These wrappers were developed by Florian Begusch as part of his internship at Medical University of Graz, Center for Medical Research. (https://zmf.medunigraz.at/, https://zmf.medunigraz.at/en/core-facilities/cf-computational-bioanalytics/)
He would like to give his thanks 1) to Marija Durdevic for support on wrapper development and testing. 2) to Slave Trajanoski, who explained general metagenomics terminology and Galaxy (https://usegalaxy.org/). 3) to Alexander Mahnert, who provided data and was a key figure while testing some of the more exotic qiime commands. |
Name | Description | Version | Minimum Galaxy Version |
---|---|---|---|
- Alpha rarefaction curves | 2019.4 | 16.01 | |
- Simplicial Linear mixed effects regression | 2019.4 | 16.01 | |
- Isometric Log-ratio Transform applied to a hierarchical clustering | 2019.4 | 16.01 | |
- Generate interactive volatility plot | 2019.4 | 16.01 | |
- adonis PERMANOVA test for beta group significance | 2019.4 | 16.01 | |
- Deblur sequences using a 16S positive filter. | 2019.4 | 16.01 | |
- bioenv | 2019.4 | 16.01 | |
- Principal Coordinate Analysis | 2019.4 | 16.01 | |
- Dendrogram heatmap. | 2019.4 | 16.01 | |
- Beta diversity group significance | 2019.4 | 16.01 | |
- Alpha diversity comparisons | 2019.4 | 16.01 | |
- Construct a phylogenetic tree with IQ-TREE. | 2019.4 | 16.01 | |
- Convert to relative frequencies | 2019.4 | 16.01 | |
- Filter features from table | 2019.4 | 16.01 | |
- Microbial maturity index prediction. | 2019.4 | 16.01 | |
- Filter samples from table | 2019.4 | 16.01 | |
- Summarize table | 2019.4 | 16.01 | |
- Alpha diversity | 2019.4 | 16.01 | |
- Dereplicate sequences. | 2019.4 | 16.01 | |
- Denoise and dereplicate single-end pyrosequences | 2019.4 | 16.01 | |
- Paired pairwise distance testing and boxplots | 2019.4 | 16.01 | |
- Demultiplex paired-end sequence data with barcodes in- sequence. | 2019.4 | 16.01 | |
- Beta diversity | 2019.4 | 16.01 | |
- Simplicial Ordinary Least Squares Regression | 2019.4 | 16.01 | |
- Add pseudocount to table | 2019.4 | 16.01 | |
- Generate a heatmap representation of a feature table | 2019.4 | 16.01 | |
- Quality filter based on sequence quality scores. | 2019.4 | 16.01 | |
- Find and remove adapters in demultiplexed single-end sequences. | 2019.4 | 16.01 | |
- Train and test a cross-validated supervised learning classifier. | 2019.4 | 16.01 | |
- Assigns ids on internal nodes in the tree, and makes sure that they are consistent with the table columns. | 2019.4 | 16.01 | |
- Principal Coordinate Analysis Biplot | 2019.4 | 16.01 | |
- Core diversity metrics (non-phylogenetic) | 2019.4 | 16.01 | |
- Filter features from sequences | 2019.4 | 16.01 | |
- Use trained classifier to predict target values for new samples. | 2019.4 | 16.01 | |
- Visualize and Interact with Principal Coordinates Analysis Biplot | 2019.4 | 16.01 | |
- Alpha diversity correlation | 2019.4 | 16.01 | |
- Plot longitudinal feature volatility and importances | 2019.4 | 16.01 | |
- Split a feature table into training and testing sets. | 2019.4 | 16.01 | |
- Identify core features in table | 2019.4 | 16.01 | |
- Export data from a QIIME 2 Artifact or Visualization. | 2019.4 | 16.01 | |
- Fit a supervised learning classifier. | 2019.4 | 16.01 | |
- Generate heatmap of important features. | 2019.4 | 16.01 | |
- Compute first differences or difference from baseline between sequential states | 2019.4 | 16.01 | |
- Summarize counts per sample. | 2019.4 | 16.01 | |
- De novo multiple sequence alignment with MAFFT | 2019.4 | 16.01 | |
- Isometric Log-ratio Transform applied to a phylogenetic tree | 2019.4 | 16.01 | |
- Visualize and Interact with a procrustes plot | 2019.4 | 16.01 | |
- Apply ANCOM to identify features that differ in abundance. | 2019.4 | 16.01 | |
- Hierarchical clustering using gradient information. | 2019.4 | 16.01 | |
- Compare query (observed) vs. reference (expected) sequences. | 2019.4 | 16.01 | |
- Export data from a QIIME 2 Artifact or Visualization. | 2019.4 | 16.01 | |
- Remove features from table if they're not present in tree. | 2019.4 | 16.01 | |
- Evaluate expected vs. observed taxonomic assignments | 2019.4 | 16.01 | |
- Export data from a QIIME 2 Artifact or Visualization. | 2019.4 | 16.01 | |
- Summarize parameter and feature extraction information for a trained estimator. | 2019.4 | 16.01 | |
- Run k-nearest-neighbors on a labeled distance matrix. | 2019.4 | 16.01 | |
- Combine multiple tables | 2019.4 | 16.01 | |
- Construct a phylogenetic tree with bootstrap supports using RAxML. | 2019.4 | 16.01 | |
- Join paired-end reads. | 2019.4 | 16.01 | |
- Paired difference testing and boxplots | 2019.4 | 16.01 | |
- Nested cross-validated supervised learning classifier. | 2019.4 | 16.01 | |
- Nested cross-validated supervised learning regressor. | 2019.4 | 16.01 | |
- Beta diversity (phylogenetic) | 2019.4 | 16.01 | |
- Demultiplex single-end sequence data with barcodes in- sequence. | 2019.4 | 16.01 | |
- Nonparametric microbial interdependence test | 2019.4 | 16.01 | |
- Convert to presence/absence | 2019.4 | 16.01 | |
- Closed-reference clustering of features. | 2019.4 | 16.01 | |
- Rarefy table | 2019.4 | 16.01 | |
- De novo chimera filtering with vsearch. | 2019.4 | 16.01 | |
- ANOVA test | 2019.4 | 16.01 | |
- Visualize and Interact with Principal Coordinates Analysis Plots | 2019.4 | 16.01 | |
- Evaluate expected vs. observed taxonomic composition of samples | 2019.4 | 16.01 | |
- Visualize Deblur stats per sample. | 2019.4 | 16.01 | |
- Insert fragment sequences using SEPP into reference phylogenies like Greengenes 13_8 | 2019.4 | 16.01 | |
- Subsample paired-end sequences without replacement. | 2019.4 | 16.01 | |
- De novo clustering of features. | 2019.4 | 16.01 | |
- Transpose a feature table. | 2019.4 | 16.01 | |
- Combine collections of feature sequences | 2019.4 | 16.01 | |
- Combine collections of feature taxonomies | 2019.4 | 16.01 | |
- Demultiplex paired-end sequence data generated with the EMP protocol. | 2019.4 | 16.01 | |
- Open-reference clustering of features. | 2019.4 | 16.01 | |
- Fit a supervised learning regressor. | 2019.4 | 16.01 | |
- Positional conservation and gap filtering. | 2019.4 | 16.01 | |
- Construct a phylogenetic tree with IQ-TREE with bootstrap supports. | 2019.4 | 16.01 | |
- Subsample single-end sequences without replacement. | 2019.4 | 16.01 | |
- Beta diversity correlation | 2019.4 | 16.01 | |
- Construct a phylogenetic tree with RAxML. | 2019.4 | 16.01 | |
- Import data into a new QIIME 2 Artifact. | 2019.4 | 16.01 | |
- Denoise and dereplicate paired-end sequences | 2019.4 | 16.01 | |
- Group samples or features by a metadata column | 2019.4 | 16.01 | |
- Find and remove adapters in demultiplexed paired-end sequences. | 2019.4 | 16.01 | |
- Denoise and dereplicate single-end sequences | 2019.4 | 16.01 | |
- Train and test a cross-validated supervised learning regressor. | 2019.4 | 16.01 | |
- Create a distance matrix from a numeric Metadata column | 2019.4 | 16.01 | |
- Filter fragments in tree from table. | 2019.4 | 16.01 | |
- Core diversity metrics (phylogenetic and non- phylogenetic) | 2019.4 | 16.01 | |
- Beta diversity rarefaction | 2019.4 | 16.01 | |
- Hierarchical clustering using feature correlation. | 2019.4 | 16.01 | |
- Compute first distances or distance from baseline between sequential states | 2019.4 | 16.01 | |
- Convert (and merge) positive numeric metadata (in)to feature table. | 2019.4 | 16.01 | |
- Midpoint root an unrooted phylogenetic tree. | 2019.4 | 16.01 | |
- Deblur sequences using a user-specified positive filter. | 2019.4 | 16.01 | |
- Procrustes Analysis | 2019.4 | 16.01 | |
- Reference-based chimera filtering with vsearch. | 2019.4 | 16.01 | |
- Interactively explore Metadata in an HTML table | 2019.4 | 16.01 | |
- Subsample table | 2019.4 | 16.01 | |
- Make 2D scatterplot and linear regression of regressor predictions. | 2019.4 | 16.01 | |
- Merge features and taxonomy into a single biom file. | 2019.4 | 16.01 | |
- Exclude sequences by alignment | 2019.4 | 16.01 | |
- Make a confusion matrix from sample classifier predictions. | 2019.4 | 16.01 | |
- View sequence associated with each feature | 2019.4 | 16.01 | |
- Filter samples from a distance matrix. | 2019.4 | 16.01 | |
- Construct a phylogenetic tree with FastTree. | 2019.4 | 16.01 | |
- Apply the Mantel test to two distance matrices | 2019.4 | 16.01 | |
- Use trained regressor to predict target values for new samples. | 2019.4 | 16.01 | |
- Alpha diversity (phylogenetic) | 2019.4 | 16.01 | |
- Feature volatility analysis | 2019.4 | 16.01 | |
- Build a phylogenetic tree using fasttree and mafft alignment | 2019.4 | 16.01 | |
- Quality filter based on joined sequence quality scores. | 2019.4 | 16.01 | |
- Linear mixed effects modeling | 2019.4 | 16.01 | |
- Demultiplex sequence data generated with the EMP protocol. | 2019.4 | 16.01 | |
- Alpha diversity (phylogenetic) - alternative implementation | 2019.7 | 16.01 | |
- Filter samples out of demultiplexed data. | 2019.7 | 16.01 | |
- ALPHA Hybrid classifier: VSEARCH exact match + sklearn classifier | 2019.7 | 16.01 |