1 | #Please insert up references in the next lines (line starts with keyword UP) |
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2 | UP arb.hlp |
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3 | UP glossary.hlp |
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4 | |
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5 | |
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6 | TITLE Estimation of Bootstrap by Parsimony |
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7 | |
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8 | OCCURRENCE ARB_PARSIMONY/Tree/Calculate Upper Bootstrap Limit |
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9 | |
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10 | DESCRIPTION Given a large tree, traditional ways to calculate bootstrap |
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11 | values are by magnitudes to slow. So a faster algorithm was |
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12 | developed: |
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13 | |
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14 | the bootstrap value for each branch is calculated under |
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15 | the assumption that all other branches have a 100% value. |
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16 | Doing this we get an upper limit for the real bootstrap values. |
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17 | |
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18 | |
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19 | NOTES The program does not use the traditional Monte Carlo method to |
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20 | estimate the bootstrap values, but calculates them correctly |
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21 | under the assumption that the tree changes only locally. |
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22 | Try different filters and see the effect on the tree. |
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23 | |
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24 | SECTION ALGORITHM |
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25 | |
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26 | For each branch B do: |
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27 | |
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28 | a b |
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29 | \ / |
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30 | >-------------< |
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31 | / B \ |
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32 | c d |
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33 | |
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34 | exchange a with b ( or a with d ) and count all columns in the alignment |
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35 | with a greater/smaller/equal minimal number of mutations |
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36 | than the original tree. |
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37 | |
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38 | result: n_plus, n_minus, n_equal |
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39 | freq_n_plus = n_plus/ (seq_len) |
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40 | ... |
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41 | |
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42 | Bootstrap value = sum of |
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43 | |
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44 | for all i = 1.. seqlen do |
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45 | for all combinations of np, nm,ne with np - nm == i do |
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46 | sum += freq_n_plus ^ np * |
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47 | freq_n_minus ^ nm * |
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48 | freq_n_equal ^ ne * |
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49 | seq_len! / np! /nm! /ne! |
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50 | done |
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51 | done |
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52 | |
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53 | SECTION PUBLIC |
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54 | |
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55 | This algorithm is not published and I am not going to publish |
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56 | it. If you feel the strong need to do this, please don't forget |
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57 | to mention me (Oliver Strunk). |
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58 | |
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59 | WARNINGS Use filters to exclude superfluous gaps and to |
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60 | increase bootstrap values |
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61 | |
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62 | BUGS Does not work with weights |
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63 | Does not work with proteins |
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