Distance- and ML-based algorithms using reversible models can't find the root of trees. A classical method to root a tree is to use an outgroup (not outlier), which is a species/sequence known to directly descend from the root of the tree. In your case, it is relatively easy: add a chicken or fish ortholog to your dataset and put the root on the chicken/fish branch.
Outgroup works well for single-copy genes used in species tree construction (e.g. ADH). However, it doesn't always work. The culprit is the "known" part. Say a gene has two copies A and B in vertebrates. Copy A was lost in rodent and copy B lost in primate. If you choose a chicken A gene as the outgroup, the correct tree should be ((primary-A,chicken-A),rodent-B). Without knowing the true history, you may forcefully put the root at the chicken-A branch and build a wrong tree ((primate-A,rodent-B),chicken-A).
There are a few other tree rooting methods. An easy approach is to put the root at the longest branch in the tree, assuming the presence of molecular clock. When the species tree is known, you can root a gene tree by minimizing the number gene duplication/loss events in the history. I generally prefer the latter approach when the relevant information is available.
Distance- and ML-based algorithms using reversible models can't find the root of trees. A classical method to root a tree is to use an outgroup (not outlier), which is a species/sequence known to directly descend from the root of the tree. In your case, it is relatively easy: add a chicken or fish ortholog to your dataset and put the root on the chicken/fish branch.
Outgroup works well for single-copy genes used in species tree construction (e.g. ADH). However, it doesn't always work. The culprit is the "known" part. Say a gene has two copies A and B in vertebrates. Copy A was lost in rodent and copy B lost in primate. If you choose a chicken A gene as the outgroup, the correct tree should be ((primary-A,chicken-A),rodent-B). Without knowing the true history, you may forcefully put the root at the chicken-A branch and build a wrong tree ((primate-A,rodent-B),chicken-A).
There are a few other tree rooting methods. An easy approach is to put the root at the longest branch in the tree, assuming the presence of molecular clock. When the species tree is known, you can root a gene tree by minimizing the number gene duplication/loss events in the history. I generally prefer the latter approach when the relevant information is available.
you may use the OD-seq algorithm to filter outliers in a multiple alignment. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0702-1
you may use the OD-seq algorithm to filter outliers in a multiple alignment.https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0702-1
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Distance- and ML-based algorithms using reversible models can't find the root of trees. A classical method to root a tree is to use an outgroup (not outlier), which is a species/sequence known to directly descend from the root of the tree. In your case, it is relatively easy: add a chicken or fish ortholog to your dataset and put the root on the chicken/fish branch.
Outgroup works well for single-copy genes used in species tree construction (e.g. ADH). However, it doesn't always work. The culprit is the "known" part. Say a gene has two copies A and B in vertebrates. Copy A was lost in rodent and copy B lost in primate. If you choose a chicken A gene as the outgroup, the correct tree should be ((primary-A,chicken-A),rodent-B). Without knowing the true history, you may forcefully put the root at the chicken-A branch and build a wrong tree ((primate-A,rodent-B),chicken-A).
There are a few other tree rooting methods. An easy approach is to put the root at the longest branch in the tree, assuming the presence of molecular clock. When the species tree is known, you can root a gene tree by minimizing the number gene duplication/loss events in the history. I generally prefer the latter approach when the relevant information is available.
Distance- and ML-based algorithms using reversible models can't find the root of trees. A classical method to root a tree is to use an outgroup (not outlier), which is a species/sequence known to directly descend from the root of the tree. In your case, it is relatively easy: add a chicken or fish ortholog to your dataset and put the root on the chicken/fish branch.
Outgroup works well for single-copy genes used in species tree construction (e.g. ADH). However, it doesn't always work. The culprit is the "known" part. Say a gene has two copies A and B in vertebrates. Copy A was lost in rodent and copy B lost in primate. If you choose a chicken A gene as the outgroup, the correct tree should be ((primary-A,chicken-A),rodent-B). Without knowing the true history, you may forcefully put the root at the chicken-A branch and build a wrong tree ((primate-A,rodent-B),chicken-A).
There are a few other tree rooting methods. An easy approach is to put the root at the longest branch in the tree, assuming the presence of molecular clock. When the species tree is known, you can root a gene tree by minimizing the number gene duplication/loss events in the history. I generally prefer the latter approach when the relevant information is available.
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