PySAFE

Local Development

Install pre-requisites

You'll need to install the following pre-requisites in order to build SAFE applications

• .NET SDK 8.0 or higher
• Node 18 or higher
• NPM 9 or higher
• Python 3.10 or higher

Install

• run setup.cmd

.. or ..

1. dotnet tool restore
2. py -m venv .venv
3. .\.venv\Scripts\python.exe -m pip install -r .\src\FastAPI\requirements.txt

Run

• .\build.cmd run starts SAFE stack

plus in another terminal run:

1. activate local python environment: .\.venv\Scripts\Activate.ps1
2. navigate to fastapi folder: cd .\src\FastAPI\
3. start fastapi backend: python -m uvicorn app.main:app --reload

Activate Email notification (optional)

Set user-secrets in the following schema:

{
  "email": {
    "NET_EMAIL_EMAIL": "placeholder@mail.de",
    "NET_EMAIL_ACCOUNTNAME": "PlaceholderAccountName",
    "NET_EMAIL_PASSWORD": "HelloWorld1234",
    "NET_EMAIL_SERVER": "smtp.placeholdermail.de",
    "NET_EMAIL_PORT": 587
  }
}

Request Workflow

sequenceDiagram
    participant py as Python ML
    participant net as F#35; Server
    participant c as Client
    actor u as User
    u -->> c: Gives data
    c -->>+net: sends user data
    par start analysis
    net-)+py: sends data, trigger eval
    py-)net: returns binned data
    and return request information
    net -) c: returns `request-ID`
    end
    critical ⚠️
    u -->> c: copies and stores `request-ID`
    end
    opt email
    u -->> c: give email address
    c -->> net: give id + email to store
    end
    opt check status
    u -->> c: use `request-ID` to check status
    end
    py-)net: send last package
    deactivate py
    net-->>net: run q-value calculation
    net-->>net: store results
    deactivate net
    opt gave email
    net-)u: send email
    end
    u -->> c: request data
    c-->>net: get data
    net-->>c: return data
    c-->>u: download data

Result

Explanations of Chloropred ,Qchloro, Mitopred,Qmito,Secrpred,Qsecr, and FinalPred.

Chloropred

Prediction score indicating the likelihood of the protein being localized to the Chloroplast. A higher scores suggest a stronger prediction that the protein is localized in the Chloroplast.

Qchloro

q-value associated with the Chloroplast prediction score. Provides a measure of statistical significance for the Chloroplast prediction. Lower q-values indicate higher statistical significance.

Mitopred

Prediction score for the localization of the protein to the Mitochondria. A higher scores suggest a stronger prediction of Mitochondrial localization.

Qmito

q-value associated with the Mitochondria prediction score. Indicates the statistical significance of the Mitochondria localization prediction. Lower q-values suggest a more reliable prediction.

Secrpred

Prediction score for identifying the protein as a Secretory Protein.A higher scores indicate a stronger likelihood that the protein functions as a Secretory Protein.

Qsecr

q-value for the Secretory Protein prediction. Provides a measure of the statistical significance of the Secretory Protein prediction. Lower q-values are indicative of more statistically significant predictions.

FinalPred

Represents the model's final prediction of the protein's localization based on the highest score and its corresponding q-value. The final localization is determined by comparing the q-values and prediction scores against preset cutoffs. If all q-values exceed the cutoff, the protein is classified as "Cytoplasmic."

Cutoff

The threshold q-value below which a prediction is considered statistically significant. Set to 0.05 by default, meaning that predictions with q-values below this threshold are classified as significant. This parameter helps in distinguishing between statistically significant and non-significant predictions, reducing the chance of false-positive localizations.