/var/lib/mobyle/programs/msaprobs.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>msaprobs</name>
<version>0.9.4</version>
<doc>
<title>MSAProbs</title>
<description>
<text lang="en">is a protein multiple sequence alignment algorithm based on pair hidden Markov models and partition function posterior probabilities</text>
</description>
<authors>Yongchao Liu, Bertil Schmidt and Douglas L. Maskell</authors>
<reference doi="doi:10.1093/bioinformatics/btq338">
Yongchao Liu, Bertil Schmidt and Douglas L. Maskell (Bioinformatics 2010 26(16): 1958-1964)
MSAProbs: multiple sequence alignment based on pair hidden Markov models and
partition function posterior probabilities.
</reference>
<sourcelink>http://sourceforge.net/projects/msaprobs/files/MSAProbs-0.9.4.tar.gz/download</sourcelink>
<homepagelink>http://sourceforge.net/projects/msaprobs/</homepagelink>
<comment>
<div xmlns="http://www.w3.org/1999/xhtml">
<p>MSAProbs is an open-source protein multiple sequence ailgnment algorithm,
achieving the stastistically highest alignment accuracy on popular benchmarks:
<a href="http://www-bio3d-igbmc.u-strasbg.fr/balibase/">BALIBASE</a>,
<a href="http://www.drive5.com/muscle/prefab.htm">PREFAB</a>,
<a href="http://bioinformatics.vub.ac.be/databases/databases.html">SABMARK</a>,
<a href="http://www.compbio.dundee.ac.uk/Software/Oxbench/oxbench.html">OXBENCH</a>,
compared to ClustalW, MAFFT, MUSCLE, ProbCons and Probalign.</p>
</div>
</comment>
</doc>
<category>alignment:multiple</category>
<command>msaprobs</command>
</head>
<parameters>
<parameter ismandatory="1" issimple="1">
<name>sequences</name>
<prompt lang="en">Sequences File ( a file containing several sequences ).</prompt>
<type>
<biotype>Protein</biotype>
<datatype>
<class>Sequence</class>
</datatype>
<dataFormat>FASTA</dataFormat>
</type>
<format>
<code proglang="perl">" $sequences"</code>
<code proglang="python">" " + str( sequences )</code>
</format>
<argpos>1000</argpos>
</parameter>
<paragraph>
<name>accuracy</name>
<prompt lang="en">Accuracy Options</prompt>
<parameters>
<parameter>
<name>consitency</name>
<prompt lang="en">passes of consistency transformation( 0 >= REPS >= 5 default: 2 )</prompt>
<type>
<datatype>
<class>Integer</class>
</datatype>
</type>
<vdef>
<value>2</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value != $vdef)" -c $value " : ""</code>
<code proglang="python">("" , " -c "+str(value))[ value is not None and value != vdef ]</code>
</format>
<ctrl>
<message>
<text lang="en">use 0 >= REPS >= 5</text>
</message>
<code proglang="perl">$value >=0 and $value<=5</code>
<code proglang="python">value >=0 and value<=5</code>
</ctrl>
<comment>
<text lang="en">A probabilistic consistency transformation is used to re-estimate more
accurate posterior probabilities of each sequence pair x and y by introducing
another sequence z. Instead of re-computing the posterior probabilities
based on three-sequence alignments, the transformation is performed
based on the already computed probability matrices estimated from
pairwise alignments.
To avoid a biased sampling of sequences, we therefore derive a weighed
probabilistic consistency transformation approach
This motivation of the weighted approach is to obtain more accurate
alignments than the non-weighted one. The transformations are further
performed for a fixed number of iterations to refine the probabilities. In
MSAProbs, two iterations (the default value) are used. This default value
offers a good trade-off between alignment accuracy and execution time.</text>
</comment>
</parameter>
<parameter>
<name>iterative_refinement</name>
<prompt lang="en">passes of iterative-refinement ( use 0 >= REPS >= 1000 default: 10 )</prompt>
<type>
<datatype>
<class>Integer</class>
</datatype>
</type>
<vdef>
<value>10</value>
</vdef>
<format>
<code proglang="perl">(defined $value and $value != $vdef)" -ir $value " : ""</code>
<code proglang="python">("" , " -ir "+str(value))[ value is not None and value != vdef ]</code>
</format>
<ctrl>
<message>
<text lang="en">use 0 >= REPS >= 1000</text>
</message>
<code proglang="perl">$value >=0 and $value<=100</code>
<code proglang="python">value >=0 and value<=100</code>
</ctrl>
<comment>
<text lang="en">
As a post-processing step, a randomized iterative alignment is employed
to further improve alignment accuracy. This refinement randomly partitions
S into two non-overlapped subsets, and then performs a profile–profile
alignment of the two subsets. MSAProbs designs its own pseudo random
number generator based on the linear congruential method for the random
partition of S. The iterative refinement is designed to complete after a fixed
number of iterations (10 iterations, by default).
</text>
</comment>
</parameter>
</parameters>
</paragraph>
<paragraph>
<name>output_opt</name>
<prompt lang="en">Output Options</prompt>
<parameters>
<parameter>
<name>annotation</name>
<prompt lang="en">write annotation for multiple alignment to FILENAME</prompt>
<type>
<datatype>
<class>Filename</class>
</datatype>
</type>
<format>
<code proglang="perl">(defined $value)" -annot $value " : ""</code>
<code proglang="python">("" , " -annot " + str(value))[ value is not None ]</code>
</format>
<comment>
<text lang="en">The score of each column of the final alignment, from the leftmost to the right most, will be report on this annotation file.</text>
</comment>
</parameter>
</parameters>
</paragraph>
<parameter isstdout="1">
<name>alignment</name>
<prompt lang="en">Alignment file</prompt>
<type>
<biotype>Protein</biotype>
<datatype>
<class>Alignment</class>
</datatype>
<dataFormat>FASTA</dataFormat>
</type>
<filenames>
<code proglang="perl">"msaprobs.out"</code>
<code proglang="python">"msaprobs.out"</code>
</filenames>
</parameter>
<parameter isout="1">
<name>annotation_file</name>
<prompt lang="en">Annotation file</prompt>
<precond>
<code proglang="perl">defined $annotation</code>
<code proglang="python">annotation is not None</code>
</precond>
<type>
<datatype>
<class>MSAProbsAnnotation</class>
<superclass>Report</superclass>
</datatype>
</type>
<filenames>
<code proglang="perl">$annotation</code>
<code proglang="python">annotation</code>
</filenames>
<comment>
<text lang="en">Each line represents the score of each column of the final alignment from the leftmost to the right most.</text>
</comment>
</parameter>
</parameters>
</program>
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