein-coding data, you can try out the extension of PhyML to codon models (which include selection), we called it CodonPhyML - the source-code and executables for Macs and Windows are available here: http://sourceforge.net/projects/codonphyml

The manuscript describing CodonPhyML is currently under review (but I can send the review version on request). The user manual is also included in source-forge distribution.

Finally, in my experience PAML package provides a number of nice evolutionary models including codon models: http://abacus.gene.ucl.ac.uk/software/paml.html

For reviews on codon models or detecting positive selection on proteins see some of my recent reviews: Anisimova and Kosiol (2009), Kosiol and Anisimova (2012), Anisimova and Liberles (2007; 2012). You can find all thee pdfs on my website:

http://www.inf.ethz.ch/personal/anmaria/publications.html

Hope it's helpful :-))

You should first find the best nucleotide substitution model for each gene (see jmodeltest and other software). I will suggest also to combine all the genes in one analysis and not to separate them and to use different methods for estimating the phylogeny of your plant species. Some software like Mrbayes and Beast (Bayesian inference for both) or Garli (Maximum likelihood) are able to handle it. Beast could also be used for dating the node of your tree.

I think that you can construct one integrative tree using three methods (MP, ML and BI). Please see one of my papers in ResearchGate. "The mitochondrial genome of the Cinnamon Bittern, Ixobrychus cinnamomeus (Pelecaniformes: Ardeidae): sequence, structure and phylogenetic analysis. ".

Best wishes for you.

Concerning methods, the paper attached may be helpful. For ML, my personal favourite is definitely RAxML, a very powerful tool and particularly useful for large datasets:

https://github.com/stamatak/standard-RAxML

https://groups.google.com/forum/?fromgroups#!forum/raxml

maybe try RAXML for calculation of complex trees and MAFFT for multiple alignment and TreeView for Tree editing

I also like RaxML, MAFFT, and TreeView.

The phylogenic analysis include

1- Alignment using NCBI web site and select the type ( organisms, plant, human...._

2- Look at the similarity % and select the higher similarity around 10-15

3- from each type take the sequence from the same web site and put them in fasta format.

4- Make the phylogenetic tree using BIoedit and tree view (easy to download from NCBI).

and from the tree u can see the closest type to ur plant.

For Bayesian analyses, besides Beast and MrBayes, I would recommand having a look at Phylobayes, that implements a model accomodating the existence of heterogeneity along the sequences. I also have the impression that MrBayes is sometimes buggy.

Dear Raksha Bawankar,

If your sequences have a high similarity or identity, may be you need use distance methods or maximum parsimony, but if your sequences have a poor similarity or identity may be you need use character discrete methods. This not a rule, don't exist the best phylogenetic method, all depends to characteristics of the sequences that we are using. Now the best tool depends on the method you select, MEGA5 has methods NJ, UPGMA, ME, MP and ML, MrBayes for Bayesian method is a good tool. There are many similar tools, these are user friendly.

Sorry for my wrong english

Dear Raksha, I would ask you to consider Dr.Maria Anisimova's suggestion.

Dr.Chinnappa

From a philosophical perspective, the only method that has a sound and clearly articulated basis is parsimony (see Farris, 1983 The logical basis of phylogenetic analysis (a book chapter available on line if you google it)). Model-based methods are mostly justified by the accusation that parsimony may be misleading under certain circumstances. However, it is not possible to determine whether or not those circumstances are in effect unless the phylogenetic relationships of the taxa in question are already known (in which case, why would you bother?). Empirically speaking, almost all the time, the various methods (MP, ML, Bayes) produce very similar hypotheses of relationship (see Rindal and Brower 2011, Cladistics 27:331), so it probably does not make a great deal of difference what method you choose, as long as you understand how your preferred method works (which may not be so easy for some of them).

I use: Mega for single gene analysis. for multiple gene PAUP (Maximum parsimony) or RAxML analysis.

It depends on you only what you looking for :-)

I would prefer MEGA package, its easy to use and can do wonderful thinks to your data.

For my suggestion u can work with MEGA 5 or 4 software, form this u can make a phylogenetic trees, and also u can see the SNPs and INDEL s, from this u can differentiate the which are all the genes were changed or modified.

There is also possibility to perform 7 single-gene analyses with any method and combine them into a consensus tree or a consensus network. This approach is different from the one based on concatenating nucleotide sequences prior to an analysis.

The software SplitsTree can do this: http://www.splitstree.org/

@ Andrew Brower:

Philosophical justification is just an excuse. In practical terms it is not always helpful to engage in this discussion.

We do science and need to make quantitative judgements and test hypotheses.

Well, sometimesparsimony can help, although it is an ad hoc method - we do not know its statistical properties (expectation, convergence, etc.), although this can be studied in simulations for a small range of scenarios or theoretically for very-very simple cases. However, parsimony may be a good solution when no models for your data is available.

For molecular data such as DNA or amino acids, models of character substitution exist and offer great potential to study biological processes that can be modelled through parameters in our model. Parsimony cannot help here. Standard accepted (even by philosophers :-) statistical estimation methods should be used - maximum likelihood or Bayesian.

But I am probably not the best person to comment on this.

I'd like to refer you to this paper by Steel and Penny (2000 in Mol BIol Evol) on this issue:

http://www.ncbi.nlm.nih.gov/pubmed/10833190

I figured that my previous comment would whack a hornet nest of likelihoodsters.

Mats: not really interested in Plato, sorry. "Philosophy" is not a monolith with all ideas descending from ancient Greeks.

The "soundly articulated basis" is explained in the previously cited Farris paper and papers cited therein, but very briefly, here is my understanding of "the justification for parsimony."

If you think that the pattern of biological diversity is treelike, and if you think that taxonomic characters contain evidence of relationship, then the simplest explanation of a data matrix (of any sort) is the one that minimizes ad hoc hypotheses of homoplasy and has the fewest implied character state transformations. It has been shown by Steel and Penny in the above cited article, and also by Goloboff (2003, Cladistics 19:91) that when characters are treated equally, MP and ML are formally the same. Thus, the real difference between parsimony and model-based methods is that the latter assume particular differential weighting schemes for various classes of characters or character state transformations that may or may not be uniformly valid assumptions for a given data set (indeed, probably are not, but of course there is no way to tell). Pretending that these assumptions are "justified" by a fog of statistical nonsense is just that. Likelihood people can't even agree on a criterion for selecting the "best" (off-the-rack) model for analyzing their data (see Riplinger & Sullivan 2008, Syst. Biol. 57:76).

The MP approach represents the lower limit of ad hoc metaphysical assumptions necessary to make phylogenetic inferences.

Obviously, one could get into an infinite regress (a la Elliott Sober) about why "parsimony" is justified at all, but I guess, like Feynman, that I would rather eat the steak than worry about whether or not it exists.

Hi Mats

In English, in an informal context such as this chat, "think" is a verb that, to me, means the same thing as "have a conviction that" or "believe" or "assume" or "suppose," or whatever. Quibbling about figures of speech is just semantic gamesmanship.

Likewise, many systematists refer to those branching diagrams as "trees," and I can't think of any other than pedants who refer to them as "graphs."

I am glad you think that my explanation of parsimony is self-evident. The same can certainly not be said for ML or Bayes.

Have you read David Williams and Malte Ebach's 2008, "Foundations of Systematics and Biogeography"? They equate the following:

component = group = node = taxon = shared character = homology

I suggest you take the non-equivalence of entities and classes up with them.

I would be pretty happy to do parsimony analyses of butterflies for the rest of my life, and will, if I can keep getting away with it!

cheers,

AB

You can use mega, phylip, geneious etc. You can contact me if there is any other problem

Geneious is foe phylogenetic analysis with diverse options, it is also user friendly.

As far as I know, Geneious is not free, or trial version is available. MEGA 5 for beginers seems quite friendly :) and is free, too.

Try poy.

MEGA should be the top pick for your analysis.

Thanks Joao coehlo . for giving us clear idea for the phylogeny software..

:)

Geneious is a great choice for assembly, annotation and alignment of sequences. It presents good plugins for phylogenetic reconstruction, but these plugins have some limitations not present in the original softwares (eg PhyML, MrBayes)

You can SaTe for ML .. this tool is specifically for ML.. http://phylo.bio.ku.edu/software/sate/sate.html

MEGA5 and PHYLIP are two good options.

MEGA5 is very user friendly and help is available for your queries (key word based).

Both are free.

Start trying MEGA5 first.

You can use BioEdit programme, it is the easy way to draw the phylogenetic tree. You can also download it from Internet

MEGA5 is a great place to start. I like how SPLITSTREE represents uncertainty in your networks.

MEGA5 is a great option for starting phylogenetic analyses but if you want extensive and refined analyses you must be used next software:

- Parsimony, TNT.

- ML, GARLI or PHYML.

- Bayesian, MrBayes or BEAST.

You don´t try to use Neighbor-joining because is not consider a phylogenetic analyses, this method is used for molecular taxonomy (phenetics, barcode, etc).

Greetings!

no doubt MEGA5. if you want extensive list of tools go for phylip

I've had robust ML analyses with RAxML. It's rapid and will let you run your bootstraps at the same time for assessment of confidence. It's totally open source, and it runs in the command line. It runs GTR +I +Gamma, or just GTR +Gamma. These are the most generalized forms of ML models, so they encompass all the variations (HKY, K2P for example). In my opinion, the program doesn't suffer much in speed from the extra parameters in the GTR.

It also has a lot of other functionality that you might like later for hypothesis testing (like the ability to generate KH, SH, etc. And site-likelihoods for CONSEL and the AU).

You will need to generate your alignment in some other software. I recommend MUSCLE or MAFFT both of which have been empirically demonstrated to produce more reliable alignments than Clustal.

@Ian. "Free" is just how I roll man,..... ;-)

I really like PhyML for sequences. Best

POWER (PhylOgenetic Web Repeater) - is a good phylogenetic tool . It allows users to carry out phylogenetic analysis on most programs of PHYLIP package repeatedly. POWER provide two pipelines to process the analysis. One of them includes multiple sequence alignment (MSA) at the beginning of the pipeline whereas the other begin phylogenetic analysis with aligned sequence. Very user friendly.

PAUP is more reliable, you can also try RAxML and Mr. Bayes. PHYML is also OK. But I prefer PAUP for its robustness and acuracy.

PAUP* is the best in view but at present its not working properly on mac latest version

As other colleagues have mentioned, the software to be used depends on the aim of your research. This is a workflow I started to follow recently in order to estimate a phylogeny for a group of taxa:

(1) Multiple alignment of the sequences. For non-coding sequences and other sequences such as mitochondrial ribosomal loci you can use MAFFT. This software provides different strategies according to the characteristics of the sequences (e.g. E-INS-i, Q-INS-i). You can download the software for Mac, Windows or Linux from here http://mafft.cbrc.jp/alignment/software/ or you can use the online version (http://mafft.cbrc.jp/alignment/server/). For protein-coding sequences you can use the Perl script transAlign (http://www.molekularesystematik.uni-oldenburg.de/en/34011.html) of Bininda-Emonds.

(2) Select the nucleotide substitution model and the partition scheme that fits better to your alignment. For this you can use PartitionFinder (http://www.robertlanfear.com/partitionfinder/). This software performs the two tasks simultaneously and represents a great improvement in the use of time. The Perl scripts seqCat and seqConverter (also from the website of Bininda-Emonds) can help to prepare the files that would be required for PartitionFinder and further analyses.

(3) Estimate the phylogenetic hypothesis. I recommend to use both the maximum likelihood (ML) and Bayesian approaches. For the ML approach you can use RAxML (e.g. https://github.com/stamatak/EPA-WorkBench). For the Bayesian procedure you can use MrBayes (http://mrbayes.sourceforge.net/).Here you can find a comparison between RAxML and MrBayes (http://sco.h-its.org/exelixis/Phylo100225.pdf). If you need to estimate divergence times then you can use BEAST (http://beast.bio.ed.ac.uk/Main_Page).

Hope this help.

for parsimony analyses: TNT

LOL.I think ARB rocks. Especially when you facing 16S rDNA

For ML analyses, one of the very best options available is Garli. If you need a fast program (e.g., if your dataset is large), then go with RAxML.

For Bayesian analyses, with no doubt MrBayes. If you would like to infer also time since divergence events using the best possible approach these days (a relaxed-clock approach), then Beast is the way to go.

If you still consider using parsimony for your analyses, then PAUP and TNT are your options. The latter is free and very fast. Opinions vary with regard to which of these two is better (i.e. provides you with the shortest tree), but there is an increasing trend among those who still use parsimony to prefer TNT.

There are four things you should bear in mind: 1) what you are trying to do is essentially a statistical inference: we just don't know the history of life, so we try to make the best approximation from the data we have; 2) what you expect is a phylogenetic tree, which is a geometrical depiction of such history (a caricature if you wish) and has geometric properties that are relevant to compare it with similar trees; 3) you need to sort out what information is useful (i.e. phylogenetic signal) and what is basically noise (distorting that signal); and 4) beware of artifacts that are unavoidable and can be quite misleading. Of course you are not expected to do this at once, but these points are most important when you are to decide what algortithm you want to use. So, distance-based methods such as NJ may be popular among microbiologists for example, but they are not phylogenetic inferences --just similarity clustering. When using DNA data, a further issue is most relevant: given that you have only four possible character states (four possible nucleotides in each position), reversals are expected over time, thus confusing or even erasing the phylogenetic signal. That is why you need to have a model of evolutionary change. So, here is what I woud recommend: use MODELTEST or a similar program to find out what model best fits your data. Then go to www.phylogeny.fr and paste your sequences there. It's very user-friendly, reliable, fast and free. Make sure you select the adequate evolutionary model. The default method is a powerful maximum-likelihood method --trust it. The tree you get can be shown in different ways. Then read about what the different steps of the process are, and get familiar with what the machine did. You will have a reasonable understanding and a robust tree. Afterwards you may decide to go deeper into phylogenetics.

MEGA (http://www.megasoftware.net/) aims to be the end-all be-all tool, soup to nuts from alignment, model selection, and then your choice of phylogeny. It is very straightforward and easily installed. It also easily enables you to BLAST GenBank and easily add sequences. However, whatever I do, I will check with PAUP*, the de facto phylogenetic analysis tool. MUSCLE is the alignment tool of choice, but I always check manually.

There is no simple answer to this question. I have used PAUP*, MrBayes and BEAST for many analyses of various types of data (morphological only, molecular sequences, combined data). All of these software packages have advantages and disadvantages. Choice really depends on what type of output you need (e.g., single tree, posterior distribution of trees) and what method may be preferred for you particular dataset (parsimony, liklihood, Bayesian). 

I like maximum likelihood analysis using the RAxML. Sometims, I use the Mrbayes. I don't like PAUP program and the maximum parsimony analysis. I think they are out-dated

I use MEGA, it is one full package and is user friendly. 

There are 3 methods for doing the tree.

1-MEGA 6 is the best for phylogenetic tree. For my analyses, I use ClustalW2 for DNA. 

2- Bioedit and tree view  

3- from NCBI balst (Distance tree of results)

But for me I prefer and trust MEGA 6

Regards 

Sure HMA! MEGA6 is very ok compared to its previous versions as it gives you the least best possible model (Bayesian vs Akaike) for the phylogenetic plot of your dataset. More so, you have the new Sub-prunning grafting tool very robustic for analysis. However, it is best to ascertain the alignments using external tools like GBLOCK or better still MAFFT/GUIDANCE, which MEGA is yet to provide.  

it depends on the data you have and the analysis, but for Maximum Likelihood you can use Mega7 or PhyML, however, PhyML has more robust algorithm.

I am attempting to develop a "charting" tool that can chart the descendants of mankind in the last 2,000 years using YDNA (YSTRs and YSNPs). Around ten years ago, we used Phylip to create charts based on YSTRs only. But as YSNP testing started revealing a robust haplotree of mankind, we realized that YSTRs are just too noisy for accuracy by themselves. Around two to three years ago, we realized that Phylip and all network joining programs have a basic flaw in the assumption that all YSTR mutations have equal probabilities. Along each path that develops over time, probability theory requires that dependent mutation events (an independent event followed by dependent event) are much much rarer (square of the mutation rate of each YSTR marker).

So we have modified our phylip trees (and network joining tools) to choose parallel YSTR mutations (of the exact same mutation that is independent over dependent mutations). The accuracy of charting dramatically improved. We can calculate the number of dependent events expected to allow some dependent events but not more than mathematically allowed by probability theory.

Then along came the explosion of YSNPs into the very recent time frame (my haplogroup R-L226 has 750 testers with 67 markers and now has 97 YSNPs that are under 1,500 years old). 30 % of these YSNP branches are under 1000 AD (when most European surnames started being used by the general public). Extensive YSNP testing under R-L226 now makes the YSTR noisiness become less of an issue since YSTRs patterns develop over time and are constrained to only belonging to certain parts of the haplotree based on YSNP testing.

This technology is based on binary logistic regression models in two phases: 1) for YSNP branches between 1500 YBP and 2500 YBP, the YSTRs are highly correlated YSNPs. Using BLR models, for 80 % of the haplogroups, YSNP prediction is over 99 % accurate (around 20 % of the time, convergence of YSTR patterns create an overlap that makes prediction drop to 80 to 95 %). Signature recognition methodology has been extended down to recent time with 60 to 95 % accuracy via charting with YSTRs and YSNPs combined.

Genetic Genealogy (the study of DNA for genealogical purposes) now has wide acceptance by the genealogical community with over 20,000,000 tests (but only 1,000,000 YDNA tests of any usable amount of information). This $100M market (growing 30 to 40 % annually) is not well studied by academics. The only interest has been law enforcement's recent usage of atNDA which is now widespread. I would think that having a very detailed tree of mankind would help with tracing genetic diseases in the medical community as well as the evolution of diseases over a long term.

I apologize for the long post - but here are a couple of my YouTubes, a current charting tool (that continues to improve over time) and a paper on signature BLR models for YSNP prediction:

https://www.youtube.com/watch?v=1W4pw9yrNRU

https://www.youtube.com/watch?v=AD1HHb0Cwfs

http://www.jdvtools.com/SAPP/

http://www.rcasey.net/DNA/R_L21/Math_behind_R_L21_SNP_Predictor_20180202A.pdf