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<a href="Bio-module.html">Package Bio</a> ::
<a href="Bio.Align-module.html">Package Align</a> ::
<a href="Bio.Align.AlignInfo-module.html">Module AlignInfo</a> ::
Class SummaryInfo
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class SummaryInfo</h1><p class="nomargin-top"><span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo">source code</a></span></p>
<p>Calculate summary info about the alignment.</p>
<p>This class should be used to caclculate information summarizing the
results of an alignment. This may either be straight consensus info or
more complicated things.</p>
<!-- ==================== INSTANCE METHODS ==================== -->
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<td align="left"><span class="table-header">Instance Methods</span></td>
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<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
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<td><span class="summary-sig"><a href="Bio.Align.AlignInfo.SummaryInfo-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">alignment</span>)</span><br />
Initialize with the alignment to calculate information on.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo.__init__">source code</a></span>
</td>
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</td>
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<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Bio.Align.AlignInfo.SummaryInfo-class.html#dumb_consensus" class="summary-sig-name">dumb_consensus</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">threshold</span>=<span class="summary-sig-default">0.7</span>,
<span class="summary-sig-arg">ambiguous</span>=<span class="summary-sig-default"><code class="variable-quote">'</code><code class="variable-string">X</code><code class="variable-quote">'</code></span>,
<span class="summary-sig-arg">consensus_alpha</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">require_multiple</span>=<span class="summary-sig-default">0</span>)</span><br />
Output a fast consensus sequence of the alignment.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo.dumb_consensus">source code</a></span>
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Bio.Align.AlignInfo.SummaryInfo-class.html#gap_consensus" class="summary-sig-name">gap_consensus</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">threshold</span>=<span class="summary-sig-default">0.7</span>,
<span class="summary-sig-arg">ambiguous</span>=<span class="summary-sig-default"><code class="variable-quote">'</code><code class="variable-string">X</code><code class="variable-quote">'</code></span>,
<span class="summary-sig-arg">consensus_alpha</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">require_multiple</span>=<span class="summary-sig-default">0</span>)</span><br />
Same as dumb_consensus(), but allows gap on the output.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo.gap_consensus">source code</a></span>
</td>
</tr>
</table>
</td>
</tr>
<tr class="private">
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Bio.Align.AlignInfo.SummaryInfo-class.html#_guess_consensus_alphabet" class="summary-sig-name" onclick="show_private();">_guess_consensus_alphabet</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">ambiguous</span>)</span><br />
Pick an (ungapped) alphabet for an alignment consesus sequence.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo._guess_consensus_alphabet">source code</a></span>
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Bio.Align.AlignInfo.SummaryInfo-class.html#replacement_dictionary" class="summary-sig-name">replacement_dictionary</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">skip_chars</span>=<span class="summary-sig-default"><code class="variable-group">[</code><code class="variable-group">]</code></span>)</span><br />
Generate a replacement dictionary to plug into a substitution matrix</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo.replacement_dictionary">source code</a></span>
</td>
</tr>
</table>
</td>
</tr>
<tr class="private">
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Bio.Align.AlignInfo.SummaryInfo-class.html#_pair_replacement" class="summary-sig-name" onclick="show_private();">_pair_replacement</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">seq1</span>,
<span class="summary-sig-arg">seq2</span>,
<span class="summary-sig-arg">weight1</span>,
<span class="summary-sig-arg">weight2</span>,
<span class="summary-sig-arg">start_dict</span>,
<span class="summary-sig-arg">ignore_chars</span>)</span><br />
Compare two sequences and generate info on the replacements seen.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo._pair_replacement">source code</a></span>
</td>
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</td>
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<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
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<td><span class="summary-sig"><a name="_get_all_letters"></a><span class="summary-sig-name">_get_all_letters</span>(<span class="summary-sig-arg">self</span>)</span><br />
Returns a string containing the expected letters in the alignment.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo._get_all_letters">source code</a></span>
</td>
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</table>
</td>
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<tr class="private">
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
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<td><span class="summary-sig"><a href="Bio.Align.AlignInfo.SummaryInfo-class.html#_get_base_replacements" class="summary-sig-name" onclick="show_private();">_get_base_replacements</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">skip_items</span>=<span class="summary-sig-default"><code class="variable-group">[</code><code class="variable-group">]</code></span>)</span><br />
Get a zeroed dictonary of all possible letter combinations.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo._get_base_replacements">source code</a></span>
</td>
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</td>
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<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
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<td><span class="summary-sig"><a href="Bio.Align.AlignInfo.SummaryInfo-class.html#pos_specific_score_matrix" class="summary-sig-name">pos_specific_score_matrix</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">axis_seq</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">chars_to_ignore</span>=<span class="summary-sig-default"><code class="variable-group">[</code><code class="variable-group">]</code></span>)</span><br />
Create a position specific score matrix object for the alignment.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo.pos_specific_score_matrix">source code</a></span>
</td>
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</table>
</td>
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<tr class="private">
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
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<tr>
<td><span class="summary-sig"><a name="_get_base_letters"></a><span class="summary-sig-name">_get_base_letters</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">letters</span>)</span><br />
Create a zeroed dictionary with all of the specified letters.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo._get_base_letters">source code</a></span>
</td>
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</table>
</td>
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<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
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<td><span class="summary-sig"><a href="Bio.Align.AlignInfo.SummaryInfo-class.html#information_content" class="summary-sig-name">information_content</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">start</span>=<span class="summary-sig-default">0</span>,
<span class="summary-sig-arg">end</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">e_freq_table</span>=<span class="summary-sig-default">None</span>,
<span class="summary-sig-arg">log_base</span>=<span class="summary-sig-default">2</span>,
<span class="summary-sig-arg">chars_to_ignore</span>=<span class="summary-sig-default"><code class="variable-group">[</code><code class="variable-group">]</code></span>)</span><br />
Calculate the information content for each residue along an
alignment.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo.information_content">source code</a></span>
</td>
</tr>
</table>
</td>
</tr>
<tr class="private">
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
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<tr>
<td><span class="summary-sig"><a href="Bio.Align.AlignInfo.SummaryInfo-class.html#_get_letter_freqs" class="summary-sig-name" onclick="show_private();">_get_letter_freqs</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">residue_num</span>,
<span class="summary-sig-arg">all_records</span>,
<span class="summary-sig-arg">letters</span>,
<span class="summary-sig-arg">to_ignore</span>)</span><br />
Determine the frequency of specific letters in the alignment.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo._get_letter_freqs">source code</a></span>
</td>
</tr>
</table>
</td>
</tr>
<tr class="private">
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Bio.Align.AlignInfo.SummaryInfo-class.html#_get_column_info_content" class="summary-sig-name" onclick="show_private();">_get_column_info_content</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">obs_freq</span>,
<span class="summary-sig-arg">e_freq_table</span>,
<span class="summary-sig-arg">log_base</span>,
<span class="summary-sig-arg">random_expected</span>)</span><br />
Calculate the information content for a column.</td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo._get_column_info_content">source code</a></span>
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"> </span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a name="get_column"></a><span class="summary-sig-name">get_column</span>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">col</span>)</span></td>
<td align="right" valign="top">
<span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo.get_column">source code</a></span>
</td>
</tr>
</table>
</td>
</tr>
</table>
<!-- ==================== METHOD DETAILS ==================== -->
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<td align="left"><span class="table-header">Method Details</span></td>
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><span class="options">[<a href="#section-MethodDetails"
class="privatelink" onclick="toggle_private();"
>hide private</a>]</span></td>
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<a name="__init__"></a>
<div>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">alignment</span>)</span>
<br /><em class="fname">(Constructor)</em>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo.__init__">source code</a></span>
</td>
</tr></table>
<p>Initialize with the alignment to calculate information on. ic_vector
attribute. A dictionary. Keys: column numbers. Values:</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="dumb_consensus"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">dumb_consensus</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">threshold</span>=<span class="sig-default">0.7</span>,
<span class="sig-arg">ambiguous</span>=<span class="sig-default"><code class="variable-quote">'</code><code class="variable-string">X</code><code class="variable-quote">'</code></span>,
<span class="sig-arg">consensus_alpha</span>=<span class="sig-default">None</span>,
<span class="sig-arg">require_multiple</span>=<span class="sig-default">0</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo.dumb_consensus">source code</a></span>
</td>
</tr></table>
<p>Output a fast consensus sequence of the alignment.</p>
<p>This doesn't do anything fancy at all. It will just go through the
sequence residue by residue and count up the number of each type of
residue (ie. A or G or T or C for DNA) in all sequences in the alignment.
If the percentage of the most common residue type is greater then the
passed threshold, then we will add that residue type, otherwise an
ambiguous character will be added.</p>
<p>This could be made a lot fancier (ie. to take a substitution matrix
into account), but it just meant for a quick and dirty consensus.</p>
<p>Arguments: o threshold - The threshold value that is required to add a
particular atom. o ambiguous - The ambiguous character to be added when
the threshold is not reached. o consensus_alpha - The alphabet to return
for the consensus sequence. If this is None, then we will try to guess
the alphabet. o require_multiple - If set as 1, this will require that
more than 1 sequence be part of an alignment to put it in the consensus
(ie. not just 1 sequence and gaps).</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="gap_consensus"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">gap_consensus</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">threshold</span>=<span class="sig-default">0.7</span>,
<span class="sig-arg">ambiguous</span>=<span class="sig-default"><code class="variable-quote">'</code><code class="variable-string">X</code><code class="variable-quote">'</code></span>,
<span class="sig-arg">consensus_alpha</span>=<span class="sig-default">None</span>,
<span class="sig-arg">require_multiple</span>=<span class="sig-default">0</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo.gap_consensus">source code</a></span>
</td>
</tr></table>
<p>Same as dumb_consensus(), but allows gap on the output.</p>
<p>Things to do: Let the user define that with only one gap, the result
character in consensus is gap. Let the user select gap character, now it
takes the same is input.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="_guess_consensus_alphabet"></a>
<div class="private">
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">_guess_consensus_alphabet</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">ambiguous</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo._guess_consensus_alphabet">source code</a></span>
</td>
</tr></table>
<p>Pick an (ungapped) alphabet for an alignment consesus sequence.</p>
<p>This just looks at the sequences we have, checks their type, and
returns as appropriate type which seems to make sense with the sequences
we've got.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="replacement_dictionary"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">replacement_dictionary</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">skip_chars</span>=<span class="sig-default"><code class="variable-group">[</code><code class="variable-group">]</code></span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo.replacement_dictionary">source code</a></span>
</td>
</tr></table>
<p>Generate a replacement dictionary to plug into a substitution
matrix</p>
<p>This should look at an alignment, and be able to generate the number
of substitutions of different residues for each other in the aligned
object.</p>
<p>Will then return a dictionary with this information: {('A', 'C') : 10,
('C', 'A') : 12, ('G', 'C') : 15 ....}</p>
<p>This also treats weighted sequences. The following example shows how
we calculate the replacement dictionary. Given the following multiple
sequence alignments:</p>
<p>GTATC 0.5 AT--C 0.8 CTGTC 1.0</p>
<p>For the first column we have: ('A', 'G') : 0.5 * 0.8 = 0.4 ('C', 'G')
: 0.5 * 1.0 = 0.5 ('A', 'C') : 0.8 * 1.0 = 0.8</p>
<p>We then continue this for all of the columns in the alignment, summing
the information for each substitution in each column, until we end up
with the replacement dictionary.</p>
<p>Arguments: o skip_chars - A list of characters to skip when creating
the dictionary. For instance, you might have Xs (screened stuff) or Ns,
and not want to include the ambiguity characters in the dictionary.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="_pair_replacement"></a>
<div class="private">
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">_pair_replacement</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">seq1</span>,
<span class="sig-arg">seq2</span>,
<span class="sig-arg">weight1</span>,
<span class="sig-arg">weight2</span>,
<span class="sig-arg">start_dict</span>,
<span class="sig-arg">ignore_chars</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo._pair_replacement">source code</a></span>
</td>
</tr></table>
<p>Compare two sequences and generate info on the replacements seen.</p>
<p>Arguments: o seq1, seq2 - The two sequences to compare. o weight1,
weight2 - The relative weights of seq1 and seq2. o start_dict - The
dictionary containing the starting replacement info that we will modify.
o ignore_chars - A list of characters to ignore when calculating
replacements (ie. '-').</p>
<p>Returns: o A replacment dictionary which is modified from initial_dict
with the information from the sequence comparison.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="_get_base_replacements"></a>
<div class="private">
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">_get_base_replacements</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">skip_items</span>=<span class="sig-default"><code class="variable-group">[</code><code class="variable-group">]</code></span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo._get_base_replacements">source code</a></span>
</td>
</tr></table>
<p>Get a zeroed dictonary of all possible letter combinations.</p>
<p>This looks at the type of alphabet and gets the letters for it. It
then creates a dictionary with all possible combinations of these letters
as keys (ie. ('A', 'G')) and sets the values as zero.</p>
<p>Returns: o The base dictionary created o A list of alphabet items to
skip when filling the dictionary.Right now the only thing I can imagine
in this list is gap characters, but maybe X's or something else might be
useful later. This will also include any characters that are specified to
be skipped.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="pos_specific_score_matrix"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">pos_specific_score_matrix</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">axis_seq</span>=<span class="sig-default">None</span>,
<span class="sig-arg">chars_to_ignore</span>=<span class="sig-default"><code class="variable-group">[</code><code class="variable-group">]</code></span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo.pos_specific_score_matrix">source code</a></span>
</td>
</tr></table>
<p>Create a position specific score matrix object for the alignment.</p>
<p>This creates a position specific score matrix (pssm) which is an
alternative method to look at a consensus sequence.</p>
<p>Arguments: o chars_to_ignore - A listing of all characters not to
include in the pssm. If the alignment alphabet declares a gap character,
then it will be excluded automatically. o axis_seq - An optional argument
specifying the sequence to put on the axis of the PSSM. This should be a
Seq object. If nothing is specified, the consensus sequence, calculated
with default parameters, will be used.</p>
<p>Returns: o A PSSM (position specific score matrix) object.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="information_content"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
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<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">information_content</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">start</span>=<span class="sig-default">0</span>,
<span class="sig-arg">end</span>=<span class="sig-default">None</span>,
<span class="sig-arg">e_freq_table</span>=<span class="sig-default">None</span>,
<span class="sig-arg">log_base</span>=<span class="sig-default">2</span>,
<span class="sig-arg">chars_to_ignore</span>=<span class="sig-default"><code class="variable-group">[</code><code class="variable-group">]</code></span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo.information_content">source code</a></span>
</td>
</tr></table>
<p>Calculate the information content for each residue along an
alignment.</p>
<p>Arguments: o start, end - The starting an ending points to calculate
the information content. These points should be relative to the first
sequence in the alignment, starting at zero (ie. even if the 'real' first
position in the seq is 203 in the initial sequence, for the info content,
we need to use zero). This defaults to the entire length of the first
sequence. o e_freq_table - A FreqTable object specifying the expected
frequencies for each letter in the alphabet we are using (ie. {'G' : 0.4,
'C' : 0.4, 'T' : 0.1, 'A' : 0.1}). Gap characters should not be included,
since these should not have expected frequencies. o log_base - The base
of the logathrim to use in calculating the information content. This
defaults to 2 so the info is in bits. o chars_to_ignore - A listing of
characterw which should be ignored in calculating the info content.</p>
<p>Returns: o A number representing the info content for the specified
region.</p>
<p>Please see the Biopython manual for more information on how
information content is calculated.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="_get_letter_freqs"></a>
<div class="private">
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
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<table width="100%" cellpadding="0" cellspacing="0" border="0">
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<h3 class="epydoc"><span class="sig"><span class="sig-name">_get_letter_freqs</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">residue_num</span>,
<span class="sig-arg">all_records</span>,
<span class="sig-arg">letters</span>,
<span class="sig-arg">to_ignore</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo._get_letter_freqs">source code</a></span>
</td>
</tr></table>
<p>Determine the frequency of specific letters in the alignment.</p>
<p>Arguments: o residue_num - The number of the column we are getting
frequencies from. o all_records - All of the SeqRecords in the alignment.
o letters - The letters we are interested in getting the frequency for. o
to_ignore - Letters we are specifically supposed to ignore.</p>
<p>This will calculate the frequencies of each of the specified letters
in the alignment at the given frequency, and return this as a dictionary
where the keys are the letters and the values are the frequencies.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
<a name="_get_column_info_content"></a>
<div class="private">
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">_get_column_info_content</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">obs_freq</span>,
<span class="sig-arg">e_freq_table</span>,
<span class="sig-arg">log_base</span>,
<span class="sig-arg">random_expected</span>)</span>
</h3>
</td><td align="right" valign="top"
><span class="codelink"><a href="Bio.Align.AlignInfo-pysrc.html#SummaryInfo._get_column_info_content">source code</a></span>
</td>
</tr></table>
<p>Calculate the information content for a column.</p>
<p>Arguments: o obs_freq - The frequencies observed for each letter in
the column. o e_freq_table - An optional argument specifying the expected
frequencies for each letter. This is a SubsMat.FreqTable instance. o
log_base - The base of the logathrim to use in calculating the info
content.</p>
<dl class="fields">
</dl>
</td></tr></table>
</div>
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