The expression of a "bootstrap" comes from the tales of Baron von Münchhausen, whom found himself stuck in a deep hole. To get out, a grabbed his boots by the boostraps and pulled himself upwards, unto he got step out of the hole. (This is of cause impossible and the Baron's lies are famous!)

In the statistical context, bootstrapping refers to using the data at hand to infer the uncertainty of said data. I.e. improve the statistic by pulling on its bootstraps. In practice, this is achieved by sampling or permuting the input data.

As an example, consider a sample of 100 sheep of which you have tested their buoyancy. You can give the mean and standard deviation of these 100 sheep, but we wish to infer more about the general buoyancy of sheep. However, after our experiments, we are banned from the farm and cannot test more sheep. When taking a sample of a population, we assume they are representative of the entire population; thus if we repetitive sample by replacement from our 100 sheep and calculate the same test statistics, we get an idea of the whole population. This is the basic idea of bootstrapping.

In terms of your phylogenetic tree, the bootstrapping values indicates how many times out of 100 (in your case) the same branch was observed when repeating the phylogenetic reconstruction on a re-sampled set of your data. If you get 100 out of 100 (and your data is sufficiently large to support this), we are pretty damned sure that the observed branch is not due to a single extreme datapoint. If you get 50 out of 100, we cannot be as certain. 

Bootstrapping

=====================================

* A computational technique for estimating a statistics for which the underlying distribution is unknown.

* Re sampling technique, estimates sampling distribution by repeatedly sampling data from the original data set.

* Used as a means to estimate confidence levels of clades within a phylogenetic tree.

* Example: if you recover the same node through 95 of 100 iterations of taking out one character and resampling your tree(which is done by the tree maker automatically), then you have a good idea that the node is well supported (your bootstrap value in that case would be 0.95 or 95%).

* Often if bootstrap values that are lower than (say 50%) are not included on a tree

Hi

If I get a bootstrap value of "0" what does it mean? Is it acceptable or not?

Thank you

 I find it hard to judge because I  never worked with your particular program but to me these values look more like branch lengths, rather than bootstrap values (note the scale bar in your graph). And all the clones whose branches have a zero to them have no branch lengths in the graph. You also should not find bootstrap values on terminal branches, as they indicate support for nodes. I don't think bootstrap values of 0 are possible because, as Atai Rabby points out, this would mean that a particular clade is never retrieved (and thus it should not show up in a phylogeny).

All u include in the tree is branch length that indicate to the amount genetic change per time.

The expression of a "bootstrap" comes from the tales of Baron von Münchhausen, whom found himself stuck in a deep hole. To get out, a grabbed his boots by the boostraps and pulled himself upwards, unto he got step out of the hole. (This is of cause impossible and the Baron's lies are famous!)

In the statistical context, bootstrapping refers to using the data at hand to infer the uncertainty of said data. I.e. improve the statistic by pulling on its bootstraps. In practice, this is achieved by sampling or permuting the input data.

As an example, consider a sample of 100 sheep of which you have tested their buoyancy. You can give the mean and standard deviation of these 100 sheep, but we wish to infer more about the general buoyancy of sheep. However, after our experiments, we are banned from the farm and cannot test more sheep. When taking a sample of a population, we assume they are representative of the entire population; thus if we repetitive sample by replacement from our 100 sheep and calculate the same test statistics, we get an idea of the whole population. This is the basic idea of bootstrapping.

In terms of your phylogenetic tree, the bootstrapping values indicates how many times out of 100 (in your case) the same branch was observed when repeating the phylogenetic reconstruction on a re-sampled set of your data. If you get 100 out of 100 (and your data is sufficiently large to support this), we are pretty damned sure that the observed branch is not due to a single extreme datapoint. If you get 50 out of 100, we cannot be as certain. 

What is a bootstrap value?. Available from: https://www.researchgate.net/post/What_is_a_bootstrap_value [accessed Apr 13, 2017].

Bootstrap value in phylogenetic analysis gives your result a confident support and the conclusion is the same irrespective of the program or software used. However, each of the phylogenetic analysis (MP, ML, BI) have a range of bootstrap support values.

According to Felsenstein, Bootstrap value provides assessment of “confidence” for each clade of an observed tree.

bootstrap is the assessment of confidence level of relatedness or similarily between the elements in a clade

I would relate it in this way, higher the bootstrap value, higher the confidence level of the clade in the phylogenetic tree. It tells you if 1000 times this tree is made using a particular data, this much is the confidence value (Bootstrap value). zero value will show unrelatedness.

A similar question pointed me here. So here's a copy of my answer (of the relevant bits to answer this):

A good starting read for the bootstrap (BS)-unaware is this post by David Morrison:

Some things you probably don't know about the bootstraphttps://phylonetworks.blogspot.com/2014/04/some-things-you-probably-dont-know.html

For those looking for the hard stuff, Felsenstein's (2004) book, pointed to in a comment above, is indeed still a good read:

Felsenstein J. 2004. Inferring phylogenies. Sunderland, MA, U.S.A.: Sinauer Associates Inc.

The process: We bootstrap the matrix and infer a tree based on this pseudo-replicate of our entire data, we repeat this process and then count how often a certain phylogenetic split, a taxon bipartition, occurs in the pseudo-replicate tree sample. It's important to realise that a phylogenetic tree is a one-dimensional graph put together by a series of such taxon bipartitions, and the standard BS approach is to optimise a tree, and then map the support from the BS sample on that tree. Note, that e.g. RAxML has an option to plot branch support values against each other, and to map different tree samples on tree not inferred simultaneously, e.g. you can read in a Bayesian tree sample and map the values on the ML tree; or read in the highest-probability Bayesian tree and map the ML-BS support on that tree.

Because of the process, although BS support may converge to the probability that a branch is correct, it is not a probability (hence, we don't write BS = 0.9 but BS = 90, also "bootstrap percentage", BP = 90) but effectively a measure of how robust a certain splits is supported by the characters in your matrix.

Accepted values of confidence. There are generally accepted thresholds for "good" and "moderate" support, e.g. BS > 70/80 is considered good, and 50/60 < BS < 70/80 is considered moderate. But this is an artificial choice (detailed below), as BS = 69 is as good as a BS = 71, and there's no reason to accept BS > 50 as moderate, but BS = 49 as "no support" (which is still very common in phylogenetic literature). So, one should always just give the actual value, and not use the largely meaningless thresholds.

If your BS ~ 0, then this aspect of your tree, this branch, has simply no character support. This is usually because your data lacks any resolution capacity to resolve this bit of the tree; in some case this is because the replicate BS trees prefer an alternative solution, a split incompatible with the inferred tree. The latter can happen when you e.g. combined data from genes that are the product of different genealogies and is not uncommon the closer you come to the leaves of the Tree of Life.

Why you should not rely on commonly accepted thresholds – reasons for BS

Are you sure that the sequences in the matrix have been aligned? I am not!

It is the probability of getting the same tree or phylogenetic relationship if you redraw tree using the same data 1000 or 100 bootstrap value means if you draw the tree using the same data sets you get a particular number at the node which specifies the correctness of the tree.

Hope I made something clear

Hi Shomini,

Unfortunately, you got it pretty wrong. Don't worry, very common errors; see also my related post: https://researchinpeace.blogspot.com/2019/03/how-to-interpret-bootstrap-values.html; and this one on Genealogical World of Phylogenetic Networks:

D Morrison (2014) Some things you probably don't know about the bootstrap

https://phylonetworks.blogspot.com/2014/04/some-things-you-probably-dont-know.html

Bootstrap (BS) percentages are not probabilities in a mathematical sense, and algorithm-wise (only Bayesian posterior probabilities are). At best, they are approximations of probabilities.

Also each BS tree is inferred based on a different data set. That's why we call it a "pseudoreplicate": some sites in the matrix are dropped, others are duplicated to replace the dropped ones. The BS pseudoreplicate matrices have hence the same number of characters but each one includes only a (different) subset of characters of the full matrix. The tree is not re-drawn, instead new trees are inferred based on changing subsets of characters.

Finally (a very common error), BS values give the frequency of a certain taxon bipartition in the pseudoreplicate trees, thus are branch supports and not node supports. A taxon bipartition, also known as taxon split, divides the taxon set into two parts; just like an internode (branch) does in a tree (or, more generally, edges in a network). A tree node doesn't reflect a taxon bipartiion, it connects three branches (in a classic tree). By rooting the tree (and branches become clades), we transfer the branch support to one of the two nodes of the internode, the root-distal one. Hence, by re-rooting a tree the same branch support may support different clade (or be moved to the next node).

In summary, BS supports are not measures of the "correctness" of a tree but the "robustness" of the underlying data. How much of the data are in agreement with the split/branch seen in a tree. In the optimal case: how many character splits support a branch in a tree. But see also this post relating to an alternative support measure implemented in IQTree:

R Lanfear (2018) Calculating and interpreting gene- and site-concordance factors in phylogenomics

http://www.robertlanfear.com/blog/files/concordance_factors.html

建议去看下MEGA的online manual，其中有相关的说明。

没法直接放入超链接，以下是MEGAX-Help (megasoftware.net)中的内容。

“One of the most commonly used tests of the reliability of an inferred tree is Felsenstein's (1985) bootstrap test, which is evaluated using Efron's (1982) bootstrap resampling technique.  If there are m sequences, each with n nucleotides (or codons or amino acids), a phylogenetic tree can be reconstructed using some tree building method.  From each sequence, n nucleotides are randomly chosen with replacements, giving rise to m rows of n columns each.  These now constitute a new set of sequences.  A tree is then reconstructed with these new sequences using the same tree building method as before.  Next the topology of this tree is compared to that of the original tree.  Each interior branch of the original tree that is different from the bootstrap tree the sequence it partitions is given a score of 0; all other interior branches are given the value 1.  This procedure of resampling the sites and the subsequent tree reconstruction is repeated several hundred times, and the percentage of times each interior branch is given a value of 1 is noted.  This is known as the bootstrap value.  As a general rule, if the bootstrap value for a given interior branch is 95% or higher, then the topology at that branch is considered "correct".  See Nei and Kumar (2000) (chapter 9) for further details.

This test is available for four different methods: Neighbor Joining, Minimum Evolution, Maximum Parsimony, UPGMA, and Maximum Likelihood.”

It assess the confidence levels of similarity in clade elements. I would recommend following article in this regard:

https://projecteuclid.org/journals/statistical-science/volume-18/issue-2/Applying-the-Bootstrap-in-Phylogeny-Reconstruction/10.1214/ss/1063994980.pdf