/var/lib/mobyle/programs/targetp.xml is in mobyle-programs 5.1.2-1.
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<!-- XML Authors: Corinne Maufrais, Nicolas Joly and Bertrand Neron, -->
<!-- 'Biological Software and Databases' Group, Institut Pasteur, Paris. -->
<!-- Distributed under LGPLv2 License. Please refer to the COPYING.LIB document. -->
<program>
<head>
<name>targetp</name>
<version>1.1</version>
<xi:include xmlns:xi="http://www.w3.org/2001/XInclude" href="Entities/cbs_package.xml"/>
<doc>
<title>targetp</title>
<description>
<text lang="en">predicts the subcellular location of eukaryotic proteins.</text>
</description>
<sourcelink>http://www.cbs.dtu.dk/cgi-bin/nph-sw_request?targetp</sourcelink>
<authors>Olof Emanuelsson, olof@sbc.su.se</authors>
<reference> Predicting subcellular localization of proteins based on their N-terminal amino acid sequence.
Olof Emanuelsson, Henrik Nielsen, Søren Brunak and Gunnar von Heijne.
J. Mol. Biol., 300: 1005-1016, 2000.
</reference>
<reference>
Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.
Henrik Nielsen, Jacob Engelbrecht, Søren Brunak and Gunnar von Heijne.
Protein Engineering, 10:1-6, 1997.
</reference>
<doclink>http://www.cbs.dtu.dk/services/TargetP/</doclink>
<comment>
<div xmlns="http://www.w3.org/1999/xhtml">
<p>targetp predicts the subcellular location of eukaryotic protein sequences. The assignment is based on the
predicted presence of any of the N-terminal presequences: chloroplast transit peptide (cTP), mitochondrial
targeting peptide (mTP) or secretory pathway signal peptide (SP).</p>
<p>targetp comes in two versions, one for plant proteins (-P) and one for non-plant proteins (-N). In the lat‐
ter case cTP is a forbidden prediction. For the sequences predicted to contain an N-terminal presequence a
prediction of its length can be provided (-c).</p>
<p><strong>CAVEATS : </strong><br />
Submit if possible 130 N-terminal residues. The suggested length is due to the fact that targetp was
trained taking into account the 130 N-terminal residues, and the fact that using longer sequences does not
influence the prediction in any way (apart from making it slower). The cTP and mTP cleavage site predic‐
tions are restricted to search for a potential cleavage site within the 100 or 120 N-terminal amino acids,
respectively.</p>
</div>
</comment>
</doc>
<category>sequence:protein:localization</category>
</head>
<parameters>
<parameter ishidden="1" iscommand="1">
<name>targetp</name>
<type>
<datatype>
<class>String</class>
</datatype>
</type>
<format>
<code proglang="perl">"targetp "</code>
<code proglang="python">"targetp "</code>
</format>
</parameter>
<parameter ismandatory="1" issimple="1" ismaininput="1">
<name>sequence</name>
<prompt lang="en">Input Sequence</prompt>
<type>
<datatype>
<class>Sequence</class>
</datatype>
<dataFormat>FASTA</dataFormat>
</type>
<format>
<code proglang="perl">" $value"</code>
<code proglang="python">" " + str( value )</code>
</format>
<argpos>50</argpos>
<example>
>P48786; PATHOGENESIS-RELATED HOMEODOMAIN PROTEIN (PRHP).
MEEISDPKPNALEQVLPTVPNGKCTAPVQMESLAVDVQKVSGEAKVRICSCWCEIVRSPEDLTKLVPCNDFAEDIKLFDS
DPMQQEAESSIGIPLIPKQVTMSHNHDHESGSEMVSNEVMQENHVIATENTYQKSDFDRINMGQKETMPEEVIHKSFLES
STSSIDILLNNHNSYQSGLPPENAVTDCKQVQLGHRSDDAIKNSGLVELVIGQKNVAKSPSQLVETGKRGRGRPRKVQTG
LEQLVIGQKTAAKSSSQLGDTGKRSRGRPRKVQNSPTSFLENINMEQKETIPEQVTQNSILESLTIPTDNQSRTYNSDQS
ELPPENAAKNCNHAQFGHQSDDTTKISGFKELVIGQETVAKSPSQLVDAGKRGRGRPRKVQTGLEQLVPVQETAAKSSSQ
LGDTGKRSRGRPRKVQDSPTSLGGNVKVVPEKGKDSQELSVNSSRSLRSRSQEKSIEPDVNNIVADEGADREKPRKKRKK
RMEENRVDEFCRIRTHLRYLLHRIKYEKNFLDAYSGEGWKGQSLDKIKPEKELKRAKAEIFGRKLKIRDLFQRLDLARSE
GRLPEILFDSRGEIDSEDIFCAKCGSKDVTLSNDIILCDGACDRGFHQFCLDPPLLKEYIPPDDEGWLCPGCECKIDCIK
LLNDSQETNILLGDSWEKVFAEEAAAAASGKNLDDNSGLPSDDSEDDDYDPGGPDLDEKVQGDDSSTDESDYQSESDDMQ
VIRQKNSRGLPSDDSEDDEYDPSGLVTDQMYKDSSCSDFTSDSEDFTGVFDDYKDTGKAQGPLASTPDHVRNNEEGCGHP
EQGDTAPLYPRRQVESLDYKKLNDIEFSKMCDILDILSSQLDVIICTGNQEEYGNTSSDSSDEDYMVTSSPDKNNSDKEA
TAMERGRESGDLELDQKARESTHNRRYIKKFAVEGTDSFLSRSCEDSAAPVAGSKSTSKTLHGEHATQRLLQSFKENQYP
QRAVKESLAAELALSVRQVSNWFNNRRWSFRHSSRIGSDVAKFDSNDTPRQKSIDMSGPSLKSVLDSATYSEIEKKEQDT
ASLGLTEGCDRYMTLNMVADEGNVHTPCIAETREEKTEVGIKPQQNPL
</example>
</parameter>
<parameter ismandatory="1" issimple="1">
<name>type</name>
<prompt lang="en"> Use the plant or non-plant version.</prompt>
<type>
<datatype>
<class>Choice</class>
</datatype>
</type>
<vdef>
<value>null</value>
</vdef>
<vlist>
<velem undef="1">
<value>null</value>
<label>Choose a type of organism</label>
</velem>
<velem>
<value>p</value>
<label>plant</label>
</velem>
<velem>
<value>np</value>
<label>non-plant</label>
</velem>
</vlist>
<format>
<code proglang="perl">( $value eq 'p')? " -P ": " -N "</code>
<code proglang="python">( " -N ", " -P ")[ value == 'p' ]</code>
</format>
<argpos>10</argpos>
</parameter>
<parameter issimple="1">
<name>cleavege</name>
<prompt lang="en">Include cleavage site prediction (-c).</prompt>
<type>
<datatype>
<class>Boolean</class>
</datatype>
</type>
<vdef>
<value>0</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value ne $vdef)? " -c " : ""</code>
<code proglang="python">( "" , " -c ")[ value is not None and value != vdef ]</code>
</format>
<argpos>20</argpos>
</parameter>
<paragraph>
<name>cutoffs</name>
<prompt lang="en">Cutoffs</prompt>
<comment>
<text lang="en">predefined set of cutoffs that yielded this specificity on the TargetP test sets.</text>
</comment>
<parameters>
<parameter>
<name>predefined_cutoff</name>
<prompt lang="en"></prompt>
<type>
<datatype>
<class>Choice</class>
</datatype>
</type>
<vdef>
<value>null</value>
</vdef>
<flist>
<felem undef="1">
<value>null</value>
<label>no cutoffs; winner-takes-all (default)</label>
<code proglang="perl">''</code>
<code proglang="python">''</code>
</felem>
<felem>
<value>cutoff_95</value>
<label>specificity >0.95</label>
<code proglang="perl">( $type eq 'p')? " -p 0.73 -t 0.86 -s 0.43 -o 0.84 " : " -t 0.78 -s 0.00 -o 0.73 "</code>
<code proglang="python">( " -t 0.78 -s 0.00 -o 0.73 " , " -p 0.73 -t 0.86 -s 0.43 -o 0.84 " )[ type == 'p' ]</code>
</felem>
<felem>
<value>cutoff_90</value>
<label>specificity >0.90</label>
<code proglang="perl">( $type eq 'p')? " -p 0.62 -t 0.76 -s 0.00 -o 0.53 " : " -t 0.65 -s 0.00 -o 0.52 "</code>
<code proglang="python">( " -t 0.65 -s 0.00 -o 0.52 " , " -p 0.62 -t 0.76 -s 0.00 -o 0.53 " )[ type == 'p' ]</code>
</felem>
</flist>
<argpos>30</argpos>
</parameter>
<paragraph>
<name>user_cutoffs</name>
<prompt lang="en">define your own Cutoffs</prompt>
<precond>
<code proglang="python">not predefined_cutoff</code>
</precond>
<comment>
<text lang="en">The user cutoffs will be ignored if a predefine set of cutoffs is specified</text>
</comment>
<argpos>40</argpos>
<parameters>
<parameter>
<name>cTP</name>
<prompt lang="en">cTP</prompt>
<type>
<datatype>
<class>Float</class>
</datatype>
</type>
<vdef>
<value>0.0</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value ne $vdef)? " -p $value " : ""</code>
<code proglang="python">( "" , " -p " + str( value ) )[ value is not None and value != vdef ]</code>
</format>
<ctrl>
<message>
<text lang="en">The value must be between 0.0 and 1.0</text>
</message>
<code proglang="perl">$value >= 0.0 or $value <= 1.0</code>
<code proglang="python">value >= 0.0 or value <= 1.0</code>
</ctrl>
<comment>
<text lang="en">In order to increase the specificity of cTP prediction, use Pcut as a cutoff for predicting cTP: if
the winning score is the chloroplast (cTP) score, specifying Pcut means that the score also has to
be above that value; if not, the sequence will be left unpredicted, and an asterisk (*) will be out‐
put in the Loc column.</text>
<text lang="en">The value of Pcut must be between 0.0 and 1.0.</text>
</comment>
</parameter>
<parameter>
<name>mTP</name>
<prompt lang="en">mTP</prompt>
<type>
<datatype>
<class>Float</class>
</datatype>
</type>
<vdef>
<value>0.0</value>
</vdef>
<format>
<code proglang="perl">( defined $value and $value ne $vdef)? " -t $value" : ""</code>
<code proglang="python">( "" , " -t " + str( value ) )[ value is not None and value != vdef ]</code>
</format>
<ctrl>
<message>
<text lang="en">The value must be between 0.0 and 1.0</text>
</message>
<code proglang="perl">$value >= 0.0 or $value <= 1.0</code>
<code proglang="python">value >= 0.0 or value <= 1.0</code>
</ctrl>
<comment>
<text lang="en">In order to increase the specificity of mTP prediction, use Tcut as a cutoff for predicting mTP: if
the winning score is the mithochondrial (mTP) score, specifying Tcut means that the score also has to
be above that value; if not, the sequence will be left unpredicted, and an asterisk (*) will be out‐
put in the Loc column.</text>
<text lang="en">The value of Tcut must be between 0.0 and 1.0.</text>
</comment>
</parameter>
<parameter>
<name>SP</name>
<prompt lang="en">SP</prompt>
<type>
<datatype>
<class>Float</class>
</datatype>
</type>
<vdef>
<value>0.0</value>
</vdef>
<format>
<code proglang="perl">( defined $value and $value ne $vdef)? " -s $value" : ""</code>
<code proglang="python">( "" , " -s " + str( value ) )[ value is not None and value != vdef ]</code>
</format>
<ctrl>
<message>
<text lang="en">The value must be between 0.0 and 1.0</text>
</message>
<code proglang="perl">$value >= 0.0 or $value <= 1.0</code>
<code proglang="python">value >= 0.0 or value <= 1.0</code>
</ctrl>
<comment>
<text lang="en">In order to increase the specificity of SP prediction, use Scut as a cutoff for predicting SP: if
the winning score is the Secretory pathway (SP) score, specifying Scut means that the score also has to
be above that value; if not, the sequence will be left unpredicted, and an asterisk (*) will be out‐
put in the Loc column.</text>
<text lang="en">The value of Scut must be between 0.0 and 1.0.</text>
</comment>
</parameter>
<parameter>
<name>other</name>
<prompt lang="en">other</prompt>
<type>
<datatype>
<class>Float</class>
</datatype>
</type>
<vdef>
<value>0.0</value>
</vdef>
<format>
<code proglang="perl">( defined $value and $value ne $vdef)? " -o $value" : ""</code>
<code proglang="python">( "" , " -o " + str( value ) )[ value is not None and value != vdef ]</code>
</format>
<ctrl>
<message>
<text lang="en">The value must be between 0.0 and 1.0</text>
</message>
<code proglang="perl">$value >= 0.0 or $value <= 1.0</code>
<code proglang="python">value >= 0.0 or value <= 1.0</code>
</ctrl>
<comment>
<text lang="en">In order to increase the specificity of any other location prediction, use Ocut as
a cutoff for predicting any other location : if
the winning score is the other location score, specifying Ocut means that the score also has to
be above that value; if not, the sequence will be left unpredicted, and an asterisk (*) will be out‐
put in the Loc column.</text>
<text lang="en">The value of Ocut must be between 0.0 and 1.0.</text>
</comment>
</parameter>
</parameters>
</paragraph>
</parameters>
</paragraph>
<parameter isstdout="1">
<name>results</name>
<prompt lang="en">targetp report</prompt>
<type>
<datatype>
<superclass>Report</superclass>
<class>targetp</class>
</datatype>
</type>
<filenames>
<code proglang="perl">"targetp.out"</code>
<code proglang="python">"targetp.out"</code>
</filenames>
<comment>
<div xmlns="http://www.w3.org/1999/xhtml">
<p>The output is in plain text; it will go to stdout. For each input sequence the following is printed (on one line):</p>
<ul>
<li><strong>Name : </strong>Sequence name truncated to 20 characters.</li>
<li><strong>Len : </strong>Sequence length.</li>
<li><strong>cTP, mTP, SP, other : </strong>Final NN scores on which the final prediction is based (Loc, see below). Note that the scores are
not really probabilities, and they do not necessarily add to one. However, the location with the
highest score is the most likely according to targetp, and the relationship between the scores (the
reliability class, see below) may be an indication of how certain the prediction is.</li>
<li><strong>Loc : </strong>Prediction of localization, based on the scores above; the codes are:
<ul>
<li><strong>C : </strong>Chloroplast, i.e. the sequence contains cTP, a chloroplast transit peptide;</li>
<li><strong>M : </strong>Mitochondrion, i.e. the sequence contains mTP, a mitochondrial targeting peptide;</li>
<li><strong>S : </strong>Secretory pathway, i.e. the sequence contains SP, a signal peptide;</li>
<li><strong>_ : </strong>any other location;</li>
<li><strong>* : </strong>"don't know". This character appears if cutoff
restrictions were demanded (-p, -t, -s, -o, see
below) and the winning network output score was below the requested cutoff for that category.</li>
</ul>
</li>
<li><strong>RC : </strong>Reliability class, from 1 to 5, where 1 indicates the strongest prediction. RC is a measure of the
size of the difference ('diff') between the highest (winning) and the second highest output scores.
There are 5 reliability classes, defined as follows:
<ol>
<li>diff > 0.8</li>
<li>0.800 > diff > 0.600</li>
<li>0.600 > diff > 0.400</li>
<li>0.400 > diff > 0.200</li>
<li>0.200 > diff</li>
</ol>
Thus, the lower the value of RC the safer the prediction.
</li>
<li><strong>TPlen : </strong>predicted presequence length (only when the -c option is given).</li>
</ul>
</div>
</comment>
</parameter>
</parameters>
</program>
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