-<html>\r
-<head><title>Principal Component Analysis</title></head>\r
-<body>\r
-<p><strong>Principal Component Analysis</strong></p>\r
-<p>This calculation creates a spatial representation of the\r
-similarities within a selected group, or all of the sequences in\r
-an alignment. After the calculation finishes, a 3D viewer displays the\r
-set of sequences as points in 'similarity space', and similar\r
-sequences tend to lie near each other in the space.</p>\r
-<p>Note: The calculation is computationally expensive, and may fail for very large sets of sequences -\r
- usually because the JVM has run out of memory. The next release of\r
- Jalview release will execute this calculation through a web service.</p>\r
-<p>Principal components analysis is a technique for examining the\r
-structure of complex data sets. The components are a set of dimensions\r
-formed from the measured values in the data set, and the principle\r
-component is the one with the greatest magnitude, or length. The\r
-sets of measurements that differ the most should lie at either end of\r
-this principle axis, and the other axes correspond to less extreme\r
-patterns of variation in the data set.\r
-</p>\r
-\r
-<p>In this case, the components are generated by an eigenvector\r
-decomposition of the matrix formed from the sum of BLOSUM scores at\r
-each aligned position between each pair of sequences. The basic method\r
-is described in the paper by G. Casari, C. Sander and\r
-A. Valencia. Structural Biology volume 2, no. 2, February 1995 (<a\r
-href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=7749921">pubmed</a>)\r
- and implemented at the SeqSpace server at the EBI.\r
-</p>\r
-\r
-<p><strong>The PCA Viewer</strong></p>\r
-<p>This is an interactive display of the sequences positioned within\r
- the similarity space. The colour of each sequence point is the same\r
- as the sequence group colours, white if no colour has been\r
- defined for the sequence, and green if the sequence is part of a\r
- the currently selected group.\r
-</p>\r
- <p>The 3d view can be rotated by dragging the mouse with the\r
- <strong>left mouse button</strong> pressed. The view can also be\r
- zoomed in and out with the up and down <strong>arrow\r
- keys</strong>. Labels will be shown for each sequence\r
- if the entry in the View menu is checked, and the plot background\r
- colour changed from the View→Background Colour.. dialog\r
- box. The File menu allows the view to be saved (File→Save\r
- submenu) as an EPS or PNG image or printed, and the original\r
- alignment data and matrix resulting from its PCA analysis to be\r
- retrieved.\r
-</p>\r
- </p>\r
-<p>A tool tip gives the sequence ID corresponding to a point in the\r
- space, and clicking a point toggles the selection of the\r
- corresponding sequence in the alignment window. Rectangular region\r
- based selection is also possible, by holding the 'S' key whilst\r
- left-clicking and dragging the mouse over the display.\r
-</p>\r
-<p>Initially, the display shows the first three components of the\r
- similarity space, but any eigenvector can be used by changing the selected\r
- dimension for the x, y, or z axis through each ones menu located\r
- below the 3d display.\r
-</p>\r
-<p>\r
-\r
-</body>\r
-</html>\r
+<html>
+<head><title>Principal Component Analysis</title></head>
+<body>
+<p><strong>Principal Component Analysis</strong></p>
+<p>This calculation creates a spatial representation of the
+similarities within a selected group, or all of the sequences in
+an alignment. After the calculation finishes, a 3D viewer displays the
+set of sequences as points in 'similarity space', and similar
+sequences tend to lie near each other in the space.</p>
+<p>Note: The calculation is computationally expensive, and may fail for very large sets of sequences -
+ usually because the JVM has run out of memory. A future release of
+ Jalview will be able to avoid this by executing the calculation via a web service.</p>
+<p>Principal components analysis is a technique for examining the
+structure of complex data sets. The components are a set of dimensions
+formed from the measured values in the data set, and the principle
+component is the one with the greatest magnitude, or length. The
+sets of measurements that differ the most should lie at either end of
+this principle axis, and the other axes correspond to less extreme
+patterns of variation in the data set.
+</p>
+
+<p>In this case, the components are generated by an eigenvector
+decomposition of the matrix formed from the sum of BLOSUM scores at
+each aligned position between each pair of sequences. The basic method
+is described in the paper by G. Casari, C. Sander and
+A. Valencia. Structural Biology volume 2, no. 2, February 1995 (<a
+href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=7749921">pubmed</a>)
+ and implemented at the SeqSpace server at the EBI.
+</p>
+
+<p><strong>The PCA Viewer</strong></p>
+<p>This is an interactive display of the sequences positioned within
+ the similarity space. The colour of each sequence point is the same
+ as the sequence group colours, white if no colour has been
+ defined for the sequence, and green if the sequence is part of a
+ the currently selected group.
+</p>
+ <p>The 3d view can be rotated by dragging the mouse with the
+ <strong>left mouse button</strong> pressed. The view can also be
+ zoomed in and out with the up and down <strong>arrow
+ keys</strong> (and the roll bar of the mouse if present). Labels
+ will be shown for each sequence if the entry in the View menu is
+ checked, and the plot background colour changed from the
+ View→Background Colour.. dialog box. The File menu allows the
+ view to be saved (File→Save submenu) as an EPS or PNG image or
+ printed, and the original alignment data and matrix resulting from
+ its PCA analysis to be retrieved.
+</p>
+ </p>
+<p>A tool tip gives the sequence ID corresponding to a point in the
+ space, and clicking a point toggles the selection of the
+ corresponding sequence in the alignment window. Rectangular region
+ based selection is also possible, by holding the 'S' key whilst
+ left-clicking and dragging the mouse over the display.
+</p>
+<p>Initially, the display shows the first three components of the
+ similarity space, but any eigenvector can be used by changing the selected
+ dimension for the x, y, or z axis through each ones menu located
+ below the 3d display.
+</p>
+<p>
+
+</body>
+</html>