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README.md

@@ -20,9 +20,8 @@ DITTO scores for variants can be obtained by the below 3 ways. Webapp and API ar
 local setup is for batch/bulk variant predictions.
 
 ### Webapp
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+
 DITTO is available for public use at this [website](https://cgds-ditto.streamlit.app/).
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 ### API
 
@@ -69,9 +68,7 @@ Please follow the steps mentioned in [install_openCravat.md](docs/install_openCr
 
 > ***NOTE:*** Current version of OpenCravat that we're using doesn't support "Spanning or overlapping deletions"
 > variants i.e. variants with `*` in `ALT Allele` column. More on these variants
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 > [here](https://gatk.broadinstitute.org/hc/en-us/articles/360035531912-Spanning-or-overlapping-deletions-allele-).
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 > These will be ignored when running the pipeline.
 
 #### Run DITTO pipeline

docs/build_DITTO.md

@@ -84,14 +84,12 @@ the below command
 
 ```sh
 python src/annotation_parsing/parse_single_sample.py -i clinvar.vcf.gz.variant.csv -e parse \
--o clinvar.vcf.gz.variant.csv_parsed.csv.gz -c configs/opencravat_train_config.json
+    -o clinvar.vcf.gz.variant.csv_parsed.csv.gz -c configs/opencravat_train_config.json
 ```
 
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 Filter and process the annotations as shown in this [python
 notebook](../src/annotation_parsing/opencravat_clinvar_filtering_80-20-20.ipynb). This will output training and testing
 data to train the neural network.
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 ## Tune and Train DITTO
 
@@ -120,7 +118,6 @@ Follow the below steps to install and add more databases for annotation and befo
 4. Follow the steps from Preprocessing above to parse, filter, process, tune and train DITTO.
 
 ## Benchmarking
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 Please follow the [python notebook](../src/analysis/opencravat_latest_benchmarking-Consequence_80_20.ipynb) to benchmark
 DITTO with other pathogenicity predition tools. It also has code snippets to generate testing metrics and SHAP plots.
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