about each model, see the <a href="scorematrices.html">list of
built-in score matrices.</a></li>
<li><strong>Sequence Feature Similarity</strong><br>Trees
- are constructed from a distance matrix formed from the normalised
- hamming distance between the sequence features observed in each column of
- the alignment.<br> <br>Distances are computed based on
- the currently displayed feature types. Sequences with similar
- distributions of features of the same type will be grouped
- together in trees computed with this metric.</li>
+ are constructed from a distance matrix formed from Jaccard
+ distances between sequence features observed at each column of the
+ alignment.
+ <ul>
+ <li>Similarity at column <em>i</em> = (Total number of
+ features displayed - Sum of number of features in common at <em>i</em>)
+ <br />Similarities are summed over all columns and divided by
+ the number of columns. <br />Since the total number of
+ feature types is constant over all columns of the alignment,
+ we do not scale the matrix, so tree distances can be
+ interpreted as the average number of features that differ over
+ all sites in the aligned region.
+ </li>
+
+ </ul> Distances are computed based on the currently displayed feature
+ types. Sequences with similar distributions of features of the
+ same type will be grouped together in trees computed with this
+ metric. <em>This measure was introduced in Jalview 2.9</em></li>
</ul>
</p>
<p><strong>Tree Construction Methods</strong></p>