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karbalayghareh · Feb 21, 2023 · 767adb4 · 767adb4
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diff --git a/README.md b/README.md
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-# ENCODE-E2G
+# ENCODE-E2G
+
+## Install requirements
+
+`conda create --name <env> --file requirements.txt`
+
+## Run 
+
+You need three files to run ENCODE-E2G models:
+
+    - TSS file
+    - CRISPRi E-G (enhancer-gene) dataset
+    - Feature table
+
+[tss](https://github.com/karbalayghareh/ENCODE-E2G/tree/main/tss) contains the TSS file we have used. [crispri](https://github.com/karbalayghareh/ENCODE-E2G/tree/main/data/crispri) has the CRISPRi datasets for training the ENCODE-E2G and ENCODE-E2G_Extended models. Feature tables are binary matrices which specify the features to be used in each model. We have included the the full and ablated models of ENCODE-E2G and ENCODE-E2G_Extended in [feature_table](https://github.com/karbalayghareh/ENCODE-E2G/tree/main/data/feature_table).
+
+This [demo](https://github.com/karbalayghareh/ENCODE-E2G/blob/main/demo.ipynb) file show step-by-step how to run the ENCODE-E2G models, save them, predict the CRISPRi E-G pair, plot the analysis figures, and perform the genome-wide predictions for the provided E-G pairs for every ENCODE cell types. 
diff --git a/data/feature_table/.~lock.model_ablation_encode_e2g.tsv# b/data/feature_table/.~lock.model_ablation_encode_e2g.tsv#
diff --git a/data/feature_table/.~lock.model_ablation_encode_e2g_extended.tsv# b/data/feature_table/.~lock.model_ablation_encode_e2g_extended.tsv#