atac-asap

v0.2.2 safe
3.0
Low Risk

A package for Allele-specific ATAC-seq prediction (ASAP)

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal signs of potential risks with no network calls or shell executions. While there is some obfuscation and metadata risk due to an incomplete maintainer profile, these alone do not conclusively point towards malicious intent.

  • No network calls detected.
  • Incomplete maintainer profile.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
  • Obfuscation: The patterns observed are typical of model evaluation code and do not indicate malicious obfuscation.
  • Credentials: No suspicious patterns related to credential harvesting were detected.
  • Metadata: The maintainer has an incomplete profile and appears to be new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.6/10)

○ 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://github.com/BoevaLab/ASAP/wiki
  • Detailed PyPI description (11071 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 105 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 50 commits in BoevaLab/ASAP
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • te_dict(state_dict) model.eval() model.to(device) # Add predictions to the datafra
  • ay, dict]: self.model.eval() try: gen.dataset.margin_size
  • bin_size self.model.eval() with torch.no_grad(): # Loop over data
  • rank, ddp_enabled): model.eval() # Set the model to evaluation mode try: margi
✓ 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: inf.ethz.ch>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository BoevaLab/ASAP appears legitimate

⚠ 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 atac-asap 
Create a mini-application that leverages the 'atac-asap' package to predict allele-specific ATAC-seq data from given genomic sequences. This application will serve as a user-friendly interface for researchers to input their genomic sequences and receive predictions on ATAC-seq activity for different alleles. The application should have the following functionalities:

1. **User Input Interface**: Design a simple web-based form where users can upload their FASTA files containing genomic sequences. Additionally, allow users to specify any necessary parameters such as the length of the sequence window for analysis.
2. **Prediction Engine**: Utilize the 'atac-asap' package to process the uploaded sequences and predict ATAC-seq activity. Ensure that the application can handle both single and multiple sequences efficiently.
3. **Visualization Module**: Implement a feature to visualize the predicted ATAC-seq activity across the genomic sequences. Use plots such as heatmaps or line graphs to display the intensity of ATAC-seq signals for each allele.
4. **Result Download**: Provide an option for users to download the predicted results in various formats like CSV or PDF, alongside the visualizations.
5. **Documentation and Help Section**: Include comprehensive documentation explaining how to use the application effectively, along with FAQs and troubleshooting tips.

The 'atac-asap' package is utilized primarily for its ability to predict ATAC-seq activity based on input genomic sequences. Users will benefit from this tool by gaining insights into allele-specific regulatory elements within their genomic regions of interest without needing extensive bioinformatics expertise.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!