Updated app.py to include CRISPR model selection

app.py

@@ -8,6 +8,9 @@ ENTRY_METHODS = dict(
     manual='Manual entry of single transcript',
     fasta="Fasta file upload (supports multiple transcripts if they have unique ID's)"
 )
+CRISPR_MODELS = ['Cas9', 'Cas12', 'Cas13']
+selected_model = st.selectbox('Select CRISPR model:', CRISPR_MODELS, key='selected_model')
+
 
 
 @st.cache_data
@@ -32,6 +35,12 @@ def progress_update(update_text, percent_complete):
 
 def initiate_run():
 
+
+    # Placeholder for dynamic module import based on selected_model
+    # model_module = get_model_module(selected_model)
+    # You will need to implement get_model_module function to import the correct module (cas9, cas12, cas13)
+
+    # ... rest of the initiate_run function ...
     # initialize state variables
     st.session_state.transcripts = None
     st.session_state.input_error = None

tiger.md

@@ -1,30 +1,4 @@
-TIGER Online Tool for Cas13 Efficacy Prediction
-Welcome to TIGER! This online tool accompanies our recent study from the labs of David Knowles and Neville Sanjana. TIGER's ability to make accurate on- and off-target predictions enables users to 1) design highly effective gRNAs and 2) precisely modulate transcript expression by engineered gRNA-target mismatches.
-
-If you use the TIGER Online Tool in your study, please consider citing:
-
-Prediction of on-target and off-target activity of CRISPR–Cas13d guide RNAs using deep learning. Wessels, H.-H.*, Stirn, A.*, Méndez-Mancilla, A., Kim, E. J., Hart, S. K., Knowles, D. A.#, & Sanjana, N. E.# Nature Biotechnology (2023). https://doi.org/10.1038/s41587-023-01830-8
-
-Please note that this precompiled, online tool differs from the manuscript slightly. First, this version of TIGER predicts using just target and guide sequence (see Figure 3c). Second, we map TIGER's predictions to the unit interval (0,1) to make estimates more interpretable: A Guide Score close to 1 corresponds to high gRNA activity (i.e. desirable for on-target guides). A Guide Score near 0 denotes no/minimal activity (i.e. desirable for predicted off-targets to minimize the activity of these gRNAs on unintended targets). This transformation is monotonic and therefore preserves Spearman, AUROC, and AUPRC performance. These estimates (transformations of log-fold-change predictions from TIGER) appear in the Guide Score column of this online tool’s output.
-
-Using the TIGER Online Tool
-The tool supports two methods for transcript entry:
-
-Manual entry of a single transcript
-Uploading a FASTA file that can contain one or more transcripts. Each transcript must have a unique ID.
-The tool has three run modes:
-
-Report all on-target gRNAs for each provided transcript.
-Report the top 10 most active, on-target gRNAs for each provided transcript. This mode allows for the optional identification of off-target effects. For off-target avoidance, please note that a higher Guide Score (closer to 1) corresponds to more likely off-target effects.
-Report the top 10 most active, on-target gRNAs for each provided transcript and their titration candidates (all possible single mismatches). A higher Guide Score (closer to 1) corresponds to greater transcript knockdown.
-The tool uses Gencode v19 (protein-coding and non-coding RNAs) to identify potential off-target transcripts. Due to computational limitations, the online tool only supports off-target predictions for the top 10 most active, on-target gRNAs per transcript.
-
-Future Development Plans
-Off-target scanning speed improvements
-Off-target scanning for titration (engineered mismatch) mode
-Allow users to select more than the top ten guides per transcript
-Incorporate non-scalar features (target accessibility, hybridization energies, etc...)
-To report bugs or to request additional features, please click the "Community" button in the top right corner of this screen and start a new discussion. Alternatively, please email Andrew Stirn.
+CRISPR Online Tool for Cas9/Cas12/Cas13 Efficacy Prediction
 
 Version
-You are using version 2.0 of this tool. All hugging face versions are marked with a vX.x tag. The code used to train this model can be found here--specifically, please see tiger_trainer.py therein. This GitHub repository has matching vX.x tags. We will increment the major number when a change causes a difference in predictions (e.g. retraining the model). We will otherwise increment the minor number (e.g. changes to the user interface, speed improvements, etc...).
+You are using version 0.1 of this beta tool.