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The TOPAS portal allows exploration of proteome and phospho-proteome profiles of patients processed with our clinical-proteomics pipeline from LC/MS data.In addition, other omics modalities such as transcriptomics and genomics can be integrated for analysis and comparisons.

This app is deployed athttps://topas-portal.kusterlab.org/

The following variables are required to be set in your user's~/.bashrc or as part of a CI/CD pipeline:

export DB_PASSWORD=<your password>export CONFIG_FILE_PATH=<path to your portal config file>

TheDB_PASSWORD you can choose yourself and is needed to login to the admin tools panel in the portal.

TheCONFIG_FILE_PATH is ajson file with the following format:

{    "local_http": "http://localhost:3832",    "basket_annotation_path": "TOPAS_SCORING_4th gen_5th gen 231212.xlsx",    "drug_annotation_path": "Drug_List.xlsx",    "patient_annotation_path": {        "INFORM": "METADATA.xlsx"    },    "report_directory": {        "INFORM": "TOPAS_pipeline_results"    },    "sample_annotation_path": {        "INFORM": "sample_annotation.csv"    },    "FP": {        "INFORM": 1    },    "PP": {        "INFORM": 1    },    "transcriptomics_path_z_scored": "fpkms.csv",    "transcriptomics_path_not_z_scored": "un_normalized_fpkms.csv",    "genomics_path": "genomics_df2portal.csv",    "oncokb_path": "onkokb2portal.csv",    "meta_data_columns_config": "meta_columns_config.json",    "pspFastaFile": "Phosphosite_seq.fasta",    "pspAnnotationFile": "Phosphorylation_site_dataset",    "pspRegulatoryFile": "Regulatory_sites"}

• basket_annotation_path: path to an Excel file with the following columns:

  'GROUP'        : '(R)TK', 'SIGNALING', 'OTHER''BASKET'       : e.g. 'KIT', 'PDGFRA', 'PDGFRB', 'FGFR1', 'FGFR4', 'FGFR2', 'FGFR3''SUBBASKET'    : e.g. 'KIT', 'PDGFRA', 'PDGFRB', 'Ligand', 'FGFR1', 'FGFR4', 'FGFR2''LEVEL'        : 'phosphorylation', 'expression', 'kinase activity''SCORING RULE' : 'highest protein phosphorylation score ,  'highest z-score', 'highest kinase score'highest z-score''WEIGHT'       : e.g.  2. ,  nan,  0.5,  3. , -1. , -0.5,  0. 'GENE NAME'    : e.g. 'KIT', 'PDGFRA', 'PDGFRB', ..., 'FBLN2', 'ITGA5', 'COL3A1''Site positions (MQ identified - PSP)' : e.g. 'P09619_Y751', 'P11362_Y654', 'P11362_Y653''Kinase'       : e.g. 'AXL;CSK;Chk1;Fyn;Lck;PDGFRA;PDGFRB;PKACA;Src''MODIFIED SEQUENCE' : e.g. '_LLLATpYARPPR_', '_EPPPpYQEPRPR_', 'Literature' : optional'Comment'    : optional

• drug_annotation_path: path to an Excel file with the following columns:

  "Drug""Kinobeads Target List""Designated targets""Other targets""Clinical Phase"

• report_directory: dictionary ofcohort_name -> path to a pipeline results folder (https://github.com/kusterlab/topas-pipeline).

• sample_annotation_path : dictionary ofcohort_name -> path to csv file with the following columns. Replicates should each get their own row.

  Sample nameBatch NameTMT ChannelQCis_reference

• FP andPP: dictionary ofcohort_name -> value, where value is1 (available) or0 (unavailable)

• patient_annotation_path: dictionary ofcohort_name -> excel file with meta data.The column 'Sample name' is mandatory and has to corresponds to the same record insample_annotation_path. Additional columns containing metadata can be added freely.Replicates of the same sample should not be repeated in this file.

• meta_data_columns_config: path to json file with additional metadata columns inpatient_annotation_path with the following format:

  {    "front_end_col_names": [        {            "dataField": "case_submitter_id",            "dataType": "string",            "width": "70",            "visible": "false"        },            {            "dataField": "ethnicity",            "dataType": "string",            "width": "70",            "visible": "false"        },        {            "dataField": "gender",            "dataType": "string",            "width": "70",            "visible": "false"        },    ]}

1. ssh into the server
2. clone this repo:

   git clone https://github.com/kusterlab/topas-portal.git`cd topas-portal

3. export password and path to config file (see above):

   export DB_PASSWORD=<your password>export CONFIG_FILE_PATH=<path to your portal config file>

4. deploy backend and frontend

   make db            # this should deploy the backend and postgres database as two different containersmake frontend      # this should deploy the frontend as a separate container

5. Open the portal athttp://localhost:8080. To upload cohort data, go toOther tools -> Admin tools and press theReload all cohorts button at the bottom of the page.

Check theMakefile for additional functionalities.

1. clone this repo:

   git clone https://github.com/kusterlab/topas-portal.git`cd topas-portal

2. install and activate a conda env on python 3.9.12:

   conda create --name portal python=3.9.12conda activate portal

3. install the dependencies:

   • option 1: using poetry (recommended)

     pip install poetry==1.8.3cd flask_backendpoetry install

   • option 2: using pip (not recommended)

     pip install -r flask-backend/requirements.txt

4. export password and path to config file (see above):

   export DB_PASSWORD=<your password>export CONFIG_FILE_PATH=<path to your portal config file>export VUE_APP_API_HOST=http://localhost:3832

5. deploy the backend in debug mode:

   make test_flask

6. Check that the backend is running by openinghttp://localhost:3832/config in your browser. Your config file should be displayed.

7. Open a new shell and deploy the frontend by running:

   cd vue-frontendnpm installnpm run serve

8. Open the portal athttp://localhost:8080. To upload cohort data, go toOther tools -> Admin tools and press theReload all cohorts button at the bottom of the page.

1. clone this repo:

   git clone https://github.com/kusterlab/topas-portal.git`cd topas-portal

2. Run docker-compose to start the backend:

   make create_docker_networksdocker-compose -f docker-compose-backend.local.yml builddocker-compose -f docker-compose-backend.local.yml up -d

3. The backend is available athttp://localhost:3832 (to test, tryhttp://localhost:3832/config)
   • in the debug mode the config file which is used by default can be found at flask-backend/config_mtb_portal_mintest.json
   • in the production mode the config file path is in settings.py
4. Deploy the frontend:

   cd vue-frontendsudo docker build --build-arg NODE_ENV=development --build-arg VUE_APP_API_HOST=http://$(hostname -I | awk '{print $1}'):3832 -t mtb:dev . sudo docker run -d --name mtb_portal_frontend -it -p 3834:3834 --rm mtb:dev

5. Open the portal athttp://localhost:3834. To upload cohort data, go toOther tools -> Admin tools and press theReload all cohorts button at the bottom of the page.

If you have a lot of data and not enough memory, consider switching to PostgreSQL as the database backend:

1. SetDATABASE_MODE = True insettings.py.
2. Start a local backend with docker (see above), this creates and starts a PostgreSQL database docker.
3. Import the data into the postgreSQL database (this takes 5-10 minutes for a small cohort):

   make database_import

4. Deploy the frontend (see above)

• DataGrid tables show "Unexpected server response" error: probably NaN values in the flask response. Replace NaNs with empty strings should solve it.
• The frontend of the portal uses eslint which can lead to vue complaining about improper linting. Runmake lint to fix these errors.