Mercurial > repos > iuc > idba_hybrid
diff idba_hybrid.xml @ 2:5c5806e23338 draft default tip
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/idba_ud commit 9c0a7395ca18043ae2dd957f39f47b65c938750a
author | iuc |
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date | Thu, 21 Jul 2022 13:35:37 +0000 |
parents | a81c5169e9fc |
children |
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--- a/idba_hybrid.xml Tue Aug 04 05:48:07 2020 -0400 +++ b/idba_hybrid.xml Thu Jul 21 13:35:37 2022 +0000 @@ -2,6 +2,7 @@ <description> Iterative de Bruijn Graph Assembler for hybrid sequencing data </description> + <expand macro="bio_tools"/> <macros> <import>macros.xml</import> </macros> @@ -61,7 +62,7 @@ <param name="ref_source|reference" value="reference.fa" ftype="fasta"/> <param name="ref_source|ref_source_sel" value="history"/> <param name="read" value="merged.fa" ftype="fasta"/> - <output name="output" file="out/scaffold.fa" ftype="fasta"/> + <output name="output" file="out/scaffold.fa" ftype="fasta" compare="re_match"/> <assert_command> <has_text text="--min_region 300" /> <has_text text="--max_gap 3" /> @@ -72,7 +73,7 @@ <param name="ref_source|reference" value="test_id"/> <param name="ref_source|ref_source_sel" value="cached"/> <param name="read" value="merged.fa" ftype="fasta"/> - <output name="output" file="out/scaffold.fa" ftype="fasta"/> + <output name="output" file="out/scaffold.fa" ftype="fasta" compare="re_match"/> </test> <!-- tool specific parameters --> <test> @@ -85,7 +86,7 @@ <has_text text="--min_region 299" /> <has_text text="--max_gap 2" /> </assert_command> - <output name="output" file="out/scaffold.fa" ftype="fasta"/> + <output name="output" file="out/scaffold.fa" ftype="fasta" compare="re_match"/> </test> </tests> <expand macro="help" more_help="IDBA-Hybrid is an iterative De Bruijn Graph De Novo Assembler for hybrid sequencing. It is an extension of IDBA-UD algorithm. It aims at using a closed related reference genome to help de novo assembly, especially when sequencing depth is low. IDBA-Hybrid does alignment between reads and reference first to extract similar regions in the reference genome, and then it correct the similar regions based on the alignment results and apply local assembly technique to resolve potential structure virations. Finally, it groups all the reads and the contigs got from those similar regions to do de novo assembly. The expriments showed it outperforms all existing de novo or hybrid assembly algorithms, especilly when the sequencing depth is low and the reference genome is similar to the target genome."/>