# HG changeset patch # User q2d2 # Date 1661804445 0 # Node ID 53ff572d6f11e88c9434a383624ecf8d92a9b92a planemo upload for repository https://github.com/qiime2/galaxy-tools/tree/main/tools/suite_qiime2__quality_control commit 9023cfd83495a517fbcbb6f91d5b01a6f1afcda1 diff -r 000000000000 -r 53ff572d6f11 qiime2__quality_control__evaluate_composition.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/qiime2__quality_control__evaluate_composition.xml Mon Aug 29 20:20:45 2022 +0000 @@ -0,0 +1,123 @@ + + + + + Evaluate expected vs. observed taxonomic composition of samples + + quay.io/qiime2/core:2022.8 + + q2galaxy version quality_control + q2galaxy run quality_control evaluate_composition '$inputs' + + + + + + + + + hasattr(value.metadata, "semantic_type") and value.metadata.semantic_type in ['FeatureTable[RelativeFrequency]'] + + + + + + hasattr(value.metadata, "semantic_type") and value.metadata.semantic_type in ['FeatureTable[RelativeFrequency]'] + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + value != "1" + + + + + + + + + +
+
+ + + + + +QIIME 2: quality-control evaluate-composition +============================================= +Evaluate expected vs. observed taxonomic composition of samples + + +Outputs: +-------- +:visualization.qzv: <no description> + +| + +Description: +------------ +This visualizer compares the feature composition of pairs of observed and expected samples containing the same sample ID in two separate feature tables. Typically, feature composition will consist of taxonomy classifications or other semicolon-delimited feature annotations. Taxon accuracy rate, taxon detection rate, and linear regression scores between expected and observed observations are calculated at each semicolon-delimited rank, and plots of per-level accuracy and observation correlations are plotted. A histogram of distance between false positive observations and the nearest expected feature is also generated, where distance equals the number of rank differences between the observed feature and the nearest common lineage in the expected feature. This visualizer is most suitable for testing per-run data quality on sequencing runs that contain mock communities or other samples with known composition. Also suitable for sanity checks of bioinformatics pipeline performance. + + +| + + + + @article{cite1, + author = {Bokulich, Nicholas A and Kaehler, Benjamin D and Rideout, Jai Ram and Dillon, Matthew and Bolyen, Evan and Knight, Rob and Huttley, Gavin A and Caporaso, J Gregory}, + journal = {Microbiome}, + title = {Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin}, + volume = {In Press}, + year = {2018} +} + + 10.1038/s41587-019-0209-9 + +
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