<p><a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0027731" rel="nofollow noreferrer">This paper claims</a> that FastTree is almost as accurate as RAxML, while being much faster. You just have to be careful, however, that the support values output by FastTree are not bootstrap values, they are <a href="http://www.microbesonline.org/fasttree/" rel="nofollow noreferrer">based on the Shimodaira-Hasegawa test</a>. (Also, <a href="http://darlinglab.org/blog/2015/03/23/not-so-fast-fasttree.html" rel="nofollow noreferrer">see this comment</a> for the case you have very short branch lengths).   [<strong>update:</strong> However, according to <a href="http://biorxiv.org/content/early/2017/05/25/142323" rel="nofollow noreferrer">the recent comparison paper mentioned below</a> FastTree performed quite poorly in comparison to RAxML or IQ-tree.]</p>

<p>From what I understand, you should use ExaML only if your data is too large to be handled by RAxML in a single node. ExaML should perform like RAxML but with some parallelization overhead. For all effects I treat them as the same. I don't know of relevant advantages of phyML over RAxML (for me, it's easier to use but I am very used to phyML).</p>

<p>I am not familiar with IQ-tree, but <a href="https://academic.oup.com/mbe/article-lookup/doi/10.1093/molbev/msu300" rel="nofollow noreferrer">its authors claim</a> that even given the same time as RAxML or phyML, IQ-tree already finds better likelihoods more often than not (although by default it takes a bit longer to converge). <a href="http://biorxiv.org/content/early/2017/05/25/142323" rel="nofollow noreferrer">A recent comparison between all these programs</a> favoured IQ-TREE for both single-gene and concatenation analyses (with RAxML very close). It may also estimate branch support through a SH-like test only, but I'm not sure.   [<strong>update:</strong> IQ-tree offers 3 measures of support, standard bootstrap, aLRT, and ultrafast bootstrap. See OP's comment below for details.]</p>

<p>However, since you have few missing data, you might also want to try a single-locus tree inference followed by gene tree clustering (using <a href="http://onlinelibrary.wiley.com/doi/10.1111/1755-0998.12676/abstract" rel="nofollow noreferrer">treescape</a> or <a href="https://academic.oup.com/mbe/article-lookup/doi/10.1093/molbev/msw038" rel="nofollow noreferrer">treeCL</a>) to see how spread your data is, or to see the effect of removal of outliers, or to use ideas similar to <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0129183" rel="nofollow noreferrer">statistical binning</a>.</p>

CC BY-SA 3.0
2017-06-11T23:20:07.337Z
In the recent comparison you mention, I note that RAxML only performed better than IQtree when they did 10 independent searches per replicate. So one might just favour IQtree here too, noting that it's sensible to do a bunch of independent searches. Also FastTree performed very poorly in all comparisons of that ms.
roblanf
CC BY-SA 3.0
2017-06-11T23:22:03.933Z
Measures of support in IQtree: (i) standard bootstrap; (ii) aLRT (approximate likelihood ratio test, which is something like asking whether a given branch length is significantly >0); (iii) Ultrafast bootstrap (not the same interpretation as a standard bootstrap, more like a posterior probability if I've understood it right).
roblanf
CC BY-SA 3.0
2017-06-12T08:33:08.173Z
Note that likelihood comparisons between program outcomes also depend on the choice of model and data partitioning. I haven't looked at the details, but some programs may implement models that are not available in others. This can be a criterion of choice.
bli
CC BY-SA 3.0
2017-06-12T08:50:37.777Z
Thank you for the comments @roblanf, I took the liberty of updating my answer with this info.
Leo Martins
CC BY-SA 3.0
2017-06-12T15:28:55.957Z
note also that there is a newly released re-write of RAxML available [here](https://github.com/amkozlov/raxml-ng). It combines the best parts of RAxML and ExaML, while being faster in general. However not all current RAxML features have been implemented yet.
Pierre Barbera
CC BY-SA 3.0
2017-06-10T11:58:10.863Z
2017-10-27T16:10:32.497Z
2017-10-27T16:10:32.497Z
Leo Martins
<p>I have a large phylogenomic alignment of &gt;1000 loci (each locus is ~1000bp), and &gt;100 species. I have relatively little missing data (&lt;10%).</p>
<p>I want to estimate a maximum-likelihood phylogenetic tree from this data, with measures of statistical support on each node.</p>
<p>There are many phylogenetics programs that claim to be able to analyse datasets like this (e.g. RAxML, ExaML, IQtree, FastTree, PhyML etc). Given that I have access to a big server (512GB RAM, 56 cores), what are the pros and cons of each program.</p>
<p>Which programme likely to give me the most accurate estimate of the ML tree for a dataset of this size within a reasonable timeframe?</p>

CC BY-SA 3.0
2017-06-10T10:51:07.423Z
You want us to review all the phylogenetic programs or just the ones you listed: RAxML, ExaML, IQtree, FastTree, PhyML (Which still I think is quite broad). How do you measure the accuracy of the ML tree?
llrs
CC BY-SA 3.0
2017-06-11T23:17:58.910Z
I'm interested in any and all opinions, evidence, and links to comparisons between any software capable of estimating ML trees from large datasets like this. There would be many ways of measuring accuracy, including: (i) evidence from simulation; (ii) comparisons of Likelihood scores of estimated trees in a common framework.
roblanf
CC BY-SA 4.0
2018-12-28T21:22:04.200Z
RAxML is the goto program. The problem with FastTree is that its accuracy is limited to the datasets used, i.e. yours might be an outlier.
M__
<phylogenetics><phylogeny>
What is the best method to estimate a phylogenetic tree from a large dataset of >1000 loci and >100 species
# What is the best method to estimate a phylogenetic tree from a large dataset of >1000 loci and >100 species

(Asked by: username_0 on 2017-06-10)

I have a large phylogenomic alignment of >1000 loci (each locus is ~1000bp), and >100 species. I have relatively little missing data (<10%).

I want to estimate a maximum-likelihood phylogenetic tree from this data, with measures of statistical support on each node.

There are many phylogenetics programs that claim to be able to analyse datasets like this (e.g. RAxML, ExaML, IQtree, FastTree, PhyML etc). Given that I have access to a big server (512GB RAM, 56 cores), what are the pros and cons of each program.

Which programme likely to give me the most accurate estimate of the ML tree for a dataset of this size within a reasonable timeframe?

**Tags:** phylogenetics, phylogeny
> **Comments:**
> **username_1**: You want us to review all the phylogenetic programs or just the ones you listed: RAxML, ExaML, IQtree, FastTree, PhyML (Which still I think is quite broad). How do you measure the accuracy of the ML tree?  
> **username_0**: I'm interested in any and all opinions, evidence, and links to comparisons between any software capable of estimating ML trees from large datasets like this. There would be many ways of measuring accuracy, including: (i) evidence from simulation; (ii) comparisons of Likelihood scores of estimated trees in a common framework.  
> **username_2**: RAxML is the goto program. The problem with FastTree is that its accuracy is limited to the datasets used, i.e. yours might be an outlier.  

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## 1 answers

### Answer by username_3 on 2017-06-10. Score: 11 | Accepted answer

[This paper claims](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0027731) that FastTree is almost as accurate as RAxML, while being much faster. You just have to be careful, however, that the support values output by FastTree are not bootstrap values, they are [based on the Shimodaira-Hasegawa test](http://www.microbesonline.org/fasttree/). (Also, [see this comment](http://darlinglab.org/blog/2015/03/23/not-so-fast-fasttree.html) for the case you have very short branch lengths). [**update:** However, according to [the recent comparison paper mentioned below](http://biorxiv.org/content/early/2017/05/25/142323) FastTree performed quite poorly in comparison to RAxML or IQ-tree.]

From what I understand, you should use ExaML only if your data is too large to be handled by RAxML in a single node. ExaML should perform like RAxML but with some parallelization overhead. For all effects I treat them as the same. I don't know of relevant advantages of phyML over RAxML (for me, it's easier to use but I am very used to phyML).

I am not familiar with IQ-tree, but [its authors claim](https://academic.oup.com/mbe/article-lookup/doi/10.1093/molbev/msu300) that even given the same time as RAxML or phyML, IQ-tree already finds better likelihoods more often than not (although by default it takes a bit longer to converge). [A recent comparison between all these programs](http://biorxiv.org/content/early/2017/05/25/142323) favoured IQ-TREE for both single-gene and concatenation analyses (with RAxML very close). It may also estimate branch support through a SH-like test only, but I'm not sure. [**update:** IQ-tree offers 3 measures of support, standard bootstrap, aLRT, and ultrafast bootstrap. See OP's comment below for details.]

However, since you have few missing data, you might also want to try a single-locus tree inference followed by gene tree clustering (using [treescape](http://onlinelibrary.wiley.com/doi/10.1111/1755-0998.12676/abstract) or [treeCL](https://academic.oup.com/mbe/article-lookup/doi/10.1093/molbev/msw038)) to see how spread your data is, or to see the effect of removal of outliers, or to use ideas similar to [statistical binning](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0129183).

> **Comments:**
> **username_0**: In the recent comparison you mention, I note that RAxML only performed better than IQtree when they did 10 independent searches per replicate. So one might just favour IQtree here too, noting that it's sensible to do a bunch of independent searches. Also FastTree performed very poorly in all comparisons of that ms.  
> **username_0**: Measures of support in IQtree: (i) standard bootstrap; (ii) aLRT (approximate likelihood ratio test, which is something like asking whether a given branch length is significantly >0); (iii) Ultrafast bootstrap (not the same interpretation as a standard bootstrap, more like a posterior probability if I've understood it right).  
> **username_4**: Note that likelihood comparisons between program outcomes also depend on the choice of model and data partitioning. I haven't looked at the details, but some programs may implement models that are not available in others. This can be a criterion of choice.  
> **username_3**: Thank you for the comments @roblanf, I took the liberty of updating my answer with this info.  
> **username_5**: note also that there is a newly released re-write of RAxML available [here](https://github.com/amkozlov/raxml-ng). It combines the best parts of RAxML and ExaML, while being faster in general. However not all current RAxML features have been implemented yet.  

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