The <strong>Web Services→Disorder</strong> menu in the
alignment window allows access to protein disorder prediction
services provided by the configured <a
- href="http://www.compbio.dundee.ac.uk/jabaws"
- >JABAWS servers</a>. Each service operates on sequences in the
- alignment or currently selected region (<em>since Jalview
- 2.8.0b1</em>) to identify regions likely to be unstructured or
- flexible, or alternately, fold to form globular domains.
+ href="http://www.compbio.dundee.ac.uk/jabaws">JABAWS
+ servers</a>. Each service operates on sequences in the alignment or
+ currently selected region (<em>since Jalview 2.8.0b1</em>) to
+ identify regions likely to be unstructured or flexible, or
+ alternately, fold to form globular domains.
</p>
<p>
Predictor results include both <a
- href="../features/seqfeatures.html"
- >sequence features</a> and sequence associated <a
- href="../features/annotation.html"
- >alignment annotation</a> rows. Features display is controlled from
- the <a href="../features/featuresettings.html">Feature Settings</a>
+ href="../features/seqfeatures.html">sequence features</a> and
+ sequence associated <a href="../features/annotation.html">alignment
+ annotation</a> rows. Features display is controlled from the <a
+ href="../features/featuresettings.html">Feature Settings</a>
dialog box. Clicking on the ID for a disorder prediction annotation
row will highlight or select (if double clicked) the associated
sequence for that row. You can also use the <em>Sequence
Associated</em> option in the <a
- href="../colourSchemes/annotationColouring.html"
- >Colour By Annotation</a> dialog box to colour sequences according to
- the results of predictors shown as annotation rows.
+ href="../colourSchemes/annotationColouring.html">Colour
+ By Annotation</a> dialog box to colour sequences according to the
+ results of predictors shown as annotation rows.
</p>
<p>JABAWS 2.0 provides four disorder predictors which are
described below:</p>
<td>Sequence Feature &<br />Annotation Row
</td>
<td>Predicts loops/coils according to DSSP definition<a
- href="#dsspstates"
- >[1]</a>.<br />Features mark range(s) of residues predicted as
- loops/coils, and annotation row gives raw value for each
- residue. Value over 0.516 indicates loop/coil.
+ href="#dsspstates">[1]</a>.<br />Features mark range(s)
+ of residues predicted as loops/coils, and annotation row gives
+ raw value for each residue. Value over 0.516 indicates
+ loop/coil.
</td>
</tr>
<tr>
<p>
<strong><a name="ronn"></a><a
- href="http://www.strubi.ox.ac.uk/RONN"
- >RONN</a></strong> <em>a.k.a.</em> Regional Order Neural Network<br />This
- predictor employs an approach known as the 'bio-basis' method to
- predict regions of disorder in sequences based on their local
- similarity with a gold-standard set of disordered protein sequences.
- It yields a set of disorder prediction scores, which are shown as
- sequence annotation below the alignment.
+ href="http://www.strubi.ox.ac.uk/RONN">RONN</a></strong> <em>a.k.a.</em>
+ Regional Order Neural Network<br />This predictor employs an
+ approach known as the 'bio-basis' method to predict regions of
+ disorder in sequences based on their local similarity with a
+ gold-standard set of disordered protein sequences. It yields a set
+ of disorder prediction scores, which are shown as sequence
+ annotation below the alignment.
</p>
<table border="1">
<tr>
</p>
<p>
<strong><a name="iupred"></a><a
- href="http://iupred.enzim.hu/Help.php"
- >IUPred</a></strong><br /> IUPred employs an empirical model to estimate
- likely regions of disorder. There are three different prediction
- types offered, each using different parameters optimized for
- slightly different applications. It provides raw scores based on two
- models for predicting regions of 'long disorder' and 'short
- disorder'. A third predictor identifies regions likely to form
- structured domains.
+ href="http://iupred.enzim.hu/Help.php">IUPred</a></strong><br />
+ IUPred employs an empirical model to estimate likely regions of
+ disorder. There are three different prediction types offered, each
+ using different parameters optimized for slightly different
+ applications. It provides raw scores based on two models for
+ predicting regions of 'long disorder' and 'short disorder'. A third
+ predictor identifies regions likely to form structured domains.
</p>
<table border="1">
<tr>
</table>
<p>
<strong><a name="globplot"></a><a
- href="http://globplot.embl.de/"
- >GLOBPLOT</a></strong><br /> Defines regions of globularity or natively
- unstructured regions based on a running sum of the propensity of
- residues to be structured or unstructured. The propensity is
- calculated based on the probability of each amino acid being
- observed within well defined regions of secondary structure or
- within regions of random coil. The initial signal is smoothed with a
- Savitzky-Golay filter, and its first order derivative computed.
- Residues for which the first order derivative is positive are
- designated as natively unstructured, whereas those with negative
- values are structured.<br />
+ href="http://globplot.embl.de/">GLOBPLOT</a></strong><br /> Defines
+ regions of globularity or natively unstructured regions based on a
+ running sum of the propensity of residues to be structured or
+ unstructured. The propensity is calculated based on the probability
+ of each amino acid being observed within well defined regions of
+ secondary structure or within regions of random coil. The initial
+ signal is smoothed with a Savitzky-Golay filter, and its first order
+ derivative computed. Residues for which the first order derivative
+ is positive are designated as natively unstructured, whereas those
+ with negative values are structured.<br />
<table border="1">
<tr>
<td><strong>Name</strong></td>
git://source.jalview.org / jalview.git / blobdiff