🤖 AI Analysis

Final verdict: SAFE

The package GenomeViewer v0.1.4 presents minimal risks based on the analysis. It lacks network calls, shows no signs of obfuscation or credential harvesting, and has a low metadata risk score.

No network calls
No obfuscation
No credential harvesting

Per-check LLM notes

Network: No network calls detected, which is normal and expected.
Shell: Execution of commands without specifying 'git' may indicate unexpected behavior but does not necessarily imply malicious intent; further investigation is recommended.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
Metadata: Low activity and lack of classifiers suggest low effort, but no clear malicious indicators.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

not just git p = subprocess.Popen([c] + args, cwd=cwd, env=env,

✓ 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 liyao001/PyGV appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Li Yao" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with GenomeViewer

Your task is to develop a user-friendly genome visualization tool using the Python package 'GenomeViewer'. This tool will allow researchers and students to easily visualize genomic data such as DNA sequences, gene locations, and other genetic markers. Your application should provide a simple yet powerful interface for exploring and understanding complex genomic information.

Step-by-Step Guide:
1. Set up your development environment with Python, GenomeViewer, and any additional libraries you might need such as Flask for web serving.
2. Design the user interface, ensuring it is intuitive and easy to navigate. Consider incorporating features like zooming in/out, panning across the genome, and highlighting specific regions of interest.
3. Implement functionality to load and display different types of genomic data (e.g., DNA sequences, gene annotations). Use GenomeViewer's capabilities to render these data points accurately on the screen.
4. Add interactive elements like tooltips that provide more detailed information when hovering over genes or other genomic features.
5. Integrate search functionality so users can quickly find specific genes or genomic regions.
6. Finally, deploy your application either locally or on a cloud service so others can access and use it.

Suggested Features:
- Zooming and panning controls
- Interactive tooltips with detailed information about genomic features
- Search bar for finding specific genes or genomic regions
- Different color schemes for better differentiation between various types of data
- Support for multiple data sources and file formats (e.g., FASTA, GFF)

How to Utilize GenomeViewer:
GenomeViewer simplifies the process of visualizing genomic data by providing a set of tools and functions specifically designed for this purpose. Use its plotting methods to draw DNA sequences, gene locations, and other genomic annotations directly onto your application's canvas. Additionally, leverage GenomeViewer's support for custom styling and layout options to enhance the clarity and usability of your visualizations.