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# Estimation of Bootstrap by Parsimony

- OCCURRENCE
- DESCRIPTION
- NOTES
- ALGORITHM
- PUBLIC
- WARNINGS
- BUGS

## OCCURRENCE

### ARB_NT/Tree/Parsimony

## DESCRIPTION

### Given a large tree, traditional ways to calculate bootstrap values are by magnitudes to slow. So a faster algorithm was developed:

the bootstrap value for each branch is calculated under the assumption that all other branches have a 100% value. Doing this we get an upper limit for the real bootstrap values.

## NOTES

### The program does not use the traditional Monte Carlo method to estimate the bootstrap values, but calculates them correctly under the assumption that the tree changes only locally. Try different filters and see the effect on the tree.

## ALGORITHM

### For each branch B do:

    a                 b
     \               /
      >-------------<
     /        B      \
    c                 d

### exchange a with b ( or a with d ) and count all columns in the alignment with a greater/smaller/equal minimal number of mutations than the original tree.

    result:         n_plus, n_minus, n_equal
                    freq_n_plus = n_plus/ (seq_len)
                    ...

### Bootstrap value = sum of

    for all i = 1.. seqlen do
            for all combinations of np, nm,ne with np - nm == i do
                    sum +=  freq_n_plus  ^ np *
                            freq_n_minus ^ nm *
                            freq_n_equal ^ ne *
                            seq_len! / np! /nm! /ne!
            done
    done

## PUBLIC

### This algorithm is not published and I am not going to publish it. If you feel the strong need to do this, please don't forget to mention me (Oliver Strunk <strunk@mikro.biologie.tu-muenchen.de>)

## WARNINGS

### Use filters to exclude superfluous gaps and to increase bootstrap values

## BUGS

### Does not work with weights Does not work with proteins