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< meta http-equiv ="X-UA-Compatible " content ="IE=edge ">
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- < meta http-equiv ="last-modified " content ="2022-10-20 16:21:04 -0400 ">
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+ < meta http-equiv ="last-modified " content ="2022-10-20 23:33:00 -0400 ">
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< meta name ="viewport " content ="width=device-width, initial-scale=1 ">
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< meta name ="search-domain " value ="https://bioinfoaps.github.io/ ">
@@ -316,12 +316,12 @@ <h2>Overview</h2>
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One way to show this is using bootstrap values,
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which is the probability that a particular node
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(i.e. a dichotomous branching with a particular set of samples in each branch)
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- appearing among a large number of trees generated
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+ appears among a large number of trees generated
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while resampling within the sequence alignment.
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Bootstrap values are often used in the same fashion as confidence intervals.</ p >
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< p > A common method for creating bootstrapped trees using RAxML consists of
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- 3 step approach:</ p >
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+ a 3 step approach:</ p >
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< ul >
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< li > Generate a few maximum likelihood (ML) trees.</ li >
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< li > Generate many bootstrapped trees.</ li >
@@ -332,7 +332,7 @@ <h3 id="generating-an-ml-tree">Generating an ML tree</h3>
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< p > Maximum likelihood tree generation is computationally expensive,
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but the resulting tree is considered superior to other rapid methods.
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- Thus, we’ll generate a small number of ML trees.</ p >
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+ Thus, we’ usually generate a small number of ML trees (16 in the following example) .</ p >
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< div class ="language-bash highlighter-rouge "> < div class ="highlight "> < pre class ="highlight "> < code > < span class ="gp "> $ </ span > raxmlHPC < span class ="nt "> -T</ span > 8 < span class ="nt "> -m</ span > GTRGAMMA < span class ="nt "> -p</ span > 144 -# 16 < span class ="nt "> -s</ span > input.fasta < span class ="nt "> -n</ span > treeML < span class ="nt "> -w</ span > outdir
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