Mercurial > repos > metexplore > met4j
comparison tools/mapping/NameMatcher/NameMatcher.xml @ 10:6a112eaf8f38 draft
planemo upload for repository https://forgemia.inra.fr/metexplore/met4j-galaxy commit 71071300dd662ad01bd064abcf6866a192eeea95
| author | metexplore |
|---|---|
| date | Mon, 03 Feb 2025 15:59:46 +0000 |
| parents | 0976a6257300 |
| children | 40c15b7467f1 |
comparison
equal
deleted
inserted
replaced
| 9:0976a6257300 | 10:6a112eaf8f38 |
|---|---|
| 1 <?xml version="1.0" encoding="UTF-8" standalone="no"?> | 1 <?xml version="1.0" encoding="UTF-8" standalone="no"?> |
| 2 <tool id="met4j_NameMatcher" name="NameMatcher" version="develop"> | 2 <tool id="met4j_NameMatcher" name="NameMatcher" version="2.0.0"> |
| 3 <description>This tool runs edit-distance based fuzzy matching to perform near-similar name matching between a metabolic model and a list of chemical names in a dataset. A harmonization processing is performed on chemical names with substitutions of common patterns among synonyms, in order to create aliases on which classical fuzzy matching can be run efficiently.</description> | 3 <description>This tool runs edit-distance based fuzzy matching to perform near-similar name matching between a metabolic model and a list of chemical names in a dataset. A harmonization processing is performed on chemical names with substitutions of common patterns among synonyms, in order to create aliases on which classical fuzzy matching can be run efficiently.</description> |
| 4 <xrefs> | 4 <xrefs> |
| 5 <xref type="bio.tools">met4j</xref> | 5 <xref type="bio.tools">met4j</xref> |
| 6 </xrefs> | 6 </xrefs> |
| 7 <requirements> | 7 <requirements> |
| 8 <container type="singularity">oras://registry.forgemia.inra.fr/metexplore/met4j/met4j-singularity:develop</container> | 8 <container type="singularity">oras://registry.forgemia.inra.fr/metexplore/met4j/met4j-singularity:2.0.0</container> |
| 9 </requirements> | 9 </requirements> |
| 10 <command detect_errors="exit_code"><![CDATA[sh /usr/bin/met4j.sh mapping.NameMatcher#if str($n): | 10 <command detect_errors="exit_code"><![CDATA[sh /usr/bin/met4j.sh mapping.NameMatcher#if str($n): |
| 11 -nMatch "$n" | 11 -nMatch "$n" |
| 12 #end if | 12 #end if |
| 13 #if str($nSkip): | 13 #if str($nSkip): |
| 58 <outputs> | 58 <outputs> |
| 59 <data format="tsv" name="outputFile"/> | 59 <data format="tsv" name="outputFile"/> |
| 60 </outputs> | 60 </outputs> |
| 61 <tests/> | 61 <tests/> |
| 62 <help><![CDATA[Metabolic models and Metabolomics Data often refer compounds only by using their common names, which vary greatly according to the source, thus impeding interoperability between models, databases and experimental data. This requires a tedious step of manual mapping. Fuzzy matching is a range of methods which can potentially helps fasten this process, by allowing the search for near-similar names. Fuzzy matching is primarily designed for common language search engines and is frequently based on edit distance, i.e. the number of edits to transform a character string into another, effectively managing typo, case and special character variations, and allowing auto-completion. However, edit-distance based search fall short when mapping chemical names: As an example, alpha-D-Glucose et Glucose would require more edits than between Fructose and Glucose. | 62 <help><![CDATA[Metabolic models and Metabolomics Data often refer compounds only by using their common names, which vary greatly according to the source, thus impeding interoperability between models, databases and experimental data. This requires a tedious step of manual mapping. Fuzzy matching is a range of methods which can potentially helps fasten this process, by allowing the search for near-similar names. Fuzzy matching is primarily designed for common language search engines and is frequently based on edit distance, i.e. the number of edits to transform a character string into another, effectively managing typo, case and special character variations, and allowing auto-completion. However, edit-distance based search fall short when mapping chemical names: As an example, alpha-D-Glucose et Glucose would require more edits than between Fructose and Glucose. |
| 63 | |
| 64 This tool runs edit-distance based fuzzy matching to perform near-similar name matching between a metabolic model and a list of chemical names in a dataset. A harmonization processing is performed on chemical names with substitutions of common patterns among synonyms, in order to create aliases on which classical fuzzy matching can be run efficiently.]]></help> | 63 This tool runs edit-distance based fuzzy matching to perform near-similar name matching between a metabolic model and a list of chemical names in a dataset. A harmonization processing is performed on chemical names with substitutions of common patterns among synonyms, in order to create aliases on which classical fuzzy matching can be run efficiently.]]></help> |
| 65 <citations/> | 64 <citations/> |
| 66 </tool> | 65 </tool> |