galaxyproject · tflowers15 · Jul 30, 2026 · May 17, 2026 · May 17, 2026 · May 18, 2026
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+---
+destination:
+  type: library
+  name: GTN - Material
+  description: Galaxy Training Network Material
+  synopsis: Galaxy Training Network Material. See https://training.galaxyproject.org
+items:
+- name: Variant Analysis
+  description: Materials for variant analysis tutorials
+  items:
+  - name: Calculating CHEK2 variant effect scores from MAVE data with CountESS
+    items:
+    - name: 'DOI: 10.5281/zenodo.20250971'
+      description: latest
+      items:
+      - url: https://zenodo.org/records/20250971/files/chek2_frequency_summary.csv
+        src: url
+        ext: csv
+        info: https://doi.org/10.5281/zenodo.20250971
+      - url: https://zenodo.org/records/20250971/files/chek2_rcs_countess.ini
+        src: url
+        ext: txt
+        info: https://doi.org/10.5281/zenodo.20250971
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+@article{McCarthyLeo2023,
+  title = {Comprehensive analysis of the functional impact of single nucleotide variants of human CHEK2},
+  author = {McCarthy-Leo, Claire E. and Brush, George S. and Pique-Regi, Roger and Luca, Francesca and Tainsky, Michael A. and Finley, Russell L.},
+  year = {2023},
+  doi = {10.1101/2023.12.22.573093},
+  url = {https://doi.org/10.1101/2023.12.22.573093},
+  journal = {bioRxiv}
+}
+
+@online{MaveDBCHEK2,
+  title = {Rad53 complementation scores for coding SNVs of human CHEK2},
+  author = {{MaveDB}},
+  url = {https://mavedb.org/score-sets/urn:mavedb:00001203-a-2},
+  urldate = {2026-05-17}
+}
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+---
+layout: tutorial_hands_on
+
+title: Calculating CHEK2 variant effect scores from MAVE data with CountESS
+subtopic: human-genetics-cancer
+zenodo_link: https://zenodo.org/records/20250971
+questions:
+- What is a multiplexed assay of variant effect?
+- How can variant frequencies before and after selection be transformed into functional scores?
+- How can a saved CountESS workflow be run in Galaxy?
+objectives:
+- Explain how deep mutational scanning and MAVE experiments connect variant frequencies to functional effects.
+- Use CountESS in Galaxy to calculate RAD53 Complementation Scores for CHEK2 variants.
+- Compare calculated log-ratio scores with scores deposited in MaveDB.
+time_estimation: 45M
+key_points:
+- MAVE experiments can measure functional effects for thousands of variants in parallel.
+- Variant frequencies before and after selection can be transformed into functional scores using log-ratio calculations.
+- CountESS workflows saved from the CountESS GUI can be reused in Galaxy for reproducible table processing.
+tags:
+- MAVE
+- DMS
+- functional genomics
+- clinical genomics
+- CountESS
+contributions:
+  authorship:
+  - plushz
+---
+
+Multiplexed assays of variant effect (MAVEs), including deep mutational scanning (DMS)
+experiments, measure the functional consequences of many genetic variants in parallel.
+Instead of testing variants one at a time, a library of variants is assayed in a pooled
+experiment. Sequencing before and after selection then estimates how each variant changed
+in frequency during the assay.
+
+In this tutorial, we will use a CHEK2 variant effect dataset from MaveDB
+(`urn:mavedb:00001203-a-2`) to calculate a functional score with CountESS. The original
+study tested coding single nucleotide variants (SNVs) in the human *CHEK2* open reading
+frame for their ability to complement a *Saccharomyces cerevisiae* strain lacking the yeast
+ortholog *RAD53* {% cite McCarthyLeo2023 %}. The resulting RAD53 Complementation Score
+(RCS) describes whether a CHEK2 variant was depleted or retained after selection.
+
+The training data for this tutorial starts from a preprocessed variant frequency summary,
+not raw sequencing reads. The two frequency columns used here are:
+
+- `Ave_LibAandB_x1000`: average variant frequency before selection, from the two input library aliquots.
+- `Ave_S1toS4_x1000`: average variant frequency after selection, from four complementation screens.
+
+CountESS will calculate the log-ratio score:
+
+```text
+RCS_this_SNV = log2(Ave_S1toS4_x1000 / Ave_LibAandB_x1000)
+```
+
+Negative scores indicate variants depleted after selection, consistent with impaired CHEK2
+function in this assay. Scores near zero indicate variants with similar frequencies before
+and after selection, suggesting little detectable effect in this assay. Positive scores
+indicate variants that increased in frequency after selection and were retained better
+than the average variant population.
+
+> <agenda-title></agenda-title>
+>
+> In this tutorial, we will cover:
+>
+> 1. TOC
+> {:toc}
+>
+{: .agenda}
+
+# Data preparation
+
+The CountESS workflow and input data are available from Zenodo.
+
+> <hands-on-title>Data upload</hands-on-title>
+>
+> 1. Create a new history for this tutorial.
+>
+>    {% snippet faqs/galaxy/histories_create_new.md %}
+>
+> 2. Import the following files from [Zenodo]({{ page.zenodo_link }}):
+>
+>    ```
+>    https://zenodo.org/records/20250971/files/chek2_frequency_summary.csv
+>    https://zenodo.org/records/20250971/files/chek2_rcs_countess.ini
+>    ```
+>
+>    {% snippet faqs/galaxy/datasets_import_via_link.md %}
+>
+> 3. Set the datatype of `chek2_frequency_summary.csv` to `csv` if Galaxy does not detect it automatically.
+>
+>    {% snippet faqs/galaxy/datasets_change_datatype.md datatype="csv" %}
+>
+> 4. Set the datatype of `chek2_rcs_countess.ini` to `txt` if Galaxy does not detect it automatically.
+>
+>    {% snippet faqs/galaxy/datasets_change_datatype.md datatype="txt" %}
+>
+{: .hands_on}
+
+# Inspect the input table
+
+The input CSV contains one row per CHEK2 variant and includes variant identifiers,
+HGVS-style variant descriptions, and frequency summaries from before and after selection.
+
+> <question-title></question-title>
+>
+> 1. Which two columns will be used to calculate the RCS score?
+>
+> > <solution-title></solution-title>
+> >
+> > The workflow uses `Ave_LibAandB_x1000` and `Ave_S1toS4_x1000`.
+> >
+> {: .solution}
+>
+{: .question}
+
+# Run the CountESS workflow
+
+CountESS is a workflow-like table processing tool. In this tutorial, we use a CountESS
+configuration file that was already created in the CountESS GUI and saved as an `.ini` file.
+Galaxy will run this saved CountESS workflow and connect Galaxy datasets to the input and
+output nodes.
+
+The saved workflow has three CountESS nodes:
+
+1. `Load CHEK2 frequencies`: load the CSV input table.
+2. `Calculate RCS`: calculate the log-ratio score.
+3. `Save CHEK2 RCS scores`: write the output CSV.
+
+This workflow is intentionally small so that the score calculation is easy to inspect.
+CountESS workflows created in the GUI can be more complex, and the Galaxy tool can run
+those saved workflows too.
+
+> <hands-on-title>Calculate CHEK2 RCS scores with CountESS</hands-on-title>
+>
+> 1. Run {% tool [CountESS](toolshed.g2.bx.psu.edu/repos/iuc/countess/countess/0.1.19+galaxy0) %} with the following parameters:
+>
+>    - {% icon param-file %} *"CountESS configuration file"*: `chek2_rcs_countess.ini`
+>
+>    - In *"Input file mapping"*:
+>      - *"CountESS input node name"*: `Load CHEK2 frequencies`
+>      - *"CountESS filename parameter"*: `files.0.filename`
+>      - {% icon param-file %} *"Galaxy input dataset"*: `chek2_frequency_summary.csv`
+>      - *"Staged filename"*: `chek2_frequency_summary.csv`
+>
+>    - In *"Output file mapping"*:
+>      - *"CountESS save node name"*: `Save CHEK2 RCS scores`
+>      - *"CountESS output filename parameter"*: `filename`
+>      - *"Output filename"*: `chek2_rcs_scores.csv`
+>
+>    - *"Log level"*: `INFO`
+>
+{: .hands_on}
+
+CountESS produces a collection of output files. Open the output collection and inspect
+`chek2_rcs_scores.csv`.
+
+# Interpret the output
+
+The output table contains the calculated `RCS_this_SNV` values. These scores are log2
+ratios of the average post-selection variant frequency over the average pre-selection
+variant frequency.
+
+For example, a variant with lower frequency after selection than before selection will have
+a negative RCS value. A variant with similar frequencies before and after selection will
+have an RCS close to zero. A variant with higher frequency after selection than before
+selection will have a positive RCS value. In the CHEK2 RAD53 complementation assay,
+strongly negative RCS values indicate variants that were depleted during selection and are
+therefore more likely to impair CHEK2 function in this assay.
+
+> <question-title></question-title>
+>
+> 1. What do negative, near-zero, and positive RCS values mean in this experiment?
+>
+> > <solution-title></solution-title>
+> >
+> > A negative RCS value means the variant was less frequent after selection than before
+> > selection, consistent with reduced ability of that CHEK2 variant to complement the yeast
+> > *rad53* mutant. A near-zero RCS means the variant frequency changed little during
+> > selection. A positive RCS means the variant was more frequent after selection than before
+> > selection, suggesting it was retained better in this assay.
+> >
+> {: .solution}
+>
+{: .question}
+
+# Compare with MaveDB
+
+The MaveDB score set used for this tutorial is:
+
+```text
+urn:mavedb:00001203-a-2
+```
+
+The calculated `RCS_this_SNV` values from CountESS should match the `RCS_this_SNV` column
+available from MaveDB for this score set:
+
+```text
+https://api.mavedb.org/api/v1/score-sets/urn:mavedb:00001203-a-2/scores
+```
+
+MaveDB also contains a final `score` column for this record. That final score is not the
+direct log-ratio calculated in this tutorial. It is derived downstream from the RCS values
+using a logistic regression model. In this tutorial, we focus on reproducing the transparent
+log-ratio component of the analysis.
+
+> <details-title>How was the CountESS configuration file created?</details-title>
+>
+> The provided `.ini` file was created in the CountESS GUI. CountESS GUI is external
+> software: to create or edit this configuration yourself, CountESS needs to be installed
+> and the GUI needs to be run outside Galaxy first. You do not need to recreate the `.ini`
+> file to complete this tutorial, but the workflow is intentionally simple:
+>
+> 1. Add a **CSV Load** node and load `chek2_frequency_summary.csv`.
+> 2. Add an **Expression** node connected to the CSV Load node.
+> 3. In the Expression node, enter:
+>
+>    ```python
+>    RCS_this_SNV = log2(Ave_S1toS4_x1000 / Ave_LibAandB_x1000)
+>    ```
+>
+> 4. Add a **CSV Save** node connected to the Expression node.
+> 5. Save the CountESS configuration as `chek2_rcs_countess.ini`.
+>
+{: .details}