ai4nucleome-glmap

v0.0.1 safe
3.0
Low Risk

GLMap: Profiling genomic language models as individuals in a population (placeholder release; full v1.0.0 forthcoming with the paper)

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks as indicated by the low scores across all checks. It appears safe for use pending further releases and more detailed documentation.

  • No network or shell execution risks detected.
  • Minimal metadata risk due to package novelty and lack of maintainer information.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or system manipulation.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity related to secret theft.
  • Metadata: The package shows some red flags due to its newness and lack of maintainer details, but there are no clear signs of malicious intent.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1227 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
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in ai4nucleome/GLMap
  • Single author but highly active (100 commits)

🔬 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ai4nucleome/GLMap appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" 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 ai4nucleome-glmap 
Create a web-based application using Python's Flask framework that leverages the 'ai4nucleome-glmap' package to profile genomic language models within a population. This application will allow researchers to upload their genomic data, process it through the GLMap algorithm provided by the 'ai4nucleome-glmap' package, and visualize the results in an interactive manner.

Step 1: Set up the Flask environment and install necessary packages including 'ai4nucleome-glmap'. Ensure all dependencies are managed properly.

Step 2: Design the user interface where users can upload their genomic datasets. The application should validate the format of uploaded files and provide feedback on file acceptance or rejection.

Step 3: Implement the backend functionality to process the uploaded genomic data using the GLMap algorithm from 'ai4nucleome-glmap'. The processing should include steps like normalization, profiling, and statistical analysis to identify unique characteristics of individual genomic models within the dataset.

Step 4: Develop a visualization component that allows users to explore the processed data interactively. Use libraries such as Plotly or Bokeh to create dynamic charts and graphs that highlight differences between genomic models based on the GLMap profiling.

Step 5: Integrate a feature that enables users to download the processed data and visualizations for further analysis outside the application.

Suggested Features:
- Real-time progress tracking during data processing.
- Advanced filtering options for refining search criteria.
- Comparative analysis tools allowing side-by-side comparisons of different genomic models.
- Export functionalities supporting various formats (CSV, PDF).

How 'ai4nucleome-glmap' is Utilized:
This package serves as the backbone of the application's analytical capabilities, providing the GLMap algorithm which profiles genomic language models. Users' genomic data is fed into this algorithm, enabling the application to generate insightful profiles that help in understanding the uniqueness and similarities among different genomic models within a given population.