feat: clearer instructions for location of test results

data/testResults/README.md

@@ -2,10 +2,10 @@
 
 The file here contains results from the [MACAW](https://github.com/Devlin-Moyer/macaw) `dead_end_test` and `duplicate_test` tests, and from cell-line specific gene essentiality prediction based on the [Hart _et al._ (2015)](https://doi.org/10.1016/j.cell.2015.11.015) dataset.
 
-The test results shown here were obtained by the GitHub Actions run in:
+The test results shown in this folder were last modified by the GitHub Actions run in:
 
-- **PR #929** (MACAW)
-- **PR #929** (gene essentiality)
+- **PR #997** (MACAW)
+- **PR #973** (gene essentiality)
 
 The results will be updated by any subsequent pull request. Summary results are shown as a comment in the corresponding pull request.
 
@@ -25,4 +25,4 @@ It is possible for a single reaction to fit in multiple of the above categories.
 ### Cell-line specific gene essentiality
 Evaluate gene essentiality predictions in 5 cell-line specific GEMs with experimental fitness data gathered from the [Hart _et al._ (2015)](https://doi.org/10.1016/j.cell.2015.11.015).
 
-Cell-line specific GEMs are constructed with tINIT2 for DLD1, GBM, HCT116, HeLa and RPE1 cell lines. Then, the `metabolicTasks_Essential.txt` list of tasks is used to identify essential genes in each of these models. The predicted gene essentiality is compared to results from a high-throughput CRISPR-Cas9 screen for identifying genes that affect fitness. Only the summary statistics of this comparison are kept. 
+Cell-line specific GEMs are constructed with tINIT2 for DLD1, GBM, HCT116, HeLa and RPE1 cell lines. Then, the `metabolicTasks_Essential.txt` list of tasks is used to identify essential genes in each of these models. The predicted gene essentiality is compared to results from a high-throughput CRISPR-Cas9 screen for identifying genes that affect fitness. Only the summary statistics of this comparison are kept.