AI Analysis
Final verdict: SAFE
The package has a low risk score due to the absence of network calls, shell executions, obfuscations, and credential harvesting. However, the incomplete author information slightly increases the metadata risk.
- No network calls detected
- Incomplete author information
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communications.
- Shell: No shell execution patterns detected, indicating the package does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, suggesting no immediate risk to secrets or credentials.
- Metadata: The author information is incomplete, which raises some suspicion but does not necessarily indicate malicious intent.
Package Quality Overall: Low (4.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "documentation" -> https://alphadia.readthedocs.io/en/latest/Detailed PyPI description (12615 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
✦ High
Multiple Contributors
8.0
Active multi-contributor project
3 unique contributor(s) across 100 commits in MannLabs/alphadiaSmall but multi-author team (3–4 contributors)
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: biochem.mpg.de>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository MannLabs/alphadia appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with alphadia
Create a Python-based mini-application that leverages the Alphadia package to process and analyze Data-Independent Acquisition (DIA) mass spectrometry data. This application will serve as a user-friendly tool for researchers in proteomics to upload their DIA datasets, perform end-to-end transfer learning for enhanced protein identification, and visualize the results. The application should include the following features: 1. **Data Upload Interface**: Users should be able to upload their DIA raw data files. 2. **Preprocessing Module**: Implement a module within the application that preprocesses the uploaded data using Alphadia’s preprocessing capabilities to prepare it for analysis. 3. **Transfer Learning Engine**: Utilize Alphadia's core feature of end-to-end transfer learning to train models on the preprocessed data, improving the accuracy of protein identification. 4. **Results Visualization**: Develop a visualization component that displays the identified proteins and their abundances in an interactive manner, allowing users to explore different aspects of their data. 5. **Export Functionality**: Provide an option for users to export the analyzed data and visualizations in common formats such as CSV, PDF, or interactive HTML. 6. **User Documentation and Help**: Include comprehensive documentation and a help section to guide users through the application’s features and functionalities. The application should be designed with a focus on ease of use and robustness, ensuring that it can handle large datasets efficiently while providing valuable insights into the proteomic content of samples.