🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its network and shell execution activities, despite no direct evidence of malicious intent. The low author engagement and metadata quality raise concerns about the project's maintenance and security practices.

moderate network risk
potential misuse of subprocess execution
low author engagement

Per-check LLM notes

Network: The network call suggests downloading files, which could be legitimate if the package requires external datasets.
Shell: Subprocess execution is used to run 'snakemake', which might be part of the package's workflow but warrants scrutiny for potential misuse.
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 secrets or credentials.
Metadata: Low author engagement and poor metadata quality suggest potential low effort or abandoned project.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

}...") response = requests.get(url) with open(self.output / dataset / fname, 'w

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

en]Running snakemake...") subprocess.run( [ "snakemake", "-s",

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: rddeboutte.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository maxplanck-ie/ATACofthesnake appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" 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 ATACofthesnake

Create a small project named 'GeneExplorer' using the Python package 'ATACofthesnake'. GeneExplorer will be a command-line tool designed for bioinformaticians and researchers to explore gene expression patterns based on ATAC-seq data. The application should allow users to input a dataset, filter genes based on certain criteria such as peak intensity or genomic location, and visualize the results either through a simple text-based interface or by generating plots.

Steps to follow:
1. Install the necessary packages including 'ATACofthesnake', pandas, matplotlib, and numpy.
2. Design a user-friendly command-line interface that accepts inputs like file paths, thresholds for filtering, and visualization preferences.
3. Implement functionality within GeneExplorer to read and parse the input ATAC-seq data using 'ATACofthesnake'.
4. Allow users to filter genes based on peak intensity, distance from transcription start sites (TSS), or any other relevant metric.
5. Integrate a feature to generate visual representations of the filtered data, such as bar charts or heatmaps, using matplotlib.
6. Ensure the application outputs a summary of the filtered gene set and saves the visualizations in a specified format.
7. Test the application thoroughly with different datasets to ensure reliability and accuracy.

Suggested Features:
- Option to specify multiple filtering criteria simultaneously.
- Support for exporting filtered gene lists to CSV or TSV files.
- Interactive mode allowing users to adjust parameters and see real-time changes in visualizations.
- Detailed documentation and examples for easy usage.