🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all categories, with no network calls, shell executions, obfuscations, or credential harvesting attempts. The metadata risk slightly increases due to the maintainer having only one package.

Low risk in all primary categories
Metadata risk due to single package from maintainer

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external API interactions.
Shell: No shell executions detected, indicating the package does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, which could indicate a new or less active account.

📦 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://alphapepttools.readthedocs.io/
Detailed PyPI description (6259 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

○ Low Type Annotations 1.0

No type annotations detected

No type annotations, py.typed marker, or stub files detected

✦ High Multiple Contributors 10.0

Active multi-contributor project

5 unique contributor(s) across 100 commits in MannLabs/alphapepttools
Active community — 5 or more distinct 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/alphapepttools appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "MannLabs" 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 alphapepttools

Your task is to develop a Python-based mini-application that leverages the 'alphapepttools' package to interpret proteomics data. This tool will serve as a bridge between complex proteomics datasets and biological insights, enabling researchers to gain deeper understanding from their experiments. Here’s a step-by-step guide on how to build this application:

1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have Python 3.x installed, and then install the necessary packages including 'alphapepttools'. If it's not available via pip, refer to the official documentation or GitHub repository for installation instructions.

2. **Data Input**: Design a user-friendly interface where users can upload their proteomics data files. These files could be in various formats such as mzML, mzXML, or TSV/CSV depending on the data source. Use libraries like 'pandas' for handling these files efficiently.

3. **Data Processing**: Utilize 'alphapepttools' to process the uploaded data. This includes parsing the raw data into a format suitable for analysis, normalizing values, and performing any necessary pre-processing steps as per the package documentation.

4. **Search and Quantification**: Implement functionality within your application that allows users to perform search and quantification tasks directly on their data. Leverage 'alphapepttools' to handle these operations seamlessly. Users should be able to specify parameters such as peptide sequences, modification sites, and quantification methods.

5. **Biological Interpretation**: One of the key features of 'alphapepttools' is its ability to provide biological interpretations of the processed data. Integrate this capability into your app so that users can explore biological pathways, protein-protein interactions, and other relevant information derived from their proteomics data.

6. **Visualization**: Enhance the usability of your application by adding visualization capabilities. Allow users to visualize their data in various forms such as heatmaps, scatter plots, or bar charts using libraries like 'matplotlib', 'seaborn', or 'plotly'.

7. **Reporting**: Finally, enable users to generate comprehensive reports based on their analyses. Reports should include key findings, visualizations, and any additional notes or comments provided by the user during the analysis.

Suggested Features:
- Real-time feedback and progress updates during data processing and analysis.
- Support for multiple file formats and data sources.
- Customizable search and quantification parameters.
- Integration with external databases for biological annotations.
- Export options for results in common file formats (CSV, PDF, etc.).

By following these steps and incorporating these features, you will create a powerful yet accessible tool for interpreting proteomics data, significantly enhancing the capabilities of researchers in the field.