--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/locarna_best_subtree.xml	Fri Dec 16 07:35:29 2016 -0500
@@ -0,0 +1,190 @@
+<tool id="locarna_best_subtree" name="locarna_best_subtree" version="0.1.0" >
+	<requirements>
+		<requirement type="package" version="0.1">graphclust-wrappers</requirement>
+		<requirement type="package" version='1.8.10'>locarna</requirement>
+		<requirement type="package" version='2.1'>rnaz</requirement>
+		<requirement type="package" version='0.07'>perl-math-round</requirement>
+	</requirements>
+	<stdio>
+		<exit_code range="1:" />
+	</stdio>
+	<command>
+		<![CDATA[
+
+		    'locARNAGraphClust.pl'  '$center_fa_file' '$tree_file' '$tree_matrix' $p $max_diff_am $tau $max_diff '' '$data_map' $plfold_minlen
+        ]]>
+	</command>
+	<inputs>
+		<param type="data" name="center_fa_file" label="centers" format="fa, fasta" help="fasta format" />
+		<param type="data" name="tree_file" label="trees" format="text" help="text format" />
+		<param type="data" name="tree_matrix" label="tree_matrix" format="text" help="text format" />
+		<param type="data" name="data_map" label="data_map" format="txt" help="text format" />
+		<param name="p" type="float" value="0.001" size="5" label="minimal probability" help="-p"/>
+		<param name="max_diff_am" type="integer" value="50" size="5" label=" maximal difference for sizes of matched arcs" help="--max-diff-am"/>
+		<param name="tau" type="integer" value="50" size="5" label="Tau factor in percent" help="--tau"/>
+		<param name="max_diff" type="integer" value="100" size="5" label="maximal difference for alignment traces" help="--max-diff"/>
+		<param name="plfold_minlen" type="integer" value="210" size="5" label="Minimal length of a sequences for which RNAplfold is used" />
+	</inputs>
+	<outputs>
+
+		<data name="model_tree_stk" format="stockholm" label="model.tree.stk" from_work_dir="MODEL/best_subtree.aln" />
+	</outputs>
+	<tests>
+		<test>
+			<param name="tree_file" value="1.1.tree"/>
+			<param name="center_fa_file" value="1.1.center.fa"/>
+			<param name="data_map" value="data.map"/>
+			<param name="tree_matrix" value="1.1.matrix.tree"/>
+			<param name="p" value="0.001"/>
+			<param name="max-diff-am" value="50"/>
+			<param name="tau" value="50"/>
+			<param name="max-diff-am" value="100"/>
+			<output name="model_tree_stk" file="best_subtree.aln"/>
+		</test>
+	</tests>
+	<help>
+		<![CDATA[
+**What it does**
+
+MLocARNA computes a multiple sequence-structure alignment of RNA sequences.
+It uses *treefile* - file with guide tree in NEWICK format. The given tree is used as guide tree for the progressive alignment.
+This saves the calculation of pairwise all-vs-all similarities and construction of the guide tree.
+
+
+
+]]>
+	</help>
+	<citations>
+		<citation type="bibtex">@inproceedings{costa2010fast,
+        title={Fast neighborhood subgraph pairwise distance kernel},
+        author={Costa, Fabrizio and De Grave, Kurt},
+        booktitle={Proceedings of the 26th International Conference on Machine Learning},
+        pages={255--262},
+        year={2010},
+        organization={Omnipress}
+      }
+      </citation>
+			<citation type="bibtex">@Article{Will_Joshi_Hofacker-LocAR_Accur_bound-2012,
+  author =	 {Will, Sebastian and Joshi, Tejal and Hofacker, Ivo L. and
+                  Stadler, Peter F. and Backofen, Rolf},
+  title =	 {{LocARNA}-{P}: {Accurate} boundary prediction and improved
+                  detection of structural {RNAs}},
+  journal =	 {RNA},
+  year =	 {2012},
+  volume =	 {18},
+  number =	 {5},
+  pages =	 {900-14},
+  user =	 {will},
+  pmid =	 {22450757},
+  doi = 	 {10.1261/rna.029041.111},
+  issn = 	 {1469-9001},
+  issn = 	 {1355-8382},
+  abstract =	 {Current genomic screens for noncoding RNAs (ncRNAs) predict
+                  a large number of genomic regions containing potential
+                  structural ncRNAs. The analysis of these data requires
+                  highly accurate prediction of ncRNA boundaries and
+                  discrimination of promising candidate ncRNAs from weak
+                  predictions. Existing methods struggle with these goals
+                  because they rely on sequence-based multiple sequence
+                  alignments, which regularly misalign RNA structure and
+                  therefore do not support identification of structural
+                  similarities. To overcome this limitation, we compute
+                  columnwise and global reliabilities of alignments based on
+                  sequence and structure similarity; we refer to these
+                  structure-based alignment reliabilities as STARs. The
+                  columnwise STARs of alignments, or STAR profiles, provide a
+                  versatile tool for the manual and automatic analysis of
+                  ncRNAs. In particular, we improve the boundary prediction of
+                  the widely used ncRNA gene finder RNAz by a factor of 3 from
+                  a median deviation of 47 to 13 nt. Post-processing RNAz
+                  predictions, LocARNA-P's STAR score allows much stronger
+                  discrimination between true- and false-positive predictions
+                  than RNAz's own evaluation. The improved accuracy, in this
+                  scenario increased from AUC 0.71 to AUC 0.87, significantly
+                  reduces the cost of successive analysis steps. The
+                  ready-to-use software tool LocARNA-P produces
+                  structure-based multiple RNA alignments with associated
+                  columnwise STARs and predicts ncRNA boundaries. We provide
+                  additional results, a web server for LocARNA/LocARNA-P, and
+                  the software package, including documentation and a pipeline
+                  for refining screens for structural ncRNA, at
+                  http://www.bioinf.uni-freiburg.de/Supplements/LocARNA-P/.}
+}
+				</citation>
+				<citation type="bibtex">@Article{Will:etal:_infer_non_codin_rna_famil:PLOS2007,
+  author =	 {Sebastian Will and Kristin Reiche and Ivo L. Hofacker and
+                  Peter F. Stadler and Rolf Backofen},
+  title =	 {Inferring Non-Coding {RNA} Families and Classes by Means of
+                  Genome-Scale Structure-Based Clustering},
+  journal =	 {PLoS Comput Biol},
+  year =	 2007,
+  volume =       {3},
+  number =       {4},
+  pages =        {e65},
+  issn =         {1553-7358},
+  issn =         {1553-734X},
+  pmid =         {17432929},
+  doi =          {10.1371/journal.pcbi.0030065},
+  user =	 {will},
+  abstract =	 {The RFAM database defines families of ncRNAs by means of
+                  sequence similarities that are sufficientto establish
+                  homology. In some cases, such as microRNAs, box H/ACA
+                  snoRNAs, functional commonalities define classes of RNAs
+                  that are characterized by structural similarities, and
+                  typically consist ofmultiple RNA families. Recent advances
+                  in high-throughput transcriptomics and comparative genomics
+                  have produced very large sets of putative non-coding RNAs
+                  and regulatory RNA signals. For many ofthem, evidence for
+                  stabilizing selection acting on their secondary structures
+                  has been derived, and at least approximate models of their
+                  structures have been computed. The overwhelming majority of
+                  these hypo-thetical RNAs cannot be assigned to established
+                  families or classes. We present here a structure-based
+                  clustering approach that is capable of extracting putative
+                  RNA classesfrom genome-wide surveys for structured RNAs. The
+                  LocARNA tool implements a novel variant of theSankoff
+                  algorithm that is sufficiently fast to deal with several
+                  thousand candidate sequences. The method is also robust
+                  against false positive predictions, i.e., a contamination of
+                  the input data with unstructured ornon-conserved
+                  sequences. We have successfully tested the LocARNA-based
+                  clustering approach on the sequences of the
+                  RFAM-seedalignments. Furthermore, we have applied it to a
+                  previously published set of 3332 predicted structured
+                  elements in the Ciona intestinalis genomes (Missal et al.,
+                  Bioinformatics 21(S2), i77-i78). In addition torecovering
+                  e.g. tRNAs as a structure-based class, the method identifies
+                  several RNA families, including microRNA and snoRNA
+                  candidates, and suggests several novel classes of ncRNAs for
+                  which to-date norepresentative has been experimentally
+                  characterized.}
+}
+
+					</citation>
+					<citation type="bibtex">@Article{Smith:Heyne:Richter:Freib_RNA_Tools:NAR2010,
+  author =	 {Smith, Cameron and Heyne, Steffen and Richter, Andreas S.
+                  and Will, Sebastian and Backofen, Rolf},
+  title =	 {Freiburg {RNA} {Tools}: a web server integrating {IntaRNA},
+                  {ExpaRNA} and {LocARNA}},
+  journal =	 NAR,
+  year =	 {2010},
+  volume =	 {38 Suppl},
+  number =	 {},
+  pages =	 {W373-7},
+  user =	 {arichter},
+  pmid =	 {20444875},
+  doi = 	 {10.1093/nar/gkq316},
+  issn = 	 {0305-1048},
+  issn =	 {1362-4962},
+  abstract =	 {The Freiburg RNA tools web server integrates three tools
+                  for the advanced analysis of RNA in a common web-based user
+                  interface. The tools IntaRNA, ExpaRNA and LocARNA support
+                  the prediction of RNA-RNA interaction, exact RNA matching
+                  and alignment of RNA, respectively. The Freiburg RNA tools
+                  web server and the software packages of the stand-alone
+                  tools are freely accessible at
+                  http://rna.informatik.uni-freiburg.de.}
+}
+						</citation>
+	</citations>
+</tool>