text: user5116: I ran a calculation in RAxML to determine the majority consensus phylogeny of a maximum likelihood bootstrap (How to show bootstrap values on a phylogenetic tree constructed with RAxML), and I got three output files: 
  
  RAxML_bipartitions.output_bootstrap.tre
  RAxML_bipartitionsBranchLabels.output_bootstrap.tre
  RAxML_info.output_bootstrap.tre
  
  What is the difference between the 
  
  RAxML_bipartitions.output_bootstrap.tre file and 
  RAxML_bipartitionsBranchLabels.output_bootstrap.tre? 
  
  
  user3848: In summary,
  RAxML_bipartitions.output_bootstrap.tre
  
  Is the only file of interest. The reason this is true in this context is really complicated and you have to understand the statistics of likelihood and how they are interpreted within phylogeny to understand why. This file is simply the final output of a non-parametric bootstrap analysis performed by maximum likelihood.
  What on earth is a non-parametric boostrap?
  A non-parametric bootstrap is resampling each alignment position with replacement. Thus if we have alignment positions 1,2,3,4,5
  A bootstrap resample for 2 replicates might be,
  Replicate 1
  1,1,3,5,2
  
  Replicate 2
  4,2,5,2,1
  
  The ML algorithm will make trees of replicates 1 and 2 and find the consensus between them. If you think about it in any other context a bootstrap replicate is pretty meaningless because it no longer reflects the true biological sequence. Thus information on how the consensus was derived, are not really of interest to us providing we are confident this has been done correctly, viz. RAxML_bipartitionsBranchLabels.output_bootstrap.tre and RAxML_bipartitionsBranchLabels.output_bootstrap.tre
  So why is this output of limited use?
  There are situations to some investigators this information is useful, but assess the robustness of a tree topology its not needed. The only thing we want is a phylogram (bestTree) with the bootstrap values superimposed on them. We really don't need complicated stuff such as the tree to be represented for example as a polytomy (non-bifurcating tree) because we can just read the bootstraps to make that deduction (values >> 75%). In addition, there is not perfect consensus what boostrap value constitutes robustness, but generally most agree >80% is robust.
  
  What output files have useful information in them?
  The information that is important are the files associated with "bestTree", that was the single maximum likelihood tree performed on the intact native sequence. The "info" file for this contains 3 really important parameters:
  
  -lnL ... very important!! 
  Gamma distribution parameter "alpha",
  PINVAR, proportion of invariant sites, 
  
  -lnL is the highest log-likelihood (probability) of the phylogeny. It is usually a very small number for which where is an enormous amount of theory over it. 
  Alpha parameter of the gamma distribution this is the shape parameter of the mutation rate, if it is very low (<1) the distribution of mutations across the alignment is very tight clustered and approximates to a negative binomial distribution. This means some sites don't mutate at all and a small number of sites mutate alot. If it is very large >200 (which is never observed) it approximates to the Poisson distribution, meaning the mutation distribution is randomised across the alignment. 
  PINVAR this is a straight percentage/frequency and simply means the sites that don't mutate.
  How are they calculated?
  PINVAR and alpha are not emperically calculated, i.e. if you look at an alignment and say 'no mutations at that position', PINVAR would of course agree but may consider other invariant depending on the phylogeny. These parameters are calculated by maximum likelihood and you can begin to see why the calculation takes so long ... alpha and PINVAR affect the tree topology (which affects -lnL), but the topology affects alpha and PINVAR. Thus, is a multidimensional search of tree and parameter space.
  So what stuff do I report in my Results?
  Anyway reporting -lnL is good technique and shows the reader you've done maximum likelihood, citing PINVAR and alpha from gamma distribution helps ('Methods' parameters were calculated reiteratively under maximum likelihood). This is only useful for bestTree. The -lnL, PINVAR and gamma's alpha are also calculated for every single bootstrap replicate, but these values are of limited to use, because we have resampled the data, only the consensus tree counts... Obviously presenting the bootstrapped phylogram is extremely important.
  Welcome to the technical world of phylogeny! 
  The amino acid matrix you used BTW .. LG is in vogue right now. 
  How do I do it?
  When I do this stuff its via Biopython and ETE3, I capture the values within the pipeline and don't examine the output files of RAxML because I generate my own.
  
original_text: 5116: <p>I ran a calculation in RAxML to determine the majority consensus phylogeny of a maximum likelihood bootstrap (<a href="https://bioinformatics.stackexchange.com/questions/10801/how-to-show-bootstrap-values-on-a-phylogenetic-tree-constructed-with-raxml">How to show bootstrap values on a phylogenetic tree constructed with RAxML</a>), and I got three output files: </p>
  
  <ol>
  <li><strong><em>RAxML_bipartitions.output_bootstrap.tre</em></strong></li>
  <li><strong><em>RAxML_bipartitionsBranchLabels.output_bootstrap.tre</em></strong></li>
  <li><strong><em>RAxML_info.output_bootstrap.tre</em></strong></li>
  </ol>
  
  <p>What is the difference between the </p>
  
  <ul>
  <li><strong><em>RAxML_bipartitions.output_bootstrap.tre</em></strong> file and </li>
  <li><strong><em>RAxML_bipartitionsBranchLabels.output_bootstrap.tre</em></strong>? </li>
  </ul>
   3848: <p>In summary,</p>
  
  <pre><code>RAxML_bipartitions.output_bootstrap.tre
  </code></pre>
  
  <p>Is the only file of interest. The reason this is true in this context is really complicated and you have to understand the statistics of likelihood and how they are interpreted within phylogeny to understand why. This file is simply the final output of a non-parametric bootstrap analysis performed by maximum likelihood.</p>
  
  <p><strong>What on earth is a non-parametric boostrap?</strong></p>
  
  <p>A non-parametric bootstrap is resampling each alignment position with replacement. Thus if we have alignment positions 1,2,3,4,5
  A bootstrap resample for 2 replicates might be,</p>
  
  <p>Replicate 1</p>
  
  <pre><code>1,1,3,5,2
  </code></pre>
  
  <p>Replicate 2</p>
  
  <pre><code>4,2,5,2,1
  </code></pre>
  
  <p>The ML algorithm will make trees of replicates 1 and 2 and find the consensus between them. If you think about it in any other context a bootstrap replicate is pretty meaningless because it no longer reflects the true biological sequence. Thus information on how the consensus was derived, are not really of interest to us providing we are confident this has been done correctly, viz. RAxML_bipartitionsBranchLabels.output_bootstrap.tre and RAxML_bipartitionsBranchLabels.output_bootstrap.tre</p>
  
  <p><strong>So why is this output of limited use?</strong></p>
  
  <p>There are situations to some investigators this information is useful, but assess the robustness of a tree topology its not needed. The only thing we want is a phylogram (bestTree) with the bootstrap values superimposed on them. We really don't need complicated stuff such as the tree to be represented for example as a polytomy (non-bifurcating tree) because we can just read the bootstraps to make that deduction (values >> 75%). In addition, there is not perfect consensus what boostrap value constitutes robustness, but generally most agree >80% is robust.</p>
  
  <hr>
  
  <p><strong>What output files have useful information in them?</strong></p>
  
  <p>The information that is important are the files associated with "bestTree", that was the single maximum likelihood tree performed on the intact native sequence. The "info" file for this contains 3 really important parameters:</p>
  
  <ul>
  <li>-lnL ... very important!! </li>
  <li>Gamma distribution parameter "alpha",</li>
  <li>PINVAR, proportion of invariant sites, </li>
  </ul>
  
  <p><strong>-lnL</strong> is the highest log-likelihood (probability) of the phylogeny. It is usually a very small number for which where is an enormous amount of theory over it. </p>
  
  <p><strong>Alpha parameter of the gamma distribution</strong> this is the shape parameter of the mutation rate, if it is very low (&lt;1) the distribution of mutations across the alignment is very tight clustered and approximates to a negative binomial distribution. This means some sites don't mutate at all and a small number of sites mutate alot. If it is very large >200 (which is never observed) it approximates to the Poisson distribution, meaning the mutation distribution is randomised across the alignment. </p>
  
  <p><strong>PINVAR</strong> this is a straight percentage/frequency and simply means the sites that don't mutate.</p>
  
  <p><strong>How are they calculated?</strong></p>
  
  <p>PINVAR and alpha are not emperically calculated, i.e. if you look at an alignment and say 'no mutations at that position', PINVAR would of course agree but may consider other invariant depending on the phylogeny. These parameters are calculated by maximum likelihood and you can begin to see why the calculation takes so long ... alpha and PINVAR affect the tree topology (which affects -lnL), but the topology affects alpha and PINVAR. Thus, is a multidimensional search of tree and parameter space.</p>
  
  <p><strong>So what stuff do I report in my Results?</strong></p>
  
  <p>Anyway reporting -lnL is good technique and shows the reader you've done maximum likelihood, citing PINVAR and alpha from gamma distribution helps ('Methods' parameters were calculated reiteratively under maximum likelihood). This is only useful for bestTree. The -lnL, PINVAR and gamma's alpha are also calculated for every single bootstrap replicate, but these values are of limited to use, because we have resampled the data, only the consensus tree counts... Obviously presenting the bootstrapped phylogram is extremely important.</p>
  
  <p>Welcome to the technical world of phylogeny! </p>
  
  <p>The amino acid matrix you used BTW .. LG is in vogue right now. </p>
  
  <p><strong>How do I do it?</strong></p>
  
  <p>When I do this stuff its via Biopython and ETE3, I capture the values within the pipeline and don't examine the output files of RAxML because I generate my own.</p>
  