some edits

tiger.md

@@ -2,7 +2,7 @@
 
 Welcome to TIGER!
 This Hugging Face space is an online tool that accompanies our Nature Biotechnology article.
-TIGER's ability to make accurate on- and off-target predictions enables biologists to both design highly effective gRNAs and precisely modulate transcript expressing by engineering gRNA mismatches.
+TIGER's ability to make accurate on- and off-target predictions enables biologists to both design highly effective gRNAs and precisely modulate transcript expression by engineering gRNA mismatches.
 If you utilize our model, please consider citing us:
 
 > Wessels, H.-H., Stirn, A., Méndez-Mancilla, A., Kim, E. J., Hart, S. K., Knowles, D. A., & Sanjana, N. E. (2023). Prediction of on-target and off-target activity of CRISPR–Cas13d guide RNAs using deep learning. Nature Biotechnology. https://doi.org/10.1038/s41587-023-01830-8
@@ -15,10 +15,10 @@ If you utilize our model, please consider citing us:
 
 This tool differs from our manuscript in two ways.
 First, this version of TIGER predicts using just target and guide sequence, which will marginally reduce performance (fig 3c).
-Second, we map TIGER's outputs to the unit interval to make estimates more interpretable: a one corresponds to high gRNA activity and a zero denotes no activity.
+Second, we map TIGER's predictions to the unit interval to make estimates more interpretable: a one corresponds to high gRNA activity and a zero denotes no activity.
 This transformation, maps estimates with no detectable Cas13 activity to (0,0.025] and the most active 2.5% of estimates to [0.975,1)
 This transformation is monotonically decreasing and therefore preserves Spearman, AUROC, and AUPRC performance.
-We label these transformed LFC estimates as `Guide Score` in our prediction tables.
+These transformed LFC estimates appear in the `Guide Score` column of our prediction tables.
 
 
 ### Using TIGER
@@ -30,7 +30,7 @@ We support two methods for transcript entry:
 We currently offer three run modes:
 - We report all on-target gRNAs for each provided transcript. This mode does not support off-target identification due to current computational constraints.
 - We report the top ten most active, on-target gRNAs for each provided transcript. This mode allows for the optional identification of off-target effects.
-- We report the top ten most active on-target gRNAs for each provided transcript and their titration candidates (all possible single mismatches). This mode also does not support off-target identification due to current computational constraints.
+- We report the top ten most active, on-target gRNAs for each provided transcript and their titration candidates (all possible single mismatches). This mode also does not support off-target identification due to current computational constraints.
 
 We use version 19 of gencode (protein-coding and lncRNA) to identify off-target candidates.