Rust-covpyo3

v0.3.1 safe
4.0
Medium Risk

Fast Rust-backed per-base coverage computation over genomic regions from BAM files.

🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with no direct signs of malicious intent. However, the low engagement and limited history of the maintainer introduce some uncertainty.

  • No network or shell execution detected
  • Low engagement and limited maintainer history
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository's low engagement and the maintainer's limited history suggest potential risks.

🔬 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: protonmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 Rust-covpyo3 
Create a mini-application named 'GenomicCoverageAnalyzer' using Python that leverages the Rust-covpyo3 package to compute per-base coverage over specified genomic regions from BAM files. This tool will be particularly useful for researchers working in genomics who need to analyze the depth of sequencing data across specific regions of interest. Here’s a step-by-step guide on how to develop this application:

1. **Setup Project Environment**: Initialize a new Python virtual environment and install the Rust-covpyo3 package along with any other necessary dependencies such as pysam for handling BAM files.
2. **Input Handling**: Design a user-friendly interface that allows users to input the path to their BAM file and specify genomic regions of interest (e.g., chromosome names and start/end positions).
3. **Data Processing**: Utilize Rust-covpyo3 to calculate the coverage for each base within the specified genomic regions. Ensure that the application efficiently handles large BAM files.
4. **Visualization**: Implement a feature to visualize the coverage data using matplotlib or seaborn, allowing users to see the distribution of coverage across the specified regions.
5. **Output Options**: Provide options for users to export the coverage data to a CSV file or directly view it within the application.
6. **Error Handling and Logging**: Incorporate robust error handling to manage issues like incorrect file paths or malformed genomic region inputs. Additionally, implement logging to track the application's operations and potential errors.
7. **Documentation**: Write comprehensive documentation explaining how to use the GenomicCoverageAnalyzer, including examples of input formats and expected outputs.

By following these steps, you'll create a powerful yet easy-to-use tool for genomic research that showcases the capabilities of Rust-covpyo3 in handling complex biological data.