Merge pull request #6295 from bernt-matthias/topic/fix-urls

Fix broken URLs

tool_collections/fastx_toolkit/macros.xml

@@ -47,8 +47,8 @@
                     author = "Assaf Gordon",
                     title = "FASTQ/A short-reads pre-processing tools",
                     year = "2010",
-                    note = "http://hannonlab.cshl.edu/fastx_toolkit/",
-                    url = "http://hannonlab.cshl.edu/fastx_toolkit/"}
+                    note = "https://github.com/agordon/fastx_toolkit",
+                    url = "https://github.com/agordon/fastx_toolkit"}
             </citation>
         </citations>
     </xml>

tool_collections/snpsift/snpSift_macros.xml

@@ -24,7 +24,7 @@ SnpSift dbnsfp 2>&1|head -n 1
     <token name="@EXTERNAL_DOCUMENTATION@">
 For details about this tool, please go to:
 
-- http://snpeff.sourceforge.net/SnpEff_manual.html
+- https://pcingola.github.io/SnpEff/
     </token>
     <xml name="citations">
         <citations>

tool_collections/snpsift/snpsift/snpSift_filter.xml

@@ -214,13 +214,10 @@ Filtering based on SnpEff annotations (``ANN`` or ``EFF`` fields):
 
 For information regarding HGVS and Sequence Ontology terms versus classic names:
 
-- https://pcingola.github.io/SnpEff/se_commandline/ for the options: ``-classic``, ``-hgvs``, and ``-sequenceOntology``
-- https://pcingola.github.io/SnpEff/se_inputoutput/#effect-prediction-details for the table containing the classic name and sequence onology term for each effect
+- https://pcingola.github.io/SnpEff/snpeff/commandline/ for the options: ``-classic``, ``-hgvs``, and ``-sequenceOntology``
+- https://pcingola.github.io/SnpEff/snpeff/inputoutput/ for the table containing the classic name and sequence onology term for each effect
 
 @EXTERNAL_DOCUMENTATION@
-- https://pcingola.github.io/SnpEff/ss_filter/
-
-The second link in particular has further details and more examples about the tool's expression syntax.
     ]]></help>
     <expand macro="citations" />
 </tool>

tools/breseq/breseq.xml

@@ -296,9 +296,8 @@ breseq's primary advantages over other software programs are that it can:
 - Produce annotated output describing biologically relevant mutational events.
 
 breseq was initially developed to analyze data from the Lenski long-term
-evolution experiment with `E. coli`_. References: barrick2009a_ barrick2009b_.
+evolution experiment with E. coli. References: barrick2009a_ barrick2009b_.
 
-.. _`E. coli`: http://myxo.css.msu.edu/ecoli/
 .. _barrick2009a: http://barricklab.org/twiki/pub/Lab/ToolsBacterialGenomeResequencing/documentation/references.html#barrick2009a
 .. _barrick2009b: http://barricklab.org/twiki/pub/Lab/ToolsBacterialGenomeResequencing/documentation/references.html#barrick2009b
 

tools/delly/cnv.xml

@@ -190,7 +190,7 @@ $gc.nowindowselection
 
 **Input**
 
-Delly *cnv* requires are a sample (BAM), a genome (FASTA) and a mappability map (FASTA), which is available `here <https://gear.embl.de/data/delly/>`_. Intervals (BED), scanning regions (BED) and a delly SV file for breakpoint refinement (BCF) can be provided optionally.
+Delly *cnv* requires are a sample (BAM), a genome (FASTA) and a mappability map (FASTA), which is available `here <https://gear-genomics.embl.de/data/delly/>`_. Intervals (BED), scanning regions (BED) and a delly SV file for breakpoint refinement (BCF) can be provided optionally.
 
 **Output**
 

tools/disco/disco.xml

@@ -430,8 +430,8 @@ minEdgeLengthToBe1MinFlow = $third_assembly_iter_param.minEdgeLengthToBe1MinFlow
     <help><![CDATA[
 DISCO is a multi threaded and multiprocess distributed memory overlap-layout-consensus (OLC) metagenome assembler. Disco was developed as a scalable assembler to assemble large metagenomes from billions of Illumina sequencing reads of complex microbial communities. Disco was parallelized for computer clusters in a hybrid architecture that integrated shared-memory multi-threading, point-to-point message passing, and remote direct memory access. The assembly and scaffolding were performed using an iterative overlap graph approach.
 
-The detailed user manual of the assembler and how to use it to acheive best results is provided here:
- http://disco.omicsbio.org/user-manual. This is a quick start guide generally for developers and testers. Users with limited experience with genome assembly are advised to use the user manual.
+The detailed user manual of the assembler and how to use it to achive best results is provided here:
+https://github.com/abiswas-odu/Disco.
     ]]></help>
     <citations>
     </citations>

tools/dnaweaver/dnaweaver.xml

@@ -86,7 +86,7 @@ Method
 
 Here is a schema of the supply network used:
 
-.. image:: https://raw.githubusercontent.com/brsynth/synbiocad-galaxy-wrappers/master/DNAWeaver/img/image4.png
+.. image:: https://github.com/brsynth/galaxytools/blob/main/tools/dnaweaver/img/image4.png?raw=true
     :width: 30 %
     :align: center
 

tools/genehunter_modscore/genehunter_modscore.xml

@@ -463,7 +463,7 @@ Due to the stochastic nature of the analysis, a random seed can be set by the us
 
 Many more configurable options are outlined in the the official manual_.
 
-.. _manual: https://www.helmholtz-muenchen.de/fileadmin/GENEPI/downloads/ghm-3.0.pdf
+.. _manual: https://web.archive.org/web/20131010145407/https://www.helmholtz-muenchen.de/fileadmin/GENEPI/downloads/ghm-3.0.pdf
 
 ]]>
     </help>

tools/gprofiler/macros.xml

@@ -19,10 +19,9 @@
 
     <token name="@REFERENCES@"><![CDATA[
 
-This tool is part of the `g:Profiler`_ from the `University of Tartu`_.
+This tool is part of the `g:Profiler`_ from the University of Tartu.
 
   .. _g:Profiler: https://biit.cs.ut.ee/gprofiler/
-  .. _University of Tartu: https://ut.ee/en/
 
 **Resources**
   * `A complete list of suppоrted organism IDs`_

tools/rp2biosensor/rp2biosensor.xml

@@ -46,9 +46,9 @@
 rp2biosensor
 ================
 
-**rp2biosensor** is an open-source Python software that extracts from the retrosynthetic network generated by `RetroPath2.0 <https://www.sciencedirect.com/science/article/pii/S1096717617301337>`_ the subnetwork of interest, linking the biosensor to the compound to be detected, and produce an interactive web page showing the transducing reactions.
+**rp2biosensor** is an open-source Python software that extracts from the retrosynthetic network generated by `RetroPath2.0 <https://doi.org/10.1016/j.ymben.2017.12.002>`_ the subnetwork of interest, linking the biosensor to the compound to be detected, and produce an interactive web page showing the transducing reactions.
 
-Briefly, rp2biosensor parses the retrosynthesis network outputted by RetroPath2.0, completes the predicted reactions by putting back co-substrates and co-products omitted during the retrosynthesis using the `rxn_rebuild <https://www.sciencedirect.com/science/article/pii/S1096717617301337>`_ Python package, enumerates the shortest path linking the compound of interest, i.e. the biosensor, to the compound to be detected, e.g. lactate, and finally outputs the resulting subnetwork as an interactive web page to let the user browse the results.
+Briefly, rp2biosensor parses the retrosynthesis network outputted by RetroPath2.0, completes the predicted reactions by putting back co-substrates and co-products omitted during the retrosynthesis using the `rxn_rebuild <https://doi.org/10.1016/j.ymben.2017.12.002>`_ Python package, enumerates the shortest path linking the compound of interest, i.e. the biosensor, to the compound to be detected, e.g. lactate, and finally outputs the resulting subnetwork as an interactive web page to let the user browse the results.
 
 Input
 -----

tools/rptools/rpreport.xml

@@ -54,7 +54,7 @@ rpReport
 
 Generates HTML pages to explore the main characteristics (thermodynamics,
 fluxes, number of metabolic steps, reaction rule score) of pathways predicted
-with `RetroPath suite <https://www.sciencedirect.com/science/article/pii/S1096717617301337>`_.
+with `RetroPath suite <https://doi.org/10.1016/j.ymben.2017.12.002>`_.
 
 
 Input

tools/rptools/rpviz.xml

@@ -82,7 +82,7 @@
 RPVIZ
 ================
 
-Visualize pathways from the `RetroPath Suite <https://www.sciencedirect.com/science/article/pii/S1096717617301337>`_.
+Visualize pathways from the `RetroPath Suite <https://pubmed.ncbi.nlm.nih.gov/29233745/>`_.
 
 Input
 -----

tools/slamdunk/slamdunk.xml

@@ -175,7 +175,7 @@ SLAM-seq
 
 SLAM-seq is a novel sequencing protocol that directly uncovers 4-thiouridine incorporation events in RNA by high-throughput sequencing. When combined with metabolic labeling protocols, SLAM-seq allows to study the intracellular RNA dynamics, from transcription, RNA processing to RNA stability.
 
-Original publication: `Herzog et al., Nature Methods, 2017; doi:10.1038/nmeth.4435 <https://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.4435.html>`_
+Original publication: `Herzog et al., Nature Methods, 2017; doi:10.1038/nmeth.4435 <https://pubmed.ncbi.nlm.nih.gov/28945705/>`_
 
 Slamdunk
 ========
@@ -220,7 +220,7 @@ Parameter  Description
 T>C conversions
 ---------------
 
-Depending on the use case, more stringent or more lenient measures of T>C conversion and T>C reads are required such as 2 T>C by `Muhar et al., Science, 2018; http://doi.org/10.1126/science.aao2793 <http://science.sciencemag.org/content/early/2018/04/04/science.aao2793>`_
+Depending on the use case, more stringent or more lenient measures of T>C conversion and T>C reads are required such as 2 T>C by `Muhar et al., Science, 2018; doi:10.1126/science.aao2793 <https://pubmed.ncbi.nlm.nih.gov/29622725/>`_
 
 This can be controlled by the following parameter:
 

tools/structure/structure.xml

@@ -533,7 +533,7 @@ You will find other sample data sets: here_
 
 .. _Clumpp: https://rosenberglab.stanford.edu/clumpp.html
 .. _Distruct: https://rosenberglab.stanford.edu/distruct.html
-.. _Structure-harvester: http://taylor0.biology.ucla.edu/structureHarvester/
+.. _Structure-harvester: http://alumni.soe.ucsc.edu/~dearl/software/structureHarvester/
 
     ]]></help>
     <citations>

tools/structureharvester/structureharvester.xml

@@ -89,7 +89,7 @@
 
 Structure_Harvester_ is a program for parsing the output of Pritchard's STRUCTURE_ and for performing the Evanno method.
 
-.. _Structure_Harvester: http://taylor0.biology.ucla.edu/structureHarvester/
+.. _Structure_Harvester: http://alumni.soe.ucsc.edu/~dearl/software/structureHarvester/
 .. _STRUCTURE: http://web.stanford.edu/group/pritchardlab/structure.html
 
     ]]></help>

tools/transit/transit_gumbel.xml

@@ -123,8 +123,8 @@ Note: Technically, Bayesian models are used to calculate posterior probabilities
 
 See  `TRANSIT documentation`
 
-- TRANSIT: https://transit.readthedocs.io/en/latest/index.html
-- `TRANSIT Gumbel`: https://transit.readthedocs.io/en/latest/transit_methods.html#gumbel
+- TRANSIT: https://transit.readthedocs.io/en/v@TOOL_VERSION@/index.html
+- `TRANSIT Gumbel`: https://transit.readthedocs.io/en/v@TOOL_VERSION@/transit_methods.html#gumbel
     ]]></help>
 
     <expand macro="citations" />

tools/transit/transit_hmm.xml

@@ -120,6 +120,19 @@ State Call                                      State Classification (ES = Essen
 =============================================   ========================================================================================================================
 
 Note: Libraries that are too sparse (e.g. < 30%) or which contain very low read-counts may be problematic for the HMM method, causing it to label too many Growth-Defect genes
+
+
+-------------------
+
+**More Information**
+
+-------------------
+
+See  `TRANSIT documentation`
+
+- TRANSIT: https://transit.readthedocs.io/en/v@TOOL_VERSION@/index.html
+- `TRANSIT Gumbel`: https://transit.readthedocs.io/en/v@TOOL_VERSION@/transit_methods.html#hmm
+
 ]]></help>
 
     <expand macro="citations" />

tools/transit/transit_resampling.xml

@@ -144,8 +144,8 @@ p-adj.                                          Adjusted p-value controlling for
 
 See  `TRANSIT documentation`
 
-- TRANSIT: https://transit.readthedocs.io/en/latest/index.html
-- `TRANSIT Gumbel`: https://transit.readthedocs.io/en/latest/transit_methods.html#re-sampling
+- TRANSIT: https://transit.readthedocs.io/en/v@TOOL_VERSION@/index.html
+- `TRANSIT Gumbel`: https://transit.readthedocs.io/en/v@TOOL_VERSION@/transit_methods.html#re-sampling
 ]]></help>
 
     <expand macro="citations" />

tools/transit/transit_tn5gaps.xml

@@ -113,8 +113,8 @@ Note: Technically, Bayesian models are used to calculate posterior probabilities
 
 See  `TRANSIT documentation`
 
-- TRANSIT: https://transit.readthedocs.io/en/latest/index.html
-- `TRANSIT Tn5Gaps`: https://transit.readthedocs.io/en/latest/transit_methods.html#tn5gaps
+- TRANSIT: https://transit.readthedocs.io/en/v@TOOL_VERSION@/index.html
+- `TRANSIT Tn5Gaps`: https://transit.readthedocs.io/en/v@TOOL_VERSION@/transit_methods.html#tn5gaps
     ]]></help>
 
     <expand macro="citations" />

tools/velvet/macros.xml

@@ -11,10 +11,10 @@
       <!-- Anything other than zero is an error -->
       <exit_code level="fatal" range="1:"/>
       <exit_code level="fatal" range=":-1"/>
-      <!-- In case the return code has not been set propery check stderr too -->
+      <!-- In case the return code has not been set properly check stderr too -->
       <regex level="fatal" match="Error:" source="both" />
       <regex level="fatal" match="Exception:" source="both" />
-      <regex level="fatal_oom" match="Can't calloc" source="both" />
+      <regex level="fatal_oom" match="Can't alloc" source="both" />
     </stdio>
   </xml>
   <xml name="citation">
@@ -24,5 +24,17 @@
           </citation>
       </citations>
   </xml>
+  <token name="@WHATITDOES@">
+**What it does**
+
+Velvet is a de novo genomic assembler specially designed for short-read sequencing technologies, such as Solexa or 454, developed by Daniel Zerbino and Ewan Birney at the
+European Bioinformatics Institute (EMBL-EBI), near Cambridge, in the United Kingdom.
+
+Velvet currently takes in short read sequences, removes errors, and then produces high-quality unique contigs. It then uses paired-end read and long read information, when available, to retrieve the repeated areas between contigs.
+
+Read the Velvet `documentation`__ for details on using the Velvet Assembler.
+
+.. __: https://github.com/dzerbino/velvet/blob/master/Manual.pdf
+  </token>
 </macros>
 

tools/velvet/velvetg.xml

@@ -214,17 +214,7 @@
     </test>
   </tests>
   <help><![CDATA[
-**What it does**
-
-Velvet_ is a de novo genomic assembler specially designed for short read sequencing technologies, such as Solexa or 454, developed by Daniel Zerbino and Ewan Birney at the European Bioinformatics Institute (EMBL-EBI), near Cambridge, in the United Kingdom.
-
-Velvet currently takes in short read sequences, removes errors then produces high quality unique contigs. It then uses paired-end read and long read information, when available, to retrieve the repeated areas between contigs.
-
-Read the Velvet `documentation`__ for details on using the Velvet Assembler.
-
-.. _Velvet: http://www.ebi.ac.uk/~zerbino/velvet/
-
-.. __: http://www.ebi.ac.uk/~zerbino/velvet/Manual.pdf
+@WHATITDOES@
 
 ------
 

tools/velvet/velveth.xml

@@ -134,17 +134,7 @@
     </test>
   </tests>
   <help><![CDATA[
-**What it does**
-
-Velvet_ is a de novo genomic assembler specially designed for short read sequencing technologies, such as Solexa or 454, developed by Daniel Zerbino and Ewan Birney at the European Bioinformatics Institute (EMBL-EBI), near Cambridge, in the United Kingdom.
-
-Velvet currently takes in short read sequences, removes errors then produces high quality unique contigs. It then uses paired-end read and long read information, when available, to retrieve the repeated areas between contigs.
-
-Read the Velvet `documentation`__ for details on using the Velvet Assembler.
-
-.. _Velvet: http://www.ebi.ac.uk/~zerbino/velvet/
-
-.. __: http://www.ebi.ac.uk/~zerbino/velvet/Manual.pdf
+@WHATITDOES@
 
 ------