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<div class="document" id="last-map-probs">
<h1 class="title">last-map-probs</h1>

<p>This script reads alignments of DNA reads to a genome, and estimates
the probability that each alignment represents the genomic source of
the read.</p>
<p>It writes the alignments with &quot;mismap&quot; probabilities, i.e. the
probability that the alignment does not represent the genomic source
of the read.  By default, it discards alignments with mismap
probability &gt; 0.01.</p>
<div class="section" id="typical-usage">
<h2>Typical usage</h2>
<p>These commands map DNA reads to the human genome:</p>
<pre class="literal-block">
lastdb -m1111110 hu human/chr*.fa
lastal -Q1 -e120 hu reads.fastq | last-map-probs.py -s150 &gt; myalns.maf
</pre>
</div>
<div class="section" id="explanation-of-typical-usage">
<h2>Explanation of typical usage</h2>
<p>These commands find alignments with mismap probability &lt;= 0.01 and
score &gt;= 150 (-s150).  The score threshold should be high enough to
avoid random, spurious alignments: otherwise, the mismap probabilities
will not be reliable.  A threshold of 150 is often reasonable.  For
instance, if we compare 50 bp reads to the human genome, we expect a
random alignment with score &gt;= 150 once every few thousand reads.</p>
<p>The lastal command finds alignments with score &gt;= 120 (-e120).  This
is because the mismap probability of an alignment with score 150 may
depend on alignments with score &lt; 150.  Setting e = s-30 seems to work
well.</p>
<p>(We used to recommend option -d108, but this is no longer necessary.)</p>
</div>
<div class="section" id="options">
<h2>Options</h2>
<blockquote>
<table class="docutils option-list" frame="void" rules="none">
<col class="option" />
<col class="description" />
<tbody valign="top">
<tr><td class="option-group">
<kbd><span class="option">-h</span>, <span class="option">--help</span></kbd></td>
<td>Print a help message and exit.</td></tr>
<tr><td class="option-group" colspan="2">
<kbd><span class="option">-m <var>M</var></span>, <span class="option">--mismap=<var>M</var></span></kbd></td>
</tr>
<tr><td>&nbsp;</td><td>Don't write alignments with mismap probability &gt; M.</td></tr>
<tr><td class="option-group" colspan="2">
<kbd><span class="option">-s <var>S</var></span>, <span class="option">--score=<var>S</var></span></kbd></td>
</tr>
<tr><td>&nbsp;</td><td>Don't write alignments with score &lt; S.</td></tr>
</tbody>
</table>
</blockquote>
</div>
<div class="section" id="details">
<h2>Details</h2>
<ul class="simple">
<li>This script can read alignments in either of the formats produced by
lastal (maf or tabular).</li>
<li>The script reads one batch of alignments at a time (by looking for
lines starting with &quot;# batch&quot;).  If the batches are huge
(e.g. because there are no lines starting with &quot;# batch&quot;), it might
need too much memory.</li>
</ul>
</div>
<div class="section" id="using-multiple-cpus">
<h2>Using multiple CPUs</h2>
<p>This will run the pipeline on all your CPU cores:</p>
<pre class="literal-block">
parallel-fastq &quot;lastal -Q1 -e120 hu | last-map-probs.py -s150&quot; &lt; reads.fastq &gt; myalns.maf
</pre>
<p>It requires GNU parallel to be installed
(<a class="reference external" href="http://www.gnu.org/software/parallel/">http://www.gnu.org/software/parallel/</a>).</p>
</div>
<div class="section" id="limitations">
<h2>Limitations</h2>
<ul class="simple">
<li>It is possible that two or more alignments reflect the origin of one
query sequence, for instance if the query arose by splicing.  This
script makes no allowance for that possibility.</li>
</ul>
</div>
<div class="section" id="method">
<h2>Method</h2>
<p>Suppose one query sequence has three alignments, with scores: s1, s2,
s3.  The probability that the first alignment is the one that reflects
the origin of the query, is:</p>
<pre class="literal-block">
exp(s1/t) / [exp(s1/t) + exp(s2/t) + exp(s3/t)]
</pre>
<p>Here, t is a parameter that depends on the scoring scheme: it is
written in the lastal header.</p>
</div>
<div class="section" id="reference">
<h2>Reference</h2>
<p>For more information, please see this article:</p>
<blockquote>
Incorporating sequence quality data into alignment improves DNA read
mapping.  Frith MC, Wan R, Horton P.  Nucleic Acids Research 2010
38:e100.</blockquote>
</div>
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