# HG changeset patch
# User immuneml
# Date 1627381631 0
# Node ID 45ca02982e1f0c980dd06a62788d77bf296261fc
# Parent  2d3dd9ff7e845d53165897fdfe4a948822e72e99
"planemo upload commit 8aef44a2b3bc8fc00a1efe0ce7ecab83eded053f-dirty"

diff -r 2d3dd9ff7e84 -r 45ca02982e1f immuneml_simulate_dataset.xml
--- a/immuneml_simulate_dataset.xml	Tue Jul 27 09:30:50 2021 +0000
+++ b/immuneml_simulate_dataset.xml	Tue Jul 27 10:27:11 2021 +0000
@@ -39,7 +39,7 @@
         - Labels, which can be used as a target when training ML models
 
         Note that since these labels are randomly assigned, they do not bear any meaning and it is not possible to train a ML model with high classification accuracy on this data.
-        Meaningful labels can be added using the `Simulate immune events into existing repertoire/receptor dataset <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_simulation>`_ Galaxy tool.
+        Meaningful labels can be added using the `Simulate immune events into existing repertoire/receptor dataset <root?tool_id=immuneml_simulation>`_ Galaxy tool.
 
         For the exhaustive documentation of this tool and an example YAML specification, see the tutorial `How to simulate an AIRR dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_simulate_dataset.html>`_.
 
diff -r 2d3dd9ff7e84 -r 45ca02982e1f immuneml_simulate_events.xml
--- a/immuneml_simulate_events.xml	Tue Jul 27 09:30:50 2021 +0000
+++ b/immuneml_simulate_events.xml	Tue Jul 27 10:27:11 2021 +0000
@@ -37,7 +37,7 @@
         Any type of repertoire dataset (experimental or simulated) can be used as a starting point for an immune event simulation, as long as it contains amino acid sequences.
 
         If you instead want to simulate a synthetic dataset from scratch, start with the
-        tool `Simulate a synthetic immune receptor or repertoire dataset <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_simulate_dataset>`_.
+        tool `Simulate a synthetic immune receptor or repertoire dataset <root?tool_id=immuneml_simulate_dataset>`_.
 
         For the exhaustive documentation of this tool and an example YAML specification, see the tutorial `How to simulate immune events into an existing AIRR dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_simulate_signals.html>`_.
 
diff -r 2d3dd9ff7e84 -r 45ca02982e1f immuneml_train_ml_model.xml
--- a/immuneml_train_ml_model.xml	Tue Jul 27 09:30:50 2021 +0000
+++ b/immuneml_train_ml_model.xml	Tue Jul 27 10:27:11 2021 +0000
@@ -46,8 +46,8 @@
       which include ML models and their parameters, encodings and preprocessing steps. Nested cross-validation is used to identify the optimal combination of ML settings.
 
       This is a YAML-based Galaxy tool, if you prefer a button-based interface that assumes less ML knowledge,
-      see `Train immune receptor classifiers (easy interface) <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_train_classifiers>`_ and
-      `Train immune repertoire classifiers (easy interface) <https://galaxy.immuneml.uio.no/root?tool_id=novice_immuneml_interface>`_.
+      see `Train immune receptor classifiers (easy interface) <root?tool_id=immuneml_train_classifiers>`_ and
+      `Train immune repertoire classifiers (easy interface) <root?tool_id=novice_immuneml_interface>`_.
 
       For more details on how to train ML models in Galaxy, see `the documentation <https://docs.immuneml.uio.no/latest/galaxy/galaxy_train_ml_models.html>`_.
 
diff -r 2d3dd9ff7e84 -r 45ca02982e1f immuneml_train_recept.xml
--- a/immuneml_train_recept.xml	Tue Jul 27 09:30:50 2021 +0000
+++ b/immuneml_train_recept.xml	Tue Jul 27 10:27:11 2021 +0000
@@ -96,7 +96,7 @@
         antigen specificity. One or more ML models are trained to classify receptors based on the information within the CDR3 sequence(s). Finally, the performance
         of the different methods is compared.
         Alternatively, if you want to predict a property per immune repertoire, such as disease status, check out the
-        `Train immune repertoire classifiers (simplified interface) <https://galaxy.immuneml.uio.no/root?tool_id=novice_immuneml_interface>`_ tool instead.
+        `Train immune repertoire classifiers (simplified interface) <root?tool_id=novice_immuneml_interface>`_ tool instead.
 
         The full documentation can be found `here <https://docs.immuneml.uio.no/latest/galaxy/galaxy_simple_receptors.html>`_.
 
@@ -190,12 +190,12 @@
           This .zip file can subsequently be used as an input when `applying previously trained ML models to a new AIRR dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_apply_ml_models.html>`_.
 
         - receptor_classification.yaml: the YAML specification file that was used by immuneML internally to run the analysis. This file can be
-          downloaded, altered, and run again by immuneML using the  `Train machine learning models <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_train_ml_model>`_ Galaxy tool.
+          downloaded, altered, and run again by immuneML using the  `Train machine learning models <root?tool_id=immuneml_train_ml_model>`_ Galaxy tool.
 
         **More analysis options**
 
         A limited selection of immuneML options is available through this tool. If you wish to have full control of the analysis, consider using
-        the `Train machine learning models <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_train_ml_model>`_ Galaxy tool.
+        the `Train machine learning models <root?tool_id=immuneml_train_ml_model>`_ Galaxy tool.
         This tool provides other encodings and machine learning methods to choose from, as well as
         data preprocessing and settings for hyperparameter optimization. The interface of the YAML-based tool expects more independence and knowledge about
         machine learning from the user.
diff -r 2d3dd9ff7e84 -r 45ca02982e1f immuneml_train_repert.xml
--- a/immuneml_train_repert.xml	Tue Jul 27 09:30:50 2021 +0000
+++ b/immuneml_train_repert.xml	Tue Jul 27 10:27:11 2021 +0000
@@ -115,7 +115,7 @@
         a disease status. One or more ML models are trained to classify repertoires based on the information within the sets of CDR3 sequences. Finally, the performance
         of the different methods is compared.
         Alternatively, if you want to predict a property per immune receptor, such as antigen specificity, check out the
-        `Train immune receptor classifiers (simplified interface) <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_train_classifiers>`_ tool instead.
+        `Train immune receptor classifiers (simplified interface) <root?tool_id=immuneml_train_classifiers>`_ tool instead.
 
         The full documentation can be found `here <https://docs.immuneml.uio.no/latest/galaxy/galaxy_simple_repertoires.html>`_.
 
@@ -219,12 +219,12 @@
           This .zip file can subsequently be used as an input when `applying previously trained ML models to a new AIRR dataset in Galaxy <https://docs.immuneml.uio.no/latest/galaxy/galaxy_apply_ml_models.html>`_.
 
         - repertoire_classification.yaml: the YAML specification file that was used by immuneML internally to run the analysis. This file can be
-          downloaded, altered, and run again by immuneML using the  `Train machine learning models <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_train_ml_model>`_ Galaxy tool.
+          downloaded, altered, and run again by immuneML using the  `Train machine learning models <root?tool_id=immuneml_train_ml_model>`_ Galaxy tool.
 
         **More analysis options**
 
         A limited selection of immuneML options is available through this tool. If you wish to have full control of the analysis, consider using
-        the `Train machine learning models <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_train_ml_model>`_ Galaxy tool.
+        the `Train machine learning models <root?tool_id=immuneml_train_ml_model>`_ Galaxy tool.
         This tool provides other encodings and machine learning methods to choose from, as well as
         data preprocessing and settings for hyperparameter optimization. The interface of the YAML-based tool expects more independence and knowledge about
         machine learning from the user.
diff -r 2d3dd9ff7e84 -r 45ca02982e1f immuneml_yaml.xml
--- a/immuneml_yaml.xml	Tue Jul 27 09:30:50 2021 +0000
+++ b/immuneml_yaml.xml	Tue Jul 27 10:27:11 2021 +0000
@@ -44,11 +44,10 @@
         This Galaxy tool can be used to run any possible YAML-based immuneML analysis in Galaxy.
 
         It is typically recommended to use the analysis-specific Galaxy tools for
-        `creating datasets <https://galaxy.immuneml.uio.no/root?tool_id=immune_ml_dataset>`_,
-        `simulating synthetic data <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_simulate_dataset>`_,
-        `implanting synthetic immune signals <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_simulation>`_ or
-        `training <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_train_ml_model>`_ and
-        `applying <https://galaxy.immuneml.uio.no/root?tool_id=immuneml_apply_ml_model>`_ ML models instead of this tool.
+        `creating datasets <root?tool_id=immune_ml_dataset>`_,
+        `simulating synthetic data <root?tool_id=immuneml_simulate_dataset>`_,
+        `implanting synthetic immune signals <root?tool_id=immuneml_simulation>`_ or
+        `training <root?tool_id=immuneml_train_ml_model>`_ ML models instead of this tool.
         These other tools are able to export the relevant output files to Galaxy history elements.
 
         However, when you want to run the `ExploratoryAnalysis <https://docs.immuneml.uio.no/latest/specification.html#exploratoryanalysis>`_ instruction,